Antipsychotic use and psychiatric disorders in COVID-19
Bibliographic record
Abstract
Huazhen Yang and colleagues' study, published in The Lancet Healthy Longevity, which examined the association between psychiatric disorders and clinical outcomes of COVID-19, is timely, novel, and potentially carries broad implications.1Yang H Chen W Hu Y et al.Pre-pandemic psychiatric disorders and risk of COVID-19: a UK Biobank chort analysis.Lancet Healthy Longev. 2020; 1: e69-e79Summary Full Text Full Text PDF PubMed Scopus (65) Google Scholar The study's main finding was that patients with prepandemic-confirmed psychiatric illness had increased odds of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (adjusted odds ratio 1·44, 95% CI 1·28–1·62), hospitalisation (1·55, 1·34–1·78), and COVID-19-related death (2·03, 1·59–2·59). Although we acknowledge the perils of disentangling causal pathways (ie, the estimation of total causal effects requires less stringent assumptions than the identification of direct and indirect effects), further understanding of the mechanisms underlying the association between psychiatric illness and COVID-19 remains warranted. As such, in addition to the potential mechanisms pointed out by the authors (eg, immune response, impairment mediated by the endocrine axis, and cytokine production dysregulation), the use of antipsychotic medications requires attention. People who use antipsychotics are at increased risk of infection, including community-acquired pneumonia,2Dzahini O Singh N Taylor D Haddad PM Antipsychotic drug use and pneumonia: systematic review and meta-analysis.J Psychoparmacol. 2018; 32: 1167-1181Crossref PubMed Scopus (41) Google Scholar urinary tract infections,3van Strien AM Souverein PC Keijsers CJPW Heerdink ER Derijks HJ van Marum RJ Association between urinary tract infections and antipsychotic drug use in older adults.J Clin Psychoparmacol. 2018; 24: 296-301Crossref Scopus (8) Google Scholar and bloodstream infections.4Augusto F, Belen B, Tentoni N, et al. Antipsychotic use and bloodstream infections among patients with chronic obstructive pulmonary disease: a cohort study. J Clin Psychiatry (in press).Google Scholar Because patients in Yang and colleagues' study were mainly diagnosed with depression, substance misuse, and anxiety—conditions that are increasingly treated with antipsychotic agents—we believe that such risk might partly explain the observed association between psychiatric illness, COVID-19, and its severity. The findings of strong associations between psychotic disorders and risk of poor outcomes of COVID-19 might point to antipsychotics as potential culprits. Thus, we believe the study by Yang and colleagues emphasises a clear need for further research evaluating the association between psychiatric disorders, antipsychotic use, the dysregulation of the immune response, and the occurrence of life-threatening infections. Future studies should assess the role of antipsychotic treatment in infection, to shed light on the underlying mechanisms of the effect of psychiatric disorders on SARS-CoV-2 infection. Comparison between different antipsychotic medications would be of special relevance given that the risk does not appear to be uniform across agents—ie, both clozapine and pure dopaminergic agents might carry a higher infection risk.4Augusto F, Belen B, Tentoni N, et al. Antipsychotic use and bloodstream infections among patients with chronic obstructive pulmonary disease: a cohort study. J Clin Psychiatry (in press).Google Scholar, 5Govind R Fonseca de Freitas D Pritchard M Hayes RD MacCabe JH Clozapine treatment and risk of COVID-19 infection: retrospective cohort study.Br J Psychiatry. 2020; (published online July 27.)https://doi:10.1192/bjp.2020.151Crossref PubMed Scopus (55) Google Scholar We declare no competing interests. Pre-pandemic psychiatric disorders and risk of COVID-19: a UK Biobank cohort analysisOur findings suggest that pre-existing psychiatric disorders are associated with an increased risk of COVID-19. These findings underscore the need for surveillance of and care for populations with pre-existing psychiatric disorders during the COVID-19 pandemic. Full-Text PDF Open AccessDoes antipsychotic use mediate the effect of psychiatric disorders on COVID-19? – Authors' replyWe thank Augusto Ferraris and colleagues for their important comments on our study.1 We share their interest in advancing understanding on the potential mechanisms that underlie our observed associations. One possibility is the potential role of psychotropic drugs, such as antipsychotic medications, as suggested by Ferraris and colleagues. Full-Text PDF Open Access
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".