Braz J Psychiatry - BJP Article Pre-Proof (as accepted) Page 1 of 23 Braz J Psychiatry - Pre-Proof - http://doi.org/10.47626/1516-4446-2023-3076 BJP PRE-PROOF (article published as accepted) Original Article Comorbidity Patterns and Mortality Among Hospitalized Patients with Psychiatric Disorders and COVID-19 Marina Sánchez-Rico, Katayoun Rezaei, Alfonso Delgado-Álvarez, Frédéric Limosin, Nicolas Hoertel, Jesús M. Alvarado http://doi.org/10.47626/1516-4446-2023-3076 Submitted: 07-Feb-2023 Accepted: 10-May-2023 This is a preliminary, unedited version of a manuscript that has been accepted for publication in the Brazilian Journal of Psychiatry. As a service to our readers, we are providing this early version of the manuscript. The manuscript will still undergo copyediting, typesetting, and review of the resulting proof before it is published in final form. The final version may present slight differences in relation to the present version . Braz J Psychiatry - BJP Article Pre-Proof (as accepted) Page 2 of 23 Braz J Psychiatry - Pre-Proof - http://doi.org/10.47626/1516-4446-2023-3076 Comorbidity Patterns and Mortality Among Hospitalized Patients with Psychiatric Disorders and COVID-19 Running title: Comorbidity patterns and COVID-19 mortality Marina Sánchez-Rico1,2, Katayoun Rezaei1, Alfonso Delgado-Álvarez2, Frédéric Limosin1,3,4, Nicolas Hoertel1,3,4, Jesús M. Alvarado2 1. DMU Psychiatrie et Addictologie, AP-HP. Centre, Hôpital Corentin-Celton, Issy- les-Moulineaux, F-92130, France. 2. Department of Psychobiology & Behavioral Sciences Methods, Faculty of Psychology, Universidad Complutense de Madrid, Campus de Somosaguas, Pozuelo de Alarcon, Spain. 3. INSERM 1266, Institut de Psychiatrie et Neurosciences de Paris, Paris, France. 4. Université Paris Cité, Faculté de Santé, UFR de Médecine, Paris, France . Corresponding author: Marina Sánchez Rico, marinals@ucm.es 4 Parv. Corentin Celton, 92130 Issy-les-Moulineaux ABSTRACT Objective: To examine the association between psychiatric and non-psychiatric comorbidity and 28-day mortality among patients with psychiatric disorders and COVID-19. Methods: We performed a multicenter observational retrospective cohort study of adult patients with psychiatric disorders hospitalized with laboratory-confirmed COVID-19 at 36 Greater Paris University hospitals (January 2020-May 2021) (N=3,768). First, we searched for different subgroups of patients according to their psychiatric and non-psychiatric comorbidities through cluster analysis. Next, we compared 28-day all-cause mortality rates across the identified clusters, while taking into account sex, age, and the number of medical conditions. Results: We found 5 clusters of patients with distinct psychiatric and non-psychiatric comorbidity patterns. Twenty-eight-day mortality in the cluster of patients with mood disorders was significantly lower than in other clusters. There were no significant differences in mortality across other clusters. Conclusions: All psychiatric and non-psychiatric conditions may be associated with increased mortality in patients with psychiatric disorders and COVID-19. The lower risk of death among patients with mood disorders might be in line with the potential beneficial effect of certain antidepressants in COVID-19, but requires further research. These findings may mailto:marinals@ucm.es Braz J Psychiatry - BJP Article Pre-Proof (as accepted) Page 3 of 23 Braz J Psychiatry - Pre-Proof - http://doi.org/10.47626/1516-4446-2023-3076 help identify at-risk patients with psychiatric disorders who should benefit from vaccine booster prioritization and other prevention measures. Keywords: COVID-19, psychiatric disorders, comorbidity, mortality, clustering, mood disorders INTRODUCTION After the unprecedented infectious disease crisis worldwide created by the global spread of the novel coronavirus SARS-CoV-2 and its variants,1 the causative agents of coronavirus disease 2019 (COVID-19), several studies2–9 have suggested that psychiatric disorders, including schizophrenia spectrum disorders,5,6,8,9 mood disorders, 2,8,9 anxiety disorders,5 intellectual and developmental disabilities,3 substance-induced psychiatric disorders,8 and dementia,10 were associated with higher COVID-19-related mortality. Studying risk factors of COVID-19-related mortality in people with psychiatric disorders is of utmost importance to prevent and treat these medical risk factors in this vulnerable population, and to reduce health disparities. 5,11,12 Prior work supports that comorbid medical illnesses are more frequent among people with psychiatric disorders than in the general population13 and associated with increased all-cause mortality11 and COVID-19-related mortality.14 However, it remains poorly known whether specific psychiatric or non-psychiatric disorders, or specific combinations of disorders, or the total number of disorders whatever they are, predict the risk of COVID-19-related death among patients with psychiatric disorders.12,15 In this report, we used data from the Assistance Publique-Hôpitaux de Paris (AP-HP) Health Data Warehouse,16–23 which includes data on all patients with laboratory-confirmed COVID- 19 who had been consecutively admitted to any of the 36 AP-HP Greater Paris University hospitals, to examine the association of psychiatric and non-psychiatric comorbidities with 28-day all-cause mortality among inpatients with psychiatric disorders and COVID-19. We took advantage of two unsupervised machine learning techniques, i.e. Uniform Manifold Approximation and Projection (UMAP)24 and hierarchical cluster analysis, to identify different subgroups of patients, and used multivariable logistic regression models to compare mortality rates, while adjusting for sex, age, and the total number of psychiatric and non- psychiatric disorders. METHODS Setting and Cohort Assembly We conducted a multicenter observational retrospective cohort study at 36 AP-HP (Assistance Publique – Hôpitaux de Paris) University hospitals from the beginning of the Braz J Psychiatry - BJP Article Pre-Proof (as accepted) Page 4 of 23 Braz J Psychiatry - Pre-Proof - http://doi.org/10.47626/1516-4446-2023-3076 epidemic in France, i.e. January 24th, 2020 until May 1st, 2021.16–23 We included all adults aged 18 years and older who had been admitted to one of these centers for laboratory- confirmed COVID-19. COVID-19 was ascertained by a positive reverse-transcriptase– polymerase-chain-reaction (RT-PCR) test from analysis of nasopharyngeal or oropharyngeal swab specimens. This observational study using routinely collected data received approval from the Institutional Review Board of the AP-HP clinical data warehouse (decision CSE-20- 20_COVID19, IRB00011591, April 8th, 2020). AP-HP clinical Data Warehouse initiatives ensure patient information and consent regarding the different approved studies through a transparency portal in accordance with European Regulation on data protection and authorization n°1980120 from National Commission for Information Technology and Civil Liberties (CNIL). Data sources AP-HP Health Data Warehouse (‘Entrepôt de Données de Santé (EDS)’) contains all available clinical data on all inpatient visits for COVID-19 to 36 Greater Paris University hospitals.16–23 The data included patient demographic characteristics, vital signs, laboratory test and RT-PCR test results, medication administration data, current medical diagnoses, and death certificates. ICD-10 diagnosis codes Patient information regarding recorded diagnosis at the time of hospitalization was obtained through electronic health records, based on the International Statistical Classification of Diseases and Related Health Problems (ICD-10) diagnosis codes, including infectious and parasitic diseases (A00-B99); neoplasms and diseases of the blood (C00-D89); endocrine disorders (E00-E89); mental disorders (F01-F99); diseases of the nervous system (G00- G99); eye-ear-nose-throat disorders (H00-H95); cardiovascular disorders (I00-I99); respiratory disorders (J00-J99); digestive disorders (K00-K95); dermatological disorders (L00-L99); diseases of the musculoskeletal system (M00-M99); and diseases of the genitourinary system (N00-N99). Diagnoses were grouped at a two-digit level (e.g., intestinal infectious diseases (A0), mood disorders (F3), hypertensive diseases (I1)), including a total of 138 potential two-digit diagnoses. To avoid ‘empty variables’, we only kept diagnoses with a frequency of at least 0.5% in the full sample. From the 138 two-digit diagnoses included in the sample, 96 (69.6%) had a frequency of at least 0.5% and were included in the analyses. Patient characteristics From the same electronic health records, we extracted information on patient characteristics at the time of the hospitalization. These variables included: sex; age, which was categorized Braz J Psychiatry - BJP Article Pre-Proof (as accepted) Page 5 of 23 Braz J Psychiatry - Pre-Proof - http://doi.org/10.47626/1516-4446-2023-3076 into 4 classes as previously recommended16–18,21,23 (i.e. 18-50, 51-70, 71-80, 81+); and the number of second-digit ICD-10 diagnosis for each participant. To further explore the severity of the patient based on their current comorbidities, we additionally computed the Elixhauser Comorbidity Index, based on the Swiss weights modification.25 Study baseline and outcome Study baseline was defined as the date of hospital admission with COVID-19. The outcome was 28-day all-cause mortality from study baseline. Patients who were discharged from the hospital before day 28 or died after day 28 were considered to be alive. Ongoing use of psychotropic medication Data on psychotropic medication use was also recorded. These medications included antidepressants (i.e., amitriptyline, amoxapine, citalopram, clomipramine, duloxetine, escitalopram, fluvoxamine, fluoxetine, mianserine, mirtazapine, paroxetine, sertraline, tianeptine, venlafaxine, and vortioxetine), antipsychotics (i.e., amisulpride, aripiprazole, chlorpromazine, clozapine, cyamemazine, flupentixol, haloperidol, levomepromazine, loxapine, olanzapine, quetiapine, paliperidone, penfluridol, propericiazine, risperidone, tiapride and zuclopenthixol), benzodiazepines or Z-drugs (i.e., alprazolam, bromazepam, clobazam, clonazepam, clorazepate, diazepam, lorazepam, nitrazepam, oxazepam, prazepam, nordazepam, midazolam, lormetazepam, zolpidem and zopiclone), mood stabilizer medications (i.e., carbamazepine, divalproate, gabapentin, oxcarbazepine, lamotrigine, lithium, phenobarbital, pregabaline and valpromide), and Functional Inhibitors of Acid Sphingomyelinase Activity (FIASMA)19 psychotropic medications (i.e., amitriptyline, aripiprazole, chlorpromazine, clomipramine, duloxetine, escitalopram, flupentixol, fluvoxamine, fluoxetine, paroxetine, and sertraline).20 Medication use was defined as having an ongoing prescription at hospital admission of each medication, defined by one prescription at hospital admission and at least one prior prescription of the same molecule dating from the last six months. Statistical procedure Comorbidity groups In order to identify subgroups of patients according to their psychiatric and non-psychiatric comorbidities, we performed a hierarchical cluster analysis among two-digit ICD-10 diagnosis codes. To minimize potential consistency and computational issues due to the large number of diagnoses,26 we transformed the binary matrix of diagnoses into a two-dimensional projection using UMAP.24 In order to select the most suitable classification, we performed a statistical procedure based on the combination of study conditions for UMAP (4 x 2) and the clustering algorithm (4 x 16). The conditions manipulated in UMAP included: the number of Braz J Psychiatry - BJP Article Pre-Proof (as accepted) Page 6 of 23 Braz J Psychiatry - Pre-Proof - http://doi.org/10.47626/1516-4446-2023-3076 neighbors (15, 50, 100, and 200) and the minimum embedding distance (0.1 and 0.5). The Manhattan equation for distance was used for UMAP and did not vary, as previously recommended.24 The clustering algorithm was performed using Euclidean distance27 and included the following manipulated conditions: linkage function (average, ward, complete, centroid) and number of clusters (5 to 20). In total, we performed 512 (4 x 2 x 4 x 16) models according to different configurations, and selected the best one based on the average Silhouette coefficient (SC).28 Associations with mortality We calculated frequencies of all diagnoses forming each cluster at both levels of ICD-10 grouping diagnoses. We also studied the distribution of patient characteristics within each cluster. To compare the association between each cluster and 28-day mortality, we used logistic regression models. In order to reduce the effects of confounding, the main analysis was a multivariable logistic regression model adjusted for age, sex, and the number of medical (psychiatric and non-psychiatric) conditions based on two-digit ICD-10 diagnosis codes. We obtained adjusted odds ratios and 95% CIs for the association of each cluster with 28-day mortality for all analyses. As a sensitivity analysis, we reproduced the main analysis while using the Elixhauser Comorbidity index25 instead of the number of medical (psychiatric and non-psychiatric) conditions based on two-digit ICD-10 diagnosis codes. We performed additional analyses and used chi-squared tests to compare the prevalence of psychotropic medication groups, i.e. antidepressants, antipsychotics, benzodiazepines or Z- drugs, mood stabilizer medications, and FIASMA psychotropic medications, across clusters. For all associations, we performed residual analyses to assess the fit of the data to the model, checked assumptions, and examined the potential influence of outliers. Because we did not have a single hypothesis in this study and our analyses were exploratory, statistical significance in the main analysis was fixed a priori at two-sided p-value<0.05. However, to reduce the risk of type I error due to multiple testing, we applied Bonferroni correction was used in the additional analyses, including pairwise comparisons of psychotropic medications across clusters. However, Bonferroni correction was used in the additional pairwise comparison analyses to correct for multiple-comparison testing. All analyses were conducted in R software version 4.1.3 (R Project for Statistical Computing). RESULTS Characteristics of the cohort Of the 51,265 adult patients hospitalized with COVID-19, ascertained by a positive RT-PCR test, 2,176 patients (4.2%) were excluded because of missing data. Of the remaining 49,089 Braz J Psychiatry - BJP Article Pre-Proof (as accepted) Page 7 of 23 Braz J Psychiatry - Pre-Proof - http://doi.org/10.47626/1516-4446-2023-3076 patients, 3,768 (7.7%) had at least one ICD-10 diagnosis of mental, behavioral and neurodevelopmental disorder (F01-F99) (Figure 1). Twenty-eight-day-mortality occurred in 842 (22.3%) patients. Sex, age, and number of medical conditions were significantly associated with 28-day mortality (Tables S1 and S2). Figure 1. Study cohort. Model selection and clusters The model with the highest Average Silhouette Coefficient (ASC=0.81) was a two- dimensional UMAP projection, with 100 neighbors and a minimum embedding distance of 0.1, using the centroid linkage function for the clustering, and 5 clusters as cutoff. All five clusters showed great consistency, with an average Silhouette coefficient of at least 0.75 for all cases (Figure 2; Table S3). Braz J Psychiatry - BJP Article Pre-Proof (as accepted) Page 8 of 23 Braz J Psychiatry - Pre-Proof - http://doi.org/10.47626/1516-4446-2023-3076 Figure 2. UMAP two-dimensional space projection by cluster in the selected model. The distribution of diagnoses by cluster is presented in Figure 3. The main psychiatric diagnosis in Cluster 1 (N=585; ASC=0.82) was anxiety disorders (F40-F48) (68% of individuals from this cluster), and the main non-psychiatric medical condition was influenza or pneumonia (J09-J18). In Cluster 2 (N=1,999; ASC=0.82), 98.6% of patients presented illness-induced psychiatric disorders (F01-F09), and almost half of them had malnutrition (E40-E46, 47%), influenza or pneumonia (J09-J18, 46.7%), or hypertensive diseases (I10- I16, 46.2%). Cluster 3 (N=694; ASC=0.75) comprised 98.6% of patients with substance- induced psychiatric disorders (F10-F19), 63.8% of them presented with influenza or pneumonia (J09-J18), and 47.7% of them with other diseases of the pleura and post- procedural disorders of respiratory system (J90-J99). Almost all individuals from the Cluster 4 (N=405; ASC=0.81) had mood disorders (F30-F39, 99.0%) and half of them (49.9%) had also a diagnosis of influenza or pneumonia (J09-J18). Cluster 5 (N=85; ASC=0.92) was the smallest and more compact cluster, with 44.7% of patients presenting with a diagnosis of intellectual disability and 54.9% with a diagnosis of influenza or pneumonia (J09-J18). Braz J Psychiatry - BJP Article Pre-Proof (as accepted) Page 9 of 23 Braz J Psychiatry - Pre-Proof - http://doi.org/10.47626/1516-4446-2023-3076 Figure 3. Distribution of ICD-10 two-digit diagnoses within each cluster. Note: B90-B99, other infectious diseases; E08-E16, diabetes mellitus or other disorders of glucose regulation; E40-E46, malnutrition; E50-E64, other nutritional deficiencies; E65-E68, overweight, obesity and other hyper alimentation; E70-E89, post-procedural endocrine and metabolic complications and disorders; F01-F09, illness-induced psychiatric disorders; F10-F19, substance-induced psychiatric disorders; F20-F29, psychotic disorders; F30-F39, mood disorders; F40-F48, anxiety disorders; F60-F69, disorders of adult personality and behavior; F70-F79, intellectual disabilities; F80-F89, pervasive and specific developmental disorders; G30-F37, other degenerative and demyelinating diseases of the central nervous system; G40-F47, episodic and paroxysmal disorders; I10-I16, hypertensive diseases; I20-I28, ischemic and pulmonary heart diseases; I40-I49, other forms of heart disease; J09-J18, influenza and pneumonia; J40-J47, chronic lower respiratory diseases; J80-J86, other respiratory diseases affecting the interstitium and suppurative and necrotic conditions Braz J Psychiatry - BJP Article Pre-Proof (as accepted) Page 10 of 23 Braz J Psychiatry - Pre-Proof - http://doi.org/10.47626/1516-4446-2023-3076 of the lower respiratory tract; J90-J99, other diseases of the pleura and post-procedural disorders of respiratory system; N10-N19, renal tubulo-interstitial diseases and acute kidney failure and chronic kidney disease. The distributions of patient characteristics by cluster are shown in Table S4. Clusters 2 and 4 included a higher rate of older women with a greater number of medical conditions, while cluster 3 mainly comprised younger men with a greater number of medical conditions. Clusters 1 and 5 did not show a statistically different proportion of men and women, and mainly included younger patients. Associations between clusters and mortality Death occurred in 19.0% (111/585) of patients in cluster 1, 27.8% (556/1,999) in cluster 2, 15.9% (110/694) in cluster 3, 13.1% (53/405) in cluster 4, and 14.1% (12/85) in cluster 5. In the main analysis adjusting for sex, age, and number of comorbidities, patients from the cluster 4 had a significantly reduced 28-day mortality when compared with those from cluster 1 (adjusted odds ratio (AOR)=0.53, 95%CI=0.37-0.77, p=0.001), cluster 2 (AOR=0.62; 95%CI=0.45-0.85; p=0.003), cluster 3 (AOR=0.67; 95%CI=0.45-0.97; p=0.036), and cluster 5 (AOR=0.45; 95%CI=0.22-0.94; p=0.027). There were no significant differences in mortality across other clusters. Results were similar in the sensibility analysis using the Elixhauser Comorbidity index instead of the number of comorbidities (Table 1). Braz J Psychiatry - BJP Article Pre-Proof (as accepted) Page 11 of 23 Braz J Psychiatry - Pre-Proof - http://doi.org/10.47626/1516-4446-2023-3076 Table 1. Association between clusters and 28-day mortality among patients with psychiatric disorders hospitalized with COVID-19 (N=3,768). Multivariable logistic regression model adjusted for sex, age and number of comorbidities – AOR (95% CI) Number of events / Number of patients (%) Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 1 111 / 585 (19.0) Ref. 1.16 (0.9 - 1.5; 0.245) 1.25 (0.92 - 1.71; 0.159) 1.88 (1.30 - 2.74; 0.001)* 0.84 (0.44 - 1.72; 0.607) Cluster 2 556 / 1999 (27.8) 0.86 (0.67 - 1.11; 0.245) Ref. 1.08 (0.82 - 1.41; 0.593) 1.62 (1.18 - 2.24; 0.003)* 0.72 (0.38 - 1.46; 0.332) Cluster 3 110 / 694 (15.9) 0.8 (0.58 - 1.09; 0.159) 0.93 (0.71 - 1.22; 0.593) Ref. 1.50 (1.03 - 2.20; 0.036)* 0.67 (0.35 - 1.37; 0.245) Cluster 4 53 / 405 (13.1) 0.53 (0.37 - 0.77; 0.001)* 0.62 (0.45 - 0.85; 0.003)* 0.67 (0.45 - 0.97; 0.036)* Ref. 0.45 (0.22 - 0.94; 0.027)* Cluster 5 12 / 85 (14.1) 1.20 (0.58 - 2.30; 0.607) 1.39 (0.68 - 2.63; 0.332) 1.50 (0.73 - 2.87; 0.245) 2.25 (1.06 - 4.48; 0.027)* Ref. Multivariable logistic regression model adjusted for sex, age and Elixhauser comorbidity index – AOR (95% CI) Number of events / Number of patients (%) Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 1 111 / 585 (19.0) Ref. 1.16 (0.90 - 1.50; 0.244) 1.19 (0.87 - 1.63; 0.266) 1.85 (1.28 - 2.69; 0.001)* 0.93 (0.49 - 1.91; 0.835) Cluster 2 556 / 1999 (27.8) 0.86 (0.67 - 1.11; 0.244) Ref. 1.03 (0.78 - 1.35; 0.856) 1.59 (1.16 - 2.21; 0.004)* 0.80 (0.43 - 1.62; 0.509) Cluster 3 110 / 694 (15.9) 0.84 (0.61 - 1.14; 0.266) 0.98 (0.74 - 1.28; 0.856) Ref. 1.55 (1.07 - 2.27; 0.023)* 0.78 (0.41 - 1.60; 0.470) Cluster 4 53 / 405 (13.1) 0.54 (0.37 - 0.78; 0.001)* 0.63 (0.45 - 0.86; 0.004)* 0.64 (0.44 - 0.94; 0.023)* Ref. 0.50 (0.25 - 1.06; 0.058) Cluster 5 12 / 85 (14.1) 1.07 (0.52 - 2.05; 0.835) 1.25 (0.62 - 2.35; 0.509) 1.28 (0.63 - 2.44; 0.470) 1.99 (0.94 - 3.94; 0.058) Ref. * Two-sided p-value p<0.05 Abbreviations: HR, hazard ratio; CI, confidence interval. Braz J Psychiatry - BJP Article Pre-Proof (as accepted) Page 12 of 23 Braz J Psychiatry - Pre-Proof - http://doi.org/10.47626/1516-4446-2023-3076 Prevalence of psychotropic medications by cluster Antipsychotic use was more prevalent among patients in cluster 1 (21.7%), but this prevalence was only significantly different when compared with patients in clusters 2 (13.1%) and 3 (5.5%). The use of benzodiazepines or Z-drugs was relatively similar across all clusters, except in cluster 3 (2.9%), in which the prevalence was significantly lower than in other clusters. Mood stabilizers were significantly more prevalent in cluster 4 (15.1%) than in clusters 1 (8.2%), 2 (6.5%) and 3 (4.9%). Antidepressant use was significantly more prevalent in cluster 4 (27.9%) than in all other clusters (cluster 1, 14.7%; cluster 2, 19.2%; cluster 3, 7.2%; cluster 5, 8.2%). Similarly, the use of FIASMA psychotropic medications was significantly more prevalent in cluster 4 (19.0%) than in all other clusters (cluster 1, 12.1%; cluster 2, 10.8%; cluster 3, 5.3%; cluster 5, 7.1%) (Figure 4, Table S5). Figure 4. Prevalence of psychotropic medications by cluster. DISCUSSION In a multicenter observational retrospective cohort study of 3,768 adult patients with psychiatric disorders hospitalized with laboratory-confirmed COVID-19, we found five distinct clusters of patients based on their medical psychiatric and non-psychiatric comorbidities. Following adjustments for sex, age, and number of medical conditions, there were no significant differences in mortality across clusters, except for cluster 4, in which almost all patients had a diagnosis of mood disorder and for which mortality rate was significantly lower than in other clusters. Twenty-eight-day mortality did not significantly differ across most clusters. This result supports that all psychiatric and non-psychiatric conditions could be associated with increased mortality in patients with psychiatric disorders and COVID-19, as previously suggested.12,15 It also suggests that the relationship of medical psychiatric and non- Braz J Psychiatry - BJP Article Pre-Proof (as accepted) Page 13 of 23 Braz J Psychiatry - Pre-Proof - http://doi.org/10.47626/1516-4446-2023-3076 psychiatric disorders with mortality may be better explained by the number and the severity of the disorders rather than by specific individual psychiatric or non-psychiatric disorders or specific combinations of disorders. More broadly, this finding is in line with the central role of comorbidity in a cumulative way in the relationships between psychiatric disorders and medical and social adverse outcomes.12,29–33 A notable exception was the patients from the cluster 4, in which almost all patients had a diagnosis of mood disorder and for which mortality was significantly lower than in all other clusters. Patients from cluster 4 were significantly more likely to take antidepressants and FIASMA psychotropic medications than in all other clusters, while the prevalence of other psychotropic medication groups did not significantly differ as compared to other clusters. This finding is in line with the potential beneficial effect of certain antidepressants in COVID-19, specifically antidepressant with high FIASMA activity, as suggested by prior work, including preclinical data,34–36 observational studies19–21,37 and clinical trials38–40, but needs to be confirmed in other studies. Our study has several limitations. First, an inherent bias in observational studies is unmeasured confounding. Second, inflation of type I error might have occurred in this study due to multiple testing. Therefore, the present results should be considered in light of this limitation and need to be confirmed by other studies. Third, there is a potential underreporting of psychiatric disorders, medical illnesses, and ongoing medications in our sample in a context of overwhelmed hospital units during the peak incidence. Fourth, the precise date of the diagnoses of psychiatric disorders during the visit (e.g. at hospital admission or at the end of the visit) was not available. Fifth, diagnoses of psychiatric disorders were based on ICD-10 diagnosis codes made by the practitioners in charge of the patients during the hospitalization for COVID-19 and were not ascertained by psychiatrists. Finally, despite the multicenter design, our results relied on a cohort study of hospitalized patients with COVID-19 in Paris and may not be generalizable to outpatients and other countries. Our results suggest that all psychiatric and non-psychiatric conditions may be associated with increased mortality in patients with psychiatric disorders and COVID-19. The potentially lower risk of death among patients with mood disorders might be in line with the potential beneficial effect of certain antidepressants in COVID-19, but requires replication. These findings may help identify at-risk patients with psychiatric disorders who should benefit from vaccine booster prioritization and other prevention measures. DISCLOSURE Funding: N.H., M.S.R., and F.L. are inventors on a patent application related to methods of treating COVID-19, filled by Assistance Publique – Hopitaux de Paris in France. Braz J Psychiatry - BJP Article Pre-Proof (as accepted) Page 14 of 23 Braz J Psychiatry - Pre-Proof - http://doi.org/10.47626/1516-4446-2023-3076 Ethical statement: This observational study using routinely collected data received approval from the Institutional Review Board of the AP-HP clinical data warehouse (decision CSE-20- 20_COVID19, IRB00011591, April 8th, 2020). AP-HP clinical Data Warehouse initiatives ensure patient information and consent regarding the different approved studies through a transparency portal in accordance with European Regulation on data protection and authorization n°1980120 from National Commission for Information Technology and Civil Liberties (CNIL). Conflicts of interest: The authors report no conflicts of interest. Author contributions: Study protocol: M.S.R., J.M.A. Formal analysis: M.S.R. Writing – original draft: M.S.R, K.R. Writing – review & editing: K.R., A.D.A., N.H., F.L., J.M.A. REFERENCES 1. 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Braz J Psychiatry - BJP Article Pre-Proof (as accepted) Page 18 of 23 18 SUPPLEMENTARY MATERIAL Table S1. Association of patient characteristics with 28-day mortality among patients with psychiatric disorders hospitalized with COVID-19 (N=3,768). Full sample (N=3,768) With the outcome (N=842) Without the outcome (N=2,926) Crude logistic regression model Multivariable logistic regression model N (%) / Mean (SD) N (%) / Mean (SD) N (%) / Mean (SD) OR (95% CI) AOR (95% CI) Collinearity diagnosis (GVIF) Sex 1.09 Women 1858 (49.3) 379 (45.0) 1479 (50.5) Ref. Ref. Men 1910 (50.7) 463 (55.0) 1447 (49.5) 1.25 (1.07 - 1.46; 0.005)* 1.72 (1.46 - 2.03; <0.001)* Age 1.10 18 to 50 400 (10.6) 15 (1.78) 385 (13.2) Ref. Ref. 51 to 70 913 (24.2) 119 (14.1) 794 (27.1) 3.85 (2.29 - 6.94; <0.001)* 3.31 (1.96 - 5.99; <0.001)* 71 to 80 780 (20.7) 191 (22.7) 589 (20.1) 8.32 (5.01 - 14.9; <0.001)* 7.42 (4.44 - 13.33; <0.001)* More than 80 1675 (44.5) 517 (61.4) 1158 (39.6) 11.46 (7.02 - 20.25; <0.001)* 11.3 (6.86 - 20.10; <0.001)* Number of medical conditions┼ 1.01 ≤ 7 1038 (27.5) 141 (16.7) 897 (30.7) Ref. Ref. 8-10 853 (22.6) 187 (22.2) 666 (22.8) 1.79 (1.41 - 2.27; <0.001)* 1.53 (1.20 - 1.97; 0.001)* 11-14 953 (25.3) 238 (28.3) 715 (24.4) 2.12 (1.68 - 2.67; <0.001)* 1.67 (1.32 - 2.13; <0.001)* ≥ 15 924 (24.5) 276 (32.8) 648 (22.1) 2.71 (2.16 - 3.41; <0.001)* 2.07 (1.64 - 2.62; <0.001)* ┼ Number of medical conditions based in the sum of second-level ICD-10 codes. * Two-sided p-value p<0.05 Abbreviations: HR, hazard ratio; CI, confidence interval. Braz J Psychiatry - BJP Article Pre-Proof (as accepted) Page 19 of 23 19 Table S2. Association of patient characteristics with 28-day mortality among patients with psychiatric disorders hospitalized with COVID- 19 (N=3,768). Full sample (N=3,768) With the outcome (N=842) Without the outcome (N=2,926) Crude logistic regression model Multivariable logistic regression model N (%) / Mean (SD) N (%) / Mean (SD) N (%) / Mean (SD) OR (95% CI) AOR (95% CI) Collinearity diagnosis (GVIF) Sex 1.10 Women 1858 (49.3) 379 (45.0) 1479 (50.5) Ref. Ref. Men 1910 (50.7) 463 (55.0) 1447 (49.5) 1.25 (1.07 - 1.46; 0.005)* 1.69 (1.43 – 2.00; <0.001)* Age 1.11 18 to 50 400 (10.6) 15 (1.78) 385 (13.2) Ref. Ref. 51 to 70 913 (24.2) 119 (14.1) 794 (27.1) 3.85 (2.29 - 6.94; <0.001)* 3.70 (2.20 - 6.70; <0.001)* 71 to 80 780 (20.7) 191 (22.7) 589 (20.1) 8.32 (5.01 - 14.9; <0.001)* 8.14 (4.87 - 14.63; <0.001)* More than 80 1675 (44.5) 517 (61.4) 1158 (39.6) 11.46 (7.02 - 20.25; <0.001)* 12.47 (7.56 - 22.23; <0.001)* Weighted Elixhauser Comorbidity index ┼ 1.05 ≤ 0 1434 (38.1) 242 (28.7) 1192 (40.7) Ref. Ref. (0, 5] 535 (14.2) 108 (12.8) 427 (14.6) 1.25 (0.97 - 1.60; 0.088) 0.99 (0.76 - 1.28; 0.927) (5, 14] 927 (24.6) 220 (26.1) 707 (24.2) 1.53 (1.25 - 1.88; <0.001)* 1.06 (0.86 - 1.32; 0.582) > 14 872 (23.1) 272 (32.3) 600 (20.5) 2.23 (1.83 - 2.73; <0.001)* 1.56 (1.26 - 1.92; <0.001)* ┼ Weighted Elixhauser Comorbidity Index (29) * Two-sided p-value p<0.05 Abbreviations: HR, hazard ratio; CI, confidence interval. Braz J Psychiatry - BJP Article Pre-Proof (as accepted) Page 20 of 23 20 Table S3. Top five models according to average silhouette coefficient. Number of neighbors Minimum embedding distance Clustering method Number of clusters Average silhouette coefficient 100 0.1 Centroid 5 0,809 100 0.1 Average 5 0,809 100 0.1 Complete 5 0,809 100 0.5 Average 5 0,784 100 0.5 Centroid 5 0,784 Braz J Psychiatry - BJP Article Pre-Proof (as accepted) Page 21 of 23 21 Table S4. Distribution of patient characteristics within each cluster. Cluster 1 (N = 585) Cluster 2 (N = 1,999) Cluster 3 (N=694) Cluster 4 (N=405) Cluster 5 (N=85) N (%) N (%) N (%) N (%) N (%) Sex Women 300 (51.3) 1135 (56.8) 146 (21.0) 243 (60.0) 34 (40.0) Men 285 (48.7) 864 (43.2) 548 (79.0) 162 (40.0) 51 (60.0) Age 18 to 50 125 (21.4) 27 (1.35) 160 (23.1) 55 (13.6) 33 (38.8) 51 to 70 210 (35.9) 194 (9.7) 345 (49.7) 129 (31.9) 35 (41.2) 71 to 80 121 (20.7) 424 (21.2) 130 (18.7) 95 (23.5) 10 (11.8) More than 80 129 (22.1) 1354 (67.7) 59 (8.5) 126 (31.1) 7 (8.2) Number of medical conditions ≤ 7 206 (35.2) 445 (22.3) 190 (27.4) 146 (36.0) 51 (60.0) 8-10 126 (21.5) 465 (23.3) 158 (22.8) 81 (20.0) 23 (27.1) 11-14 134 (22.9) 538 (26.9) 181 (26.1) 91 (22.5) 9 (10.6) ≥ 15 119 (20.3) 551 (27.6) 165 (23.8) 87 (21.5) 2 (2.4) Elixhauser comorbidity index ┼ ≤ 1 295 (50.4) 547 (27.4) 339 (48.8) 207 (51.1) 46 (54.1) (1, 3] 95 (16.2) 281 (14.1) 89 (12.8) 56 (13.8) 14 (16.5) (3, 4] 91 (15.6) 618 (30.9) 118 (17.0) 82 (20.2) 18 (21.2) > 4 104 (17.8) 553 (27.7) 148 (21.3) 60 (14.8) 7 (8.2) ┼ Elixhauser index computed with the Swiss weights modification by Sharma et al.25 Braz J Psychiatry - BJP Article Pre-Proof (as accepted) Page 22 of 23 22 Table S5. Pairwise comparison of the prevalence of psychotropic medications in each cluster. Cluster A % Cluster B % Χ2 (p-value) Antidepressants Cluster 1 14.7 Cluster 2 19.2 5.88 (0.015)* Cluster 3 7.2 17.99 (<0.001)** Cluster 4 27.9 25.15 (<0.001)** Cluster 5 8.2 2.08 (0.149) Cluster 2 19.2 Cluster 3 7.2 54.04 (<0.001)** Cluster 4 27.9 14.99 (<0.001)** Cluster 5 8.2 5.74 (0.017)* Cluster 3 7.2 Cluster 4 27.9 85.10 (<0.001)** Cluster 5 8.2 0.02 (0.901) Cluster 4 27.9 Cluster 5 8.2 13.65 (<0.001)** Antipsychotics Cluster 1 21.7 Cluster 2 13.1 25.90 (<0.001)** Cluster 3 5.5 73.00 (<0.001)** Cluster 4 16.8 3.36 (0.067) Cluster 5 16.5 0.93 (0.335) Cluster 2 13.1 Cluster 3 5.5 29.20 (<0.001)** Cluster 4 16.8 3.66 (0.056) Cluster 5 16.5 0.56 (0.455) Cluster 3 5.5 Cluster 4 16.8 36.30 (<0.001)** Cluster 5 16.5 13.00 (<0.001)** Cluster 4 16.8 Cluster 5 16.5 <0.01 (>0.99) Benzodiazepines or Z-drugs Cluster 1 34.7 Cluster 2 36.7 0.71 (0.399) Cluster 3 20.9 29.83 (<0.001)** Cluster 4 33.3 0.14 (0.705) Cluster 5 27.1 1.61 (0.204) Cluster 2 36.7 Cluster 3 20.9 57.96 (<0.001)** Cluster 4 33.3 1.53 (0.216) Cluster 5 27.1 2.89 (0.089) Cluster 3 20.9 Cluster 4 33.3 20.20 (<0.001)** Cluster 5 27.1 1.36 (0.244) Cluster 4 33.3 Cluster 5 27.1 0.99 (0.319) Mood stabilizers Cluster 1 8.2 Cluster 2 6.5 1.79 (0.181) Cluster 3 4.9 5.24 (0.022)* Cluster 4 15.1 10.79 (0.001)** Cluster 5 14.1 2.49 (0.114) Cluster 2 6.5 Cluster 3 4.9 2.05 (0.153) Cluster 4 15.1 32.57 (<0.001)** Cluster 5 14.1 6.29 (0.012)* Cluster 3 4.9 Cluster 4 15.1 32.17 (<0.001)** Cluster 5 14.1 9.98 (0.002)** Cluster 4 15.1 Cluster 5 14.1 <0.01 (0.956) FIASMA psychotropic medications Cluster 1 12.1 Cluster 2 10.8 0.68 (0.408) Cluster 3 5.3 18.15 (<0.001)** Braz J Psychiatry - BJP Article Pre-Proof (as accepted) Page 23 of 23 23 Cluster 4 19.0 8.37 (0.004)** Cluster 5 7.1 1.42 (0.234) Cluster 2 10.8 Cluster 3 5.3 17.50 (<0.001)** Cluster 4 19.0 20.43 (<0.001)** Cluster 5 7.1 0.84 (0.359) Cluster 3 5.3 Cluster 4 19.0 50.03 (<0.001)** Cluster 5 7.1 0.17 (0.684) Cluster 4 19.0 Cluster 5 7.1 6.31 (0.012)* *p-value<0.05; **p-value<0.005 (Bonferroni correction: 0.05/10)