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


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).


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.


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

21: 2175–97.

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

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

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

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

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

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

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

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

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

4 Ways COVID Leaves Its Mark on the Eye

Authors: Reena MukamalReviewed By Joseph T Nezgoda, MD MBA Sep. 14, 2021

An analysis of 121 patients dating back to the beginning of the pandemic unveils COVID’s most common effects on the eye. Share this information and remember: Widespread vaccination is key to ending the pandemic.

How does COVID reach the eyes?

People respond in different ways to COVID-19 infections. While some people develop mild to severe respiratory problems, others experience no symptoms at all. Pink eye remains the most common sign of COVID in the eyes of children and adults.

Doctors are still learning how COVID affects the eyes. But it’s clear that some people with COVID experience inflammation throughout their body. This inflammation can cause blood clots to form. These clots may travel through the body and reach the veins, arteries and blood vessels of the eye.

COVID’s effects on the retina

The new study suggests that few people with COVID will develop eye problems. But when those problems occur, they can range from mild to vision-threatening. Many of these problems affect the retina — a light-sensing layer of cells in the back of the eye that plays a key role in your vision.

Here are four of the most common eye problems that may develop after COVID infection, according to the new analysis.

1. “Cotton wool” spots

When blood clots prevent nutrients from getting to the retina, the tissue in the retina begins to swell and die. If the doctor examines your eye closely using optical coherence tomography, this area looks white and fluffy like cotton wool (shown in the image above). These spots do not typically affect a person’s vision.

2. Eye stroke (also called retinal artery occlusion)

Blood clots in the arteries of the retina can block the flow of oxygen, causing cells to die. This is known as a retinal artery occlusion, or eye stroke. The most common symptom of an eye stroke is sudden, painless vision loss.

3. Retinal vein occlusion

When a vein in the retina becomes blocked, blood can’t drain out like it should. The buildup of blood raises pressure levels inside the eye, which can cause bleeding, swelling and fluid leaks. People with this complication can develop blurry vision or even sudden, permanent blindness.

4. Retinal hemorrhage

This occurs when blood vessels in the retina start bleeding. It is sometimes caused by a retinal vein occlusion. A hemorrhage can lead to blind spots and gradual or sudden loss of vision.

Am I at risk of eye complications from COVID?

Very few people with COVID will experience serious eye-related complications. But certain people are more likely than others to develop these problems. People with the following conditions are at greatest risk:

When eye problems occur, they tend to develop within 1 to 6 weeks of experiencing COVID symptoms.

These problems have developed in people who were very sick with COVID as well as people who were apparently healthy and lacked symptoms.

Although this is the largest study to date on COVID’s impact on the retina, researchers only examined information from 121 patients. Doctors are continuing to explore how often eye problems affect people with COVID, and how to prevent these conditions.

How to protect your eyes during COVID-19

If you develop symptoms of COVID and notice changes in your vision, schedule an appointment with an ophthalmologist right away.

Ophthalmic Manifestations Of Coronavirus (COVID-19)

Authors: Katherine Hu; Jay Patel; Cole Swiston; Bhupendra C. Patel.


Since December 2019, coronavirus disease 2019 (COVID-19) has become a global pandemic caused by the highly transmissible severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).[1] Initially, there were several reports of eye redness and irritation in COVID-19 patients, both anecdotal and published, supporting conjunctivitis as an ocular manifestation of SARS-CoV-2 infection. Reports continue to emerge on further associations of COVID-19 with uveitic, retinovascular, and neuro-ophthalmic disease.

During the 2003 severe acute respiratory syndrome (SARS) outbreak, a study detected SARS-CoV in tear samples in SARS patients in Singapore.[2] Lack of eye protection was a primary risk factor of SARS-CoV transmission from SARS patients to healthcare workers in Toronto, prompting a concern that respiratory illness could be transmitted through ocular secretions.[3][4] Similar concerns have been raised with SARS-CoV-2, especially among eye care providers and those on the front lines triaging what could be initial symptoms of COVID-19.

As conjunctivitis is a common eye condition, ophthalmologists may be the first medical professionals to evaluate a patient with COVID-19. Indeed, one of the first providers to voice concerns regarding the spread of coronavirus in Chinese patients was Dr. Li Wenliang, MD, an ophthalmologist. He later died from COVID-19 and was believed to have contracted the virus from an asymptomatic glaucoma patient in his clinic.[5]

The authors of this article have attempted to collect the most up-to-date information on ophthalmic manifestations of COVID-19 as a resource for identifying symptoms, providing diagnostic pearls, and mitigating transmission.


SARS-CoV-2 is a novel enveloped, positive single-stranded RNA beta coronavirus that causes COVID-19, originally linked to an outbreak in Wuhan of China’s Hubei province.[1] Direct contact with mucous membranes, including the eye, is a suspected route of transmission.

Coronaviruses can cause severe ocular disease in animals, including anterior uveitis, retinitis, vasculitis, and optic neuritis in feline and murine species. However, ocular manifestations in humans are typically mild and rare,[6] although there are increasing numbers of associated ocular findings in patients positive for the COVID-19. There are no described ocular manifestations of Middle East respiratory syndrome (MERS) or SARS, though, as previously stated, SARS-CoV was isolated in ocular secretions.[2] Other coronaviruses have been found to cause viral conjunctivitis in humans.[7]Go to:


At the time of writing the initial article on April 4, 2020, there were 1,272,953 confirmed cases and 69,428 deaths due to COVID-19 worldwide, according to the World Health Organization (WHO), with 79,332 new cases confirmed in the previous 24 hours. At the time, the Center for Disease Control and Prevention (CDC) had reported 337,278 cases and 9,637 deaths in the United States to that date. On April 16, 2021, just over a year since our initial review, the number of deaths worldwide has crossed the 3 million mark. The severity of the pandemic is emphasized by noting the rate of deaths: it took 8.5 months after the first fatality in China to mark the loss of the first 1 million lives, 3.5 months to reach 2 million, and 3 months for the loss to cross 3 million lives. 

As of December 23, 2021, there have been 51,574,787 confirmed cases of COVID-19 and 809,300 deaths in the United States. Globally, there have been over 276 million confirmed cases of COVID-19 and 5,374,744 deaths reported to the WHO. As of December 23, 2021, a total of 8,649,057,088 vaccine doses have been administered worldwide. The United States has had the most infections to date, followed by India, Brazil, the United Kingdom, Russia, Turkey, and France.

Viral mutations leading to variants of SARS-CoV-2 have been found around the world: the B.1.1.7 in the United Kingdom in early 2020, the B.1.526 in the United States in November 2020, the B.1.525 in the United Kingdom and Nigeria in December 2020, and the B.1.351 in South Africa in late 2020. The Delta variant B.1.617.2 was initially identified in India in December 2020 and rapidly spread through over 60 countries due to a 40-60% increase in transmissibility, becoming the dominant strain globally by August 2021.[8] Most recently, the Omicron variant B.1.1.529 was named a variant of concern in late November 2021 after cases emerged out of Botswana and South Africa with rapid, exponential spread.[9]

Early studies postulated that ocular manifestations of COVID-19 were rare overall. Only 9 (0.8%) out of 1,099 patients from 552 hospitals across 30 provinces in China were reported to have “conjunctival congestion” from December 2019 through January 2020.[10] More recent data, however, have supported a much higher incidence of ocular signs and symptoms. A 2021 meta-analysis by Nasiri et al. reported a pooled prevalence of all ocular manifestations among 7,300 COVID-19 patients as 11.03%, with the most frequent ocular disease being conjunctivitis (88.8%).[11] In the same meta-analysis, dry eye or foreign body sensation (16%), eye redness (13.3%), tearing (12.8%), and itching (12.6%) were among the most frequent symptoms reported. 

A case series reported ocular symptoms in 12 (31.6%) of 38 hospitalized patients with COVID-19 in Hubei province, China.[12] These 12 of 38 patients had conjunctival hyperemia (3 patients), chemosis (7 patients), epiphora (7 patients), or increased secretions (7 patients). Of note is that one patient who had epiphora presented with epiphora as the first symptom of COVID-19. Of those with ocular manifestations, 2 (16.7%) patients had positive results of SARS-CoV-2 on reverse-transcriptase polymerase chain reaction (RT-PCR) by a conjunctival swab, as well as by nasopharyngeal swabs. Only one patient in this study presented with conjunctivitis as the first symptom.[12] The authors noted that patients with ocular symptoms had higher white blood cell and neutrophil counts, C-reactive protein, and higher levels of procalcitonin and lactate dehydrogenase compared to patients without ocular abnormalities. 

Out of 30 hospitalized patients with COVID-19 tested by Xia et al., one patient had conjunctivitis and was also the sole patient in the study to test positive for SARS-CoV-2 in ocular secretions by a conjunctival swab. This patient did not have a severe fever or respiratory symptoms at the time of testing.[13]


The pathogenesis and tissue tropism of SARS-CoV-2 relates to the binding of the viral spike protein to its cognate receptor on human host cells— the angiotensin-converting enzyme 2 (ACE-2) receptor. Efficient cell entry requires cleavage by protein transmembrane serine protease 2 (TMPRSS2). ACE-2 is expressed primarily on respiratory mucosal and alveolar epithelial cells and has been identified in other tissues, including the gastrointestinal tract, kidney, vascular endothelial cells, immune cells, and even neurons. Virulence is achieved via direct cellular invasion and death and the induction of widespread cytokine-driven inflammation and vascular leakage.[14] Immune cell and complement debris can also lead to an increased thromboembolic state.

The potential of infection through ocular secretions is currently unknown, and it remains unclear how SARS-CoV-2 accumulates in ocular secretions. Possible theories include direct inoculation of the ocular tissues from respiratory droplets or aerosolized viral particles, migration from the nasopharynx via the nasolacrimal duct, or even hematogenous spread through the lacrimal gland.[6]

Data surrounding the expression of ACE-2 and TMPRSS2 on the ocular surface are mixed. One study demonstrated the expression of both these proteins on the cornea and limbus but observed low levels on the conjunctiva.[15] Lange et al. also found the human conjunctival to have low levels of ACE-2.[16]

A case report from Rome, Italy, isolated SARS-CoV-2 by RT-PCR from conjunctival swabs in a COVID-19 patient with ocular symptoms.[17] Conjunctival swabs were collected from hospital days 3 to 27. Although conjunctivitis was clinically resolved on day 20, the patient had detectable viral SARS-CoV-2 RNA in conjunctival samples on day 21 and subsequently on day 27 after SARS-CoV-2 was negative by nasopharyngeal swab. Because SARS-CoV-2 has not been successfully cultured from human tears or conjunctival swabs, the viability and transmissibility of SARS-CoV-2 in human ocular secretions remains uncertain.[18] Limited reports suggest that tears can be both an early and late source of infection transmission, even after the patient becomes asymptomatic.[17][19]

Using RT-PCR, Azzolini et al. found SARS-CoV-2 present on the ocular surface in 52 of 91 patients with COVID-19 (57.1%).[5] They found that even when the nasopharyngeal swab was negative, the virus was detected on the ocular surface in 10 of 17 patients. It has been postulated that the viral particles in tears may be from the lacrimal gland with diffusion from a systemic load of the virus or from direct contagion from airborne droplets.[5]

History and Physical

The prevalence of ocular manifestations in patients with COVID-19 ranges from 2% to 32%.[20][21][22][23][24][11][25]


Patients infected with SARS-CoV-2 can present with acute conjunctivitis symptoms, including eye redness, ocular irritation, eye soreness, foreign body sensation, tearing, mucoid discharge, eyelid swelling, congestion and chemosis. These symptoms have more commonly affected patients with severe systemic symptoms of COVID-19, though they can rarely present as an initial manifestation of the disease.[12] Non-remitting conjunctivitis was found to be the sole manifestation of COVID-19 in five patients with confirmed SARS-CoV-2 infection on nasopharyngeal RT-PCR; these patients never developed fever, general malaise, or respiratory symptoms throughout the course of their illness.[26]

Examination findings include those consistent with mild follicular conjunctivitis, including unilateral or bilateral bulbar conjunctiva injection, follicular reaction of the palpebral conjunctiva, watery discharge, and mild eyelid edema. Bilateral chemosis alone may represent third-spacing in a critically ill patient rather than a true ocular manifestation of the virus. A case report published by Cheema et al. described the first case of keratoconjunctivitis as the presenting manifestation of COVID-19 in North America.[27] The patient’s primary symptoms included eye redness and tearing. The examination was significant for conjunctival injection, the follicular reaction of the palpebral conjunctiva, and corneal findings that developed rapidly over 3 days, including transient pseudodendritic lesions and diffuse subepithelial infiltrates with overlying epithelial defects.

Navel et al. observed a case of severe hemorrhagic conjunctivitis and pseudomembrane formation in a patient with onset 19 days after the beginning of systemic symptoms and 11 days after admission to the intensive care unit.[28]

We saw a 46-year-old male with mild respiratory symptoms and a positive nasopharyngeal test for COVID-19. Five days after the positive test, he developed a hemorrhagic bilateral conjunctivitis with pseudomembrane formation and chemosis. The left eye had been removed some years previously for melanoma. The conjunctival of the socket showed the same hemorrhagic conjunctivitis with chemosis and pseudomembrane formation. He was treated empirically with topical antibiotics and his symptoms resolved in four weeks. He did not develop any other symptoms of COVID-19.

It should be noted that in pediatric patients, COVID-19 has been strongly associated with the Kawasaki-like illness known as multisystem inflammatory syndrome in children (MIS-C). While there have been several ocular manifestations reported in this syndrome (papilledema, iritis, keratitis), the most common ocular manifestation has been conjunctivitis.[29]


There have been at least two reported cases of episcleritis onset in the setting of COVID-19 infection. Otaif et al. described a 29-year-old male with unilateral episcleritis as the initial presenting symptom of SARS-CoV-2 infection, and Mangana et al. observed nodular episcleritis in a 31-year-old female.[30][31]

Feizi et al. reported two cases of anterior scleritis in patients with COVID-19.[32] The first was a 67-year-old woman with necrotizing anterior scleritis which began 3 weeks after viral symptom onset. The second was a case of sectoral anterior scleritis which was highly responsive to topical and systemic steroids in a 33-year-old male; his ocular symptoms started 2 weeks after the onset of COVID-19.

Anterior Chamber

Beyond the ocular surface, acute anterior uveitis has also been reported both in isolation and in association with COVID-19 related multi-system inflammatory disease.[33][34] Sanjay et al. also reported a case of reactivated idiopathic anterior uveitis post-COVID-19 infection; this patient had remained quiescent for 13 years prior to this episode.[35]

Retina and Choroid

Posterior segment diseases have also been suspected to be associated with COVID-19 infection. These have varied between vascular, inflammatory, and neuronal etiologies. Both ACE-2 and TMPRSS2 are highly expressed in the human retina, and a recent case series of 3 patients discovered S and N COVID-19 proteins by immunofluorescence microscopy within retinal vascular endothelial cells, presumably containing viral particles.[36][37][36]

Both central retinal vein and artery occlusions have been reported in patients without classic systemic vascular risk factors. The hypothesized mechanism includes a complement-induced prothrombic and inflammatory state induced by the virus resulting in endothelial damage and microangiopathic injury. A striking example was reported by Walinjkar et al. with a central retinal vein occlusion (CRVO) in a 17-year-old female with COVID-19.[38] Yahalomi et al. presented a similar case in a previously healthy 33-year-old.[39] Several cases of central retinal artery occlusions (CRAO) have been reported, potentially related to viral-induced endothelial insult and vasculitis.[40][41][42]

Acute macular neuroretinopathy (AMN) and paracentral acute middle maculopathy (PAMM), conditions in which there is ischemia to the deep retinal capillary plexus, have also been observed with COVID, marked by hyperreflective changes at the level of the outer plexiform and inner nuclear layers.[43]

There have been two published case reports on Purtscher-like retinopathy observed in patients with COVID-19. Bottini et al. described a 59-year-old male who presented with multiple bilateral cotton wool spots localized to the posterior pole after a month-long hospitalization for COVID-19 pneumonia associated with multiorgan failure and severe coagulopathy.[44] Rahman and colleagues reported a 58-year-old male who presented with bilateral areas of ill-defined retinal whitening and arteriolar narrowing following a severe COVID-19 infection associated with disseminated intravascular coagulation.[45]

Optical coherence tomography (OCT) showed subclinical hyperreflective lesions at the level of the inner plexiform and ganglion cell layers in 12 adults examined after systemic disease onset; cotton wool spots and microhemorrhages were found on dilated fundus examinations in 4 of these patients.[46] Invernizzi and colleagues found retinal hemorrhages (9.25%), cotton wools spots (7.4%), dilated veins (27.7%), and tortuous vessels (12.9%) in 54 patients with COVID-19 upon screening with fundus photography.[47] These authors also found that retinal vein diameter correlated directly with disease severity, suggesting that this may be a non-invasive parameter to monitor inflammatory response and/or endothelial injury in COVID-19. Lecler et al. described abnormal MRI findings in the posterior pole of 9 patients with COVID-19 consisting of one or several hyperintense nodules in the macular region on FLAIR-weighted images.[48] These lesions were postulated to be either direct inflammatory infiltration of the retina or microangiopathic disease from viral infection.

Various forms of posterior uveitis have been observed following either acute COVID-19 infection or the COVID vaccine. Souza et al. reported a case of unilateral multifocal choroiditis, though it is noted that the temporal relationship of the viral infection could be attributed to chance alone.[49] Goyal et al. published a case of bilateral multifocal choroiditis within one week of the COVID-19 vaccine.[50] Cases of serpiginous and ampiginous choroiditis have also been reported.[51][52][51]

Immune dysregulation due to COVID-19 may contribute to the reactivation of latent herpervirus leading to acute retinal necrosis. This has been reported in two consecutive patients by Soni et al.[53]

Animal model studies have also shown the involvement of the retina with retinal vasculitis [54], retinal degeneration [55], and breakdown of the blood-retinal barrier.[56]

Optic Nerve

A wide variety of neuro-ophthalmologic manifestations have also been found in association with COVID-19, mostly related to demyelinating disease. While the mechanism of these manifestations is unknown, hypotheses include direct neuronal invasion, endothelial cell dysfunction leading to ischemia and coagulopathy, or a widespread inflammatory “cytokine storm” induced by the virus.[57] Optic neuritis has developed in several infected patients, presenting with neuromyelitis optica spectrum disorder and anti-myelin oligodendrocyte glycoprotein (anti-MOG) antibodies.[58][59][60] Patients presented with subacute vision loss, a relative afferent pupillary defect, pain with eye movements, optic disc edema, and radiographic findings of acute optic neuritis following a COVID-19 infection. There have also been reports of acute optic neuritis following vaccination for COVID-19.[61]

A case of multiple sclerosis following COVID-19 infection was reported by Palao et al. in a 24-year-old patient who presented with right optic neuritis; MRI demonstrated right optic nerve inflammation and supratentorial periventricular demyelinating lesions.[62] These cases suggest that SARS-CoV-2 can either trigger or exacerbate inflammatory and demyelinating disease.

Ophthalmologists may also be called to evaluate for papilledema in SARS-CoV-2 infected patients, as there have been cases of elevated intracranial pressure, both due to widespread inflammation and dural venous sinus thrombosis.[63] As previously mentioned, multisystem inflammatory syndrome in children (MIS-C) due to COVID-19 is also becoming recognized as a unique syndrome similar to Kawasaki disease and has been linked to both optic neuritis and elevated intracranial pressure.[64] Verkuli et al. described a case of a 14-year-old girl with pseudotumor cerebri syndrome associated with MIS-C due to COVID-19 manifesting as a new right abducens palsy, papilledema with disc hemorrhages, and lumbar puncture with an opening pressure of 36 cm H2O.[65]

Extraocular Motility, Cranial Nerves

Cranial nerve III, IV, and VI palsies associated with COVID-19 have been reported in the literature within a few days of fever and cough onset, most without remarkable radiological features.[66][67][68] Ocular cranial neuropathies and binocular diplopia with nerve enhancement on MRI have also been observed in association with post-infectious demyelinating conditions such as Miller Fisher and Guillain Barré syndrome. For example, Dinkin et al. described a 36-year-old male with left mydriasis, ptosis, and limited depression and adduction with concurrent MRI enhancement of the left oculomotor nerve.[69] He was also found to have lower extremity hyporeflexia and ataxia consistent with Miller Fisher syndrome.

Ocular myasthenia gravis has been described as a post-infectious sequela of COVID-19, with authors proposing that antibodies directed against SARS-CoV-2 proteins may cross-react with acetylcholine receptors and similar components at the neuromuscular junction.[70] Huber and colleagues described a 21-year-old patient who presented 4 weeks after COVID-19 infection with fluctuating vertical binocular diplopia and ptosis, treated successfully with intravenous immunoglobulins and oral pyridostigmine.[71]


Pupillary changes have also been observed. Several groups have described patients with mydriasis and cholinergic super-sensitivity, indicative of tonic pupils and post-ganglionic parasympathetic pupillary nerve fiber damage.[72][73][74][73]


Oscillopsia has been reported in several cases of COVID-19 with neurologic involvement. Malayala described a 20-year-old woman who presented with intractable vertigo, nausea, and vomiting with a presumed diagnosis of viral-induced vestibular neuritis secondary to COVID-19.[75] Central vestibular nystagmus has also been described in association with clinical and imaging findings consistent with rhombencephalitis.[76][77]

Visual Cortex

Perhaps the most devastating neuro-ophthalmic complication of severe COVID-19 infection is acute stroke affecting the posterior visual pathways. The incidence of stroke in these patients has been found to be 7.6 times higher than that of patients with influenza and has been occurring in a far younger than average patient population without classic vascular risk factors.[78] These patients may present with homonymous visual field deficits prompting ophthalmologic consultation. Authors at our university recently published a case of bilateral posterior cerebral artery ischemic strokes presenting as a homonymous visual field defect in a 12-year old patient with multisystem inflammatory syndrome related to COVID-19.[79]

Orbit and Ocular Adnexa

While oculoplastic and orbital manifestations of COVID-19 are uncommon, there is growing evidence to link inflammatory and infectious orbital disease to the virus. There have been two reported cases of sinusitis, orbital cellulitis, and intracranial abnormalities in adolescents with COVID-19.[80] It was postulated in this study at SARS-CoV-2 infection resulted in congestion of the upper respiratory tract and increased risk for secondary bacterial infection. This theory was expanded on by Shires et al., who reported a case of bacterial orbital abscess in a patient with COVID-19, with a unique intraoperative finding of highly avascular nasal mucosa and cultures positive for Streptococcus constellatus and Peptonipihilus indolicus, bacteria normally absent in the orbit or upper respiratory mucosa.[81] It is possible that the local microbiologic and immunologic environment was altered due to avascularity induced by thrombosis in the setting of SARS-CoV-2 infection.

There have been a growing number of reports of acute invasive fungal rhino-orbital mucormycosis co-infection with COVID-19. These opportunistic pathogens thrive in the hypoxic respiratory environment induced by SARS-CoV-2, as well as an immunocompromised state induced by high-dose steroids and immunosuppressive therapies. In patients with poorly controlled diabetes, particularly those with diabetic ketoacidosis (DKA), the risk is further increased.[82][83][84] Singh et al. published a systematic review of 101 reported cases of COVID-19 patients with mucormycosis; these patients were predominantly male (79%), 80% of which had diabetes and 15% with concomitant DKA.[85] Corticosteroids had been used in 76% of these patients and nearly 60% of the cases reported rhino-orbital involvement.[85] Another case described a 33-year-old female who presented with orbital compartment syndrome due to concurrent COVID-19 and fulminant mucormycotic infection.[86]

There have also been reports of MRI-proven orbital myositis in two separate COVID-19 patients in the absence of concomitant bacterial infection.[87][88][87] The authors postulated either direct viral orbital invasion or induced autoimmunity as possible mechanisms.

Similar processes have been proposed by Diaz et al., who reported a case of acute dacryoadenitis in a 22-year-old male with positive SARS-CoV-2 antibodies who developed partial ophthalmoplegia.[89] Providers at our university have treated one patient with typical symptoms and signs of dacryoadenitis occurring concurrently with a positive COVID-19 nasopharyngeal test. The patient responded to a slow taper of steroids over six weeks. A recently submitted cases series by our group also highlights a case of biopsy-proven chronic dacryoadenitis in a 57-year-old man with COVID-19, with symptom onset one month following his viral symptoms. Other cases in this series include idiopathic inflammation in an anophthalmic socket.

Lacrimal System

Epiphora has been described as an initial finding in patients with COVID-19.[12] This is thought to be secondary epiphora from inflammation of the conjunctiva. Direct involvement of the nasolacrimal system or the lacrimal sac has not been reported to date. 

Manifestations in Newborn Infants

There have been recent data to support frequent ocular manifestations of SARS-CoV-2 infection in newborn infants. In a study by Perez-Chimal et al. in Mexico, 15 infants were identified with positive RT-PCR nasopharyngeal swabs. All of these newborns exhibited ocular manifestations, most commonly periorbital edema (100%), followed by chemosis and hemorrhagic conjunctivitis (73%) and ciliary injection (53%). Unique findings included 6 infants (40%) with corneal edema, 1 with rubeosis and posterior synechiae, and posterior segment manifestations including retinopathy of prematurity in 3 (20%) infants.[90] Vitreous hemorrhage was observed in 1 full-term baby and subtle cotton wool spots in 2 other newborns.


A thorough history is necessary regarding the onset, duration, and characteristics of symptoms. Anterior segment examination at the slit lamp or bedside can confirm findings of conjunctivitis or episcleritis. Measurement of visual acuity, intraocular pressure, and dilated fundus examination are warranted to rule out potentially more harmful ocular diseases. The clinician should perform a careful examination of pupils and color testing to evaluate patients for evidence of optic neuropathy. Evaluation of extraocular motility may show evidence of nystagmus or cranial neuropathies. Visual field testing can detect and confirm deficits related to stroke.

SARS-CoV-2 can be detected in RT-PCR by sweeping the lower eyelid fornices to collect tears and conjunctival secretions with a virus sampling swab.[13] Additional serum or cerebrospinal fluid testing may be useful to evaluate for inflammatory, autoimmune, or demyelinating entities. Neuroimaging can be valuable in patients presenting with optic neuritis, visual field deficits, cranial neuropathies, or other associated neurologic symptoms.

All patients should be questioned about recent fever, respiratory symptoms, exposure, and travel history to assess the need for further evaluation of COVID-19. 

Treatment / Management

Chen et al. reported gradual symptomatic improvement of COVID-19 conjunctivitis in one patient with administration of ribavirin eye drops.[91] The efficacy of targeted treatment has not been studied. It is unlikely to be of long-term clinical importance in a self-limited viral illness. However, eye care providers should be mindful of trying to decrease possible viral load and potential transmission.[92]

As with other viral infections, COVID-19 conjunctivitis is presumed to be self-limited and can be managed with symptomatic care. In the absence of significant eye pain, decreased vision, or light sensitivity, many patients can be managed remotely with a trial of frequent preservative-free artificial tears, cold compresses, and lubricating ophthalmic ointment. A short course of topical antibiotics can prevent or treat bacterial superinfection based on the patient’s symptoms and risk factors (e.g. contact lens wear).[93] 

On March 18, 2020, the American Academy of Ophthalmology (AAO) urged all ophthalmologists to provide only urgent or emergent care to reduce the risk of SARS-CoV-2 transmission and to conserve disposable medical supplies. Specific criteria are presented below. In the summer of 2020, many centers had begun to resume elective surgeries and consider expanding care on a case-by-case basis based on reopening guidelines from the federal government. 

Although preliminary studies suggest that the risk of viral transmission through ocular secretions is low, large-scale research has not yet been done, and new data is emerging daily. Therefore, healthcare providers are still urged to wear proper protection of the eyes, nose, and mouth when examining patients (see below). Eye care providers and technicians may be more susceptible to infection due to the nature and proximity of the ophthalmic examination.[94] Eye care providers are encouraged to use slit lamp breath shields and should counsel patients to speak as little as possible when sitting at the slit lamp to reduce the risk of virus transmission. Disinfection and sterilization practices should be employed for shared clinic equipment such as tonometers, trial frames, pinhole occluders, B-scan probes, and contact lenses for laser procedures.[2][94] Disposable barrier protection of clinic tools should be used where possible.

Stratification of Ophthalmic Patients for Clinic Visits from early 2020 to December 2020

In the presence of life-threatening infections such as this, ophthalmologists have to achieve a balance between providing ophthalmic care and infection control. Most ophthalmic conditions are not life-threatening. Furthermore, many can be managed with some delay in treatment as they progress relatively slowly (cataracts, glaucoma, ptosis, etc.). However, some conditions like retinal detachments, acute infections (cellulitis, orbital cellulitis), severe inflammation (uveitis), and trauma require more urgent attention. To that end, the following is suggested for the management of ophthalmic patients:

1. All routine ophthalmic patients are delayed until the severity of disease spread reduces as determined by the WHO and the local Chief Medical Officer. These include chronic conditions and routine clinic annual and other follow-ups as well as new patients with chronic conditions like cataracts, ptosis, etc. 

2. New patient referrals are reviewed by the consultant surgeon to determine urgency. If necessary, telephone interviews with the referring doctor and/or the patient are held. 

3. All patients considered for a clinic visit are reviewed for three things: 

  • Presence of fever, cough, or shortness of breath
  • Any foreign travel or travel to an area with a high infection rate within the prior 14 days
  • Any contact with patients who have been diagnosed as having COVID-19

The presence of any of these would be a reason to consider the necessity of seeing and examining the patient more closely. If a patient has two of the three are referred for medical assessment. If a patient with COVID-19 or one with a fever, cough, or shortness of breath needs to be examined, the patient is seen in a separate isolation room. Ideally, only one person (physician, technician, etc.) should be present in the room (as ophthalmic rooms tend to be small) and should wear the full personal protective equipment (PPE): gown, N95 mask, face shield, and gloves. Hands are washed before and after examination for a minimum of 20 seconds with soap and water. Once the ophthalmic examination is completed, the patient is referred for further assessment by the medical team. 

Protection of Medical Workers

Although the 2003 SARS-CoV crisis did not create quite so severe a spread of infection in the United States, it was noted that health care workers (HCW) accounted for about 20% of all patients with infections.[95] Most recent figures show that HCWs make up 9% of Italy’s COVID-19 cases. In the United States, between March 2020 and April 7. 2021, more than 3699 health workers have died from COVID-19 infections with the majority being younger than 60 years of age. As of April 7, 2021, the number of coronavirus cases recorded among healthcare workers in Italy reached 129,873.  Early in the pandemic in Italy, by April 2020, more than 100 health care workers had died from COVID-19 infections, including, more than 60 doctors. Current figures are not available and figures in other countries will continue to increase. 

It is, therefore, vital that front-line medical workers wear proper protection. Secondly, it is important to monitor these health care workers for disease and implement appropriate containment measures. 

A significant number of deaths in the United States were associated with an initial severe shortage of appropriate personal protective equipment for healthcare providers. Even with the availability of appropriate protective equipment, it behooves us to choose the level of protective gear based upon the risk of infection. The following is suggested:

  • Keep the waiting room as empty as possible with available seating spaced at least 6 feet apart.
  • For all patients who have none of the three criteria mentioned above, the medical workers will wear a surgical mask, a face shield, and gloves. Hands are washed before and after every encounter.
  • If a patient is positive for any of the three criteria, the full PPE of gown, face shield, gloves, and the N95 mask are worn. 
  • It has been noted that droplets from sneezes can travel up to 6 meters.[96] To that end, inventive ophthalmic technicians at the Moran Eye Center have developed a slit-lamp shield made by passing two plastic sheets through a laminator without a paper in between and cutting openings for the eyepieces (Fig 1). Others have similarly used old X-ray films when commercially available shields are in short supply. 
  • Conversations are kept to a minimum during the consultation. Ophthalmologists are, by nature, a gregarious lot. Such temptations are to be resisted. 
  • As a shortage of surgical masks has become a reality, some institutions are storing used masks at the end of each day in a container with a view to re-sterilization if necessary. 
  • As many as a quarter of patients being injected under sedation may develop a severe involuntary sneeze.[97][98] This is more common with eyelid injections than with retrobulbar injections. Ophthalmic surgeons should be acutely aware of this to take appropriate precautions during the administration of the local anesthetic. 

Surveillance of Medical Workers

  • In Singapore, health workers reported their temperatures twice a day via an online system: this was eminently sensible as the “walk-by” temperature-testing that was practiced may not be as accurate or complete with staff arriving early, leaving late, etc.[99] As of April 16, 2021, Singapore had had 60,769 infections and only 30 deaths caused by the virus. 
  • All travel outside the state or country should be declared to the medical administration for review. This should still apply in April 2021 as there are recurring hot spots of infection in different states and countries. 
  • All health workers should self-report any symptoms, so appropriate testing may be performed: isolation and contact-tracing would then be undertaken as deemed necessary. 

Sterilization of Equipment

  • The slit-lamp shields are disinfected with 70% ethyl alcohol after each patient; 70% ethyl alcohol has been shown to reduce coronavirus infectivity.[96]
  • Slitlamps, B-scan probes, and any other tools are similarly cleaned with 70% ethyl alcohol.
  • Goldman tonometers are sterilized with a 10% diluted sodium hypochlorite solution, which inactivates coronaviruses.[100]

Differential Diagnosis

Ocular manifestations of COVID-19 have most commonly presented with conjunctivitis otherwise indistinguishable from other viral etiologies. Differential diagnosis includes a broad range of common ocular manifestations of eye redness and increased tearing:

  • Other viral conjunctivitis (e.g., adenovirus)
  • Bacterial conjunctivitis
  • Allergic conjunctivitis
  • Herpes simplex virus keratitis
  • Anterior uveitis
  • Corneal abrasion
  • Foreign body
  • Dry eye syndrome
  • Exposure keratopathy in an intubated patient
  • Chemosis in a critically ill patient

Go to:


Conjunctivitis related to COVID-19 is currently thought to be self-limited. Larger studies and long-term follow-up of patients with other ocular manifestations COVID-19 have yet to be reported. Globally, we are in the grip of grim circumstances with ebbs and flows of infections. With the rather disjointed global response to the infection and unbalanced vaccine administration, the hope that out of the pain joy will spring and new strengths arise from recognition of the weaknesses of administrations around the world is a distant hope.


Sequelae and complications from demyelinating disease and stroke require management in conjunction with other specialties such as neurology, occupational therapy, and physical medicine and rehabilitation. Ophthalmologists need to be vigilant as we continue to learn of the different ways that COVID-19 may affect the eye and periorbital tissues. 

Deterrence and Patient Education

Prevention strategies are vital to limiting the spread of disease. In addition to physical distancing and practicing good hand-washing hygiene, patients should employ behavioral changes that reduce the direct touching of the eyes and face. These are “soft” suggestions which are mostly ignored now that vaccinations are becoming available and infections in parts of the world are decreasing.

  • Refraining from wearing contact lenses during the outbreak
  • Refraining from applying cosmetics
  • Wearing glasses and sunglasses
  • Changing sheets, pillowcases, and towels regularly

Pearls and Other Issues

  • Ocular shedding of SARS-CoV-2 via tears is a distinct possibility of which ophthalmologists should be aware.
  • Conjunctivitis or tearing can be the first presentation and even sole manifestation in a patient with the COVID-19 infection.
  • Several ocular manifestations of COVID-19 have been observed, including retinovascular disease, uveitis, optic neuropathies, and orbital fungal co-infections.
  • SARS-CoV-2 may trigger or exacerbate inflammatory/demyelinating disease.
  • Patients may present with chemosis in advanced cases (Fig 2) or follicular conjunctivitis (Fig 3).
  • The ocular examination should be performed while wearing gloves and using extension instruments (cotton swabs, etc.) to avoid direct contact with secretions.
  • As many patients who visit the ophthalmic clinic are elderly, many with comorbidities, it is important to screen the need for the visit ahead of time and only see patients who need urgent care. We continue to practice telemedicine for many of these patients. 
  • As advocated in many countries, social distancing means being 6 feet away from others: this is clearly impossible in the clinical world and certainly in the small confines of ophthalmic examination lanes. One way to practice it is to have only one person in the room with the patient.
  • Anecdotally, it has been observed that physicians most at risk of becoming infected include ophthalmologists, otolaryngologists, and anesthesiologists because of the proximity of the examiners to mucosal surfaces.
  • When performing surgery under general anesthesia, it has been recommended that surgeons and other staff do not enter the room for 15 minutes after intubation or extubation. This standard is applied to all general anesthesia cases in many facilities, whether the patient is COVID-19 positive or negative.

Enhancing Healthcare Team Outcomes

Within outpatient, inpatient, and surgical settings, an interprofessional approach is necessary to manage COVID-19 patients and successfully mitigate the spread of disease.

There should be clear communication between hospital administration, provider teams, and clinic support staff regarding expectations for screening patients, wearing personal protective equipment, and utilizing new technologies for telemedicine consultations.

At least one long-term symptom seen in 37% of COVID-19 patients -study

by Reuters Wednesday, 29 September 2021 11:06 GMT

Sept 29 (Reuters) – At least one long-term COVID-19 symptom was found in 37% of patients three to six months after they were infected by the virus, a large study from Oxford University and the National Institute for Health Research showed on Wednesday.

The most common symptoms included breathing problems, fatigue, pain and anxiety, Oxford University said, after investigating symptoms in over 270,000 people recovering from COVID-19.

The symptoms were more frequent among people who had been previously hospitalised with COVID-19 and were slightly more common among women, according to the study.

The study did not provide any detailed causes of long-COVID symptoms, their severity, or how long they could last.

It, however, said older people and men had more breathing difficulties and cognitive problems, whereas young people and women had more headaches, abdominal symptoms and anxiety or depression.

“We need to identify the mechanisms underlying the diverse symptoms that can affect survivors,” said Oxford University professor Paul Harrison, who headed the study.

“This information will be essential if the long-term health consequences of COVID-19 are to be prevented or treated effectively,” Harrison added. (

The OC43 human coronavirus envelope protein is critical for infectious virus production and propagation in neuronal cells and is a determinant of neurovirulence and CNS pathology

Authors:Jenny K.Stodola1GuillaumeDubois1AlainLe CoupanecMarcDesforgesPierre J.Talbot


Coronavirus structural envelope (E) protein specific motifs involved in protein-protein interaction or in homo-oligomeric ion channel formation are needed for optimal production of recombinant infectious virus.•

Fully functional E protein of HCoV-OC43 is crucial for viral propagation in the CNS and neurovirulence.•

Fully functional E protein of HCoV-OC43 is crucial for efficient viral propagation in the central nervous system and thereby for neurovirulence.


The OC43 strain of human coronavirus (HCoV-OC43) is an ubiquitous respiratory tract pathogen possessing neurotropic capacities. Coronavirus structural envelope (E) protein possesses specific motifs involved in protein-protein interaction or in homo-oligomeric ion channel formation, which are known to play various roles including in virion morphology/assembly and in cell response to infection and/or virulence. Making use of recombinant viruses either devoid of the E protein or harboring mutations either in putative transmembrane domain or PDZ-binding motif, we demonstrated that a fully functional HCoV-OC43 E protein is first needed for optimal production of recombinant infectious viruses. Furthermore, HCoV-OC43 infection of human epithelial and neuronal cell lines, of mixed murine primary cultures from the central nervous system and of mouse central nervous system showed that the E protein is critical for efficient and optimal virus replication and propagation, and thereby for neurovirulence.

For More Information: https://www.sciencedirect.com/science/article/pii/S0042682217304361

Inflammatory brain injury higher in men with acute COVID-19, finds study

Authors: By Dr. Liji Thomas, MD

The coronavirus disease 2019 (COVID-19) pandemic has been associated with both short- and long-term neurologic complications, including stroke, brain fog and persistent tiredness.

A new study concludes that the effects of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on the central nervous system are due to the endothelial injury and inflammation that this produces in the brain.Study: Markers of brain and endothelial Injury and inflammation are acutely and sex specifically regulated in SARS-CoV-2 infection. Image Credit: Ralwell / ShutterstockStudy: Markers of brain and endothelial Injury and inflammation are acutely and sex specifically regulated in SARS-CoV-2 infection. Image Credit: Ralwell / Shutterstock

A preprint version of the study is available on the medRxiv* server, while the article undergoes peer review.

Study aims

Since the beginning of the pandemic, it has become clear that men are often more affected by COVID-19, with a higher likelihood of severe illness and a greater chance of death.

The current study focused on assessing brain injury markers (BIM) within 48 hours of hospitalization and at three months later.

BIMs are recognized as being valid indicators of injury to nerve cells and astrocytes, in human immunodeficiency virus (HIV) infection, sepsis and cardiac arrest. The current study focused on six, namely, glial fibrillary acidic protein (GFAP), neuron-specific enolase (NSE), S100B, ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1), Syndecan-1 and microtubule-associated protein 2 (MAP 2).

The scientists also examined levels of two markers of endothelial injury (Intercellular Adhesion Molecule 1, ICAM-1 and Vascular Cell Adhesion Molecule 1, VCAM-1) and of inflammation, in the form of cytokines or chemokines.

These were measured in hospitalized patients and in controls in a single hospital in Houston, Texas, USA. None of them had chronic lung, heart, neurological or psychiatric disease, cancer, or any disabling condition.

Increased markers of endothelial and brain injury

The researchers found that within 48 hours of hospitalization, that is, during the acute phase, patients had higher markers of brain injury like MAP2 and NSE than controls. The mean levels showed an increase of 60% to 145%, depending on the individual marker, relative to the controls.

Of these markers, MAP2 is a sign of dendritic injury, and was high at both acute and chronic time points. It has previously been shown to be high after traumatic brain injury and predicts long-term outcomes.

NSE is found in nerve cells and indicates damage. S100B is found in astrocytes and is high in traumatic brain injury and in strokes. Thus, this combination of BIMs shows combined nerve cell and astrocytic injury in COVID-19, worse in men than in women.

However, all markers had returned to normal at three months from hospitalization.

Markers of endothelial injury were also higher with acute infection, with the mean levels being two and three times higher than in controls, for ICAM1 and VCAM1, respectively. These were not assessed at three months.

The endothelial marker ICAM1 is released in response to IL-1b and TNFα. The effects are increased leukocyte adhesion, which reduces the barrier’s integrity and promotes leakage from the blood vessels.

Cytokines and chemokines were also much higher, in some cases, in acute infection, but others showed a decrease. Of 38 chemokines and cytokines evaluated, seven were high, while two were low. Again, these reverted to normal levels at three months.  

TNFα is a potent inflammatory mediator. Its elevation in this context indicates that the vascular injury is probably inflammatory in origin and not due to viral injury.

For More Information: https://www.news-medical.net/news/20210531/Inflammatory-brain-injury-higher-in-men-with-acute-COVID-19-finds-study.aspx