What heart and stroke patients need to know about COVID-19 in 2022

Authors: Michael Merschel, American Heart Association News

Two years into the pandemic, researchers have learned a lot about how COVID-19 affects people with heart disease and stroke survivors. But like the coronavirus itself, what everyone needs to know keeps evolving.

“You can’t assume that what was true three months ago is true now,” said Dr. James de Lemos, a cardiologist at UT Southwestern Medical Center in Dallas. Thanks to the omicron variant, “it’s a fundamentally different pandemic than it was at Thanksgiving.”

Early data suggests omicron causes less severe illness but spreads more easily than its predecessors. So heart and stroke patients need to protect themselves, starting with understanding that COVID-19 still is a threat to their health.

“Early on, we recognized that the risk was higher for those with pre-existing cardiovascular disease,” said Dr. Biykem Bozkurt, a cardiologist at Baylor College of Medicine in Houston. According to the Centers for Disease Control and Prevention, people with conditions such as heart failure, coronary artery disease and possibly high blood pressure may be more likely to get severely ill from COVID-19. So can people who have diabetes, are overweight or are recovering from a stroke.

SARS-CoV-2, the virus that causes COVID-19, also has been linked to increased risk of several cardiovascular conditions. According to a September 2021 report from the CDC, people with COVID-19 are nearly 16 times more likely to have heart inflammation, or myocarditis, than uninfected people. The report found about 150 cases per 100,000 people with COVID-19 versus about nine cases per 100,000 people without the virus.

In addition, an August 2021 study in the New England Journal of Medicine showed people with the coronavirus may have a significantly higher, albeit rare, risk of intracranial hemorrhage, or brain bleeding; heart attack; and having an arrhythmia, or abnormal heartbeat.

Researchers don’t have full data on omicron’s effects yet, Bozkurt said, but it’s still affecting people who are vulnerable. “And that’s why the hospitals right now are full.”

The risks of any one person having a severe problem from the new variant are relatively small, de Lemos said. “But the flipside is, given how many people are getting infected right now, the cumulative number of people with COVID-19 complications is still very large.”

De Lemos, who helped create the American Heart Association’s COVID-19 Cardiovascular Disease Registry, said omicron “is obviously wildly more infectious and able to evade the vaccine to some extent, although it does appear that the vaccine seems to prevent severe infections and hospitalizations.”

And overall, “we don’t know a ton about specifically why certain patients with heart disease do less well,” he said, although understanding has evolved over time.

In the beginning, de Lemos said, doctors feared the virus directly infected the heart muscle. “That doesn’t really appear to be the case,” he said.

Instead, it appears that in severe cases, the virus is inflaming the lining of blood vessels of the heart and increasing the likelihood of clotting in the smallest vessels, he said.

COVID-19 also can overwhelm the heart by making it work harder to pump oxygenated blood through the body as the lungs are overwhelmed.

But as they’ve learned more about the coronavirus, doctors have gotten better at fighting it. For example, de Lemos said, they now work proactively to treat blood-clotting disorders in hospitalized patients. And although researchers are working to understand lingering effects known as “long COVID,” it appears long-term implications for the heart look favorable.

“The vast majority of people who have mild COVID infections really appear to have nothing to worry about with their hearts,” he said. “That’s good news, I think, and doesn’t get emphasized enough.”

People with existing heart conditions or a history of stroke still need to protect themselves, and have many ways of doing so.

“Number one: Get vaccinated,” said Bozkurt, who has studied COVID-19 vaccine side effects. “And please, do get a booster.” Reports of rare cases of vaccine-related myocarditis, particularly in younger males, should not dissuade anybody with an existing condition. Most people with pre-existing cardiovascular disease are not young adult males, she noted. And regardless of age, the benefits from vaccines outweigh the risks.

Given how the vaccines don’t seem to be as protective against the spread of omicron, de Lemos said if you’re a heart disease or stroke patient, hunker down for the next several weeks until this wave passes, “and then you’ll be able to re-emerge.”

Patients should avoid indoor crowds, he said, and use a KN95 mask or, when possible, an N95 mask instead of cloth masks when being in a crowd is necessary.

Bozkurt said heart and stroke patients should keep in contact with their health care team and continue taking medications as prescribed. Anybody with symptoms that could be heart-related should seek care immediately. “Do not delay,” she said.

Both doctors said it was important to get information from reliable sources. Some false remedies promoted on social media can actually damage the heart, Bozkurt said.

De Lemos acknowledged that even from reliable sources, advice can shift. “I would say that the information is written in pencil, not in pen, because things are changing so fast.” It can be frustrating for him, even as a scientist, when experts disagree or alter their recommendations, but “that’s the way science goes.”

And even as COVID-19 “remains a bizarrely arbitrary virus in terms of who gets sick and who doesn’t,” he’s optimistic.

“Think about all the progress we’ve made in a year or two, and the remarkable effect of the vaccines, the fact that we have drugs” that should help keep people out of hospitals. Heart and stroke patients need to be extra careful right now, but “as frustrating as it is, we will not be in this situation forever. We really won’t.”

Editor’s note: Because of the rapidly evolving events surrounding the coronavirus, the facts and advice presented in this story may have changed since publication. Visit Heart.org for the latest coverage, and check with the Centers for Disease Control and Prevention and local health officials for the most recent guidance.

If you have questions or comments about this story, please email editor@heart.org.

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.

References

1 Rogers JP, Chesney E, Oliver D, et al. Psychiatric and neuropsychiatric presentations associated with severe coronavirus infections: a systematic review and meta-analysis with comparison to the COVID-19 pandemic. Lancet Psychiatry 2020; 7: 611–27.

2 Ellul MA, Benjamin L, Singh B, et al. Neurological associations of COVID-19. Lancet Neurol 2020; 19: 767–83.

3 Varatharaj A, Thomas N, Ellul MA, et al. Neurological and neuropsychiatric complications of COVID-19 in 153 patients: a UK-wide surveillance study. Lancet Psychiatry 2020; 7: 875–82.

4 Paterson RW, Brown RL, Benjamin L, et al. The emerging spectrum of COVID-19 neurology: clinical, radiological and laboratory findings. Brain 2020; 143: 3104–20.

5 Kremer S, Lersy F, Anheim M, et al. Neurologic and neuroimaging findings in patients with COVID-19: a retrospective multicenter study. Neurology 2020; 95: e1868–82.

6 Pezzini A, Padovani A. Lifting the mask on neurological manifestations of COVID-19. Nat Rev Neurol 2020; 16: 636–44.

7 Raman B, Cassar MP, Tunnicliffe EM, et al. Medium-term effects of SARS-CoV-2 infection on multiple vital organs, exercise capacity, cognition, quality of life and mental health, post-hospital discharge. EClinicalMedicine 2021; 31: 100683.

8 Iadecola C, Anrather J, Kamel H. Effects of COVID-19 on the nervous system. Cell 2020; 183: 16–27.

9 Kreye J, Reincke SM, Prüss H. Do cross-reactive antibodies cause neuropathology in COVID-19? Nat Rev Immunol 2020; 20: 645–46.

10 Meinhardt J, Radke J, Dittmayer C, et al. Olfactory transmucosal SARS-CoV-2 invasion as a port of central nervous system entry in individuals with COVID-19. Nat Neurosci 2021; 24: 168–75.

11 Rhea EM, Logsdon AF, Hansen KM, et al. The S1 protein of SARS-CoV-2 crosses the blood-brain barrier in mice. Nat Neurosci 2021; 24: 368–78.

12 Holmes EA, O’Connor RC, Perry VH, et al. Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science. Lancet Psychiatry 2020; 7: 547–60.

13 Vindegaard N, Benros ME. COVID-19 pandemic and mental health consequences: systematic review of the current evidence. Brain Behav Immun 2020; 89: 531–42.

14 Taquet M, Luciano S, Geddes JR, Harrison PJ. Bidirectional associations between COVID-19 and psychiatric disorder: retrospective cohort studies of 62 354 COVID-19 cases in the USA. Lancet Psychiatry 2021; 8: 130–40.

15 de Lusignan S, Dorward J, Correa A, et al. Risk factors for SARS-CoV-2 among patients in the Oxford Royal College of General Practitioners Research and Surveillance Centre primary care network: a cross-sectional study. Lancet Infect Dis 2020; 20: 1034–42.

16 Williamson EJ, Walker AJ, Bhaskaran K, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature 2020; 584: 430–36.

17 Slooter AJC, Otte WM, Devlin JW, et al. Updated nomenclature of delirium and acute encephalopathy: statement of ten Societies. Intensive Care Med 2020; 46: 1020–22.

18 Oldham MA, Slooter AJC, Cunningham C, et al. Characterising neuropsychiatric disorders in patients with COVID-19. Lancet Psychiatry 2020; 7: 932–33.

19 Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res 2011; 46: 399–424.

20 Haukoos JS, Lewis RJ. The propensity score. JAMA 2015; 314: 1637–38. 21 Royston P, Parmar MKB. Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Stat Med 2002;

21: 2175–97.

22 Panigada M, Bottino N, Tagliabue P, et al. Hypercoagulability of COVID-19 patients in intensive care unit: a report of thromboelastography findings and other parameters of hemostasis. J Thromb Haemost 2020; 18: 1738–42.

23 Siow I, Lee KS, Zhang JJY, Saffari SE, Ng A, Young B. Stroke as a neurological complication of COVID-19: a systematic review and meta-analysis of incidence, outcomes and predictors. J Stroke Cerebrovasc Dis 2021; 30: 105549. For the study data see https:// osf.io/7tzvy Articles www.thelancet.com/psychiatry Vol 8 May 2021 427

24 Hernandez-Fernandez F, Valencia HS, Barbella-Aponte R, et al. Cerebrovascular disease in patients with COVID-19: neuroimaging, histological and clinical description. Brain 2020; 143: 3089–103.

25 Xie Y, Bowe B, Maddukuri G, Al-Aly Z. Comparative evaluation of clinical manifestations and risk of death in patients admitted to hospital with COVID-19 and seasonal influenza: cohort study. BMJ 2020; 371: m4677.

26 Keddie S, Pakpoor J, Mousele C, et al. Epidemiological and cohort study finds no association between COVID-19 and Guillain-Barré syndrome. Brain 2020; published online Dec 14. https://doi. org/10.1093/brain/awaa433.

27 Hoffman LA, Vilensky JA. Encephalitis lethargica: 100 years after the epidemic. Brain 2017; 140: 2246–51.

28 Watson CJ, Thomas RH, Solomon T, Michael BD, Nicholson TR, Pollak TA. COVID-19 and psychosis risk: real or delusional concern? Neurosci Lett 2021; 741: 135491.

29 Casey JA, Schwartz BS, Stewart WF, Adler NE. Using electronic health records for population health research: a review of methods and applications. Annu Rev Public Health 2016; 37: 61–81.

30 Docherty AB, Harrison EM, Green CA, et al. Features of 20133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study. BMJ 2020; 369: m1985.

13 ways that the SARS-CoV-2 spike protein causes damage

Authors: Posted on January 13, 2022 by Jesse Santiano, M.D.

The SARS-CoV-2 virus has four structural proteins. The spike, membrane, envelop, and nucleocapsid proteins. The spike protein protrudes from the middle of the coronavirus and attaches to the ACE2 receptor of cells to start the process of cell entry, replication, and infection. The two major parts of the spike protein are the S1 and S2 subunit. The S1 has the receptor-binding domain.

For easier reading, this review starts with what happens after the COVID jabs, soluble spike proteins, and what it takes to have a normal blood vessel. Then I will enumerate how the spike protein damages the body.

What happens after the COVID shots?

COVID vaccination aims to produce an immune response against the spike protein in the form of neutralizing antibodies so that in future SARS-CoV-2 exposures, COVID-19 will be prevented.

The injected messenger RNA provides instructions to the cells on making the spike proteins. Once the spike protein is produced, it migrates to the outside of the cell to be anchored on the cells’ outer surface, where the immune system will recognize it and develop an immune response to it. (antibodies, T cells, B cells).

Soluble spike proteins

Ideally, the whole spike protein should stay attached to the outside of the cells. Sometimes incomplete spike proteins are produced in the form of spike peptides. As shown below, they are also presented to the immune system by cells outside the surface with an anchoring protein called the Major Histocompatibility Complex (MHC).

Anchoring to the cells is critical because once the spike protein or its pieces in the form of peptides become soluble or float in the bloodstream, they induce inflammation and clot formation in the arteries and capillaries. Scientists have found several ways that it happens.

First is that enzymes called metalloproteinases can cut the MHC1 at their bases.[1] Free-floating MHCs are found in patients with systemic lupus erythematosus SLE and cancers.[4][5]

The second is that errors can happen while RNA splicing occurs inside the nucleus. This results in variant spike proteins that are soluble.[2] Soluble S1 subunits were observed among recipients of the Moderna shots.[3

You can read more about it in this article: SARS-CoV-2 spike proteins detected in the plasma following Moderna shots.

Third, are exosomes released from cells containing MHCs with the spike proteins. [6] T-cells can interact with the spike proteins in the exosomes and cause inflammation [7]. Immunogenic spike proteins inside exosomes were demonstrated after Pfizer injection[8].

 Donor Blood Can Have Spike Protein Exosomes

The normal blood vessel

All organs in the body need an adequate blood supply, and blood vessels have to be in pristine working conditions for that to happen. They should be distensible to allow greater blood flow during exertion, smooth inside to prevent blood clot formation, and have working mechanisms to repair themselves and dissolve blood clots that may form.

All that work falls on the endothelial cells that line the inner wall of the blood vessels, andI talked about them at The Magical Endothelium. Any injury to the endothelium can elicit an inflammatory response and clot formation leading to organ dysfunctions like heart attacks, strokes, and deaths. 

Blood clots always start small, and once they develop, they initiate a chain reaction that promotes a more extensive clot. The good thing is that the body can do fibrinolysis, a built-in mechanism to dissolve clots.

13 ways Spike Proteins cause disease

The following are how the spike proteins and their S1 subunit can cause damage. They can work together and have four results, inflammationthrombosis or clot formation, auto-immunity, and amyloid formation.

Any foreign protein inside the body can elicit inflammation. That is why parts of the spike proteins in the form of their S1 subunits or shorter fragments are enough to cause damage.[9][10].

Inflammation and thrombosis

The S1 subunit activates Toll-like receptor 4 (TLR4) signaling to induce pro-inflammatory responses. It happens with the spike protein in COVID-19 [10] and the S1 subunits.[12]

The spike protein triggers cell signaling events that promote pulmonary vascular remodeling and pulmonary arterial hypertension (PAH), and other cardiovascular complications [13]Source: Suzuki and Gychka. Vaccines 2

The spike S1 initiates inflammatory responses from tumor-necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) to initiate a cytokine storm syndrome in the lungs. [14].

Spike proteins cause vascular leaks by degrading the barrier of the endothelium. [15] The leak may explain the proliferation of lymphocytes seen by German pathologists in the organs of deceased patients who died after the shots.

Spike proteins affect the cardiac pericytes, the cells that “supervise” the endothelial cells responsible for maintaining the smoothness of the blood vessels. [16]. Study shows spike proteins affect cardiac pericytes and explain why soccer players collapse

Spike proteins downregulate the ACE2 and impair endothelial function [17]

The S1 produces blood clots resistant to the body’s fibrinolysis and hospital clot-buster medications [18]. That’s why some have their limbs amputated after the shots. One woman had both legs and hands amputated. There’s a list here.

The spike protein can cause inflammation by activating the alternate complement pathway. [20]

Long COVID-Syndrome

  1. The S1 Proteins can persist in CD16+ Monocytes up to 15 Months Post-Infection and vaccination to induce chronic inflammation. This explains the symptoms of the Long COVID Syndrome. [19]

Amyloids formation and interaction

  1. Amyloids are fibrillar proteins. They are most commonly associated with neurodegenerative diseases like dementia. However, they can also form in the heart and lungs and make them rigid and form blood clots resistant to dissolution. [21The SARS-CoV-2 spike protein can form amyloids seen in lung, blood, and nervous system disorders
  2. The S1 protein contains heparin-binding sites that attract amyloids to initiate amyloid protein aggregation. Amyloid formation leads to neurodegeneration like Parkinson’s Disease, Alzheimer’s’ disease, and Frontal lobe dementia. [22]

Molecular mimicry

12. Molecular mimicry happens if protein sequences in the spike protein and peptides have similarities to human proteins. Antibodies made for those viral proteins may also attack the host proteins.[24][25][26].

This leads to several autoimmune diseases like immune thrombocytopenia (low platelet counts) [23], autoimmune liver diseasesGuillain-Barré syndromeIgA nephropathyrheumatoid arthritis, and systemic lupus erythematosus[27]

Cancer and Immune Deficiency

  1. Spike proteins impair DNA damage repair and result in ineffective antibodies and damaged tumor-suppressor genes like the BRCA1 and 53BP1 that lead to cancers. BRCA1 damage is associated with breast, ovarian, and prostate cancers.

53BP1 loss of function in tumor tissues is elated to tumor occurrence, progression, and poor prognosis in human malignancies.[30]

Parting thoughts

The disease-causing part of the SARS-CoV-2 virus is the spike protein, and it is present in COVID-19 and the COVID injections. Prevention and early treatment are possible for COVID-19. Once you have the shot, there is no way to control the spike protein.

It is unclear why not all have the adverse effects or die. What is sure is that there are over one million reported adverse effects on VAERS, and more than one hundred thousand have been killed. Vaccine-induced deaths in the U.S. and Europe are way higher than the VAERS reports!

This list is not all-inclusive, and I probably missed some. Indeed, more will be discovered in the future, and I don’t want my body to find out. Do you?

Don’t Get Sick!

Knowledge about Covid-19 is rapidly evolving. Stay current by subscribing. Feel free to share and like.

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Related:

  1. Blood Vessel Damaging Proteins of the SARS-CoV-2
  2. Cerebral Thrombosis after the Pfizer Covid-19 Vaccine
  3. The High Risk of Deadly Brain Clots in the J & J COVID Vaccine
  4. This Study shows a Ten-Fold Risk of Developing Blood Clots after the COVID Vaccines.
  5. You got the COVID shot and found that others developed blood clots. Now what?
  6. Platelet Changes Causes Blood Clots in COVID-19
  7. Unidentified Foreign Bodies in the Vaccines Form Clots
  8. Retinal complications after COVID shots
  9. U.K. Study of COVID-19 shots and Excess Rates of Guillain-Barré Syndrome
  10. mRNA Vaccination Increases the Risk of Acute Coronary Syndrome
  11. German Analysis: The Higher the Vaccination Rate, the Higher the Excess Mortality
  12. Anti-Idiotype Antibodies against the Spike Proteins may Explain Myocarditis

References:

  1. Rijkers GT, Weterings N, Obregon-Henao A, et al. Antigen Presentation of mRNA-Based and Virus-Vectored SARS-CoV-2 VaccinesVaccines (Basel). 2021;9(8):848. Published 2021 Aug 3. doi:10.3390/vaccines9080848
  2. Kowarz E, Krutzke L, Reis J, et al. “Vaccine-Induced Covid-19 Mimicry” Syndrome: Splice reactions within the SARS-CoV-2 Spike open reading frame result in Spike protein variants that may cause thromboembolic events in patients immunized with vector-based vaccines. Research Square; 2021. DOI: 10.21203/rs.3.rs-558954/v1
  3. Ogata AF. et al. Circulating SARS-CoV-2 Vaccine Antigen Detected in the Plasma of mRNA-1273 Vaccine Recipients [published online ahead of print, 2021 May 20]. Clin Infect Dis. 2021;ciab465. doi:10.1093/cid/ciab465
  4. Hervier B, Ribon M, Tarantino N, Mussard J, Breckler M, Vieillard V, Amoura Z, Steinle A, Klein R, Kötter I, Decker P. Increased Concentrations of Circulating Soluble MHC Class I-Related Chain A (sMICA) and sMICB and Modulation of Plasma Membrane MICA Expression: Potential Mechanisms and Correlation With Natural Killer Cell Activity in Systemic Lupus Erythematosus. Front Immunol. 2021 May 3;12:633658. doi: 10.3389/fimmu.2021.633658. PMID: 34012432; PMCID: PMC8126610.
  5. Salih, Helmut & Goehlsdorf, Dennis & Steinle, Alexander. (2006). Salih HR, Goehlsdorf D, Steinle A. Release of MICB molecules by tumor cells: mechanism and soluble MICB in sera of cancer patients. Hum Immunol 67: 188-195. Human immunology. 67. 188-95. 10.1016/j.humimm.2006.02.008.
  6. Edgar JR. Q&A: What are exosomes, exactly?BMC Biol. 2016;14:46. Published 2016 Jun 13. doi:10.1186/s12915-016-0268-z
  7. Raposo G, Nijman HW, Stoorvogel W, Liejendekker R, Harding CV, Melief CJ, Geuze HJ. B lymphocytes secrete antigen-presenting vesicles. J Exp Med. 1996 Mar 1;183(3):1161-72. doi: 10.1084/jem.183.3.1161. PMID: 8642258; PMCID: PMC2192324.
  8. Bansal et al. Cutting Edge: Circulating Exosomes with COVID Spike Protein Are Induced by BNT162b2 (Pfizer–BioNTech) Vaccination prior to Development of Antibodies: A Novel Mechanism for Immune Activation by mRNA Vaccines. J Immunol November 15, 2021, 207 (10) 2405-2410
  9. Nuovo, G.J. et al. (2021) Endothelial cell damage is the central part of COVID-19 and a mouse model induced by injection of the S1 subunit of the spike protein. Ann. Diagn. Pathol. 51, 151682, https://doi.org/10.1016/j.anndiagpath.2020.151682
  10. Gu, T. et al. (2020) Cytokine signature induced by SARS-CoV-2 spike protein in a mouse model. Front. Immunol. 11, 621441,
  11. Aboudounya MM, Heads RJ. COVID-19 and Toll-Like Receptor 4 (TLR4): SARS-CoV-2 May Bind and Activate TLR4 to Increase ACE2 Expression, Facilitating Entry and Causing Hyperinflammation. Mediators Inflamm. 2021 Jan 14;2021:8874339. doi: 10.1155/2021/8874339. PMID: 33505220; PMCID: PMC7811571.
  12. Shirato K, Kizaki T. SARS-CoV-2 spike protein S1 subunit induces pro-inflammatory responses via toll-like receptor 4 signaling in murine and human macrophages. Heliyon. 2021 Feb 2;7(2):e06187. doi: 10.1016/j.heliyon.2021.e06187. PMID: 33644468; PMCID: PMC7887388. https://pubmed.ncbi.nlm.nih.gov/33644468/
  13. Suzuki YJ, et al. SARS-CoV-2 Spike Protein Elicits Cell Signaling in Human Host Cells: Implications for Possible Consequences of COVID-19 VaccinesVaccines (Basel). 2021;9(1):36. Published 2021 Jan 11. doi:10.3390/vaccines9010036
  14. Cao, X. et al. (2021) Spike protein of SARS-CoV-2 activates macrophages and contributes to induction of acute lung inflammation in male mice. FASEB J. 35, e21801
  15. Biering et al. SARS-CoV-2 Spike triggers barrier dysfunction and vascular leak via integrins and TGF-β signaling. bioRxiv 2021.12.10.472112
  16. Avolio E et al.  The SARS-CoV-2 Spike protein disrupts human cardiac pericytes function through CD147 receptor-mediated signaling: a potential non-infective mechanism of COVID-19 microvascular disease. Clin Sci (Lond). 2021 Dec 22;135(24):2667-2689. doi: 10.1042/CS20210735. PMID: 34807265; PMCID: PMC8674568.
  17. Lei, Y. et al. SARS-CoV-2 Spike Protein Impairs Endothelial Function via Downregulation of ACE 2. Circulation Research. 2021;128:1323–1326
  18. Grobbelaar, L.M. et al. (2021) SARS-CoV-2 spike protein S1 induces fibrin(ogen) resistant to fibrinolysis: implications for microclot formation in COVID-19. Biosci. Rep. 41 (8)
  19. Patterson B. et al. Persistence of SARS CoV-2 S1 Protein in CD16+ Monocytes in Post-Acute Sequelae of COVID-19 (PASC) Up to 15 Months Post-Infection.bioRxiv 2021.06.25.449905
  20. Yu J, Yuan X, Chen H, Chaturvedi S, Braunstein EM, Brodsky RA. Direct activation of the alternative complement pathway by SARS-CoV-2 spike proteins is blocked by factor D inhibition. Blood. 2020.
  21. Nyström et al. Amyloidogenesis of SARS-CoV-2 Spike Protein. bioRxiv 2021.12.16.472920. 
  22. Idrees D, Kumar V. SARS-CoV-2 spike protein interactions with amyloidogenic proteins: Potential clues to neurodegeneration. Biochem Biophys Res Commun. 2021 May 21;554:94-98. doi: 10.1016/j.bbrc.2021.03.100. Epub 2021 Mar 24. PMID: 33789211; PMCID: PMC7988450
  23. Nunez-Castilla, J et al. Spike mimicry of thrombopoietin may induce thrombocytopenia in COVID-19. bioRxiv 2021.08.10.455737
  24. Ehrenfeld M et al. Covid-19 and autoimmunity. Autoimmun Rev. 2020;19:102597.
  25. Kanduc D, Shoenfeld Y. On the molecular determinants of the SARS-CoV-2 attack. Clin Immunol. 2020;215. 
  26. Vojdani A, Kharrazian D. Potential antigenic cross-reactivity between SARS-CoV-2 and human tissue with a possible link to an increase in autoimmune diseases. Clin Immunol. 2020;217:108480
  27. Chen Y, Xu Z, Wang P, Li XM, Shuai ZW, Ye DQ, Pan HF. New-onset autoimmune phenomena post-COVID-19 vaccination. Immunology. 2021 Dec 27. doi: 10.1111/imm.13443. Epub ahead of print. PMID: 34957554.
  28. Jiang H, Mei YF. SARS-CoV-2 Spike Impairs DNA Damage Repair and Inhibits V(D)J Recombination In VitroViruses. 2021;13(10):2056. Published 2021 Oct 13. doi:10.3390/v13102056
  29. BRCA1 gene. Medline [website]
  30. Mirza-Aghazadeh-Attari M, et al.  53BP1: A key player of DNA damage response with critical functions in cancer. DNA Repair (Amst). 2019 Jan;73:110-119. doi: 10.1016/j.dnarep.2018.11.008. Epub 2018 Nov 20. PMID: 30497961.

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Potential mechanisms of cerebrovascular diseases in COVID-19 patients

Authors: Manxue Lou 1Dezhi Yuan 2 3Shengtao Liao 4Linyan Tong 1Jinfang Li 5Affiliations expand

Abstract

Since the outbreak of coronavirus disease 2019 (COVID-19) in 2019, it is gaining worldwide attention at the moment. Apart from respiratory manifestations, neurological dysfunction in COVID-19 patients, especially the occurrence of cerebrovascular diseases (CVD), has been intensively investigated. In this review, the effects of COVID-19 infection on CVD were summarized as follows: (I) angiotensin-converting enzyme 2 (ACE2) may be involved in the attack on vascular endothelial cells by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), leading to endothelial damage and increased subintimal inflammation, which are followed by hemorrhage or thrombosis; (II) SARS-CoV-2 could alter the expression/activity of ACE2, consequently resulting in the disruption of renin-angiotensin system which is associated with the occurrence and progression of atherosclerosis; (III) upregulation of neutrophil extracellular traps has been detected in COVID-19 patients, which is closely associated with immunothrombosis; (IV) the inflammatory cascade induced by SARS-CoV-2 often leads to hypercoagulability and promotes the formation and progress of atherosclerosis; (V) antiphospholipid antibodies are also detected in plasma of some severe cases, which aggravate the thrombosis through the formation of immune complexes; (VI) hyperglycemia in COVID-19 patients may trigger CVD by increasing oxidative stress and blood viscosity; (VII) the COVID-19 outbreak is a global emergency and causes psychological stress, which could be a potential risk factor of CVD as coagulation, and fibrinolysis may be affected. In this review, we aimed to further our understanding of CVD-associated COVID-19 infection, which could improve the therapeutic outcomes of patients. Personalized treatments should be offered to COVID-19 patients at greater risk for stroke in future clinical practice.

For More Information: https://pubmed.ncbi.nlm.nih.gov/33534131/

Late Complications of COVID-19; a Systematic Review of Current Evidence

Authors: SeyedAhmad SeyedAlinaghi,1Amir Masoud Afsahi,2Mehrzad MohsseniPour,1Farzane Behnezhad,3Mohammad Amin Salehi,1Alireza Barzegary,4Pegah Mirzapour,1Esmaeil Mehraeen,5,* and Omid Dadras6

Introduction

Introduction:

COVID-19 is a new rapidly spreading epidemic. The symptoms of this disease could be diverse as the virus can affect any organ in the body of an infected person. This study aimed to investigate the available evidence for long-term complications of COVID-19.

Methods:

This study was a systematic review of current evidence conducted in November 2020 to investigate probable late and long-term complications of COVID-19. We performed a systematic search, using the keywords, in online databases including PubMed, Scopus, Science Direct, Up to Date, and Web of Science, to find papers published from December 2019 to October 2020. Peer-reviewed original papers published in English, which met the eligibility criteria were included in the final report. Addressing non-human studies, unavailability of the full-text document, and duplicated results in databases, were characteristics that led to exclusion of the papers from review.

Results:

The full-texts of 65 articles have been reviewed. We identified 10 potential late complications of COVID-19. A review of studies showed that lung injuries (n=31), venous/arterial thrombosis (n=28), heart injuries (n=26), cardiac/brain stroke (n=23), and neurological injuries (n=20) are the most frequent late complications of COVID-19.

For More Information: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7927752/

What Does COVID Do to Your Blood?

Authors: Panagis Galiatsatos, M.D., M.H.S., Robert Brodsky, M.D.

COVID-19 is a very complex illness. The coronavirus that causes COVID-19 attacks the body in many different ways, ranging from mild to life threatening. Different organs and tissues of the body can be affected, including the blood.

Robert Brodsky, a blood specialist who directs the Division of Hematology, and Panagis Galiatsatos, a specialist in lung diseases and critical care medicine, talk about blood problems linked to SARS-CoV-2 — the coronavirus that causes COVID-19 — and what you should know.

Coronavirus Blood Clots

Blood clots can cause problems ranging from mild to life threatening. If a clot blocks blood flow in a vein or artery, the tissue normally nourished by that blood vessel can be deprived of oxygen, and cells in that area can die.

Some people infected with SARS-CoV-2 develop abnormal blood clotting. “In some people with COVID-19, we’re seeing a massive inflammatory response, the cytokine storm that raises clotting factors in the blood,” says Galiatsatos, who treats patients with COVID-19.

“We are seeing more blood clots in the lungs (pulmonary embolism), legs (deep vein thrombosis) and elsewhere,” he says.

Brodsky notes that other serious illnesses, especially ones that cause inflammation, are associated with blood clots. Research is still exploring if the blood clots seen in severe cases of COVID-19 are unique in some way. 

The Impact of Coronavirus Blood Clots Throughout the Body

In addition to the lungs, blood clots, including those associated with COVID-19, can also harm:

The nervous system. Blood clots in the arteries leading to the brain can cause a stroke. Some previously young, healthy people who have developed COVID-19 have suffered strokes, possibly due to abnormal blood clotting.

The kidneys. Clogging of blood vessels in the kidney with blood clots can lead to kidney failure. It can also complicate dialysis if the clots clog the filter of the machine designed to remove impurities in the blood.

Peripheral blood vessels and “COVID toe.” Small blood clots can become lodged in tiny blood vessels. When this happens close to the skin, it can result in a rash. Some people who test positive for COVID-19 develop tiny blood clots that cause reddish or purple areas on the toes, which can itch or be painful. Sometimes called COVID toe, the rash resembles frostbite.

For More Information: https://www.hopkinsmedicine.org/health/conditions-and-diseases/coronavirus/what-does-covid-do-to-your-blood

Clots, Strokes and Rashes: Is COVID a Disease of the Blood Vessels?

Whether it’s strange rashes on the toes or blood clots in the brain, the widespread ravages of COVID-19 have increasingly led researchers to focus on how the novel coronavirus sabotages blood vessels.

As scientists have come to know the disease better, they have homed in on the vascular system — the body’s network of arteries, veins and capillaries, stretching more than 60,000 miles — to understand this wide-ranging disease and to find treatments that can stymie its most pernicious effects.

Some of the earliest insights into how COVID-19 can act like a vascular disease came from studying the aftermath of the most serious infections. Those reveal that the virus warps a critical piece of our vascular infrastructure: the single layer of cells lining the inside of every blood vessel, known as the endothelial cells or simply the endothelium.

For More Information: https://khn.org/news/clots-strokes-and-rashes-is-covid-a-disease-of-the-blood-vessels/