New study suggests covid increases risks of brain disorders

Authors: Frances Stead Sellers Fri, August 19, 2022  Washington Post

A study published this week in the Lancet Psychiatry showed increased risks of some brain disorders two years after infection with the coronavirus, shedding new light on the long-term neurological and psychiatric aspects of the virus.

The analysis, conducted by researchers at the University of Oxford and drawing on health records data from more than 1 million people around the world, found that while the risks of many common psychiatric disorders returned to normal within a couple of months, people remained at increased risk for dementia, epilepsy, psychosis and cognitive deficit (or brain fog) two years after contracting covid. Adults appeared to be at particular risk of lasting brain fog, a common complaint among coronavirus survivors.

The study was a mix of good and bad news findings, said Paul Harrison, a professor of psychiatry at the University of Oxford and the senior author of the study. Among the reassuring aspects was the quick resolution of symptoms such as depression and anxiety.

“I was surprised and relieved by how quickly the psychiatric sequelae subsided,” Harrison said.

David Putrino, director of rehabilitation innovation at Mount Sinai Health System in New York, who has been studying the lasting impacts of the coronavirus since early in the pandemic, said the study revealed some very troubling outcomes.

“It allows us to see without a doubt the emergence of significant neuropsychiatric sequelae in individuals that had covid and far more frequently than those who did not,” he said.

Because it focused only on the neurological and psychiatric effects of the coronavirus, the study authors and others emphasized that it is not strictly long-covid research.

“It would be overstepping and unscientific to make the immediate assumption that everybody in the [study] cohort had long covid,” Putrino said. But the study, he said, “does inform long-covid research.”

Between 7 million and 23 million people in the United States have long covid, according to recent government estimates – a catchall term for a wide range of symptoms including fatigue, breathlessness and anxiety that persist weeks and months after the acute infection has subsided. Those numbers are expected to rise as the coronavirus settles in as an endemic disease.

The study was led by Maxime Taquet, a senior research fellow at the University of Oxford who specializes in using big data to shed light on psychiatric disorders.

The researchers matched almost 1.3 million patients with a diagnosis of covid-19 between Jan. 20, 2020, and April 13, 2022, with an equal number of patients who had other respiratory diseases during the pandemic. The data, provided by electronic health records network TriNetX, came largely from the United States but also included data from Australia, Britain, Spain, Bulgaria, India, Malaysia and Taiwan.

The study group, which included 185,000 children and 242,000 older adults, revealed that risks differed according to age groups, with people age 65 and older at greatest risk of lasting neuropsychiatric affects.

For people between the ages of 18 and 64, a particularly significant increased risk was of persistent brain fog, affecting 6.4 percent of people who had had covid compared with 5.5 percent in the control group.

Six months after infection, children were not found to be at increased risk of mood disorders, although they remained at increased risk of brain fog, insomnia, stroke and epilepsy. None of those affects were permanent for children. With epilepsy, which is extremely rare, the increased risk was larger.

The study found that 4.5 percent of older people developed dementia in the two years after infection, compared with 3.3 percent of the control group. That 1.2-point increase in a diagnosis as damaging as dementia is particularly worrisome, the researchers said.

The study’s reliance on a trove of de-identified electronic health data raised some cautions, particularly during the tumultuous time of the pandemic. Tracking long-term outcomes may be hard when patients may have sought care through many different health systems, including some outside the TriNetX network.

“I personally find it impossible to judge the validity of the data or the conclusions when the data source is shrouded in mystery and the sources of the data are kept secret by legal agreement,” said Harlan Krumholz, a Yale scientist who has developed an online platform where patients can enter their own health data.

Taquet said the researchers used several means of assessing the data, including making sure it reflected what is already known about the pandemic, such as the drop in death rates during the omicron wave.

Also, Taquet said, “the validity of data is not going to be better than validity of diagnosis. If clinicians make mistakes, we will make the same mistakes.”

The study follows earlier research from the same group, which reported last year that a third of covid patients experienced mood disorders, strokes or dementia six months after infection with the coronavirus.

While cautioning that it is impossible to make full comparisons among the effects of recent variants, including omicron and its subvariants, which are currently driving infections, and those that were prevalent a year or more ago, the researchers outlined some initial findings: Even though omicron caused less severe immediate symptoms, the longer-term neurological and psychiatric outcomes appeared similar to the delta waves, indicating that the burden on the world’s health-care systems might continue even with less-severe variants.

Hannah Davis, a co-founder of the Patient-Led Research Collaborative, which studies long covid, said that finding was meaningful. “It goes against the narrative that omicron is more mild for long covid, which is not based on science,” Davis said.

“We see this all the time,” Putrino said. “The general conversation keeps leaving out long covid. The severity of initial infection doesn’t matter when we talk about long-term sequalae.

Multivariate profile and acute-phase correlates of cognitive deficits in a COVID-19 hospitalized cohort

Authors: AdamHampshireaDoris A.ChatfieldbAnne ManktelowMPhilbAmyJollyaWilliamTrenderaPeter J.HellyeraMartina DelGiovaneaVirginia F.J.NewcombebJoanne G. Outtrimb BenWarneb JunaidBhattid LindaPointond AnneElmere NyarieSitholebf JohnBradleybgh NathalieKingston lStephen J.Sawceri Edward T.Bullmorecdj…David K.Menonbck1

eClinicalMedicine

Volume 47, May 2022, 101417

Summary

Background

Preliminary evidence has highlighted a possible association between severe COVID-19 and persistent cognitive deficits. Further research is required to confirm this association, determine whether cognitive deficits relate to clinical features from the acute phase or to mental health status at the point of assessment, and quantify rate of recovery.

Methods

46 individuals who received critical care for COVID-19 at Addenbrooke’s hospital between 10th March 2020 and 31st July 2020 (16 mechanically ventilated) underwent detailed computerised cognitive assessment alongside scales measuring anxiety, depression and post-traumatic stress disorder under supervised conditions at a mean follow up of 6.0 (± 2.1) months following acute illness. Patient and matched control (N = 460) performances were transformed into standard deviation from expected scores, accounting for age and demographic factors using N = 66,008 normative datasets. Global accuracy and response time composites were calculated (G_SScore & G_RT). Linear modelling predicted composite score deficits from acute severity, mental-health status at assessment, and time from hospital admission. The pattern of deficits across tasks was qualitatively compared with normal age-related decline, and early-stage dementia.

Findings

COVID-19 survivors were less accurate (G_SScore=-0.53SDs) and slower (G_RT=+0.89SDs) in their responses than expected compared to their matched controls. Acute illness, but not chronic mental health, significantly predicted cognitive deviation from expected scores (G_SScore (p=​​0.0037) and G_RT (p = 0.0366)). The most prominent task associations with COVID-19 were for higher cognition and processing speed, which was qualitatively distinct from the profiles of normal ageing and dementia and similar in magnitude to the effects of ageing between 50 and 70 years of age. A trend towards reduced deficits with time from illness (r∼=0.15) did not reach statistical significance.

Interpretation

Cognitive deficits after severe COVID-19 relate most strongly to acute illness severity, persist long into the chronic phase, and recover slowly if at all, with a characteristic profile highlighting higher cognitive functions and processing speed.

Funding

This work was funded by the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (BRC), NIHR Cambridge Clinical Research Facility (BRC-1215-20014), the Addenbrooke’s Charities Trust and NIHR COVID-19 BioResource RG9402. AH is funded by the UK Dementia Research Institute Care Research and Technology Centre and Imperial College London Biomedical Research Centre. ETB and DKM are supported by NIHR Senior Investigator awards. JBR is supported by the Wellcome Trust (220258) and Medical Research Council (SUAG/051 G101400). VFJN is funded by an Academy of Medical Sciences/ The Health Foundation Clinician Scientist Fellowship. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

Evidence before this study

A PubMed search for articles using the terms ‘COVID-19′, ‘chronic’ and ‘cognitive impairment’ returns 85 results between 2020 and 2022, reflecting growing concern that people may suffer persistent cognitive problems after SARS-CoV-2 infection. However, most of these studies have built on either subjective report of cognitive problems or brief pen-and paper assessment scales that lack sensitivity to mild deficits and precision regarding affected cognitive domains.

Added value of this study

Using precision computerised cognitive assessment tools, we observed that 46 COVID-19 patients matched for age, gender, education and first language, 6–10 months after admission for care at Addenbrookes hospital perform less well than controls in terms of cognition. Critically, the scale of their cognitive deficits correlated with acute illness severity as recorded during the hospital stay, but not fatigue or mental health status at the time of cognitive assessment.

Implications of all the available evidence

These results suggest that the patients who have recovered from severe COVID-19 may need longer term support for cognitive deficits that persist into the chronic phase. More research is required to understand the basis of these deficits. Future work will be focused on mapping these cognitive deficits to underlying neural pathologies and inflammatory biomarkers, and to longitudinally track recovery into the chronic phase.

Introduction

There is growing evidence that COVID-19 can cause lasting cognitive and mental health problems. Recovered patients reporting psychological symptoms including fatigue, cognitive difficulties (“brain fog” and “problems finding the words”), sleep disturbances breathlessness and psychiatric disorders months after infection.1 In the UK alone, 13.7% of 20,000 individuals reported having symptoms inclusive of cognitive difficulties 12 weeks after a positive COVID-19 test (UK Office for National Statistics, April 2021). Mild cases can report persistent cognitive symptoms; however, prevalence is higher in severe cases,2 with ∼33–76% of patients suffering cognitive symptoms 3–6 months post hospitalisation.3,4

The neurobiological and psychological bases of these deficits remain unclear. Imaging biomarker studies indicate multiple likely candidates. Indeed, drawing parallels with serious acute respiratory syndrome (SARS), middle eastern respiratory syndrome (MERS) and post-critical illness/intensive care syndrome, a range of neurological/ central nervous system (CNS) complications can arise from infection.5,6 Most notably, encephalitis, ischaemia, haemorrhage, microstructural and functional changes and cerebrovascular disease (CVD) have been observed in COVID-19 patients, and more recently, evidence of brainstem inflammation using 7 Tesla magnetic resonance imaging (MRI) has been reported.7 There has been concern regarding whether cognitive deficits will remain for years as a chronic syndrome, and whether patients who develop CVD as a result of infection will experience neurodegeneration and dementia in the long-term,789 despite recovery of other acute and sub-acute symptoms.10

Key limitations for much of this early work include a reliance on self-report as opposed to objective assessment of cognitive deficits, the application of neuropsychological scales that lack sensitivity to detect subtle deficits in the formerly unimpaired or precision to differentiate deficits across cognitive domains, and uncertainty regarding longevity of deficits. Furthermore, depression, anxiety, fatigue and post-traumatic stress are elevated post COVID-19 illness,11 which might mediate the association with cognitive sequelae.

Recently, we provided preliminary results addressing some of these limitations. Specifically, we used computerised cognitive assessment technology,12,13 which has superior sensitivity and precision to gold-standard neuropsychological scales,14 to investigate objectively measurable deficits across multiple cognitive domains in a large online cohort15,16 that incidentally included people who reported infection with COVID-19 of varying severity.17 Higher cognitive functions such as spatial planning and analogical reasoning appeared to be disproportionately impaired, especially in hospitalised patients. However, our earlier analyses lacked clinical-record corroboration of self-reported illness severity or hospital treatment. Furthermore, participants primarily were in the early chronic phase ∼2–3 months post illness, which limited insight into the longevity of deficits.

Here, we use the same technology to assess patients at timepoints ranging from between ∼1 and 10 months post admission to hospital for severe COVID-19. We sought to determine whether (i) the finding of higher cognitive deficits after COVID-19 infection can be replicated in a hospital confirmed cohort, (ii) the cognitive deficits relate to features of acute illness vs. mood, anxiety, tiredness or post-traumatic stress disorder (PTSD) at the point of assessment, (iii) the deficits negatively correlate with time since illness and (iv) the scale and profile of deficits is qualitatively comparable to that observed in normal age-related decline or dementia.

Methods

Data collection

All patients admitted to Addenbrookes Hospital with COVID-19 between 10th March 2020 and 31st July 2020, who survived and consented to take part were eligible for this cohort study. This comprised 489 patients, of whom 49 were consented to the NIHR COVID-19 BioResource to participate in the study and were administered the follow up battery. The study was approved by the Cambridge Central Research Ethics Committee (17/EE/0025 0025 & IRAS ID: 220277). Of these, 46 patients (27 females, 19 males, age mean=51 years standard deviation (SD)=14 years, range 28–83 years) completed the study protocol adequately to allow analysis (Tables S1–3). Based on the effect size observed in our previous citizen science dataset,17 where people were assessed using the same technology, expected effect size for critically ill hospitalised patients would be >0.5 standard deviations. At n-46, power was sufficient to detect with one-tailed alpha at p < 0.05 a 0.5SD effect size difference as gauged by DfE scores from the linear model at 96% relative to zero and at 94% relative to the matched control group. There was statistical power of 95% to detect medium strength correlations of r = 0.50 at two tailed alpha p < 0.05. Participants completed a custom computerised cognitive assessment battery under supervised conditions via the Cognitron platform,17,18 comprising 8 tasks deployed on an iPad (Supplemental Methods), as well as standard mood, anxiety and post-traumatic stress scales, specifically, the Generalized Anxiety Disorder 7 (GAD-7),19 the Patient Health Questionnaire 9 (PHQ-9)20,21 and the PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders 5 (PCL-5)22 in a return visit to the hospital on average 179 days after illness onset (SD=62 inter-quartile range=81).

Statistical methods

All analyses were conducted in MATLAB R2020a. To enable correlation of deficit magnitude with clinical and mental health measures whilst accounting for population variables, accuracy and median reaction times were extracted for each task, comprising 16 measures (Table S4), were transformed to deviation from expected (DfE) scores (see below definition) relative to N = 66,008 normative datasets (Table S5), comprising individuals who had performed the same set of tasks. Specifically, to calculate DfE scores, linear models were trained to predict performance for each task within the normative dataset from age decade, sex (male, female, other), education level, handedness (left, right ambidextrous) and first language (English, other). The trained models were then applied to the patient demographics, to which they were naive, providing expected scores for each individual. DfE score was quantified as the difference between observed minus predicted score divided by the control standard deviation. Non-compliant individuals from the normative dataset already had been identified and removed based on responding unfeasibly fast given the response time distribution; applying the same threshold identified no non-compliant participants within the patient dataset. Four patients could not complete Verbal Analogies and one could not complete Spatial Planning as they found them too challenging. Control and patient datasets were concatenated, and composites were then calculated by taking the first unrotated principal component (Table S6) across the eight summary score measures (G_SScore), focused on accuracy, and across the eight response time scores (G_RT), focussing on speed of response. Component scores were calculated for each subject via regression of the component loadings matrix across the above measures, excluding any unavailable datapoints, and transformed to DfE score as described above. For further comparison, a set of matched controls was identified from within the normative database and processed in the same manner as the patients. Specifically, for each patient, ten unique control datasets were randomly selected who exactly matched them in terms of age decade, sex, handedness, first language and education level.

All statistical analyses applied a prior significance cut-off set to p < 0.05. T-tests, performed one-tailed with family wise error (FWE) correction for multiple comparisons, evaluated whether patient composite and individual task DfE scores were consistently poorer than expected relative to the matched normative group. Multiple regression determined whether G_SScore and G_RT DfE scores could be predicted from clinical features during the acute hospital stay or mental health measures at the time of assessment. Clinical features were World Health Organisation (WHO) COVID-19 severity score,23 highest C-reactive protein (CRP), mechanical ventilation, extrapulmonary support, days ventilated, tracheostomy, and highest D-dimer; as well as age, sex and time since illness. Mental health scores were the GAD7, PHQ9 and PCL5. Due to high correlations between some of these clinical features, the feature matrix was reduced by applying Principal Component Analysis with varimax rotation, where the number of components was defined according to the Kaiser convention of retaining components with eigenvalues >1. The relationship between G_SScore and G_RT to time since illness was further examined in isolation using bivariate correlations with one-tailed significance.

To qualitatively gauge whether the profile of COVID-19 related cognitive deficits was similar in pattern or scale to age-related decline, standard deviation differences were extracted from the normative models (that is, accounting for the other population variables listed above) for each task between people at ages aged 70–79 minus those 20–29 or 50–59 within the control dataset. For further qualitative comparison, performance data from a previously collected group of 28 early-mid stage dementia patients were submitted to the same DfE pipeline as described above and effect sizes plotted (clinical and demographic details provided in Table S7).

Role of the funding source

The funder of the study had no role in the design of the study, data collection, data analysis, interpretation or writing of the report. All authors had full access to all data within the study. The corresponding authors had final responsibility for the decision to submit for publication.

Results

T-tests of global summary score and response time composites (Figure 1a) confirmed that participants who had been hospitalised due to COVID-19 scored significantly lower and were slower in their responses than would be expected given the control population as gauged by DfE scores (G_SScore estimate=-0.538 SDs, t=-4.214 p < 0.0001; G_RT estimate=0.726SDs, t = 4.507, p < 0.0001). Repeating the analysis for the 43 chronic-phase patients >90 days post symptom onset showed a similar result (G_SScore estimate=-0.524 SDs, t=-3.875 p = 0.0004; G_RT estimate=0.715SDs, t = 4.194, p < 0.0001). Contrasting the DfE scores directly against 460 precisely matched individuals (Figure 2), 10 per patient, from the control database reinforced this observation (mean difference in G_SScore estimate=-0.525SDs, t=-4.327, p < 0.0001; mean difference in G_RT estimate=0.887SDs, t = 5.803, p < 0.0001).

Fig 1
Fig 2

Application of Principal Component Analysis to the matrix of clinical and mental health features identified three components with eigenvalues >1 capturing 74% of the variance (Figure 1b). After varimax rotation, Component 1 captured variance pertaining to general severity of acute illness, including heavy positive loadings from WHO COVID-19 severity score, highest CRP, and requirement for mechanical ventilation, extrapulmonary organ support and days ventilated, moderate positive loading with age and requirement of tracheostomy and moderate negative loading for days since illness. Component 2 had heavy positive loading of requirement for tracheostomy and days ventilated, moderate positive loading for highest D-dimer and mechanical ventilation and extrapulmonary support, and moderate negative loading for females vs. males and time from illness onset. Component 3 had heavy positive loadings for the three mental health scales.

Multiple regression of the component scores onto DfE performance composites (Figure 1c) showed a significant negative correlation between G_SScore and Component 1 (Estimate=-0.346, F(1,42)= 9.392 p = 0.00380), but not Component 2 (Estimate=0.140, F(1,42)=1.841 p = 0.18208) or Component 3 (Estimate=-0.153, F(1,42)=1.855 p = 0.18041). There was also a threshold level negative correlation between G_RT and Component 1 (Estimate=0.305, F(1,42)=4.008 p=​ 0.05178), but not Component 2 (Estimate=-0.177, F(1,42)=1.791 p = 0.21044) or Component 3 (Estimate=0.111, F(1,42)=0.592 p = 0.46861).

Bivariate correlations (Table S8) showed significant associations between G_SScore and Severity WHO COVID-19 ordinal scale, mechanical ventilation, extrapulmonary organ dysfunction support and highest CRP during admission at the one tailed uncorrected threshold. However, the hypothesised trends towards reduced underperformance over time were of small effect size and were statistically non-significant (G_SScore r = 0.15 p = 0.1542, G_RT r=-0.16 p = 0.1486 one tailed and uncorrected). Reanalysing the data focusing exclusively on either those who were or were not ventilated relative to their respective controls showed significant cognitive deficits in both sub-groups (Fig. S1 & Table S9).

Finally, DfE scores were examined at the individual task level. There was a broad pattern of reduced accuracy and slowed response compared to the 460 matched controls (Table 1Figure 2a), with multiple tasks surviving the p < 0.05 one-tailed and family wise error (FWE) corrected for multiple comparisons threshold. As predicted,17 underperformance was more substantial for tasks challenging higher cognitive functions such as Analogical Reasoning (score -0.85SDs RT +1.34SDs) and Spatial Planning (score +0.28SDs RT +0.89SDs), as well as 2D Manipulations (score -0.58SDs RT +0.57SD) and word recall (immediate score -0.43SDs RT +0.43SDs delayed score -0.051SDs RT +0.46SDs).

Table 1. T-tests contrasting patients vs. 460 matched controls (one-tailed and FWE corrected for multiple comparisons).

Empty CellEmpty CellEffect size (DfE)tp (corrected)
AccuracyVerbal analogies-0.854-6.205<0.00001
2D manipulation-0.575-4.2210.00026
Words immediate-0.432-2.8690.03863
Spatial span-0.405-3.6050.00309
Target detection-0.176-1.4501.32876
3D rotation-0.076-0.9962.87946
Words delayed-0.051-0.4585.82405
Spatial planning0.2831.5101.18614
LatencySpatial span0.2311.5431.11135
Words immediate0.4313.0350.02276
Target detection0.4442.5680.09468
Words delayed0.4632.9420.03070
2D manipulation0.5703.8790.00107
3D rotation0.6203.5220.00421
Spatial planning0.8884.7790.00002
Verbal analogies1.3377.018<0.00001
GlobalG_SScore-0.525-4.3270.00016
G_RT0.8875.803<0.00001

For comparison (Figure 2), the pattern of mean age-related differences in performance of people in their 70s minus 20s or 70s minus 50s was quite distinct, with age related differences being most pronounced for 2D Manipulations, Spatial Span and Target Detection as opposed to Spatial Planning or Verbal Analogies. Furthermore, the 28 dementia patients who undertook 6 of the tasks showed the greatest DfE score on the Word Memory task with notably higher effect size.

Discussion

Individuals who survive severe COVID-19 illness have objectively measurable cognitive deficits, lasting many months, with respect to age- and demographic-adjusted norms.17,24252627282930 Taking Cohen’s notion of effect sizes as a gauge, the scale of those deficits was large; on average the 0.52SD and 0.89SD levels of underperformance on global accuracy and response time composite measures span the medium to large effect size range. For individuals who required mechanical ventilation, both composites were in the large range at 0.90SDs and 1.0 SDs, respectively, which is somewhat larger than our previous online study using the same assessment tools.17 The deficits within specific cognitive domains were even greater, e.g., Verbal Analogies response times were 1.3SDs longer on average for all patients and 1.7SDs for those who had required mechanical ventilation. Notably, when analysing only those individuals for whom English was the native language, the same pattern of deficits was still evident. Furthermore, our analyses accounted for both first language and education level. These results accord with self-reported problems ‘finding words’31 and neuropsychological case studies indicating verbal fluency deficits in severe COVID-19 patients post recovery.32

By using a large pre-existing normative dataset to correct for normal population variability in cognitive performance, we were able to begin the process of disentangling potential contributors to cognitive deficits post COVID-19. In particular, measures of mood, post-traumatic stress and mental health at the point of assessment were sufficiently dissociable from acute illness severity to be evaluated within the predictor matrix. This distinction is critical, because it is now well established that people who have recovered from severe COVID-19 illness can have a broad spectrum of symptoms of poor mental health11 as do those suffering from Long Covid,1 which could conceivably contribute to both self-perceived and objectively measured cognitive deficits. These include problems with depression, anxiety post-traumatic stress, low motivation, fatigue, low mood, and disturbed sleep. Here, it was clearly the case that acute illness severity was the better predictor of objectively measurable global cognitive deficits during the chronic phase. At the level of individual clinical features, WHO COVID-19 severity score, highest CRP and the requirement for mechanical ventilation and multiple organ support were predictive of poorer cognitive performance.

All patients were recruited from the same hospital and following illness within a narrow timeframe, which given differences in patient treatment and virus variants across time limits our confidence when generalising these results. We believe that this limitation is somewhat mitigated by the concordance between the results presented here and our previous citizen science dataset, published in this journal.17 Nonetheless, future research should seek to determine the relationship between variants, treatment strategies and cognitive outcomes at larger scale.

Regarding how representative the cohort was, the recruited population were younger, and more frequently female, and with a higher proportion of critical care admissions (WHO Ordinal Scale >6) than those who came through the centre (Tables S10–S14). A significant proportion, though not all, of these differences is attributable to the mortality of 24% in the overall admitted population, since non-survivors were older (median age=80 inter quartile range =73–87), more often male (64%), and may have included patients in whom treatment limitation decisions may have been in place.

Our analysis of fatigue post COVID-19 illness was not in the original analysis plan. However, scores capturing self-report of fatigue in the months post illness were available for 38 patients (Tables S1–3) from the Post-Intensive Care Unit Presentation Screen, a brief functional screening tool to inform the rehabilitation needs after treatment in intensive care settings. 28 of them endorsed some level of fatigue. Fatigue score correlated robustly with the mental health composite score (r=-0.45 p = 0.005) but not with the acute illness composite score (r = 0.03 p = 0.852) or either of the cognitive composite scores (G_ACC r = 0.19 p = 0.240; G_RT r=-0.16 p = 0.343). These results indicate that although both fatigue and mental health are prominent chronic sequalae of COVID-19, their severity is likely to be somewhat independent from the observed cognitive deficits.

A further limitation was that the acute clinical features were too highly correlated with each other to dissociate. All but two of the participants requiring mechanical ventilation also required multiple organ support, and the requirement for mechanical ventilation correlated with highest CRP, a measure of inflammation, at r∼=0.8. The observed correlation with a marker of acute inflammation may reflect a causal relationship beyond the severity of respiratory problems; however, given the high correlation to other clinical features of the acute phase, work seeking to disentangle underlying clinical causes of the observed cognitive deficits will require either substantial sized cohorts with sufficient power to delineate highly correlated predictors or additional data types, such as brain imaging in order to detect associations with markers in specific types of neuropathology.

Some previous studies have observed significant recovery across time in terms of cognitive symptoms18 and imaging measures of brain function.33 In accordance with these studies, we did observe slow and non-significant trends towards reduced deficits in both accuracy and response latency as a function of time from illness. We conclude that any recovery in cognitive faculties is at best likely to be slow. It also is important to consider that trajectories of cognitive recovery may vary across individuals depending on illness severity and the neurological or psychological underpinnings, which are likely complex. Plotting recovery trajectories and untangling their multivariate relationships to clinical features will require multi-timepoint studies in larger cohorts.

At a finer multivariate grain, the profile of deficits replicates our previous report in an online cohort of disproportionate underperformance within certain cognitive domains. In concordance with a previous large scale online study this pattern includes tasks designed to assess performance accuracy of attention, memory, difficult word-based reasoning and planning.17 However, we also observed slowed processing speed. On a neurological level, this pattern of impairment aligns with the observation of sub-acute phase hypometabolism within frontoparietal systems after COVID-19 illness26 that are known to be recruited in different combinations and configurations during the performance of these tasks.12,13,34

In this latter respect, the application of an assessment battery that provides a dimensional profile spanning multiple cognitive domains is of value when offering interoperability across studies. Indeed, it was informative to note that this profile of cognitive dysfunction was quite distinct to the normal pattern of age-related decline and to the pattern of deficits observed in early-stage dementia patients. On average, the scale of deficits was most similar to that observed in normal cognitive decline between the ages of 50–70; however, when examined in more detail the pattern of cognitive deficits was most pronounced for different tasks than either age-related decline or the dementia group. These more detailed results highlight the potential value of cross comparing multivariate profile of COVID-19 cognitive deficits to a wider variety of populations in order to identify potential similarities to other neurological conditions. Future work should also expand the repertoire of disorders, especially populations who have recovered from other critical illnesses, and cross relate these detailed cognitive profiles to imaging and blood biomarker measures of neuropathology and tracking recovery and decline trajectories over a longer temporal scale.

In summary, severe COVID-19 illness is associated with significant objectively measurable cognitive deficits that persist into the chronic phase. The scale of the deficits correlates with clinical severity during the acute phase as opposed to mental health status at the time of assessment, shows at best a slow recovery trajectory and the multivariate profile of deficits is consistent with higher cognitive dysfunction as opposed to accelerated ageing or dementia.

Funding

This work was funded by the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (BRC), NIHR Cambridge Clinical Research Facility (BRC-1215-20014), the Addenbrooke’s Charities Trust and NIHR COVID-19 BioResource RG9402. AH is funded by the UK Dementia Research Institute Care Research and Technology Centre and Imperial College London Biomedical Research Centre. ETB and DKM are supported by NIHR Senior Investigator awards. JBR is supported by the Wellcome Trust (220258) and Medical Research Council (SUAG/051 G101400). VFJN is funded by an Academy of Medical Sciences/ The Health Foundation Clinician Scientist Fellowship. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

Contributors

AH and DKM had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: AH, JB, ETB, JBR, DKM

Acquisition, analysis, or interpretation of data: AH, DAC, AM, AJ, WT, PH, MDG, VFJN, JGG, JB, LP, AE, NS, JB, NK, SJS, DKM.

Drafting of the manuscript: AH.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: AH.

Obtained funding: JB, JBR, ETB, DKM.

Supervision: AH, ETB, JBR, DKM.

Data sharing statement

Requests for data should directed to the corresponding authors. Data will be available upon reasonable request.

Declaration of Interests

Dr. Hampshire reports grants from UK Dementia Research Institute, grants from NIHR Imperial Biomedical Research Centre, and grants from NIHR, outside the submitted work; and is Co-director and owner of H2 Cognitive Designs Ltd and director and owner of Future Cognition Ltd, which support online cognitive studies and develop custom cognitive assessment software, respectively. Ms. Chatfield has nothing to disclose. Ms. Manktelow has nothing to disclose. Dr. Jolly has nothing to disclose. Mr. Trender has nothing to disclose. Dr. Hellyer reports being Chief Executive of H2 Cognitive Designs LTD, which provides a platform for online cognitive tests for remote assessment and receives remuneration for role. Ms. Del Giovane has nothing to disclose. Dr. Newcombe reports grants from Academy of Medical Sciences / The Health Foundation Clinician Scientist Fellowship during the conduct of the study. Ms. Outrim has nothing to disclose. Mr. Warne has nothing to disclose. Mr. Bhatti has nothing to disclose. Ms. Pointon has nothing to declare. Ms. Elmer has nothing to disclose. Dr. Sithole has nothing to disclose. Dr. Bradley reports grants from Funding for NIHR BioResource (IS-BRC-1215-20014) during the conduct of the study. Dr. Kingston has nothing to disclose. Dr. Sawcer has nothing to disclose. Dr. Bullmore reports personal fees from GlaxoSmithKline, personal fees from Sosei Heptares, outside the submitted work; and is Honorary Treasurer and member of Council for the Academy of Medical Sciences. Dr. Rowe reports grants from Wellcome Trust, grants from NIHR, grants from Medical Research Council, during the conduct of the study. Dr. Menon reports grants from Lantmannen AB, grants from GlaxoSmithKline Ltd, personal fees from Calico LLC, personal fees from GlaxoSmithKline Ltd, personal fees from Lantmannen AB, other from Integra Neurosciences, outside the submitted work; and reports leadership and fiduciary roles for Queens’ College, Cambridge, Intensive Care National Audit and Research Centre, London, and European Brain Injury Consortium.

Acknowledgments

We thank NIHR BioResource volunteers for their participation, and gratefully acknowledge NIHR BioResource centres, NHS Trusts and staff for their contribution. We thank the National Institute for Health Research, NHS Blood and Transplant, and Health Data Research UK as part of the Digital Innovation Hub Programme. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

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References

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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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Some COVID-19 patients have brain complications, study suggests

Authors: Mary Van Beusekom | News Writer | CIDRAP News  | Jun 26, 2020

Some COVID-19 patients, including those younger than 60 years old, appear to develop neurologic and neuropsychiatric complications such as stroke, brain inflammation, psychosis, and dementia-like symptoms, according to a study published yesterday in The Lancet Psychiatry.

The early-stage study of 153 hospitalized patients with confirmed, probable, or possible COVID-19 in the United Kingdom (UK) from Apr 2 to 26 identified 125 patients with complete data, of whom 77 (62%) had a stroke.

Of 125 patients, 114 (92%) had confirmed coronavirus infection, 5 (4%) had probable infection, and 5 (4%) were classified as possibly infected.

Stroke, encephalopathy, psychiatric diagnoses

Fifty-seven of 77 stroke patients (74%) had an ischemic stroke caused by a blood clot in the brain, 9 (12%) had a stroke caused by a brain hemorrhage, and 1 (1%) had a stroke caused by inflammation in the brain’s blood vessels. Sixty-one of the 77 stroke patients for whom age was available (82%) were older than 60 years.

Thirty-nine of 125 patients (31%) had behavioral changes indicative of an altered mental state, of whom 9 (23%) had unspecified brain dysfunction known as encephalopathy, and 7 (18%) had brain inflammation, or encephalitis.

The remaining 23 patients with altered mental states had psychiatric diagnoses, including 10 with new-onset psychosis, 7 with depression or anxiety, and 6 with a dementia-like syndrome. Only 2 patients (9%) had exacerbations of a chronic mental illness, although the authors noted that they cannot exclude the possibility that cases classified as new were simply undiagnosed before the pandemic.

Of the 37 of 39 COVID-19 patients with an altered mental state for whom age was available, 18 (49%) were younger than 60 years, which could be because they were more likely to be referred to a psychiatrist or other specialist, while physicians may be likely to attribute confusion or behavioral changes in older patients to delirium without further investigation, the authors said.

Altered mental states in younger patients

While altered mental states are not uncommon in hospitalized patients with infections, especially those requiring intensive care, they occur most often in older patients.

“In this study, we observed a disproportionate number of neuropsychiatric presentations in younger patients and a predominance of cerebrovascular complications in older patients, which might reflect the state of health of the cerebral vasculature and associated risk factors, exacerbated by critical illness in older patients,” the authors said.

For More Information: https://www.cidrap.umn.edu/news-perspective/2020/06/some-covid-19-patients-have-brain-complications-study-suggests