Could tiny blood clots cause long COVID’s puzzling symptoms?

Scientists debate evidence for a micro-clot hypothesis that has some people pursuing potentially risky treatments

Authors: Cassandra Willyard Nature 608, 662-664 (2022)doi: https://doi.org/10.1038/d41586-022-02286-7

When Lara Hawthorne, an illustrator in Bristol, UK, began developing strange symptoms after having COVID-19, she hoped that they weren’t due to the virus. Her initial illness had been mild. “I’ve been triple vaccinated. I felt quite protected,” she says. But months later, she was still sick with a variety of often debilitating symptoms: earaches, tinnitus, congestion, headaches, vertigo, heart palpitations, muscle pain and more. On some days, Hawthorne felt so weak that she could not get out of bed. When she finally saw her physician, the diagnosis was what she had been dreading: long COVID.

Unable to find relief, she became increasingly desperate. After reading an opinion piece in The Guardian newspaper about how blood clots might be to blame for long COVID symptoms, Hawthorne contacted a physician in Germany who is treating people with blood thinners and a procedure to filter the blood. She hasn’t heard back yet — rumour has it that people stay on the waiting list for months — but if she has the opportunity to head there for these unproven treatments, she probably will. “I don’t want to wait on my health when I’m feeling so dreadful,” she says.

Researchers are baffled by long COVID: hundreds of studies have tried to unpick its mechanism, without much success. Now some scientists, and an increasing number of people with the condition, have been lining up behind the as-yet-unproven hypothesis that tiny, persistent clots might be constricting blood flow to vital organs, resulting in the bizarre constellation of symptoms that people experience.

Heart disease after COVID: what the data say

Proponents of the idea (#teamclots, as they sometimes refer to themselves on Twitter) include Etheresia Pretorius, a physiologist at Stellenbosch University in South Africa, and Douglas Kell, a systems biologist at the University of Liverpool, UK, who led the first team to visualize micro-clots in the blood of people with long COVID. They say that the evidence implicating micro-clots is undeniable, and they want trials of the kinds of anticoagulant treatment that Hawthorne is considering. Pretorius penned the Guardian article that caught Hawthorne’s attention.

But many haematologists and COVID-19 researchers worry that enthusiasm for the clot hypothesis has outpaced the data. They want to see larger studies and stronger causal evidence. And they are concerned about people seeking out unproven, potentially risky treatments.

When it comes to long COVID, “we’ve now got little scattered of bits of evidence”, says Danny Altmann, an immunologist at Imperial College London. “We’re all scuttling to try and put it together in some kind of consensus. We’re so far away from that. It’s very unsatisfying.”

Cascade of clots

Pretorius and Kell met about a decade ago. Pretorius had been studying the role of iron in clotting and neglected to cite some of Kell’s research. When he reached out, they began chatting. “We had a Skype meeting and then we decided to work together,” Pretorius says. They observed odd, dense clots that resist breaking down for years in people with a variety of diseases. The research led them to develop the theory that some molecules — including iron, proteins or bits of bacterial cell wall — might trigger these abnormal clots.

Blood clotting is a complex process, but one of the key players is a cigar-shaped, soluble protein called fibrinogen, which flows freely in the bloodstream. When an injury occurs, cells release the enzyme thrombin, which cuts fibrinogen into an insoluble protein called fibrin. Strands of fibrin loop and criss-cross, creating a web that helps to form a clot and stop the bleeding.

Under a microscope, this web typically resembles “a nice plate of spaghetti”, Kell says. But the clots that the team has identified in many inflammatory conditions look different. They’re “horrible, gunky, dark”, Kell says, “such as you might get if you half-boiled the spaghetti and let it all stick together.” Research by Kell, Pretorius and their colleagues suggests that the fibrin has misfolded1, creating a gluey, ‘amyloid’ version of itself. It doesn’t take much misfolding to seed disaster, says Kell. “If the first one changes its conformation, all the others have to follow suit”, much like prions, the infectious misfolded proteins that cause conditions such as Creutzfeldt–Jakob disease.

Long-COVID treatments: why the world is still waiting

Pretorius first saw these strange, densely matted clots in the blood of people with a clotting disorder2, but she and Kell have since observed the phenomenon in a range of conditions1 — diabetes, Alzheimer’s disease and Parkinson’s disease, to name a few. But the idea never gained much traction, until now.

When the pandemic hit in 2020, Kell and Pretorius applied their methods almost immediately to people who had been infected with SARS-CoV-2. “We thought to look at clotting in COVID, because that is what we do,” Pretorius says. Their assay uses a special dye that fluoresces when it binds to amyloid proteins, including misfolded fibrin. Researchers can then visualize the glow under a microscope. The team compared plasma samples from 13 healthy volunteers, 15 people with COVID-19, 10 people with diabetes and 11 people with long COVID3. For both long COVID and acute COVID-19, Pretorius says, the clotting “was much more than we have previously found in diabetes or any other inflammatory disease”. In another study4, they looked at the blood of 80 people with long COVID and found micro-clots in all of the samples.

So far, Pretorius, Kell and their colleagues are the only group that has published results on micro-clots in people with long COVID.

But in unpublished work, Caroline Dalton, a neuroscientist at Sheffield Hallam University’s Biomolecular Sciences Research Centre, UK, has replicated the results. She and her colleagues used a slightly different method, involving an automated microscopy imaging scanner, to count the number of clots in blood. The team compared 3 groups of about 25 individuals: people who had never knowingly had COVID-19, those who had had COVID-19 and recovered, and people with long COVID. All three groups had micro-clots, but those who had never had COVID-19 tended to have fewer, smaller clots, and people with long COVID had a greater number of larger clots. The previously infected group fell in the middle. The team’s hypothesis is that SARS-CoV-2 infection creates a burst of micro-clots that go away over time. In individuals with long COVID, however, they seem to persist.

Dalton has also found that fatigue scores seem to correlate with micro-clot counts, at least in a few people. That, says Dalton, “increases confidence that we are measuring something that is mechanistically linked to the condition”.

In many ways, long COVID resembles another disease that has defied explanation: chronic fatigue syndrome, also known as myalgic encephalomyelitis (ME/CFS). Maureen Hanson, who directs the US National Institutes of Health (NIH) ME/CFS Collaborative Research Center at Cornell University in Ithaca, New York, says that Pretorius and Kell’s research has renewed interest in a 1980s-era hypothesis about abnormal clots contributing to symptoms. Pretorius, Kell and colleagues found amyloid clots in the blood of people with ME/CFS, but the amount was much lower than what they’ve found in people with long COVID5. So clotting is probably only a partial explanation for ME/CFS, Pretorius says.

Micro-clot mysteries

Where these micro-clots come from isn’t entirely clear. But Pretorius and Kell think that the spike protein, which SARS-CoV-2 uses to enter cells, might be the trigger in people with long COVID. When they added the spike protein to plasma from healthy volunteers in the laboratory, that alone was enough to prompt formation of these abnormal clots6.

Bits of evidence hint that the protein might be involved. In a preprint7 posted in June, researchers from Harvard University in Boston, Massachusetts, reported finding the spike protein in the blood of people with long COVID. Another paper8 from a Swedish group showed that certain peptides in the spike can form amyloid strands on their own, at least in a test tube. It’s possible that these misfolded strands provide a kind of template, says Sofie Nyström, a protein chemist at Linköping University in Sweden and an author of the paper.

Micrographs of platelet poor plasma of a healthy volunteer showing few microclots,and post-COVID-19 infection showing microclots
Micro-clots (green) in a study participant before SARS-CoV-2 infection (left four panels) and in the same person after they developed long COVID (right four panels).Credit: E. Pretorius et al./Cardiovasc. Diabetol. (CC BY 4.0)

A California-based group found that fibrin can actually bind to the spike. In a 2021 preprint9, it reported that when the two proteins bind, fibrin ramps up inflammation and forms clots that are harder to degrade. But how all these puzzle pieces fit together isn’t yet clear.

If the spike protein is the trigger for abnormal clots, that raises the question of whether COVID-19 vaccines, which contain the spike or instructions for making it, can induce them as well. There’s currently no direct evidence implicating spike from vaccines in forming clots, but Pretorius and Kell have received a grant from the South African Medical Research Council to study the issue. (Rare clotting events associated with the Oxford–AstraZeneca vaccine are thought to happen through a different mechanism (Nature 596, 479–481; 2021).)

Raising safety concerns about the vaccines can be uncomfortable, says Per Hammarström, a protein chemist at Linköping University and Nyström’s co-author. “We don’t want to be over-alarmist, but at the same time, if this is a medical issue, at least in certain people, we have to address that.” Gregory Poland, director of the Mayo Clinic’s vaccine research group in Rochester, Minnesota, agrees that it’s an important discussion. “My guess is that spike and the virus will turn out to have a pretty impressive list of pathophysiologies,” he says. “How much of that may or may not be true for the vaccine, I don’t know.”

Dearth of data

Many researchers find it plausible and intriguing that micro-clots could be contributing to long COVID. And the hypothesis does seem to fit with other data that have emerged on clotting. Researchers already know that people with COVID-19, especially severe disease, are more likely to develop clots. The virus can infect cells lining the body’s 100,000 kilometres of blood vessels, causing inflammation and damage that triggers clotting.

Those clots can have physiological effects. Danny Jonigk, a pathologist at Hanover Medical School in Germany, and his colleagues looked at tissue samples from people who died of COVID-19. They found micro-clots and saw that the capillaries had split, forming new branches to try to keep oxygen-rich blood flowing10. The downside was that the branching introduces turbulence into the flow that can give rise to fresh clots.

How common is long COVID? Why studies give different answers

Several other labs have found signs that, in some people, this tendency towards clotting persists months after the initial infection. James O’Donnell, a haematologist and clotting specialist at Trinity College Dublin, and his colleagues found11 that about 25% of people who are recovering from COVID-19 have signs of increased clotting that are “quite marked and unusual”, he says.

What is less clear is whether this abnormal clotting response is actually to blame for any of the symptoms of long COVID, “or is it just, you know, another unusual phenomenon associated with COVID?” O’Donnell says.

Alex Spyropoulos, a haematologist at the Feinstein Institutes for Medical Research in New York City, says the micro-clot hypothesis presents “a very elegant mechanism”. But he argues that much more work is needed to tie the lab markers to clinical symptoms. “What’s a little bit disturbing is that these authors and others make huge leaps of faith,” Spyropoulos says.

Jeffrey Weitz, a haematologist and clotting specialist at McMaster University in Hamilton, Canada, points out that the method Pretorius’s team is using to identify micro-clots “isn’t a standard technique at all”. He adds: “I’d like to see confirmation from other investigators.” Micro-clots are difficult to detect. Pathologists can spot them in tissue samples, but haematologists tend to look for markers of abnormal clotting rather than the clots themselves.

Other, larger studies of long COVID have failed to find signs of clotting. Michael Sneller, an infectious-disease specialist, and his colleagues at the NIH in Bethesda, Maryland, thoroughly examined 189 people who had been infected with SARS-CoV-2, some with lingering symptoms and some without, and 120 controls12. They did not specifically look for micro-clots. But if micro-clots had been clogging the capillaries, Sneller says, they should have seen some evidence — tissue damage in capillary-rich organs such as the lungs and kidneys, for example. Micro-clots might also damage red blood cells, leading to anaemia. But Sneller and his colleagues found no signs of this in any of the lab tests.

The four most urgent questions about long COVID

Kell and Pretorius argue that just because this study didn’t find any evidence of micro-clots doesn’t mean they aren’t there. One of the key issues with long COVID is that “every single test comes back within the normal ranges”, Pretorius says. “You have desperately ill patients with no diagnostic method.” She hopes that other researchers will read their papers and attempt to replicate their results. “Then we can have a discussion,” she says. The ultimate causal proof, she adds, would be people with long COVID feeling better after receiving anticoagulant therapies.

There is some limited evidence of this. In an early version of a preprint, posted in December 2021, Kell, Pretorius and other researchers, including physician Gert Jacobus Laubscher at Stellenbosch University, reported that 24 people who had long COVID and were treated with a combination of two antiplatelet therapies and an anticoagulant experienced some relief13. Participants reported that their main symptoms resolved and that they became less fatigued. They also had fewer micro-clots. Pretorius and Kell are working to gather more data before they try to formally publish these results. But other physicians are already using these medications to treat people with long COVID. Some are even offering a dialysis-like procedure that filters fibrinogen and other inflammatory molecules from the blood. To O’Donnell, such treatment feels premature. He accepts that some people with long COVID are prone to clots, but leaping from a single small study to treating a vast number of people is “just not going to wash in 2022 in my book”, he says. Sneller agrees. “Anticoagulating somebody is not a benign thing. You basically are interfering with the blood’s ability to clot,” he says, which could make even minor injuries life-threatening.

Kell says he’s tired of waiting for a consensus on how to treat long COVID. “These people are in terrible pain. They are desperately unwell,” he says. Altmann understands that frustration. He gets e-mails almost daily, asking: “Where are the drug trials? Why does it take so long?” But even in the midst of a pandemic, he argues, researchers have to follow the process. “I’m not rubbishing anybody’s data. I’m just saying we’re not there yet,” he says. “Let’s join up the dots and do this properly.”

References

  1. Kell, D. B., Laubscher, G. J. & Pretorius, E. Biochem. J. 479, 537–559 (2022).PubMed Article Google Scholar 
  2. Pretorius, E., Briedenhann, S., Marx, J. & Franz, R. C. Ultrastruct. Pathol. 30, 167–176 (2006).PubMed Article Google Scholar 
  3. Pretorius, E. et al. Cardiovasc. Diabetol. 20, 172 (2021).PubMed Article Google Scholar 
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Who’ll Get Long COVID? Just a Look at a Patient Gives Clues

Authors: Dennis Thompson Jul 19, 2022 The Indiana Gazette

ometimes just looking at a person can give clues to their likelihood of developing long COVID after a bout with the virus.

For example, obese people are five times more likely to suffer long COVID symptoms that persist at least three months after their infection clears, a major new U.S. study finds.

Another risk factor: Experiencing hair loss during COVID-19 illness, the same study found.

Headache and sore throat during infection also greatly increase a person’s risk of long-haul symptoms, the researchers added.

However, the results also showed that other risk factors for COVID-19 infection do not necessarily mean a person will develop long COVID, noted senior researcher Eileen Crimmins, chair of gerontology for the University of Southern California and director of the USC/UCLA Center on Biodemography and Population Health.

“What’s somewhat more interesting are the things that didn’t matter,” Crimmins said. “Gender didn’t predict long COVID. Race/ethnicity didn’t predict long COVID. And having conditions like hypertension [high blood pressure], heart disease, cancer, they didn’t predict long COVID.”

Overall, 23% of people infected with COVID-19 can be expected to develop long-haul symptoms, regardless of whether their infection was severe enough to require hospitalization, Crimmins and her colleagues reported. The study was published online recently in the journal Scientific Reports.

The World Health Organization defines long COVID as symptoms that last 12 weeks or longer after the initial infection has cleared, the researchers said.

“A significant number of people may have trouble working, taking care of their families, doing the things they need to do day-to-day because they’ve had the condition,” Crimmins said. “So, it’s not a nothing disease.”

These numbers are based on the Understanding America Study COVID-19 National Panel, an ongoing regular survey of more than 8,400 U.S. adults.

Starting every two weeks in March 2020, panel members were asked to fill out a questionnaire detailing their health status and any symptoms they might be having.

During the following year, about 10% of total participants reported that they’d been diagnosed with or tested positive for COVID-19.

The researchers focused in on 308 people who had COVID-19 and had reported their health status and symptoms before, during, and at least three months after their initial diagnosis.

What factors influenced the odds of long COVID the most? Obesity increased a person’s risk of long COVID by nearly five and a half times, the results showed. Other prominent risk factors included hair loss during infection, which increased sevenfold the risk of long COVID. Headache and sore throat each increased a person’s risk by more than three times.

It’s likely that obesity and hair loss are both tied to the amount of inflammation a person suffers during their COVID-19 infection, which can wreak havoc on their body’s organs, explained Dr. William Schaffner, medical director of the National Foundation for Infectious Diseases.

“Perhaps obesity allows that inflammation to persist for a longer period of time, therefore resulting in symptoms,” Schaffner said. “Hair loss is kind of new to me, but that’s obviously going to be some sort of symptom that relates somehow to inflammation.”

Surprisingly, age, gender, race, education, smoking, and preexisting health conditions like diabetes or asthma didn’t appear to influence the risk of long COVID.

The most common symptoms people developed during COVID that persisted months later included:

  • Headache (22%)
  • Runny or stuffy nose (19%)
  • Abdominal discomfort (18%)
  • Fatigue (17%)
  • Diarrhea (13%)

The study did not find other symptoms that have been commonly reported by long COVID-19 patients, including brain fog and joint pain, Schaffner noted.

“So there are some things that reinforce what’s in the literature and some other things that are a little different,” Schaffner said.

Despite that, Schaffner praised the study as a “noteworthy addition to the literature” that should help the many long COVID centers that have opened up around the country to deal with this phenomenon.

“The main thing I take away from this is that long COVID is not unusual. In fact, it’s rather common,” Schaffner said. “It’s persistent and it will require a great deal of medical attention going forward. A lot of medical resources will have to be devoted to this, and those resources will largely be outside the hospital, including supportive care, physical therapy and even some psychological support for these patients.”

Crimmins added it could take years, and even decades, to fully understand the long-term effects of COVID-19.

Research into the 1918 influenza pandemic found that fetuses in utero when moms caught the flu had a 25% higher risk of heart disease by the time they were in their 70s, Crimmins noted.

“There are things that may happen in this population to their underlying health that may not be immediately obvious, but could have relatively significant long-term effects,” Crimmins said of long COVID patients.

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COVID-19: A Global Threat to the Nervous System

Authors: Igor J. Koralnik MD,Kenneth L. Tyler MD 07 June 2020 Annals of NeurologyVolume 88, Issue 1 p. 1-11

Abstract

In less than 6 months, the severe acute respiratory syndrome-coronavirus type 2 (SARS-CoV-2) has spread worldwide infecting nearly 6 million people and killing over 350,000. Initially thought to be restricted to the respiratory system, we now understand that coronavirus disease 2019 (COVID-19) also involves multiple other organs, including the central and peripheral nervous system. The number of recognized neurologic manifestations of SARS-CoV-2 infection is rapidly accumulating. These may result from a variety of mechanisms, including virus-induced hyperinflammatory and hypercoagulable states, direct virus infection of the central nervous system (CNS), and postinfectious immune mediated processes. Example of COVID-19 CNS disease include encephalopathy, encephalitis, acute disseminated encephalomyelitis, meningitis, ischemic and hemorrhagic stroke, venous sinus thrombosis, and endothelialitis. In the peripheral nervous system, COVID-19 is associated with dysfunction of smell and taste, muscle injury, the Guillain-Barre syndrome, and its variants. Due to its worldwide distribution and multifactorial pathogenic mechanisms, COVID-19 poses a global threat to the entire nervous system. Although our understanding of SARS-CoV-2 neuropathogenesis is still incomplete and our knowledge is evolving rapidly, we hope that this review will provide a useful framework and help neurologists in understanding the many neurologic facets of COVID-19. ANN NEUROL 2020;88:1–11 ANN NEUROL 2020;88:1–11

The novel coronavirus, now called severe acute respiratory syndrome-coronavirus type 2 (SARS-CoV-2), is the agent of coronavirus disease 2019 (COVID-19), that was first diagnosed on December 8, 2019, in a patient in the city of Wuhan in central China. Common symptoms of COVID-19 include fevercoughfatigue, and shortness of breath. Whereas most affected individuals have no or minor symptoms, some go on to develop pneumonia, acute respiratory distress syndrome (ARDS), and succumb from multiple organ failure. On January 30, 2020, the World Health Organization (WHO) declared it a Public Health Emergency of international concern. It has been estimated that the number of infected individuals during the early epidemic doubled every 2.4 days, and the R0 value, or number of people that can be infected by a single individual, may be as high as 4.7 to 6.6.1 After spreading throughout China, the disease took hold in Europe and the United States, and in view of this alarming development and the rapid growth of cases, public health officials in many jurisdictions ordered people to shelter in place beginning with the state of California on March 19, 2020. As of May 29, 2020, there have been 5.88 million confirmed cases in 188 countries and 363,000 reported deaths, and most countries are in various phases of relaxing quarantine requirements while continuing some social distancing measures.

What are coronaviruses and what makes SARS-CoV-2 so contagious? Coronaviruses, which have a diameter of approximately 100 nm, are named after their crown-like appearance on electron microscopy. They infect many animal species and are part of the family of Coronaviridae that contain four distinct Genera. Coronaviruses are positive strand, single stranded ribonucleic acid (+ss-RNA) viruses. They have the largest genome of all RNA viruses, approximately 30 kilobases in length. The full sequence of SARS-CoV-2 was published on January 7, 2020, and revealed that it is was a β-coronavirus, similar to other human coronaviruses that are responsible for 15% of all cases of acute viral nasopharyngitis, also known as “common cold.”2 However, SARS-CoV-2 contains unique sequences, including a polybasic cleavage site in the spike protein, which is a potential determinant of increased transmissibility.3

Coronaviruses have caused deadly outbreaks in the past. The first one caused by SARS-CoV, occurred in China in 2003 and affected approximately 8,000 people, with a 10% mortality rate. The Middle-East Respiratory Syndrome (MERS) outbreak began in Saudi Arabia in 2012, and affected 2,500 individuals with a 35% mortality rate. SARS-CoV-2 has approximately 80% sequence homology with SARS-CoV, but 96% homology with a bat coronavirus and 92% with a pangolin coronavirus, suggesting it arouse in animals and then spread between species to humans. The spike protein of SARS-CoV-2 binds to its cellular receptor, the angiotensin converting enzyme 2 (ACE2), which also acts as receptor for SARS-CoV. Viral entry occurs after proteolytic cleavage of the spike protein by the transmembrane protease TMPRSS2. ACE2 is expressed abundantly in lung alveolar cells, but also in many cell types and organs in the body, including the cerebral cortex, digestive tract, kidney, gallbladder, testis, and adrenal gland.4

Experience with the neurological complications of MERS and SARS provides a framework for considering both reported and potential neurological complications with SARS-CoV-2 and COVID-19.510 In both MERS and SARS, significant neurological complications were fortunately extremely rare. Reported cases of neurological disease suggests a minimum incidence of ~1:200 cases (MERS) -1:1,000 cases (SARS). It is important to recognize, however, that the total number of confirmed cases of MERS and SARS together is only ~10,500 cases. It is likely that the sheer numeracy of COVID-19 compared to MERS and SARS, with nearly 6 million cases reported worldwide to date, will bring out a broader spectrum of neurological manifestations. In MERS and SARS neurological disease could be considered in three major categories: (1) the neurological consequences of the associated pulmonary and systemic diseases, including encephalopathy and stroke, (2) direct central nervous system (CNS) invasion by virus, including encephalitis, and (3) postinfectious and potentially immune-mediated complications, including Guillain-Barre syndrome (GBS) and its variants and acute disseminated encephalomyelitis (ADEM).

Neurological Complications of Systemic COVID-19

In a review of 214 patients hospitalized in 3 dedicated COVID-19 hospitals in Wuhan, China, 36% of patients had nerurologic.11 These were further subdivided into those thought to reflect CNS, peripheral nervous system (PNS), and skeletal muscle injury. Overall, 25% of patients had symptoms considered as evidence of CNS dysfunction, including dizziness (17%), headache (13%), impaired consciousness (7.5%), acute cerebrovascular disease (3%), ataxia (0.5%), and seizures (0.5%). Confirming this low incidence of seizures, no cases of status epilepticus or new onset seizures were reported in a large cohort of over 304 hospitalized patients with COVID-19 in Hubei Province, China,12 although there have been isolated case reports describing seizures at presentation in both adult and pediatric patients with COVID-19.1314

In the series by Mao and colleagues,11 the patients were subdivided based on the severity of their pneumonia and pulmonary impairment, and among those with “severe” disease (n = 88) the incidence of CNS symptoms was higher (31%) compared to the non-severe group (21%), although the results were not statistically significant (p = 0.09). Although all the categorized CNS symptoms occurred more frequently in patients with severe disease compared to non-severe disease, only impaired consciousness (15% in severe vs 2% in non-severe, p < 0.001) and acute cerebrovascular disease (5.7% vs 0.8%; p = 0.03) were significantly different between the two groups. Diagnostic studies were limited, but the impairment of consciousness seems most consistent with encephalopathy. Not surprisingly, when compared to those with non-severe disease, the severe cohort were older (58 ± 15 years vs 49 ± 15 years), and more likely to have comorbidities, including hypertension, diabetes, malignancy, cardiac, cerebrovascular, or kidney disease (48% vs 33%; p = 0.03). The severe group also had more evidence of systemic inflammation, including elevated C-reactive protein (CRP; median 37 mg/L) and D-dimer (median 0.9 mg/L) compared to non-severe cases, and were also more likely to have evidence of hepatic (elevated alanine and aspartate aminotransferases) and renal (elevated BUN and creatinine) dysfunction.

A second survey of 58 hospitalized patients (median age 63 years) with COVID-19 ARDS at Strasbourg University Hospital found that 69% of patients had agitation, 67% had corticospinal tract signs, and 36% had a “dysexecutive” syndrome with difficulty in concentration, attention, orientation, and following commands.15 All patients studied (11/11) had evidence of frontal hypoperfusion on arterial spin label and dynamic susceptibility-weighted perfusion magnetic resonance imaging (MRI). Only seven patients had a cerebrospinal fluid (CSF) examination, none had a pleocytosis, and none had SARS-CoV-2 RNA detected by reverse transcriptase-polymerase chain reaction (RT-PCR). One patient did have elevated immunoglobulin G (IgG) levels and “mildly” elevated total protein. CSF specific oligoclonal bands (OCBs) were not detected, but one patient had “mirror pattern” OCBs in CSF and serum.

In a study of MRI abnormalities in patients in the intensive care unit (ICU) with COVID-19, 21% (50/235) of patients developed neurological symptoms.16 In this group of neurologically symptomatic patients, only 27 had MRIs performed, and of these 44% (12/27) had new acute findings. Surprisingly, 56% (15/27) had no new MRI changes. The most common new abnormalities were multifocal areas of cortical fluid-attenuated inversion recovery (FLAIR) signal (10/12), accompanied in three patients by areas of increased FLAIR signal in the subcortical and deep white matter. One patient each had new transverse sinus thrombosis and acute middle cerebral artery infarction. Five of the 10 patients with cortical FLAIR abnormalities had a CSF examination, and none of these patients had a pleocytosis elevated IgG index, or OCBs (0/3 tested), although 4 patients had an elevated protein (mean 80 mg/dl; range = 60–110). RT-PCR for SARS-CoV-2 was negative in all 5 cases tested. In another MRI series of critically ill patients on mechanical ventilation, many were found to have confluent T2 hyperintensities and restricted diffusion in the deep and subcortical white matter, in some cases, accompanied by punctate microhemorrhages in the juxtacortical and callosal white matter that resembled findings seen in delayed post-hypoxic leukoencephalopathy.17

The mechanism of encephalopathy in COVID-19 remains to be determined. From available studies, COVID-19 encephalopathy seems to be more common in patients with more severe disease, associated comorbidities, evidence of multi-organ system dysfunction, including hypoxemia, and renal and hepatic impairment, and elevated markers of systemic inflammation. Virus is not detected in CSF by RT-PCR and pleocytosis is usually absent. Some patients may have altered perfusion detectable by MRI, others have leukoencephalopathy with or without punctate microhemorrhages. This group needs to be distinguished from patients with encephalitis (who have a pleocytosis) and postinfectious immune-mediated encephalitis (see below).

In a series of five consecutive patients with COVID-19 with delayed awakening post-mechanical ventilation for ARDS, MRI showed enhancement of the wall of basal skull arteries without enlargement of the vessel wall or stenosis. Toxic-metabolic derangements and seizures were ruled out, CSF SARS-CoV-2 RT-PCR was negative in all and they showed marked improvement in alertness 48 to 72 hours after treatment with methylprednisolone 0.5 g/days iv for 5 days. These findings suggest that an endothelialitis rather than a vasculitis was responsible for the encephalopathy.18 Direct infection of endothelial cells by SARS-CoV-2 and associated endothelial inflammation has been demonstrated histologically in postmortem specimens from a variety of organs, which did not include the brain.19

However, in an autopsy series, including examination of the brain, of 20 patients with COVID-19, six had microthrombi and acute infarctions and two focal parenchymal infiltrates of T-lymphocytes, whereas the others mainly had minimal inflammation and slight neuronal loss without acute hypoxic–ischemic changes in most cases. There was no evidence of meningoencephalitis, microglial nodules, or viral inclusions, including in the olfactory bulbs and brainstem, and no demyelination. ACE2 was expressed in lung and brain capillaries. All cases had evidence of systemic inflammation.20

A second major manifestation of systemic COVID-19 disease is acute cerebrovascular disease. In the study by Mao and colleagues,11 this was present in 6 of the 214 (3%) hospitalized cases, but 5 of the 6 events occurred in those with severe disease (incidence 6%; p = 0.03 vs non-severe disease).11 Five of the six reported events were ischemic strokes, and one was hemorrhagic. In the review of cases at Strasbourg University Hospital,15 3 of 13 (23%) had cerebral ischemic stroke. In a single center retrospective study from China of 221 patients hospitalized with COVID-19, 13 had acute strokes, including 11 ischemic, 1 hemorrhagic, and 1 venous sinus thrombosis.21 The stroke patients were older, had more comorbidities, including diabetes, hypertension, and a prior stroke, and elevated inflammatory markers, including D-dimer and CRP. Another review of six consecutive patients with COVID-19 admitted to the National Hospital in Queen Square with stroke, noted that occlusions typically involved large vessels and often occurred in multiple vascular territories.22 In 5 of 6 cases, the strokes occurred 8 to 24 days after onset of COVID-19 symptoms. All patients had a highly prothrombotic state with very high D-dimer levels and elevated ferritin. Five of the six patients had detectable lupus anticoagulant, suggesting another potential prothrombotic mechanism for stroke in COVID-19. Anticardiolipin IgA and antiphospholipid IgA and IgM antibodies directed against β2-glycoprotein-1 were also found in three patients with COVID-associated multiple territory large vessel infarctions.23 Finally, a postmortem MRI study showed subcortical micro- and macro-bleeds (two decedents), cortico-subcortical edematous changes evocative of posterior reversible encephalopathy syndrome (PRES; one decedent), and nonspecific deep white matter changes (one decedent).24

Although initial reports emphasized acute cerebrovascular disease in older patients with COVID-19, a recent report described five cases of large vessel stroke as a presenting feature of COVID-19 in younger individuals, two of whom lacked classic stroke risk factors.25 These patients ranged in age from 33 to 49 years. Two of the five patients had diabetes, one of whom had had a mild prior stroke, and one had hypertension and dyslipidemia. The infarcts involved large vessel territories, including the middle cerebral artery (3), posterior cerebral artery (1), and internal carotid artery (1). Two patients had preceding COVID-19 symptoms, including fever, chills, cough, and headache; one patient had only lethargy. Surprisingly, two of the five patients had no COVID-19-related symptoms preceding their stroke presentation. These five patients had elevated prothrombin (range = 12.8–15.2 seconds) and activated partial thromboplastin times (range = 25–42.7 seconds), elevated fibrinogen (range = 370–739 mg/dl), D-dimer (range = 52–13,800 ng/ml) and ferritin (range = 7–1,564 ng/ml) consistent with a hypercoagulable state and the presence of disseminated intravascular coagulation (DIC).

COVID-19 cerebrovascular disease seems to be predominantly ischemic and to involve large vessels. In older individuals, it reflects the underlying severity of systemic disease as well as the hyperinflammatory state, whereas in younger patients, it seems to be due to hypercoagulopathy. Children with a Kawasaki disease-like multisystem inflammatory syndrome (MIS) have recently been described.2627 Patients with Kawasaki disease can develop cerebral vasculopathy and forms of neurological involvement, and in one series of 10 COVID-19 associated cases of MIS, two patients had meningeal symptoms.27 As noted, in addition to hypercoagulable states, SARS-CoV-2 can infect and injure endothelial cells. However, it remains to be determined whether virus-induced injury to endothelial cells (a vasculopathy) or even true vasculitis contributes to COVID-19 related cerebrovascular syndromes, and this determination will require additional detailed vessel imaging and neuropathological analyses. Similarly, the number of cases is too small to determine the comparative therapeutic benefit, if any, of antiplatelet or anticoagulant drugs or immunomodulatory therapies in COVID-19 associated neurovascular syndromes.

Neuroinvasion by SARS-CoV-2

In contrast to encephalopathy, in which evidence for direct invasion by virus of the CNS is absent, encephalitis occurs when direct invasion of the CNS by virus produces tissue injury and neurological dysfunction. Evidence for direct invasion of the CNS was seen in patients with SARS. Xu and colleagues described a fatal case in a 39-year-old man with delirium that progressed to somnolence and coma.10 At postmortem, the SARS-CoV antigen was detected in brain tissue by immunohistochemistry (IHC) and viral RNA by in situ hybridization (ISH). SARS-CoV virions were seen by transmission electron microscopy of brain tissue inoculated cell culture. In a postmortem analysis of four patients with SARS, low level infection of cerebral neurons with SARS-CoV (1–24% of cells) was seen in the cerebrum in all four cases by IHC and ISH, although none of the cases had virus detected in the cerebellum.28

By definition, encephalitis is an inflammatory process, with supportive evidence, including the presence of a CSF pleocytosis and elevated protein. However, in studies of transgenic mice expressing the human SARS-CoV receptor, ACE2, infection with SARS-CoV was associated with viral entry into the CNS, spread within the CNS, and neuronal injury with relatively limited inflammation.29 This suggests the possibility that, in some cases of SARS-CoV-2 CNS invasion, that signs of inflammation could be modest or even absent. Regardless of the presence or absence of inflammation, diagnostic studies may show evidence of either a generalized or focal CNS process, including areas of attenuation on computed tomography (CT), hyperintense signal on FLAIR, or T2-weighted sequences on MRI, and focal patterns, including seizures on electroencephalogram (EEG). Definitive evidence supporting direct viral invasion would include a positive CSF RT-PCR for SARS-CoV-2, demonstration of intrathecal synthesis of SARS-CoV-2-specific antibodies, or detection of SARS-CoV-2 antigen or RNA in brain tissue obtained at biopsy or autopsy.

Cases meeting strict criteria for encephalitis resulting from direct SARS-CoV-2 are currently extremely rare, although several plausible case reports have now surfaced. Moriguchi et al described a 24-year-old man with COVID-19 disease who developed nuchal rigidity, progressively decreased consciousness (Glasgow Coma Scale [GCS] = 6), and generalized seizures.30 CSF showed a slight mononuclear predominant pleocytosis (12 cells/μl3) and elevated opening pressure (>320 mm H20). Neuroimaging showed hippocampal and mesial temporal increased FLAIR signal and the CSF RT-PCR was positive for SARS-CoV-2. Unfortunately, studies to exclude other viral etiologies of encephalitis were limited. A second case involved a 41-year-old woman with headache, fever, a new onset seizure, and photophobia and nuchal rigidity, followed by hallucinations and disorientation. A head CT scan was normal and MRI was not performed. An EEG showed generalized slowing. The CSF examination showed a lymphocytic pleocytosis (70 cells/μl; 100% lymphocytes), and elevated protein (100 mg/dl), and a positive SARS-CoV-2 RT-PCR.3132

Several cases have emerged in which patients had inflammatory features consistent with encephalitis, but who did not have evidence of direct viral CNS invasion. Bernard-Valnet et al reported on two patients with “meningoencephalitis concomitant to SARS-CoV2.”33 These patients had nuchal rigidity, altered mental status, mild CSF lymphocytic pleocytosis (17–21 cells/μl3 on initial lumbar puncture [LP]), and mildly elevated CSF protein (46–47 mg/dl). However, in both patients, the MRI was normal and neither patient had a positive CSF RT-PCR for SARS-CoV-2. Similarly, Pilotto et al describe a 60-year-old man with COVID-19 who developed confusion, irritability, and then apathy progressing to “akinetic mutism” with nuchal rigidity.34 The CSF showed a mild lymphocytic pleocytosis (18 cells/μl3) and elevated protein (70 mg/dl). An EEG showed generalized slowing with an anterior predominance. The CT and MRI were normal, and CSF RT-PCR was negative twice for SARS-CoV-2. Although treated with a wide variety of medications, this patient showed improvement coincident to administration of high dose methylprednisolone.34 Another study reported on six critically ill patients with severe ARDS, elevated inflammatory markers, and depressed consciousness and/or agitation, who were considered to have “autoimmune meningoencephalitis.”35 No patient had a CSF pleocytosis but five had elevated CSF protein (52–131 mg/dL) and three had an MRI that showed cortical hyperintensities with sulcal effacement. There were no controls but patients were felt to have responded to plasma exchange. In one report, a patient with neuropsychiatric symptoms and COVID-19 had a “hematic” CSF tap with 960 “red and white blood cells” and an elevated protein (65 mg/dL) and detectable N-methyl-D-aspartate (NMDA) receptor antibodies. This currently isolated case also raises the possibility that COVID-19 may trigger auto-antibody production.36

The available studies suggest that SARS-CoV-2 can rarely produce a true encephalitis or meningoencephalitis with associated evidence of direct viral invasion of the CNS. The failure to detect virus in CSF in the other reported cases, despite evidence of inflammation as evidenced by CSF pleocytosis and elevated protein, raises the possibility that some cases of COVID-19 encephalitis may occur in the absence of direct virus invasion, and could potentially result from immune-mediated inflammatory mechanisms (see below). It is important to realize that techniques, including detection of intrathecal SARS-CoV-2 antibody synthesis or of viral antigen or nucleic acid in brain tissue, may establish evidence for viral invasion when CSF RT-PCR studies are negative. For example, detection of intrathecal antibody synthesis is significantly more sensitive than CSF nucleic acid amplification tests for diagnosis of both West Nile Virus neuroinvasive disease and Enterovirus (EV)-D68 associated acute flaccid myelitis (AFM).3739 In the case of EV-D68-associated AFM, nasopharyngeal and throat swabs are frequently positive for virus by RT-PCR when obtained early after disease onset, yet, CSF RT-PCR tests are only positive in a small minority (<3%) of cases.40 The sensitivity of SARS-CoV-2 RT-PCR in properly performed nasopharyngeal swabs for detection of acute COVID-19 is high, but data are currently too limited to evaluate sensitivity of this technique in CSF in patients with neurological disease.

Post-Infectious and Immune-Mediated Complications of SARS-CoV-2

The identification of postinfectious complications of SARS-CoV-2 would be expected to temporally lag behind those resulting from acute infection. Occasional cases of GBS and its variants and of ADEM were reported after MERS and SARS.579 Reports are now emerging of similar associations with COVID-19 and GBS, and with GBS variants, including the Miller-Fisher syndrome.4146 The largest series to date, describes five patients.47 In this series, all patients developed GBS 5 to 10 days following COVID-19 symptom onset. The clinical presentation included bilateral multi-limb flaccid weakness with areflexia. Three patients had associated respiratory failure and two had associated facial weakness. MRI showed caudal root nerve enhancement in two cases and enhancement of the facial nerve in a third case. The CSF was normocellular in all five cases, and had an elevated protein consistent with albuminocytological dissociation in three cases. Electrophysiological studies showed reduced compound motor amplitudes and prolonged distal latencies, and the overall pattern was felt to be consistent with demyelination in two cases and axonal neuropathy in three cases. Fibrillation potentials were seen by electromyography (EMG) acutely in three patients and later in a fourth patient. None of the patients had SARS-CoV-2 detected in the CSF by RT-PCR. Antiganglioside antibodies were absent in the three tested patients. All patients received intravenous immunoglobulin (ivIG) and one plasma exchange, although improvement was noted in only two cases (one “mild improvement” only).

Cases of acute necrotizing encephalopathy (ANE) have been reported in COVID-19.4849 One patient was a 50-year-old woman with COVID-19 confirmed by nasopharyngeal RT-PCR who developed altered mental status and MRI and CT findings typical of ANE, including bilateral thalamic lesions. Unfortunately, CSF studies were limited and CSF RT-PCR testing for SARS-CoV-2 was not performed. A second case occurred in a 59-year-old woman with aplastic anemia who developed seizures and reduced consciousness 10 days after onset of her COVID-19 symptoms.49 The mechanism behind ANE remains unknown, and either direct viral or postinfectious inflammatory processes have been postulated to play a role, and many cases have been reported after upper respiratory infections, including influenza. Some patients have mutations in RAN binding protein-2 (RANBP2), indicating that host genetic factors may also play a role in susceptibility.

Rare cases of ADEM were associated with MERS.6 The first case of “COVID-19 associated disseminated encephalomyelitis” was reported in a 40-year-old woman.50 This individual had COVID-19 symptoms followed 11 days later by dysarthria, dysphagia, facial weakness, and a gaze preference. A chest X-ray showed pneumonia and nasopharyngeal RT-PCR was positive for SARS-CoV-2. Head CT showed multiple areas of patchy hypoattenuation and an MRI showed areas of increased FLAIR and T2 signal in the subcortical and deep white matter that were felt to be consistent with demyelination. Her CSF was normal. A second reported case was in a 54-year-old woman who developed seizures and neurological deterioration (GCS = 12) and had chest X-ray lesions consistent with COVID-19 and a positive nasopharyngeal RT-PCR for SARS-CoV-2.51 Her MRI showed multiple periventricular T2 hyperintense, nonenhancing, lesions in the white matter of the cerebrum, brainstem, and spinal cord consistent with multifocal demyelination. Her CSF studies were unremarkable, including a negative CSF RT-PCR for SARS CoV-2. She was treated with high dose dexamethasone and her symptoms gradually resolved. A single case of acute flaccid myelitis has also been described in COVID-19.52 This patient developed upper limb weakness and a flaccid areflexic lower limb paralysis, urinary and bowel incontinence, and a T10 sensory level. Unfortunately, neither spine imaging nor CSF studies were available so the mechanism remains unknown. The most convincing example of ADEM-like pathology associated with COVID-19 was in a 71-year-old man who developed symptoms immediately following coronary bypass graft surgery that progressed to respiratory failure and a hyperinflammatory state. A postmortem examination showed brain swelling and disseminated hemorrhagic lesions and subcortical white matter pathology with perivenular myelin injury but also necrotic blood vessels and perivascular inflammation. The lesions had features of both acute hemorrhagic leukoencephalitis and of acute disseminated encephalomyelitis.53

The rarity of postinfectious potentially immune-mediated cases following COVID-19 other than GBS and its variants, and the general paucity of details, makes their status unclear. The cases of ADEM-like illness are hard to distinguish from some of the patients with acute encephalopathy and associated MRI white matter lesions, but can be differentiated from cases of encephalitis by the absence of CSF pleocytosis. GBS is a common neurological disease even in the absence of COVID-19, and identifying the magnitude of the COVID-19 risk and association will require better epidemiological data. However, the 5 cases of GBS occurring in a population of 1,000 to 1,200 patients with COVID-19 seen over a 1 month period by Toscano et al in Northern Italy suggest an incidence that is much higher than that can be expected in the general population (~1/100,000 person-years).54 The mechanism of pathogenesis will need to be identified, and the efficacy of conventional therapies, including ivIG and plasma exchange, evaluated.

Other COVID-19 Related Neurological Disorders

One of the more striking reported symptom manifestations in patients with COVID-19 is loss or perturbation of smell (anosmia or hyposmia) and/or taste (dysgeusia). The frequency of these symptoms, their specificity as a potential diagnostic clue for COVID-19 infection as opposed to influenza or other symptomatologic similar diseases, and their implication for understanding viral pathogenesis all remain uncertain. In the Wuhan COVID-19 series, impairment of smell was noted in 5% and of taste in 6% of the 214 hospitalized patients.11 It is likely that the frequency was under-represented due to incomplete evaluations in these hospitalized sick patients. A later study of 31 patients, suggested that disorders of taste occurred in 81% of COVID-19 cases (46% anosmia, 29% hyposmia, and 6% dysosmia) and disorders of taste in 94% (ageusia 45%, hypogeusia 23%, and dysgeusia 26%).55 The average duration of smell and taste disorders in the COVID-19 cases was 7.1 ± 3.1 days. A multicenter European study of 417 cases with “mild-to-moderate” COVID-19 disease found a similarly high frequency of olfactory dysfunction (86%), with 80% of those affected having anosmia and 20% hyposmia.56 Approximately 70% of patients had recovered within 8 days of symptom onset. It has been suggested that olfactory and/or gustatory dysfunction may be indicative of neuro-invasion and provide a route from the nasopharynx or oropharynx to cardiorespiratory centers in the medulla, based on studies of transgenic mice expressing the human SARS virus receptor (ACE2) and infected with SARS-CoV, however, no evidence supporting host entry via this pathway yet exists in man.29 The transient nature of the dysfunction in most patients would seem to make direct viral infection and subsequent killing of olfactory or gustatory neurons unlikely. MRI of the olfactory bulb was normal in one RT-PCR confirmed patient with anosmia.57

In the Wuhan COVID-19 series, 11% of patients were reported to have evidence of skeletal muscle injury (defined as a creatine kinase [CK] >200 U/L and skeletal muscle pain).11 Injury was significantly more common in patients with “severe” disease (19%) compared to non-severe disease (5%; p < 0.001). Unfortunately, almost no clinical details were provided beyond the presence of associated muscle pain. Subsequently two reports have emerged of rhabdomyolysis as either a presenting feature or a late complication of COVID-19.5859 One patient had limb pain and weakness with a peak CK of ~12,000 U/L and myoglobulin >12,000 μg/L, and the other had a peak CK of 13,581 U/L. Neither patient had muscle biopsy performed. The mechanism of injury remains to be determined.

Immunopathogenesis of SARS-CoV-2 and Implication for Management and Treatment of Neurologic Manifestations

One of the most puzzling features of SARS-CoV-2 infection is that it is asymptomatic or associated with minor symptoms in approximately 80% of patients, especially children and young adults, whereas 20% will develop COVID-19 with various degrees of severity. Can knowledge gathered on SARS-CoV inform us about the immunopathogenesis of SARS-CoV-2? A successful production of type I interferon (IFN) response is a key first line defense for suppressing replication of many neurotropic viruses at the site of entry and dissemination. SARS-CoV suppresses type I IFN response and downstream signaling using multiple strategies, and this dampening is closely associated with disease severity.60

Because SARS-CoV-2 shares an overall genomic similarity of 80% with SARS-CoV and uses the same receptor, it is reasonable to expect that the innate immune mechanisms involved in pathogenesis will be similar for the two viruses. SARS-CoV has developed multiple strategies to evade the innate immune response in order to optimize its replication capacity.61 It seems likely that SARS-CoV-2 uses the same strategy. The magnitude of the immune response against SARS-CoV-2 needs to be precisely calibrated to control viral replication without triggering immunopathogenic injury. A hyperinflammatory response likely plays a major role in ARDS and, in a subset of children, may contribute to the development of a Kawasaki-like multisystem inflammatory disorder.20 In a mouse model of SARS, rapid SARS-CoV replication and delay in IFN-I signaling led to inflammatory monocyte–macrophage accumulation, resulting in elevated lung cytokine/chemokine levels and associated vascular leakage and lethal pneumonia. This “cytokine storm,” in turn, was associated with a decrease in T cell counts and suboptimal T cell responses to SARS-CoV infection.62

The same pattern is found in 522 patients with COVID-19, where the number of total T cells, CD4+ and CD8+ T cells, were dramatically reduced, especially in those requiring ICU care, and T cell numbers were negatively correlated to serum IL-6, IL-10, and TNF-α concentration. Conversely, patients in the disease resolution period showed reduced IL-6, IL-10, and TNF-α levels and restored T cell counts.63 These data were corroborated by other groups who also noticed a decrease in type 1 interferon response in severely affected patients.6465 It has been suggested that reduced and delayed IFN gamma production (“too little and too late”) in the lungs and depletion of both CD4+ and CD8+ T cells may combine to potentiate viral injury, by reducing control of viral replication and enhancing the upregulation of pro-inflammatory cytokines, including TNF-α, IL-6, and IL-10 (“cytokine storm”), and that it may be the immune dysregulation as much or more than the direct viral infection that results in pulmonary epithelial cell injury, and similar mechanisms could be operative in the CNS.66

What are the possible mechanisms for the apparent immune dysregulation seen in those patients and could they have a role in the neuropathogenesis of COVID-19? The source of cytokines found in the serum in unclear, but they could be produced by lung macrophages. IL-6 could also come from infected neurons, as seen in a transgenic mouse model of SARS-Cov.29 A high level of circulating cytokines, in turn, could lead to lymphocytopenia. TNF-α, a pro-inflammatory cytokine, may cause T cell apoptosis via interacting with its receptor, TNFR1, which expression is increased in aged T cells.6768 IL-6, that has both pro-inflammatory and anti-inflammatory properties, contributes to host defense in response to infections. However, continual synthesis of IL-6 has been shown to play a pathological role in chronic inflammation and infection.6970 IL-10, an inhibitory cytokine that prevents T cell proliferation, can also induce T cell exhaustion. Interestingly, patients with COVID-19 have high levels of the PD-1 and Tim-3 exhaustion markers on their T cells.63 In turn, decreased numbers of CD4+ and CD8+ T lymphocytes will considerably weaken the cellular immune response to SARS-CoV-2 in severe cases, allowing further viral replication. This can be compounded by the use of corticosteroids. Of note, a study in convalescent patients with SARS-CoV showed that CD8+ T cell responses were more frequent and had a greater magnitude of response than CD4+ T cells.71 Finally, one autopsy series of patients with COVID-19 showed histological features suggestive of secondary hemophagocytic lymphohistiocytosis (sHLH), also known as macrophage activation syndrome. This syndrome is characterized by an imbalance of innate and adaptive immune responses with aberrant activation of macrophages, and a blunted adaptive immune response.20

This dysregulated immune response may have a role in the pathogenesis of the COVID-19 encephalopathy. High levels of circulating pro-inflammatory cytokines can cause a confusion and alteration of consciousness, whereas a weakened T cell response may be unable to eliminate virus-infected cells in the brain causing further neurologic dysfunction. Careful studies of the CSF cytokine profile and T cell response to SARS-CoV-2 as well as postmortem studies, including CNS and muscle tissues, are urgently needed to better understand the neuropathogenesis of COVID-19. These will help inform whether therapeutic strategies aimed at blocking pro-inflammatory cytokines, including the IL-6 inhibitors tocilizumab and sarilumab, could have a beneficial effect on encephalopathy or whether corticosteroids that dampened the adaptive cellular immune response to viruses are contra-indicated. As we strive to find medications to counter the deleterious inflammatory state triggered by SARS-CoV-2, lessons can also be learned from COVID-19 outcomes in patients with neurological diseases, such as multiple sclerosis or myasthenia gravis, treated with immunomodulatory therapies.

Although we are only starting to grasp the complexity of SARS-CoV-2 biology, it is already apparent that COVID-19 causes a global threat to the entire nervous system, both through its worldwide distribution and multifactorial pathogenic mechanisms (Fig). As we hope for a vaccine or a cure, neurologists will play an important role in diagnosing, investigating, and treating the many neurologic manifestations of COVID-19 (Table).72

Details are in the caption following the image
FIGURE 1Open in figure viewerPowerPointMechanisms of severe acute respiratory syndrome-coronavirus type 2 (SARS-CoV-2) neuropathogenesis. SARS-CoV-2 pathogenic effects on the nervous system are likely multifactorial, including manifestations of systemic disease, direct neuro-invasion of the central nervous system (CNS), involvement of the peripheral nervous system (PNS) and muscle, as well as through a postinfectious, immune-mediated mechanism. MOF = multi-organ failure; GBS = Guillain-Barre syndrome. *CNS inflammation (CSF pleocytosis and proteinorrachia) with no evidence of direct viral infection of CNS; §direct evidence of viral invasion (reverse transcriptase-polymerase chain reaction positive [RT-PCR+], biopsy); ADEM = acute disseminated encephalomyelitis; ANE = acute necrotizing encephalopathy. [Color figure can be viewed at www.annalsofneurology.org]

TABLE 1. Neurologic Conditions Associated with SARS-CoV-2 Infection

Disease entityPresentationSupportive Neurodiagnostic testingPathogenesis
EncephalopathyAltered mental statusMRI: non-specificEEG: abnormal (slow)CSF: nl cells and ProCSF SARS-CoV-2 RT-PCR: NEGMultiple organ failureHypoxemiaSystemic InflammationEndothelialitis
EncephalitisAltered mental status and CNS dysfunctionMRI: non-specific (? WM changes)EEG: abnormal (slow, +focal)CSF: pleocytosis & elev. ProCSF SARS-CoV-2 RT-PCR: NEGCNS inflammation
Viral encephalitisAltered mental status and CNS dysfunctionMRI: new abnormalityEEG: abnormal (slow, ±focal)CSF: Pleocytosis and elev. ProCSF SARS-CoV-2 RT-PCR: POSBrain Tissue: POS (Ag or RNA)Brain parenchymal neuro-invasion
Viral meningitisHeadache, nuchal rigidityMRI: meningeal enhancement, CSF: pleocytosis & elev. ProCSF SARS-CoV-2 RT PCR: POSSubarachnoid invasion
StrokeFocal motor or sensory deficitMRI: ischemia or bleed, abnormal coagulation factors, increased inflammatory markersCoagulopathy
Anosmia/ageusiaOlfactory or taste dysfunctionAbnormal smell/taste tests? Peripheral vs central neuro-invasion
ADEMHeadache, acute neurologic symptomsMRI: hyperintense FLAIR lesions with variable enhancementPostinfectious
Guillain-Barre syndromeFlaccid muscle weaknessCSF: increased protein, nl WBC CSF SARS-CoV-2 RT-PCR: NEGEMG/NCS: abnormalPostinfectious
Muscle injuryMyalgiaCK elevatedMyopathy or myositis?
  • ADEM = acute disseminated encephalomyelitis; CNS = central nervous system; CK= creatinine kinase; CSF = cerebrospinal fluid; EEG = electroencephalogram; EMG = electromyogram; FLAIR = fluid-attenuated inversion recovery; MRI = magnetic resonance imaging; NCS = nerve conduction study; NEG = negative; POS = positive; pro = protein; RT-PCR = reverse transcriptase-polymerase chain reaction; SARS-CoV-2 = severe acute respiratory syndrome-coronavirus type 2; WBC = white blood cell; WM = white matter.

Most long-COVID sufferers battle neurological symptoms, including some cognitive issues never seen before

Authors: Jocelyn Solis-Moreira JUNE 20, 2022 https://www.braintomorrow.com/

People continue to experience neurological problems six months after recovering from a COVID-19 infection, finds a recent study from the University of California San Diego. In fact, scientists say most coronavirus long-haulers battle brain-related issues.

The findings are part of a long-term study tracking the progression of neurological symptoms in people with long COVID. Not only do neurological symptoms persist, the researchers also found never-before-seen motor coordination and cognitive issues in long-haulers.

“It’s encouraging that most people were showing some improvement at six months, but that wasn’t the case for everyone,” says Dr. Jennifer S. Graves, associate professor at UC San Diego School of Medicine and neurologist at UC San Diego Health in a media release. “Some of these participants are high-level professionals who we’d expect to score above average on cognitive assessments, but months after having COVID-19, they’re still scoring abnormally.” 

Between October 2020 to October 2021, the research team tracked the health of 56 people who developed neurological issues after a mild to moderate COVID-19 infection. None of the people had a history of neurological conditions before becoming sick from the virus. People first received a neurological exam, cognitive test, survey questions on symptoms, and the option for a brain scan.

In the first visit, 89% of people reported fatigue, and 80% said they felt constant headaches. Other neurological symptoms ranged from memory troubles, insomnia, and loss of concentration. About 80% of people said the neurological symptoms affected their quality of life.

After a 6-month follow-up, only one-third of people fully recovered from their neurological symptoms. The other two-thirds continued to show neurological symptoms, though the symptoms decreased in severity. For those that continued to have symptoms, the most common was memory impairment and lack of focus.

One surprising finding for the team was that 7% of people had a set of symptoms that to their knowledge have never observed in people with long COVID. The symptoms included cognitive deficits, tremors, and trouble keeping their balance. The authors labeled the symptoms as Tremor, Ataxia, and Cognitive deficit (PASC-TAC).

“These are folks who had no neurological problems before COVID-19, and now they have an incoordination of their body and possible incoordination of their thoughts,” comments Dr. Graves. “We didn’t expect to find this, so we want to get the word out in case other physicians see this too.” 

There is still much work to be done to study how the virus penetrates the brain. Dr. Graves hypothesizes that inflammatory autoimmune responses in the brain caused by the infection is likely the reason behind these delayed neurological symptoms.

The study is projected to last for 10 years, with researchers following up with people every year. Other parts of the research will focus on how different COVID-19 variants and vaccines impact persistent neurological symptoms.

“To have people’s cognition and quality of life still impacted so long after infection is something we as a society need to be taking a serious look at,” says Dr. Graves. “We still need to know how common this is, what biological processes are causing this, and what ongoing health care these people will need. This work is an important first step to getting there.”

The study is published in the journal Annals of Clinical and Translational Neurology.

Warning to anyone who’s had Covid as scientists discover symptoms that can last for TWO YEARS

Authors: Vanessa Chalmers, Digital Health Reporter May 11 2022  May 12 2022

DOCTORS have discovered the symptoms of Covid that can last for two years or more. 

Their research has shown that half of patients admitted to hospital are still likely to have at least one persistent problem two years later.

The study, published in The Lancet Respiratory Medicine, has the longest follow-up period of patients to date. 

Researchers are only able to analyse what symptoms exist after two years given the coronavirus emerged in late 2019.

So it’s possible problems like fatigue and anxiety could stick around even longer.

Lead author Professor Bin Cao, of the China-Japan Friendship Hospital, China, said: “Our findings indicate that for a certain proportion of hospitalised Covid-19 survivors, while they may have cleared the initial infection, more than two years is needed to recover fully from Covid-19.”

The study involved almost 1,200 patients, aged 57 on average, who were infected with the bug in the early phase of the pandemic.

They had all been treated in Wuhan, China, then assessed six months, 12 months and two years after discharge.

Researchers looked at their walking abilities, mental health, quality of life and more.

Covid patients were generally found to be in poorer health than those in the general population two years after infection.

They reported:

  • Fatigue or muscle weakness (31 per cent of Covid patients compared to five per cent in the general population)
  • Sleep difficulties (51 per cent compared with 14 per cent)
  • Pain or discomfort (23 per cent compared with five per cent)
  • Anxiety or depression (12 per cent compared with five per cent)

Joint pain, palpitations, dizziness, and headaches were also more common among previously hospitalised Covid patients.

Not all of those hospitalised were affected, however.

More than half (55 per cent) of participants had at least one symptom of Covid at two years, and were therefore considered “long Covid” patients.

The researchers then compared the long Covid group with the group of participants who had endured Covid, but gotten better. 

Those with long Covid had more pain (35 per cent vs 10 per cent), and mobility issues (five per cent vs one per cent) than their fully recovered counterparts.

Some 13 per cent showed symptoms of anxiety and 11 per cent depression, compared with three per cent and one per cent in non-long Covid patients, respectively. 

The researchers said it’s not possible to say whether problems like these are specific to Covid, or whether other hospital patients experience them.

Long Covid is defined as someone who still battles symptoms beyond four weeks in the UK.

It may be defined as ongoing Covid (four to 12 weeks), or post-Covid syndrome (more than 12 weeks) by medics.

The symptoms may include fatigue, a cough, breathlessness, muscle or joint pain, loss of taste of smell and brain fog.

6-month neurological and psychiatric outcomes in 236,379 survivors of COVID-19: a retrospective cohort study using electronic health records

Authors: Maxime Taquet, John R Geddes, Masud Husain, Sierra Luciano, Paul J Harrison

Summary

Background Neurological and psychiatric sequelae of COVID-19 have been reported, but more data are needed to adequately assess the effects of COVID-19 on brain health. We aimed to provide robust estimates of incidence rates and relative risks of neurological and psychiatric diagnoses in patients in the 6 months following a COVID-19 diagnosis. Methods For this retrospective cohort study and time-to-event analysis, we used data obtained from the TriNetX electronic health records network (with over 81 million patients). Our primary cohort comprised patients who had a COVID-19 diagnosis; one matched control cohort included patients diagnosed with influenza, and the other matched control cohort included patients diagnosed with any respiratory tract infection including influenza in the same period. Patients with a diagnosis of COVID-19 or a positive test for SARS-CoV-2 were excluded from the control cohorts. All cohorts included patients older than 10 years who had an index event on or after Jan 20, 2020, and who were still alive on Dec 13, 2020. We estimated the incidence of 14 neurological and psychiatric outcomes in the 6 months after a confirmed diagnosis of COVID-19: intracranial haemorrhage; ischaemic stroke; parkinsonism; Guillain-Barré syndrome; nerve, nerve root, and plexus disorders; myoneural junction and muscle disease; encephalitis; dementia; psychotic, mood, and anxiety disorders (grouped and separately); substance use disorder; and insomnia. Using a Cox model, we compared incidences with those in propensity score-matched cohorts of patients with influenza or other respiratory tract infections. We investigated how these estimates were affected by COVID-19 severity, as proxied by hospitalisation, intensive therapy unit (ITU) admission, and encephalopathy (delirium and related disorders). We assessed the robustness of the differences in outcomes between cohorts by repeating the analysis in different scenarios. To provide benchmarking for the incidence and risk of neurological and psychiatric sequelae, we compared our primary cohort with four cohorts of patients diagnosed in the same period with additional index events: skin infection, urolithiasis, fracture of a large bone, and pulmonary embolism. Findings Among 236 379 patients diagnosed with COVID-19, the estimated incidence of a neurological or psychiatric diagnosis in the following 6 months was 33·62% (95% CI 33·17–34·07), with 12·84% (12·36–13·33) receiving their first such diagnosis. For patients who had been admitted to an ITU, the estimated incidence of a diagnosis was 46·42% (44·78–48·09) and for a first diagnosis was 25·79% (23·50–28·25). Regarding individual diagnoses of the study outcomes, the whole COVID-19 cohort had estimated incidences of 0·56% (0·50–0·63) for intracranial haemorrhage, 2·10% (1·97–2·23) for ischaemic stroke, 0·11% (0·08–0·14) for parkinsonism, 0·67% (0·59–0·75) for dementia, 17·39% (17·04–17·74) for anxiety disorder, and 1·40% (1·30–1·51) for psychotic disorder, among others. In the group with ITU admission, estimated incidences were 2·66% (2·24–3·16) for intracranial haemorrhage, 6·92% (6·17–7·76) for ischaemic stroke, 0·26% (0·15–0·45) for parkinsonism, 1·74% (1·31–2·30) for dementia, 19·15% (17·90–20·48) for anxiety disorder, and 2·77% (2·31–3·33) for psychotic disorder. Most diagnostic categories were more common in patients who had COVID-19 than in those who had influenza (hazard ratio [HR] 1·44, 95% CI 1·40–1·47, for any diagnosis; 1·78, 1·68–1·89, for any first diagnosis) and those who had other respiratory tract infections (1·16, 1·14–1·17, for any diagnosis; 1·32, 1·27–1·36, for any first diagnosis). As with incidences, HRs were higher in patients who had more severe COVID-19 (eg, those admitted to ITU compared with those who were not: 1·58, 1·50–1·67, for any diagnosis; 2·87, 2·45–3·35, for any first diagnosis). Results were robust to various sensitivity analyses and benchmarking against the four additional index health events. Interpretation Our study provides evidence for substantial neurological and psychiatric morbidity in the 6 months after COVID-19 infection. Risks were greatest in, but not limited to, patients who had severe COVID-19. This information could help in service planning and identification of research priorities. Complementary study designs, including prospective cohorts, are needed to corroborate and explain these findings. Funding National Institute for Health Research (NIHR) Oxford Health Biomedical Research Centre. Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Articles www.thelancet.com/psychiatry Vol 8 May 2021 417 Introduction Since the COVID-19 pandemic began on March 11, 2020, there has been concern that survivors might be at an increased risk of neurological disorders. This concern, initially based on findings from other coronaviruses,1 was followed rapidly by case series,2–4 emerging evidence of COVID-19 CNS involvement,5–7 and the identification of mechanisms by which this could occur.8–11 Similar concerns have been raised regarding psychiatric sequelae of COVID-19,12,13 with evidence showing that survivors are indeed at increased risk of mood and anxiety disorders in the 3 months after infection.14 However, we need large scale, robust, and longer term data to properly identify and quantify the consequences of the COVID-19 pandemic on brain health. Such information is required both to plan services and identify research priorities. In this study, we used an electronic health records network to investigate the incidence of neurological and psychiatric diagnoses in survivors in the 6 months after documented clinical COVID-19 infection, and we compared the associated risks with those following other health conditions. We explored whether the severity of COVID-19 infection, as proxied by hospitalization, intensive therapy unit (ITU) admission, and encephalopathy, affects these risks. We also assessed the trajectory of hazard ratios (HRs) across the 6-month period. Methods Study design and data collection For this retrospective cohort study, we used The TriNetX Analytics Network, a federated network recording anonymized data from electronic health records in 62 health-care organizations, primarily in the USA, comprising 81 million patients. Available data include demographics, diagnoses (using codes from ICD-10), medications, procedures, and measurements (eg, blood pressure and body-mass index). The health-care organizations are a mixture of hospitals, primary care, and specialist providers, contributing data from uninsured and insured patients. These organizations warrant that they have all necessary rights, consents, approvals, and authority to provide the data to TriNetX, so long as their name remains anonymous as a data source and their data are used for research purposes. By use of the TriNetX user interface, cohorts can be created on the basis of inclusion and exclusion criteria, matched for confounding variables with a built-in propensity score-matching algorithm, and compared for outcomes of interest over specified time periods. Additional details about TriNetX, its data, provenance, and functionalities, are presented in the appendix (pp 1–2). Cohorts The primary cohort was defined as all patients who had a confirmed diagnosis of COVID-19 (ICD-10 code U07.1). We also constructed two matched control cohorts: patients diagnosed with influenza (ICD-10 codes J09–11) and patients diagnosed with any respiratory tract infection including influenza (ICD-10 codes J00–06, J09–18, or J20–22). We excluded patients with a diagnosis of COVID-19 or a positive test for SARS-CoV-2 from the control cohorts. We refer to the diagnosis of COVID-19 (in the primary cohort) and influenza or other respiratory See Online for appendix For the TriNetX Analytics Network see www.trinetx.com Research in context Evidence before this study We searched Web of Science and Medline on Aug 1 and Dec 31, 2020, for studies in English, with the terms “(COVID-19 OR SARS-CoV2 OR SARS-CoV-2) AND (psychiatry* or neurology*) AND (incidence OR epidemiology* OR ‘systematic review’ or ‘meta-analysis’)”. We found case series and reviews of series reporting neurological and neuropsychiatric disorders during acute COVID-19 illness. We found one large electronic health records study of the psychiatric sequelae in the 3 months after a COVID-19 diagnosis. It reported an increased risk for anxiety and mood disorders and dementia after COVID-19 compared with a range of other health events; the study also reported the incidence of each disorder. We are not aware of any large-scale data regarding the incidence or relative risks of neurological diagnoses in patients who had recovered from COVID-19. Added value of this study To our knowledge, we provide the first meaningful estimates of the risks of major neurological and psychiatric conditions in the 6 months after a COVID-19 diagnosis, using the electronic health records of over 236000 patients with COVID-19. We report their incidence and hazard ratios compared with patients who had had influenza or other respiratory tract infections. We show that both incidence and hazard ratios were greater in patients who required hospitalization or admission to the intensive therapy unit (ITU), and in those who had encephalopathy (delirium and other altered mental states) during the illness compared with those who did not. Implications of all the available evidence COVID-19 was robustly associated with an increased risk of neurological and psychiatric disorders in the 6 months after a diagnosis. Given the size of the pandemic and the chronicity of many of the diagnoses and their consequences (eg, dementia, stroke, and intracranial hemorrhage), substantial effects on health and social care systems are likely to occur. Our data provide important evidence indicating the scale and nature of services that might be required. The findings also highlight the need for enhanced neurological follow-up of patients who were admitted to ITU or had encephalopathy during their COVID-19 illness. Articles 418 www.thelancet.com/psychiatry Vol 8 May 2021 tract infections (in the control cohorts) as index events. The cohorts included all patients older than 10 years who had an index event on or after Jan 20, 2020 (the date of the first recorded COVID-19 case in the USA), and who were still alive at the time of the main analysis (Dec 13, 2020). Additional details on cohorts are provided in the appendix (pp 2–3). Covariates We used a set of established and suspected risk factors for COVID-19 and for more severe COVID-19 illness:15,16 age, sex, race, ethnicity, obesity, hypertension, diabetes, chronic kidney disease, asthma, chronic lower respiratory diseases, nicotine dependence, substance use disorder, ischemic heart disease and other forms of heart disease, socioeconomic deprivation, cancer (and hematological cancer in particular), chronic liver disease, stroke, dementia, organ transplant, rheumatoid arthritis, lupus, psoriasis, and disorders involving an immune mechanism. To capture these risk factors in patients’ health records, we used 55 variables. More details, including ICD-10 codes, are provided in the appendix (pp 3–4). Cohorts were matched for all these variables, as described in the following subsections. Outcomes We investigated neurological and psychiatric sequelae of COVID-19 in terms of 14 outcomes occurring 1–180 days after the index event: intracranial hemorrhage (ICD-10 codes I60–62); ischemic stroke (I63); Parkinson’s disease and parkinsonism (G20–21); Guillain-Barré syndrome (G61.0); nerve, nerve root, and plexus disorders (G50–59); myoneural junction and muscle disease (neuromuscular disorders; G70–73); encephalitis (G04, G05, A86, or A85.8); dementia (F01–03, G30, G31.0, or G31.83); psychotic, mood, and anxiety disorders (F20–48), as well as each category separately; substance use disorder (F10–19), and insomnia (F51.0 or G47.0). For outcomes that are chronic illnesses (eg, dementia or Parkinson’s disease), we excluded patients who had the diagnosis before the index event. For outcomes that All patients Patients without hospitalization Patients with hospitalization Patients with ITU admission Patients with encephalopathy Cohort size 236379 (100·0%) 190077 (100·0%) 46302 (100·0%) 8945 (100·0%) 6229 (100·0%) Demographics Age, years 46 (19·7) 43·3 (19·0) 57 (18·7) 59·1 (17·3) 66·7 (17·0) Sex Male 104015 (44·0%) 81 512 (42·9%) 22 503 (48·6%) 5196 (58·1%) 3307 (53·1%) Female 131460 (55·6%) 107 730 (56·7%) 23 730 (51·3%) 3743 (41·8%) 2909 (46·7%) Other 904 (0·4%) 835 (0·4%) 69 (0·1%) 10 (0·1%) 13 (0·2%) Race White 135 143 (57·2%) 109635 (57·7%) 25 508 (55·1%) 4918 (55·0%) 3331 (53·5%) Black or African American 44459 (18·8%) 33868 (17·8%) 10591 (22·9%) 2184 (24·4%) 1552 (24·9%) Unknown 48085 (20·3%) 39841 (21·0%) 8244 (17·8%) 1457 (16·3%) 1071 (17·2%) Ethnicity Hispanic or Latino 37 772 (16·0%) 29155 (15·3%) 8617 (18·6%) 2248 (25·1%) 895 (14·4%) Not Hispanic or Latino 134075 (56·7%) 106844 (56·2%) 27 231 (58·8%) 5041 (56·4%) 3873 (62·2%) Unknown 64532 (27·3%) 54078 (28·5%) 10454 (22·6%) 1656 (18·5%) 1461 (23·5%) Comorbidities Overweight and obesity 42871 (18·1%) 30198 (15·9%) 12673 (27·4%) 3062 (34·2%) 1838 (29·5%) Hypertensive disease 71014 (30·0%) 47 516 (25·0%) 23498 (50·7%) 5569 (62·3%) 4591 (73·7%) Type 2 diabetes 36696 (15·5%) 22 518 (11·8%) 14178 (30·6%) 3787 (42·3%) 2890 (46·4%) Asthma 25 104 (10·6%) 19834 (10·4%) 5270 (11·4%) 1132 (12·7%) 755 (12·1%) Nicotine dependence 17 105 (7·2%) 12639 (6·6%) 4466 (9·6%) 1042 (11·6%) 803 (12·9%) Substance use disorder 24870 (10·5%) 18173 (9·6%) 6697 (14·5%) 1620 (18·1%) 1316 (21·1%) Ischemic heart diseases 21082 (8·9%) 11815 (6·2%) 9267 (20·0%) 2460 (27·5%) 2200 (35·3%) Other forms of heart disease 42431 (18·0%) 26066 (13·7%) 16365 (35·3%) 4678 (52·3%) 3694 (59·3%) Chronic kidney disease 15908 (6·7%) 8345 (4·4%) 7563 (16·3%) 1941 (21·7%) 1892 (30·4%) Neoplasms 45 255 (19·1%) 34362 (18·1%) 10893 (23·5%) 2339 (26·1%) 1793 (28·8%) Data are n (%) or mean (SD). Only characteristics with a prevalence higher than 5% in the whole population are displayed. Additional baseline characteristics are presented in the appendix (pp 25–27). ITU=intensive therapy unit. Table 1: Baseline characteristics for the whole COVID-19 cohort and for the non-hospitalization, hospitalization, ITU admission, and encephalopathy cohorts during the illness Articles www.thelancet.com/psychiatry Vol 8 May 2021 419 tend to recur or relapse (eg, ischaemic strokes or psychiatric diagnoses), we estimated separately the incidence of first diagnoses (ie, excluding those who had a diagnosis before the index event) and the incidence of any diagnosis (ie, including patients who had a diagnosis at some point before the index event). For other outcomes (eg, Guillain-Barré syndrome), we estimated the incidence of any diagnosis. More details, and a full list of ICD-10 codes, are provided in the appendix (pp 4–5). Finally, to assess the overall risk of neurological and psychiatric outcomes after COVID-19, we estimated the incidence of any of the 14 outcomes, and the incidence of a first diagnosis of any of the outcomes. This is lower than the sum of incidences of each outcome because some patients had more than one diagnosis. Secondary analyses We investigated whether the neurological and psychiatric sequelae of COVID-19 were affected by the severity of the illness. The incidence of outcomes was estimated separately in four subgroups: first, in those who had required hospitalization within a time window from 4 days before their COVID-19 diagnosis (taken to be the time it might take between clinical presentation and confirmation) to 2 weeks afterwards; second, in those who had not required hospitalization during that window; third, in those who had been admitted to an intensive therapy unit (ITU) during that window; and fourth, in those who were diagnosed with delirium or other forms of altered mental status during that window; we use the term encephalopathy to describe this group of patients (appendix p 5).17,18 Differences in outcome incidence between these subgroups might reflect differences in their baseline characteristics. Therefore, for each outcome, we estimated the HR between patients requiring hospitalization (or ITU) and a matched cohort of patients not requiring hospitalization (or ITU), and between patients with encephalopathy and a matched cohort of patients without encephalopathy. Finally, HRs were calculated for patients who had not required hospitalization for COVID-19, influenza, or other respiratory tract infections. To provide benchmarks for the incidence and risk of neurological and psychiatric sequelae, patients after COVID-19 were compared with those in four additional matched cohorts of patients diagnosed with health events selected to represent a range of acute presentations during the same time period. These additional four index events were skin infection, urolithiasis, fracture of a large bone, and pulmonary embolism. More details are presented in the appendix (pp 5–6). We assessed the robustness of the differences in outcomes between cohorts by repeating the analysis in three scenarios: one including patients who had died by All patients Patients without hospitalization Patients with hospitalization Patients with ITU admission Patients with encephalopathy Intracranial hemorrhage (any) 0·56% (0·50–0·63) 0·31% (0·25–0·39) 1·31% (1·14–1·52) 2·66% (2·24–3·16) 3·61% (2·97–4·39) Intracranial hemorrhage (first) 0·28% (0·23–0·33) 0·14% (0·10–0·20) 0·63% (0·50–0·80) 1·05% (0·79–1·40) 1·19% (0·82–1·70) Ischemic stroke (any) 2·10% (1·97–2·23) 1·33% (1·22–1·46) 4·38% (4·05–4·74) 6·92% (6·17–7·76) 9·35% (8·23–10·62) Ischemic stroke (first) 0·76% (0·68–0·85) 0·43% (0·36–0·52) 1·60% (1·37–1·86) 2·82% (2·29–3·47) 3·28% (2·51–4·27) Parkinsonism 0·11% (0·08–0·14) 0·07% (0·05–0·12) 0·20% (0·15–0·28) 0·26% (0·15–0·45) 0·46% (0·28–0·78) Guillain-Barré syndrome 0·08% (0·06–0·11) 0·05% (0·03–0·07) 0·22% (0·15–0·32) 0·33% (0·21–0·54) 0·48% (0·20–1·14) Nerve, nerve root, or plexus disorders 2·85% (2·69–3·03) 2·69% (2·51–2·89) 3·35% (3·02–3·72) 4·24% (3·58–5·03) 4·69% (3·81–5·77) Myoneural junction or muscle disease 0·45% (0·40–0·52) 0·16% (0·12–0·20) 1·24% (1·05–1·46) 3·35% (2·76–4·05) 3·27% (2·54–4·21) Encephalitis 0·10% (0·08–0·13) 0·05% (0·03–0·08) 0·24% (0·17–0·33) 0·35% (0·19–0·64) 0·64% (0·39–1·07) Dementia 0·67% (0·59–0·75) 0·35% (0·29–0·43) 1·46% (1·26–1·71) 1·74% (1·31–2·30) 4·72% (3·80–5·85) Mood, anxiety, or psychotic disorder (any) 23·98% (23·58–24·38) 23·59% (23·12–24·07) 24·50% (23·76–25·26) 27·78% (26·33–29·29) 36·25% (34·16–38·43) Mood, anxiety, or psychotic disorder (first) 8·63% (8·28–8·98) 8·15% (7·75–8·57) 8·85% (8·22–9·52) 12·68% (11·28–14·24) 12·96% (11·13–15·07) Mood disorder (any) 13·66% (13·35–13·99) 13·10% (12·73–13·47) 14·69% (14·09–15·32) 15·43% (14·27–16·68) 22·52% (20·71–24·47) Mood disorder (first) 4·22% (3·99–4·47) 3·86% (3·60–4·14) 4·49% (4·05–4·99) 5·82% (4·86–6·97) 8·07% (6·56–9·90) Anxiety disorder (any) 17·39% (17·04–17·74) 17·51% (17·09–17·93) 16·40% (15·76–17·06) 19·15% (17·90–20·48) 22·43% (20·65–24·34) Anxiety disorder (first) 7·11% (6·82–7·41) 6·81% (6·47–7·16) 6·91% (6·38–7·47) 9·79% (8·65–11·06) 9·24% (7·70–11·07) Psychotic disorder (any) 1·40% (1·30–1·51) 0·93% (0·83–1·04) 2·89% (2·62–3·18) 2·77% (2·31–3·33) 7·00% (6·01–8·14) Psychotic disorder (first) 0·42% (0·36–0·49) 0·25% (0·19–0·33) 0·89% (0·72–1·09) 0·70% (0·46–1·06) 2·12% (1·53–2·94) Substance use disorder (any) 6·58% (6·36–6·80) 5·87% (5·63–6·13) 8·56% (8·10–9·04) 10·14% (9·25–11·10) 11·85% (10·55–13·31) Substance use disorder (first) 1·92% (1·77–2·07) 1·74% (1·58–1·91) 2·09% (1·82–2·40) 3·15% (2·60–3·82) 2·58% (1·91–3·47) Insomnia (any) 5·42% (5·20–5·64) 5·16% (4·91–5·42) 5·95% (5·53–6·39) 7·50% (6·66–8·44) 9·82% (8·57–11·24) Insomnia (first) 2·53% (2·37–2·71) 2·23% (2·05–2·43) 3·14% (2·81–3·51) 4·24% (3·55–5·07) 5·05% (4·10–6·20) Any outcome 33·62% (33·17–34·07) 31·74% (31·22–32·27) 38·73% (37·87–39·60) 46·42% (44·78–48·09) 62·34% (60·14–64·55) Any first outcome 12·84% (12·36–13·33) 11·51% (10·98–12·07) 15·29% (14·32–16·33) 25·79% (23·50–28·25) 31·13% (27·29–35·36) Data are percentage at 6 months (95% CI). Additional outcomes are presented in the appendix (pp 27–28). ITU=intensive therapy unit. Table 2: Major outcomes for the whole COVID-19 cohort, and for the non-hospitalization, hospitalization, ITU admission, and encephalopathy cohorts during the illness Articles 420 www.thelancet.com/psychiatry Vol 8 May 2021 the time of the analysis, another restricting the COVID-19 diagnoses to patients who had a positive RNA or antigen test (and using antigen test as an index event), and another comparing the rates of sequelae of patients with COVID-19 with those observed in patients with influenza before the pandemic (ie, in 2019 or 2018). Details of these analyses are provided in the appendix (p 6). Finally, to test whether differences in sequelae between cohorts could be accounted for by differences in extent of follow-up, we counted the average number of health visits that each cohort had during the follow-up period. Statistical analysis We used propensity score matching19 to create cohorts with matched baseline characteristics, done within the TriNetX network. Propensity score with 1:1 matching used a greedy nearest neighbor matching approach with a caliper distance of 0·1 pooled SDs of the logit of the propensity score. Any characteristic with a standardized mean difference between cohorts lower than 0·1 was considered well matched.20 The incidence of each outcome was estimated by use of the KaplanMeier estimator. Comparisons between cohorts were made with a log-rank test. We calculated HRs with 95% CIs using a proportional hazard model wherein the cohort to which the patient belonged was used as the independent variable. The proportional hazard assumption was tested with the generalized Schoenfeld approach. When the assumption was violated, the time varying HR was assessed with natural cubic splines fitted to the log cumulative hazard.21 Additional details are presented in the appendix (p 6). Statistical analyses were done in R, version 3.4.3, except for the log-rank tests, which were done within TriNetX. Statistical significance was set at two-sided p-value <0⋅05. Our study was reported according to the Reporting of studies Conducted using Observational Routinely collected health Data (RECORD, appendix pp 55–60). Role of the funding source The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript. Results Our primary cohort comprised 236 379 patients diagnosed with COVID-19, and our two propensity-score matched control cohorts comprised 105579 patients diagnosed with influenza and 236 038 patients diagnosed with any respiratory tract infection including influenza. The COVID-19 cohort was divided into subgroups of patients who were not hospitalized (190077 patients), those who were hospitalized (46 302 patients), those who required ITU admission (8945 patients), and those who received a diagnosis of encephalopathy (6229 patients). The main demographic features and comorbidities of the COVID-19 cohort are summarized in table 1, with additional demographic details presented in the appendix (pp 25–27). Matched baseline characteristics of the two control cohorts are also presented in the appendix (pp 29–30 for patients with influenza, and pp 31–32 for patients with other respiratory tract infections). Adequate propensity-score matching (standardized mean dif­ference <0·1) was achieved for all comparisons and baseline characteristics. We estimated the diagnostic incidence of the neurological and psychiatric outcomes of the primary cohort in the 6 months after a COVID-19 diagnosis. In the whole cohort, 33·62% (95% CI 33·17–34·07) of patients received a diagnosis (table 2). For the cohort subgroups, these estimates were 38·73% (37·87–39·60) for patients who were hospitalized, 46·42% (44·78–48·09) for those admitted to ITU, and 62·34% (60·14–64·55) for those diagnosed with COVID-19 vs influenza (N=105 579)* COVID-19 vs other RTI (N=236038)* HR (95% CI) p value HR (95% CI) p value Intracranial hemorrhage (any) 2·44 (1·89–3·16) <0·0001 1·26 (1·11–1·43) 0·0003 Intracranial hemorrhage (first) 2·53 (1·68–3·79) <0·0001 1·56 (1·27–1·92) <0·0001 Ischemic stroke (any) 1·62 (1·43–1·83) <0·0001 1·45 (1·36–1·55) <0·0001 Ischemic stroke (first) 1·97 (1·57–2·47) <0·0001 1·63 (1·44–1·85) <0·0001 Parkinsonism 1·42 (0·75–2·67) 0·19 1·45 (1·05–2·00) 0·020 Guillain-Barré syndrome 1·21 (0·72–2·04) 0·41 2·06 (1·43–2·96) <0·0001 Nerve, nerve root, or plexus disorders 1·64 (1·50–1·81) <0·0001 1·27 (1·19–1·35) <0·0001 Myoneural junction or muscle disease 5·28 (3·71–7·53) <0·0001 4·52 (3·65–5·59) <0·0001 Encephalitis 1·70 (1·04–2·78) 0·028 1·41 (1·03–1·92) 0·028 Dementia 2·33 (1·77–3·07) <0·0001 1·71 (1·50–1·95) <0·0001 Mood, anxiety, or psychotic disorder (any) 1·46 (1·43–1·50) <0·0001 1·20 (1·18–1·23) <0·0001 Mood, anxiety, or psychotic disorder (first) 1·81 (1·69–1·94) <0·0001 1·48 (1·42–1·55) <0·0001 Mood disorder (any) 1·47 (1·42–1·53) <0·0001 1·23 (1·20–1·26) <0·0001 Mood disorder (first) 1·79 (1·64–1·95) <0·0001 1·41 (1·33–1·50) <0·0001 Anxiety disorder (any) 1·45 (1·40–1·49) <0·0001 1·17 (1·15–1·20) <0·0001 Anxiety disorder (first) 1·78 (1·66–1·91) <0·0001 1·48 (1·42–1·55) <0·0001 Psychotic disorder (any) 2·03 (1·78–2·31) <0·0001 1·66 (1·53–1·81) <0·0001 Psychotic disorder (first) 2·16 (1·62–2·88) <0·0001 1·82 (1·53–2·16) <0·0001 Substance use disorder (any) 1·27 (1·22–1·33) <0·0001 1·09 (1·05–1·12) <0·0001 Substance use disorder (first) 1·22 (1·09–1·37) 0·0006 0·92 (0·86–0·99) 0·033 Insomnia (any) 1·48 (1·38–1·57) <0·0001 1·15 (1·10–1·20) <0·0001 Insomnia (first) 1·92 (1·72–2·15) <0·0001 1·43 (1·34–1·54) <0·0001 Any outcome 1·44 (1·40–1·47) <0·0001 1·16 (1·14–1·17) <0·0001 Any first outcome 1·78 (1·68–1·89) <0·0001 1·32 (1·27–1·36) <0·0001 Additional details on cohort characteristics and diagnostic subcategories are presented in the appendix (pp 29–33). HR=hazard ratio. RTI=respiratory tract infection. *Matched cohorts. Table 3: HRs for the major outcomes in patients after COVID-19 compared with those after influenza and other RTIs Articles www.thelancet.com/psychiatry Vol 8 May 2021 421 encephalopathy. A similar, but more marked, increasing trend was observed for patients receiving their first recorded neurological or psychiatric diagnosis (table 2). Results according to sex, race, and age are shown in the appendix (p 28). The baseline characteristics of the COVID-19 cohort divided into those who did versus those who did not have a neurological or psychiatric outcome are also shown in the appendix (p 7). We assessed the probability of the major neurological and psychiatric outcomes in patients diagnosed with COVID-19 compared with the matched cohorts diagnosed with other respiratory tract infections and with influenza (table 3; figure 1, appendix pp 8–10). Most diagnostic categories were more common in patients who had COVID-19 than in those who had influenza (HR 1·44, 95% CI 1·40–1·47 for any diagnosis; 1·78, 1·68–1·89 for any first diagnosis) and those who had other respiratory tract infections (1·16, 1·14–1·17 for any diagnosis; 1·32, 1·27–1·36 for any first diagnosis). Hazard rates were also higher in patients who were admitted to ITU than in those who were not (1·58, 1·50–1·67 for any diagnosis; 2·87, 2·45–3·35 for any first diagnosis). HRs were significantly greater than 1 for all diagnoses for patients who had COVID-19 compared with those who had influenza, except for parkinsonism and Guillain-Barré syndrome, and significantly greater than 1 for all diagnoses compared with patients who had respiratory tract infections (table 3). Similar results were observed when patients who had COVID-19 were compared with those who had Figure 1: Kaplan-Meier estimates for the incidence of major outcomes after COVID-19 compared with other RTIs Shaded areas are 95% CIs. For incidences of first diagnoses, the number in brackets corresponds to all patients who did not have the outcome before the follow-up period. For diagnostic subcategories, see appendix (pp 8–10). RTI=respiratory tract infection. Number at risk COVID-19 Other RTI 0 50 100 150 200 92579 131885 67102 116315 Intracranial haemorrhage (any) 50172 103261 32705 90066 20679 77005 12775 65909 0 0·2 0·6 0·4 0·8 Outcome probability (%) 30 60 90 120 150 180 0 50 100 150 200 91998 131352 66499 115264 48528 102599 32265 89412 20361 76367 11415 63334 0 0·5 2·0 1·5 2·5 30 60 90 120 150 180 0 50 100 150 200 92193 131363 66587 115073 48488 102233 32186 88929 19962 75806 11585 62702 0 1·0 3·0 2·0 4·0 30 60 90 120 150 180 COVID-19 (n=236038) Other RTI (n=236038) COVID-19 (n=236038) Other RTI (n=236038) COVID-19 (n=236038) Other RTI (n=236038) Ischaemic stroke (any) Nerve, nerve root, or plexus disorder Number at risk COVID-19 Other RTI 0 50 100 Time since index event (days) Time since index event (days) Time since index event (days) 150 200 91646 133203 66346 115207 Myoneural junction or muscle disease 50653 102653 34259 90454 21522 76919 11895 63909 0 0·2 0·1 0·5 0·4 0·3 0·6 Outcome probability (%) 30 60 90 120 150 180 0 50 100 150 200 89958 128680 65186 113623 47578 101313 32182 88082 19593 75359 12242 62553 0 0·2 0·6 0·4 0·8 30 60 90 120 150 180 0 50 100 150 200 84435 122790 58504 103824 41026 89662 26310 75998 15885 63173 8741 51033 0 10 5 20 15 25 30 60 90 120 150 180 Dementia Mood, anxiety, or psychotic disorder COVID-19 (n=234527) Other RTI (n=234810) COVID-19 (n=230151) Other RTI (n=230495) COVID-19 (n=236038) Other RTI (n=236038) Articles 422 www.thelancet.com/psychiatry Vol 8 May 2021 one of the four other index events (appendix pp 11–14, 34), except when an outcome had a predicted relationship with the comparator condition (eg, intracranial hemorrhage was more common in association with fracture of a large bone). HRs for diagnostic subcategories are presented in the appendix (p 33). There were no violations of the proportional hazards assumption for most of the neurological outcomes over the 6 months of follow-up (appendix pp 15, 35). The only exception was for intracranial hemorrhage and ischemic stroke in patients who had COVID-19 when compared with patients who had other respiratory tract infections (p=0·012 for intracranial hemorrhage and p=0·032 for ischemic stroke). For the overall psychiatric disorder category (ICD-10 F20–48), the HR did vary with time, declining but remaining significantly higher than 1, indicating that the risk was attenuated but maintained 6 months after COVID-19 diagnosis (appendix p 9). HRs for COVID-19 diagnosis compared with the additional four index events showed more variation with time, partly reflecting the natural history of the comparator condition (appendix, pp 16–19, 36). We explored the effect of COVID-19 severity in four ways. First, we restricted analyses to matched cohorts of patients who had not required hospitalization (matched baseline characteristics in the appendix, pp 37–40). HRs remained significantly greater than 1 in this subgroup, with an overall HR for any diagnosis of 1·47 (95% CI 1·44–1·51) for patients who had COVID-19 compared with patients who had influenza, and 1·16 (1·14–1·17) compared with those who had other respiratory tract infections (table 4, appendix pp 20–21). For a first diagnosis, the HRs were 1·83 (1·71–1·96) versus patients who had influenza and 1·28 (1·23–1·33) versus those who had other respiratory tract infections. Second, we calculated HRs for the matched cohorts of patients with COVID-19 requiring hospitalization versus those who did not require hospitalization (44 927 matched patients; matched baseline characteristics are presented in the appendix, pp 41–42). This comparison showed greater hazard rates for all outcomes in the hospitalized group than in the non-hospitalized group, except for nerve, nerve root, or plexus disorders (table 5, figure 2), with an overall HR of 1·33 (1·29–1·37) for any diagnosis and 1·70 (1·56–1·86) for any first diagnosis. Third, we calculated HRs for the matched cohorts of patients with COVID-19 requiring ITU admission versus those not COVID-19 vs influenza in patients without hospitalization (N=96803)* COVID-19 vs other RTI in patients without hospitalization (N=183 731)* HR (95% CI) p value HR (95% CI) p value Intracranial hemorrhage (any) 1·87 (1·25–2·78) 0·0013 1·38 (1·11–1·73) 0·0034 Intracranial hemorrhage (first) 1·66 (0·88–3·14) 0·082 1·63 (1·11–2·40) 0·010 Ischemic stroke (any) 1·80 (1·54–2·10) <0·0001 1·61 (1·45–1·78) <0·0001 Ischemic stroke (first) 1·71 (1·26–2·33) 0·0003 1·69 (1·38–2·08) <0·0001 Parkinsonism 2·22 (0·98–5·06) 0·028 1·20 (0·73–1·96) 0·42 Guillain-Barré syndrome 0·90 (0·44–1·84) 0·99 1·44 (0·85–2·45) 0·10 Nerve, nerve root, or plexus disorders 1·69 (1·53–1·88) <0·0001 1·23 (1·15–1·33) <0·0001 Myoneural junction or muscle disease 3·46 (2·11–5·67) <0·0001 2·69 (1·91–3·79) <0·0001 Encephalitis 1·77 (0·86–3·66) 0·095 2·29 (1·28–4·10) 0·0046 Dementia 1·88 (1·27–2·77) 0·0008 1·95 (1·55–2·45) <0·0001 Mood, anxiety, or psychotic disorder (any) 1·49 (1·45–1·54) <0·0001 1·18 (1·15–1·21) <0·0001 Mood, anxiety, or psychotic disorder (first) 1·85 (1·72–1·99) <0·0001 1·40 (1·32–1·48) <0·0001 Mood disorder (any) 1·49 (1·43–1·55) <0·0001 1·22 (1·19–1·26) <0·0001 Mood disorder (first) 1·78 (1·61–1·96) <0·0001 1·37 (1·27–1·47) <0·0001 Anxiety disorder (any) 1·48 (1·43–1·54) <0·0001 1·16 (1·13–1·19) <0·0001 Anxiety disorder (first) 1·80 (1·67–1·94) <0·0001 1·37 (1·30–1·45) <0·0001 Psychotic disorder (any) 1·93 (1·63–2·28) <0·0001 1·44 (1·27–1·62) <0·0001 Psychotic disorder (first) 2·27 (1·56–3·30) <0·0001 1·49 (1·15–1·93) 0·0016 Substance use disorder (any) 1·26 (1·19–1·33) <0·0001 1·11 (1·07–1·17) <0·0001 Substance use disorder (first) 1·21 (1·05–1·38) 0·0054 0·89 (0·81–0·97) 0·013 Insomnia (any) 1·52 (1·42–1·63) <0·0001 1·18 (1·12–1·24) <0·0001 Insomnia (first) 2·06 (1·82–2·33) <0·0001 1·51 (1·38–1·66) <0·0001 Any outcome 1·47 (1·44–1·51) <0·0001 1·16 (1·14–1·17) <0·0001 Any first outcome 1·83 (1·71–1·96) <0·0001 1·28 (1·23–1·33) <0·0001 Details on cohort characteristics are presented in the appendix (pp 37–40). HR=hazard ratio. RTI=respiratory tract infection. *Matched cohorts. Table 4: HRs for the major outcomes in patients without hospitalization after COVID-19 compared with those after influenza or other RTIs Articles www.thelancet.com/psychiatry Vol 8 May 2021 423 requiring ITU admission (8942 patients; matched baseline characteristics presented in the appendix, pp 43–44), with a HR of 1·58 (1·50–1·67) for any diagnosis and 2·87 (2·45–3·35) for any first diagnosis (table 5, appendix p 22). Fourth, we calculated HRs for the matched cohorts of patients with COVID-19 who had encephalopathy diagnosed during acute illness versus those who did not (6221 patients; matched baseline characteristics presented in the appendix, pp 45–46). HRs for all diagnoses were greater for the group who had encephalopathy than for the matched cohort who did not, with an overall HR of 1·85 (1·73–1·98) for any diagnosis and 3·19 (2·54–4·00) for any first diagnosis (table 5, figure 2). We inspected other factors that might influence the findings. The results regarding hospitalization, ITU admission, or encephalopathy (which we had defined as occurring up to 14 days after diagnosis) could be confounded by admissions due to an early complication of COVID-19 rather than to COVID-19 itself. This was explored by excluding outcomes during this period, with the findings remaining similar, albeit with many HRs being reduced (appendix pp 47–49). Additionally, COVID-19 survivors had fewer health-care visits during the 6-month period compared with the other cohorts (appendix p 50). Hence the higher incidence of many diagnoses was not simply due to having had more diagnostic opportunities. The increased rates of neurological and psychiatric sequelae were robust in all three sensitivity analyses: when patients who had died by the time of the analysis were included (appendix p 51), when the COVID-19 diagnosis was confirmed by use of an RNA or antigen test (appendix p 52), and when the sequelae were compared with those observed in patients who had influenza in 2019 or 2018 (appendix pp 53). Discussion Various adverse neurological and psychiatric outcomes occurring after COVID-19 have been predicted and COVID-19 with vs without hospitalization (N=45167) COVID-19 with vs without ITU admission (N=8942) COVID-19 with vs without encephalopathy (N=6221) HR (95% CI) p value HR (95% CI) p value HR (95% CI) p value Intracranial hemorrhage (any) 3·09 (2·43–3·94) <0·0001 5·06 (3·43–7·47) <0·0001 4·73 (3·15–7·11) <0·0001 Intracranial hemorrhage (first) 3·75 (2·49–5·64) <0·0001 5·12 (2·68–9·77) <0·0001 5·00 (2·33–10·70) <0·0001 Ischemic stroke (any) 1·65 (1·48–1·85) <0·0001 1·93 (1·62–2·31) <0·0001 1·65 (1·38–1·97) <0·0001 Ischemic stroke (first) 2·82 (2·22–3·57) <0·0001 3·51 (2·39–5·15) <0·0001 3·39 (2·17–5·29) <0·0001 Parkinsonism 2·63 (1·45–4·77) 0·0016 3·90 (1·29–11·79) 0·024 1·64 (0·75–3·58) 0·24 Guillain-Barré syndrome 2·94 (1·60–5·42) 0·00094 11·01 (2·55–47·61) 0·0007 2·27 (0·76–6·73) 0·24 Nerve, nerve root, or plexus disorders 0·94 (0·83–1·06) 0·29 1·16 (0·92–1·45) 0·21 1·41 (1·07–1·87) 0·018 Myoneural junction or muscle disease 7·76 (5·15–11·69) <0·0001 11·53 (6·38–20·83) <0·0001 5·40 (3·21–9·07) <0·0001 Encephalitis 3·26 (1·75–6·06) 0·0002 1·78 (0·75–4·20) 0·22 9·98 (2·98–33·43) <0·0001 Dementia 2·28 (1·80–2·88) <0·0001 1·66 (1·12–2·46) 0·018 4·25 (2·79–6·47) <0·0001 Mood, anxiety, or psychotic disorder (any) 1·23 (1·18–1·28) <0·0001 1·34 (1·24–1·46) <0·0001 1·73 (1·58–1·90) <0·0001 Mood, anxiety, or psychotic disorder (first) 1·55 (1·40–1·71) <0·0001 2·27 (1·87–2·74) <0·0001 2·28 (1·80–2·89) <0·0001 Mood disorder (any) 1·21 (1·15–1·28) <0·0001 1·15 (1·03–1·27) 0·010 1·51 (1·35–1·70) <0·0001 Mood disorder (first) 1·53 (1·33–1·75) <0·0001 2·06 (1·57–2·71) <0·0001 2·09 (1·55–2·80) <0·0001 Anxiety disorder (any) 1·16 (1·10–1·22) <0·0001 1·39 (1·26–1·53) <0·0001 1·64 (1·45–1·84) <0·0001 Anxiety disorder (first) 1·49 (1·34–1·65) <0·0001 2·22 (1·82–2·71) <0·0001 1·91 (1·48–2·45) <0·0001 Psychotic disorder (any) 2·22 (1·92–2·57) <0·0001 1·48 (1·14–1·92) 0·0028 3·84 (2·90–5·10) <0·0001 Psychotic disorder (first) 2·77 (1·99–3·85) <0·0001 1·77 (0·98–3·20) 0·072 5·62 (2·93–10·77) <0·0001 Substance use disorder (any) 1·53 (1·42–1·64) <0·0001 1·62 (1·41–1·85) <0·0001 1·45 (1·24–1·70) <0·0001 Substance use disorder (first) 1·68 (1·40–2·01) <0·0001 2·53 (1·83–3·50) <0·0001 2·03 (1·32–3·11) 0·0015 Insomnia (any) 1·08 (0·99–1·18) 0·088 1·40 (1·19–1·66) <0·0001 1·73 (1·42–2·11) <0·0001 Insomnia (first) 1·49 (1·28–1·74) <0·0001 1·93 (1·46–2·55) <0·0001 3·44 (2·35–5·04) <0·0001 Any outcome 1·33 (1·29–1·37) <0·0001 1·58 (1·50–1·67) <0·0001 1·85 (1·73–1·98) <0·0001 Any first outcome 1·70 (1·56–1·86) <0·0001 2·87 (2·45–3·35) <0·0001 3·19 (2·54–4·00) <0·0001 Details on cohort characteristics are presented in the appendix (pp 41–46). HR=hazard ratio. ITU=intensive therapy unit. *Matched cohorts. Table 5: HRs for the major outcomes after COVID-19 for patients with vs those without hospitalization, patients with vs without ITU admission, and patients with vs without encephalopathy Articles 424 www.thelancet.com/psychiatry Vol 8 May 2021 Encephalopathy Matched cohort without encephalopathy Number at risk Encephalopathy Matched cohort without encephalopathy Hospitalization Matched cohort without hospitalization 0 50 100 150 200 Intracranial hemorrhage (any) 0 1 4 3 2 Outcome probability (%) 30 60 90 120 150 180 Ischemic stroke (any) Total 6221 6221 45167 45167 3214 3424 20486 20010 2296 2372 14717 14696 1746 2372 11818 11185 1269 1244 7766 7344 1032 1244 5232 4799 733 642 4030 3598 0 50 100 150 200 0 2·5 10·0 7·5 5·0 Total 30 60 90 120 150 180 6221 6221 45167 45167 3133 2989 20218 19587 2201 2187 14429 14515 1639 1641 10786 10792 1177 1221 7566 7464 821 758 5083 4714 634 758 3551 3133 Number at risk Encephalopathy Matched cohort without encephalopathy Hospitalization Matched cohort without hospitalization 0 50 100 150 200 Nerve, nerve root, or plexus disorder 0 1 5 4 3 2 Outcome probability (%) 30 60 90 120 150 180 Myoneural junction or muscle disease Total 0 50 100 150 200 0 1 4 3 2 Total 30 60 90 120 150 180 6221 6221 45167 45167 3277 3125 20453 19636 2317 2225 14614 14537 1701 1701 10902 10775 1231 1314 7737 7447 881 825 5062 4549 602 596 3378 2606 5906 6109 44481 44788 2996 3067 20044 20069 2127 2236 14345 16550 1836 1916 10853 13067 1292 1916 8206 10185 790 1139 5270 10185 604 635 3320 4092 Number at risk Encephalopathy Matched cohort without encephalopathy Hospitalization Matched cohort without hospitalization 0 50 100 Time since index event (days) Time since index event (days) 150 200 Dementia 0 1 5 4 3 2 Outcome probability (%) 30 60 90 120 150 180 Mood, anxiety, or psychotic disorder Total 0 50 100 150 200 0 10 40 30 20 Total 30 60 90 120 150 180 4704 5094 42434 42877 2627 2562 19428 18719 1929 2010 13970 13904 1425 1419 10567 10329 1036 986 7611 7017 717 986 5427 5174 583 986 3616 2697 6221 6221 45167 45167 2640 2729 18072 18092 1734 1910 12352 12874 1217 1417 8933 9170 888 955 6068 5925 575 633 3976 3653 351 437 2501 2001 Hospitalization Matched cohort without hospitalization Articles www.thelancet.com/psychiatry Vol 8 May 2021 425 reported.1–5,14 The data presented in this study, from a large electronic health records network, support these predictions and provide estimates of the incidence and risk of these outcomes in patients who had COVID-19 compared with matched cohorts of patients with other health conditions occurring contemporaneously with the COVID-19 pandemic (tables 2, 3, figure 1). The severity of COVID-19 had a clear effect on subsequent neurological diagnoses (tables 4, 5, figure 2). Overall, COVID-19 was associated with increased risk of neurological and psychiatric outcomes, but the incidences and HRs of these were greater in patients who had required hospitalization, and markedly so in those who had required ITU admission or had developed encephalopathy, even after extensive propensity score matching for other factors (eg, age or previous cerebrovascular disease). Potential mechanisms for this association include viral invasion of the CNS,10,11 hypercoagulable states,22 and neural effects of the immune response.9 However, the incidence and relative risk of neurological and psychiatric diagnoses were also increased even in patients with COVID-19 who did not require hospitalization. Some specific neurological diagnoses merit individual mention. Consistent with several other reports,23,24 the risk of cerebrovascular events (ischemic stroke and intracranial hemorrhage) was elevated after COVID-19, with the incidence of ischemic stroke rising to almost one in ten (or three in 100 for a first stroke) in patients with encephalopathy. A similarly increased risk of stroke in patients who had COVID-19 compared with those who had influenza has been reported.25 Our previous study reported preliminary evidence for an association between COVID-19 and dementia.14 The data in this study support this association. Although the estimated incidence was modest in the whole COVID-19 cohort (table 2), 2·66% of patients older than 65 years (appendix p 28) and 4·72% who had encephalopathy (table 2), received a first diagnosis of dementia within 6 months of having COVID-19. The associations between COVID-19 and cerebrovascular and neurodegenerative diagnoses are concerning, and information about the severity and subsequent course of these diseases is required. Whether COVID-19 is associated with Guillain-Barré syndrome remains unclear;26 our data were also equivocal, with HRs increased with COVID-19 compared with other respiratory tract infections but not with influenza (table 3), and increased compared with three of the four other index health events (appendix p 34). Concerns have also been raised about post-COVID-19 parkinsonian syndromes, driven by the encephalitis lethargica epidemic that followed the 1918 influenza pandemic.27 Our data provide some support for this possibility, although the incidence was low and not all HRs were significant. Parkinsonism might be a delayed outcome, in which case a clearer signal might emerge with a longer follow-up. The findings regarding anxiety and mood disorders were broadly consistent with 3-month outcome data from a study done in a smaller number of cases than our cohort, using the same network,14 and showed that the HR remained elevated, although decreasing, at the 6-month period. Unlike the earlier study, and in line with previous suggestions,28 we also observed a significantly increased risk of psychotic disorders, probably reflecting the larger sample size and longer duration of follow-up reported here. Substance use disorders and insomnia were also more common in COVID-19 survivors than in those who had influenza or other respiratory tract infections (except for the incidence of a first diagnosis of substance use disorder after COVID-19 compared with other respiratory tract infections). Therefore, as with the neurological outcomes, the psychiatric sequelae of COVID-19 appear widespread and to persist up to, and probably beyond, 6 months. Compared with neurological disorders, common psychiatric disorders (mood and anxiety disorders) showed a weaker relationship with the markers of COVID-19 severity in terms of incidence (table 2) or HRs (table 5). This might indicate that their occurrence reflects, at least partly, the psychological and other implications of a COVID-19 diagnosis rather than being a direct manifestation of the illness. HRs for most neurological outcomes were constant, and hence the risks associated with COVID-19 persisted up to the 6-month timepoint. Longer-term studies are needed to ascertain the duration of risk and the trajectory for individual diagnoses. Our findings are robust given the sample size, the propensity score matching, and the results of the sensitivity and secondary analyses. Nevertheless, they have weaknesses inherent to an electronic health records study,29 such as the unknown completeness of records, no validation of diagnoses, and sparse information on socioeconomic and lifestyle factors. These issues primarily affect the incidence estimates, but the choice of cohorts against which to compare COVID-19 outcomes influenced the magnitude of the HRs (table 3, appendix p 34). The analyses regarding encephalopathy (delirium and related conditions) deserve a note of caution. Even among patients who were hospitalized, only about 11% received this Figure 2: Kaplan-Meier estimates for the incidence of major outcomes after COVID-19 comparing patients requiring hospitalization with matched patients not requiring hospitalization, and comparing those who had encephalopathy with matched patients who did not have encephalopathy 95% CIs are omitted for clarity but are shown in the appendix (p 23). For incidences of first diagnoses, the total number corresponds to all patients who did not have the outcome before the follow-up period. The equivalent figure showing the comparison between patients with intensive therapy unit admission versus those without is presented in the appendix (p 22). Articles 426 www.thelancet.com/psychiatry Vol 8 May 2021 diagnosis, whereas much higher rates would be expected.18,30 Under-recording of delirium during acute illness is well known and probably means that the diagnosed cases had prominent or sustained features; as such, results for this group should not be generalized to all patients with COVID-19 who experience delirium. We also note that encephalopathy is not just a severity marker but a diagnosis in itself, which might predispose to, or be an early sign of, other neuropsychiatric or neurodegenerative outcomes observed during follow-up. The timing of index events was such that most infections with influenza and many of the other respiratory tract infections occurred earlier on during the pandemic, whereas the incidence of COVID-19 diagnoses increased over time (appendix p 24). The effect of these timing differences on observed rates of sequelae is unclear but, if anything, they are likely to make the HRs an underestimate because COVID-19 cases were diagnosed at a time when all other diagnoses were made at a lower rate in the population (appendix p 24). Some patients in the comparison cohorts are likely to have had undiagnosed COVID-19; this would also tend to make our HRs an underestimate. Finally, a study of this kind can only show associations; efforts to identify mechanisms and assess causality will require prospective cohort studies and additional study designs. In summary, the present data show that COVID-19 is followed by significant rates of neurological and psychiatric diagnoses over the subsequent 6 months. Services need to be configured, and resourced, to deal with this anticipated need. Contributors PJH and MT were granted unrestricted access to the TriNetX Analytics network for the purposes of research, and with no constraints on the analyses done or the decision to publish; they designed the study and directly accessed the TriNetX Analytics web interface to do it. MT, JRG, MH, and PJH defined cohort inclusion and exclusion criteria, and the outcome criteria and analytical approaches. MT did the data analyses, assisted by SL and PJH. All authors contributed to data interpretation. MT and PJH wrote the paper with input from JRG, MH, and SL. MT and PJH verified the data. PJH is the guarantor. PJH and MT had full access to all the data in the study, and the corresponding author had final responsibility for the decision to submit for publication. Declaration of interests SL is an employee of TriNetX. All other authors declare no competing interests. Data sharing The TriNetX system returned the results of these analyses as .csv files, which were downloaded and archived. Data presented in this paper can be freely accessed online. Additionally, TriNetX will grant access to researchers if they have a specific concern (through a third-party agreement option).

Acknowledgments

This work was supported by the NIHR Oxford Health Biomedical Research Centre (grant BRC-1215–20005). MT is an NIHR Academic Clinical Fellow. MH is supported by a Wellcome Trust Principal Research Fellowship and the NIHR Oxford Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the UK National Health Service, NIHR, or the UK Department of Health.

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COVID virus linked with headaches, altered mental status in hospitalized kids

Authors: UNIVERSITY OF PITTSBURGH Peer-Reviewed Publication

PITTSBURGH, Jan. 21, 2022 – Of hospitalized children who tested or were presumed positive for SARS-CoV-2, 44% developed neurological symptoms, and these kids were more likely to require intensive care than their peers who didn’t experience such symptoms, according to a new study led by a pediatrician-scientist at UPMC and the University of Pittsburgh School of Medicine

The most common neurologic symptoms were headache and altered mental status, known as acute encephalopathy. Published in Pediatric Neurology, these preliminary findings are the first insights from the pediatric arm of GCS-NeuroCOVID, an international, multi-center consortium aiming to understand how COVID-19 affects the brain and nervous system. 

“The SARS-CoV-2 virus can affect pediatric patients in different ways: It can cause acute disease, where symptomatic illness comes on soon after infection, or children may develop an inflammatory condition called MIS-C weeks after clearing the virus,” said lead author Ericka Fink, M.D., pediatric intensivist at UPMC Children’s Hospital of Pittsburgh, and associate professor of critical care medicine and pediatrics at Pitt. “One of the consortium’s big questions was whether neurological manifestations are similar or different in pediatric patients, depending on which of these two conditions they have.” 

To answer this question, the researchers recruited 30 pediatric critical care centers around the world. Of 1,493 hospitalized children, 1,278, or 86%, were diagnosed with acute SARS-CoV-2; 215 children, or 14%, were diagnosed with MIS-C, or multisystem inflammatory syndrome in children, which typically appears several weeks after clearing the virus and is characterized by fever, inflammation and organ dysfunction. 

The most common neurologic manifestations linked with acute COVID-19 were headache, acute encephalopathy and seizures, while youths with MIS-C most often had headache, acute encephalopathy and dizziness. Rarer symptoms of both conditions included loss of smell, vision impairment, stroke and psychosis.  

“Thankfully, mortality rates in children are low for both acute SARS-CoV-2 and MIS-C,” said Fink. “But this study shows that the frequency of neurological manifestations is high—and it may actually be higher than what we found because these symptoms are not always documented in the medical record or assessable. For example, we can’t know if a baby is having a headache.” 

The analysis showed that neurological manifestations were more common in kids with MIS-C compared to those with acute SARS-CoV-2, and children with MIS-C were more likely than those with acute illness to have two or more neurologic manifestations. 

According to Fink, the team recently launched a follow up study to determine whether acute SARS-CoV-2 and MIS-C—with or without neurologic manifestations—have lasting effects on children’s health and quality of life after discharge from hospital.  

“Another long-term goal of this study is to build a database that tracks neurological manifestations over time—not just for SARS-CoV-2, but for other types of infections as well,” she added. “Some countries have excellent databases that allow them to easily track and compare children who are hospitalized, but we don’t have such a resource in the U.S.” 

This study was partly funded by the Neurocritical Care Society Investing in Clinical Neurocritical Care Research (INCLINE) grant. 

Other researchers who contributed to the study include Courtney L. Robertson, M.D., Johns Hopkins Children’s Center; Mark S. Wainwright, M.D., Ph.D., University of Washington and Seattle Children’s Hospital; Juan D. Roa, M.D., Universidad Nacional de Colombia and Fundación Universitaria de Ciencias de la Salud; Michelle E. Schober, M.D., University of Utah, and other GCS-NeuroCOVID Pediatrics investigators who are listed in the paper. 

To read this release online or share it, visit http://www.upmc.com/media/news/012122-Fink-COVID-Children.  


JOURNAL

Pediatric Neurology

DOI

10.1016/j.pediatrneurol.2021.12.010 

METHOD OF RESEARCH

Observational study

SUBJECT OF RESEARCH

People

ARTICLE TITLE

Prevalence and Risk Factors of Neurologic Manifestations in Hospitalized Children Diagnosed with Acute SARS-CoV-2 or MIS-C

ARTICLE PUBLICATION DATE

21-Jan-2022

Acute inflammatory demyelinating polyneuropathy or Guillain-Barré syndrome associated with COVID-19: a case report

Journal of Medical Case Reports volume 15, Article number: 219 (2021) 

Abstract

Background

Coronavirus disease 2019 (COVID-19) is a global pandemic. The disease, typically characterized by bilateral pulmonary infiltrates and profound elevation of inflammatory markers, can range in severity from mild or asymptomatic illness to a lethal cytokine storm and respiratory failure. A number of recognized complications of COVID-19 infection are described in the literature. Common neurological complications include headache and anosmia. Guillain-Barré syndrome (GBS) is an uncommon complication described in isolated case reports. However, a causal relationship has yet to be established. This case report adds to the growing body of evidence that GBS is a potential COVID-19 complication.

Case presentation

A 70-year-old Caucasian woman with recently diagnosed COVID-19 infection presented to the emergency department with 4 days of gradually worsening ascending lower extremity weakness. Exam revealed bilateral lower extremity weakness, mute reflexes, and sensory loss. Soon after starting intravenous administration of immunoglobulin (IVIG), the patient developed respiratory distress, eventually requiring intubation. She remained intubated for the duration of her IVIG treatment. After five rounds of treatment, the patient was successfully extubated and transferred to acute rehab. Following 4 weeks of intense physical therapy, she was able to walk with assistance on room air.

Conclusion

At the present time, this is one of the few reports of acute inflammatory demyelinating polyneuropathy (AIDP) or GBS associated with COVID-19 in the United States. It is unclear whether a causal relationship exists given the nature of the syndrome. However, in light of the growing number of reported cases, physicians should be aware of this possible complication when evaluating COVID-19 patients.

For More Information: https://jmedicalcasereports.biomedcentral.com/articles/10.1186/s13256-021-02831-4

Coronavirus and the Nervous System

What is SARS-CoV-2 and COVID-19?

Coronaviruses are common causes of usually mild to moderate upper respiratory tract illnesses like the common cold, with symptoms that may include runny nose, fever, sore throat, cough, or a general feeling of being ill. However, a new coronavirus called Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) emerged and spread to cause the COVID-19 pandemic.

COVID-19, which means Coronavirus disease 2019, is an infectious disease that can affect people of all ages in many ways. It is most dangerous when the virus spreads from the upper respiratory tract into the lungs to cause viral pneumonia and lung damage leading to Acute Respiratory Distress Syndrome (ARDS). When severe, this impairs the body’s ability to maintain critical levels of oxygen in the blood stream—which can cause multiple body systems to fail and can be fatal.

What do we know about the effects of SARS-CoV-2 and COVID-19 on the nervous system?

Much of the research to date has focused on the acute infection and saving lives. These strategies have included preventing infection with vaccines, treating COVID-19 symptoms with medicines or antibodies, and reducing complications in infected individuals.

Research shows the many neurological symptoms of COVID-19 are likely a result of the body’s widespread immune response to infection rather than the virus directly infecting the brain or nervous system. In some people, the SARS-CoV-2 infection causes an overreactive response of the immune system which can also damage body systems. Changes in the immune system have been seen in studies of the cerebrospinal fluid, which bathes the brain, in people who have been infected by SARS-CoV-2. This includes the presence of antibodies—proteins made by the immune system to fight the virus—that may also react with the nervous system. Although still under intense investigation, there is no evidence of widespread viral infection in the brain. Scientists are still learning how the virus affects the brain and other organs in the long-term. Research is just beginning to focus on the role of autoimmune reactions and other changes that cause the set of symptoms that some people experience after their initial recovery. It is unknown if injury to the nervous system or other body organs cause lingering effects that will resolve over time, or whether COVID-19 infection sets up a more persistent or even chronic disorder.

What are the immediate (acute) effects of SARS-CoV-2 and COVID-19 on the brain?

Most people infected with SARS-CoV-2 virus will have no or mild to moderate symptoms associated with the brain or nervous system. However, most individuals hospitalized due to the virus do have symptoms related to the brain or nervous system, most commonly including muscle aches, headaches, dizziness, and altered taste and smell. Some people with COVID-19 either initially have, or develop in the hospital, a dramatic state of confusion called delirium. Although rare, COVID-19 can cause seizures or major strokes. Muscular weakness, nerve injury, and pain syndromes are common in people who require intensive care during infections. There are also very rare reports of conditions that develop after SARS-CoV-2 infection, as they sometimes do with other types of infections. These disorders of inflammation in the nervous system include Guillain-Barré syndrome (which affects nerves), transverse myelitis (which affects the spinal cord), and acute necrotizing leukoencephalopathy (which affects the brain).

Bleeding in the brain, weakened blood vessels, and blood clots in acute infection

The SARS-CoV-2 virus attaches to a specific molecule (called a receptor) on the surface of cells in the body. This molecule is concentrated in the lung cells but is also present on certain cells that line blood vessels in the body. The infection causes some arteries and veins—including those in the brain—to  become thin, weaken, and leak. Breaks in small blood vessels have caused bleeding in the brain (so-called microbleeds) in some people with COVID-19 infection. Studies in people who have died due to COVID-19 infection show leaky blood vessels in different areas of the brain that allow water and a host of other molecules as well as blood cells that are normally excluded from the brain to move from the blood stream into the brain. This leak, as well as the resulting inflammation around blood vessels, can cause multiple small areas of damage. COVID-19 also causes blood cells to clump and form clots in arteries and veins throughout the body. These blockages reduce or block the flow of blood, oxygen, and nutrients that cells need to function and can lead to a stroke or heart attack.

stroke is a sudden interruption of continuous blood flow to the brain. A stroke occurs either when a blood vessel in the brain becomes blocked or narrowed or when a blood vessel bursts and spills blood into the brain. Strokes can damage brain cells and cause permanent disability. The blood clots and vascular (relating to the veins, capillaries, and arteries in the body) damage from COVID-19 can cause strokes even in young healthy adults who do not have the common risk factors for stroke.

COVID-19 can cause blood clots in other parts of the body, too. A blood clot in or near the heart can cause a heart attack. A heart attack orInflammation in the heart, called myocarditis, can causeheart failure, and reduce the flow of blood to other parts of the body. A blood clot in the lungs can impair breathing and cause pain. Blood clots also can damage the kidneys and other organs.

Low levels of oxygen in the body (called hypoxia) can permanently damage the brain and other vital organs in the body. Some hospitalized individuals require artificial ventilation on respirators. To avoid chest movements that oppose use of the ventilator it may be necessary to temporarily “paralyze” the person and use anesthetic drugs to put the individual to sleep. Some individuals with severe hypoxia require artificial means of bringing oxygen into their blood stream, a technique called extra corporeal membrane oxygenation (ECMO). Hypoxia combined with these intensive care unit measure generally cause cognitive disorders that show slow recovery.

Diagnostic imaging of some people who have had COVID-19 show changes in the brain’s white matter that contains the long nerve fibers, or “wires,” over which information flows from one brain region to another. These changes may be due to a lack of oxygen in the brain, the inflammatory immune system response to the virus, injury to blood vessels, or leaky blood vessels. This “diffuse white matter disease” might contribute to cognitive difficulties in people with COVID-19. Diffuse white matter disease is not uncommon in individuals requiring intensive hospital care but it not clear if it also occurs in those with mild to moderate severity of COVID-19 illness.

For More Information: https://www.ninds.nih.gov/Current-Research/Coronavirus-and-NINDS/nervous-system

More than 50 Long-Term Effects of COVID-19: A Systematic Review and Meta-Analysis

Authors: López-León SWegman-Ostrosky TPerelman CSepulveda RRebolledo PACuapio AVillapol S Preprint from SSRN, 20 Jan 2021

Abstract 


Background: COVID-19, caused by SARS-CoV-2, can involve sequelae that last weeks to months after initial recovery. The objective of this systematic review and meta-analysis is to identify studies assessing the long-term effects of COVID-19 and estimate the prevalence of each symptom, sign, or laboratory parameters of patients at a post-COVID-19 stage.

Methods: In this systematic review and meta-analysis, LitCOVID (PubMed and Medline) and Embase were searched by two independent researchers. Studies published before 1st of January 2021 and with a minimum of 100 patients were included. For effects reported in two or more studies, meta-analyses using a random-effects model were performed using the MetaXL software to estimate the pooled prevalence with 95% CI. Heterogeneity was assessed using the I2 statistics. PRISMA guidelines were followed.

Findings: A total of 18,251 publications were identified, of which 15 met the inclusion criteria. The prevalence of 55 long-term effects was estimated, 21 meta-analyses were performed, and 47,910 patients were included. The follow-up time ranged from 15 to 110 days post-viral infection. The age of the study participants ranged between 17 and 87 years. It was estimated that 80% (95% CI 65-92) of the patients that were infected with SARS-CoV-2 developed one or more symptoms. The five most common symptoms were fatigue (58%), headache (44%), attention disorder (27%), hair loss (25%), and dyspnea (24%). In order to have a better understanding, there is a need for studies to stratify by sex, age, previous comorbidities, severity of COVID-19 (including asymptomatic), and duration of each symptom.

Interpretation: From the clinical perspective, multi-disciplinary teams are crucial to developing preventive measures, rehabilitation techniques, and clinical management strategies with whole-patient perspectives designed to address after-COVID-19 care.

Funding: National Institute for Neurological Disorders and Stroke (NINDS), and Houston Methodist Research Institute, Houston, TX.

Declaration of Interests: SLL is an employee of Novartis Pharmaceutical Company; the statements presented in the paper do not necessarily represent the position of the company. The remaining authors have no competing interests to declare.

For More Information: https://europepmc.org/article/PPR/PPR280403