The effect of antidepressants on severity of COVID-19 in hospitalized patients: A systematic review and meta-analysis

1 1 The effect of antidepressants on severity of COVID-19 in hospitalized patients: 2 A systematic review and meta-analysis 3 4 5 Hosein Nakhaee1, Reza Bayati1ϯ, Mohammad Rahmanian1ϯ, Amir Ghaffari Jolfayi1, Moein 6 Zangiabadian2*, Sakineh Rakhshanderou3* 7 8 1Student Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, 9 Tehran, Iran 10 11 2Department of Microbiology, School of Medicine, Shahid Beheshti University of Medical 12 Sciences, Tehran, Iran 13 14 3Environmental and Occupational Hazards Control Research Center, School of Public Health, Shahid 15 Beheshti University of Medical Sciences, Tehran, Iran 16 17 18 19 ϯ These authors contributed equally 20 21 22 23 *Correspondence: 24 25 Moein Zangiabadian 26 [email protected] 27 28 Sakineh Rakhshanderou 29 [email protected] 30 31 . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. 2 32 Abstract 33 34 Introduction: 35 Clinical depression and the subsequent low immunity is a comorbidity that can act as a risk factor 36 for severity of COVID-19 cases. Antidepressants such as SSRI and SNRI are associated with 37 immune-modulatory effects, which dismiss inflammatory response and reduce lung tissue damage. 38 The current systematic review and meta-analysis aims to evaluate the effect of antidepressant drugs 39 on prognosis and severity of COVID-19 in hospitalized patients. 40 41 Methods: 42 A systematic search was carried out in PubMed/Medline, EMBASE, and Scopus up to January 43 16, 2022. The following keywords were used: “COVID-19”, “SARS-CoV-2”, “2019-nCoV”, 44 “SSRI”, “SNRI”, “TCA”, “MAOI”, and “Antidepressant”. The pooled risk ratio (RR) with 95% 45 CI was assessed using a fixed or random-effect model. We considered P < 0.05 as statistically 46 significant for publication bias. Data were analyzed by Comprehensive Meta-Analysis software, 47 Version 2.0 (Biostat, Englewood, NJ). 48 49 Results: 50 Twelve studies were included in our systematic review. Three of them were experimental with 51 1751, and nine of them were observational with 290,950 participants. Seven out of twelve 52 articles revealed the effect of antidepressants on reducing severity of COVID-19. SSRI 53 medications, including Fluvoxamine, Escitalopram, Fluoxetine, and Paroxetine and also among 54 the SNRI drugs Venlafaxine are also reasonably associated with reduced risk of intubation or 55 death. There were four studies showing no significant effect and one study showing the negative 56 effect of antidepressants on prognosis of covid-19. The meta-analysis on clinical trials showed 57 that fluvoxamine could significantly decrease the severity outcomes of COVID-19 (RR: 0.745; 58 95% CI: 0.580-0.956) 59 60 Conclusions: Most of the evidence supports that the use of antidepressant medications, mainly 61 Fluvoxamine may decrease the severity and improve the outcome in hospitalizes patients with 62 sars-cov-2. Some studies showed contradictory findings regarding the effects of antidepressants 63 on severity of COVID-19. Further experimental studies should be conducted to clarify the effects 64 of antidepressants on severity of COVID-19. 65 Keywords: antidepressants –SSRI- COVID-19 -SARS-CoV-2 66 67 . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint 3 68 Introduction 69 70 After over two years since the first case of the novel coronavirus was detected in December 2019 71 in Wuhan, China, 483 million infected cases and 6.13 million deaths have been reported worldwide 72 up to January 21, 2022 (1). Covid19 is an acute respiratory disease, resulting in progressive 73 respiratory failure, and eventually leading to death. Common symptoms include fever, dry cough, 74 myalgia and fatigue. Severe cases develop dyspnea, hemoptysis, acute respiratory distress 75 syndrome which can result in death. The severity or mortality is higher in older population, with 76 comorbidities such as diabetes, hypertension, cardiovascular diseases and weaker immune 77 functions (2). During the COVID-19 pandemic with strict lockdown measures, psychiatric patients 78 have suffered episodes of anxiety, depression, and stress disorders on higher scales.(3) Clinical 79 depression and the subsequent low immunity can also act as risk factors for severity of COVID- 80 19 cases (4). Patients with clinical depression have lower immunity compared to those of healthy 81 controls. A history of depression is associated with higher risk of infection and also the increased 82 risk remains consistent over time (5). The suggested mechanism of action is fewer circulating 83 CD4+ T-cells and reduction in natural killer cell cytotoxic responses of lymphocyte proliferation 84 in elderly patients with clinical depression (6). Treatment of depression as an underlying morbidity 85 could decrease the risk of clinical deterioration of Covid-19 patients. On the other hand, 86 antidepressants such as SNRI and SSRI which are widely used in treatment of psychiatric patients 87 are shown to reduce corona virus infection rates as patients who were receiving these medications 88 were less likely to test positive for COVID-19 (7). Antidepressants are also associated with less 89 severe cases as they significantly reduced the risk of intubation or death in some cohort studies (8, 90 9). Several mechanisms are suggested to justify this finding. Antidepressants could be associated 91 with declined plasma levels of inflammatory cytokines, including IL-10, TNF-α, CCL-2, and IL- 92 6, which are related to COVID-19 severity and mortality (10, 11). Also Some SSRI 93 antidepressants, such as fluvoxamine which is a functional inhibitor of acid sphingomyelinase 94 activity (FIASMA), may prevent the infection of epithelial cells with SARS-CoV-2 (12). In this 95 study we aim to evaluate the effect of antidepressant drugs on prognosis and severity of COVID- 96 19 in hospitalized patients. 97 98 Method 99 100 This study was conducted and reported in accordance with the Preferred Reporting Items for 101 Systematic Reviews and Meta-Analyses statement (13). The study was registered in the 102 Systematic Review Registration: PROSPERO (pending registration ID: 313272). 103 104 Search strategy 105 We searched Pubmed/Medline, Embase and Scopus for clinical studies reporting the effect of 106 anti-depressants on reducing severity of hospitalized patients with covid-19, published up to 107 January 16, 2022. We included clinical trials, cohort and case–control studies that were written 108 in English. We used the following MeSH terms: “‘antidepressive agents’, ‘antidepressive agents, 109 second generation’, ‘antidepressive agents, tricyclic’, ‘monoamine oxidase inhibitors’, ‘serotonin 110 and noradrenaline reuptake inhibitors’, ‘serotonin uptake inhibitors’, ‘COVID-19’ and ‘SARS- 111 CoV-2’” (Table S1&2&3). Keyword searches were done with combinations of the terms “SSRI”, 112 “SNRI”, “TCA”, “MAOI”, “Antidepressant”, “2019 novel coronavirus” and “sars coronavirus . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint 4 113 2”. Lists of references of selected articles and relevant review articles were hand-searched to 114 identify further studies. 115 116 Study Selection 117 118 The records found through database searching were merged, and the duplicates were removed 119 using EndNote X8 (Thomson Reuters, Toronto, ON, Canada). Two reviewers independently 120 screened the records by title/abstract and full text to exclude those unrelated to the study 121 objectives. Any disagreements were resolved by the lead investigator. Included studies met the 122 following criteria: (i) patients were diagnosed with COVID-19 based on the WHO criteria; (ii) 123 patients were received anti-depressants; and (iii) severity predictors (hospitalization due to 124 COVID-19 and/or ARDS and/or need to NIV or mechanical ventilation and/or ICU admission 125 and/or death). Conference abstracts, editorials, reviews, study protocols, molecular or 126 experimental studies on animal models and studies with focusing on infection risk were 127 excluded. 128 129 Data extraction 130 131 Two reviewers designed a data extraction form. These reviewers extracted data from all eligible 132 studies, and differences were resolved by consensus. The following data were extracted: first 133 author name; year of publication; study duration; type of study, country/ies where the research 134 was conducted; demographics (i.e. age, sex); Detection test of COVID-19; anti-depressant type, 135 dosage and frequency; Follow-up time; the definition of case and control; total number of 136 controls and cases, severity indices, mechanism of action of anti-depressants against COVID-19 137 and Possibility of using anti-depressants in COVID-19 treatment. 138 139 Quality assessment 140 141 Two blinded reviewers assessed the quality of the studies using three different assessment tools 142 (checklists): two for observational studies (case controls and cohorts) and one for experimental 143 studies (14). Items such as study population, measure of exposures, confounding factors, extent 144 of outcomes, follow-up data, and statistical analysis were evaluated. 145 146 Statistical Analysis 147 148 The pooled risk ratios (RRs) with 95% CI were assessed using random or fixed-effect models. 149 The fixed-effects model was used because of the low estimated heterogeneity of the true effect 150 sizes. The between-study heterogeneity was assessed by Cochran’s Q and the I2 statistic. 151 Publication bias was evaluated statistically by using Egger’s and Begg’s tests as well as the 152 funnel plot (p < 0.05 was considered indicative of statistically significant publication bias; funnel 153 plot asymmetry also suggests bias) (15). All analyses were performed using “Comprehensive 154 Meta-Analysis” software, Version 2.0 (Biostat, Englewood, NJ). 155 156 Results 157 158 The selection process of articles is shown in Figure 1. Twelve articles were included and 159 classified into the followings: six cohort studies (8, 16-20), three case-control studies (21-23) and 160 three clinical trials (24-26). There were 872 cases and 879 controls in clinical trials, 15950 cases . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint 5 161 and 96638 controls in the cohort studies and 97,844 cases and 80518 controls in the case–control 162 studies with the total population of 292701 in the whole studies (114666 cases and 178035 163 controls). Four studies were conducted in USA and other studies were designed in France, 164 Turkey, Brazil, Hungary, Israel, Croatia, Scotland and Italy. The duration of studies, detection 165 test of COVID-19 and other study characteristics are shown in table 1. 166 167 168 169 Quality of the included studies 170 171 The checklists for observational studies (14) showed that the included observational studies had a 172 low risk of bias except Bora et al. study (16). (Table 2 & 3) In contrast, the checklist for 173 experimental studies (14) showed that the included experimental studies had a high risk of bias for 174 randomization, group concealment and participants, treatment delivers and outcome assessors 175 blinding except Lenze et al. study (25). (Table 4) 176 177 Patient Characteristics 178 179 According to nine studies (8, 16-18, 21-25), the mean age of total patients was 58.1 years old 180 with 40 and 56.5 years old in case and control groups in eight studies, respectively (8, 17, 18, 20, 181 21, 23-25). 49.6 percent of patients were female according to eleven studies (8, 16-21, 23-26). 182 This number was 52% in case groups and 49.3% in control groups according to nine studies (17- 183 21, 23-26). Cases and controls were matched with age, sex, comorbidities, smoking status, BMI 184 category and etc. The definition of case and control groups and severity outcomes are shown in 185 table 5. 186 187 Interventions and exposures Characteristics 188 189 Seven studies (18, 20, 21, 23-26) only used or surveyed SSRIs as anti-depressant in case groups 190 (fluvoxamine in five studies (18, 20, 24-26), fluoxetine in two studies (18, 23) and Escitalopram 191 in one study (21)). In remain five studies (8, 16, 17, 19, 22) different types of SSRIs, SNRIs and 192 TCAs were used or surveyed. Anti-depressant type, dosage, frequency and follow-up time are 193 shown in table 6. 194 195 Effect of anti-depressants on severity outcomes of COVID-19 196 197 After adjustment, seven studies (8, 16, 18, 20, 21, 23, 25) showed significant association 198 between anti-depressant use and reducing severity outcomes of COVID-19. On the other hand, 199 five studies (17, 19, 22, 24, 26) presented that either there are not any significant effect for anti- 200 depressants to reduce disease severity or anti-depressants are associated with severe COVID-19 201 in hospitalized patients. In the study that was conducted by Bora et al. (16) anti-depressants were 202 associated with reducing mortality, regardless of the anti-depressant type. Hoertel et al. (8) 203 suggested that antidepressant use (HR:0.56; 95% CI: 0.43–0.73) is significantly and substantially 204 associated with reduced risk of intubation or death, independently of patient characteristics, 205 clinical and biological markers of disease severity, and other psychotropic medications. They 206 found that SSRI (HR:0.51; 95% CI:0.316–0.72) and non-SSRI (HR:0.65; 95% CI, 0.45–0.93) 207 antidepressants, and specifically the SSRIs escitalopram, fluoxetine, and paroxetine, the SNRI . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint 6 208 venlafaxine and the α2-antagonist antidepressants mirtazapine are significantly associated with 209 reduced risk of intubation or death. Escitalopram is also effective in reducing severity according 210 to Israel et al. (21) study. Fluoxetine use was associated with an important (70%) decrease 211 of mortality (OR:0.33; 95% CI:0.16–0.68) and threefold survival in fluoxetine group according 212 to Nemeth et al. (23) study. Also another study by Oskotsky et al. (18) showed similar result that 213 fluoxetine could reduce risk of mortality (RR:0.72; 95% CI:0.54-0.97). On the other hand, 214 Rauchman et al. (19) mentioned that prior use of SSRIs or SNRIs did not reduce mortality 215 (OR:0.96; 95% CI:0.79-1.16). McKeigue et al. (22) proposed that TCAs (RR:1.1; 95% CI: 0.94- 216 1.27) and SSRIs (RR:1.18; 95% CI:1.03-1.36) like other anti-depressants (RR: 1.76; 95% CI:1.5- 217 2.07) increase the risk of severe COVID-19 because of their anti-cholinergic effect that is likely 218 to increase risk of pneumonia. However, Fei et al. (17) represented that mortality rate, respiratory 219 failure related to pneumonia and renal failure as a comorbidity within the anti-depressant treated 220 subgroup is not lower than in the other patient of the sample but suggested that anti-depressant 221 treated patient subgroup show high mean age and high size of medical comorbidity. Although, it 222 is shown in this study that ARDS is significantly lower for the anti-depressant treated subgroup 223 so mild significant lower employment of protease inhibitors and endotracheal intubation is 224 needed in this subgroup because of lower level of IL-6 in anti-depressant treated patient that 225 resulted from inhibition function of acid sphingomyelinase induced by antidepressants. 226 227 Specific effect of fluvoxamine on severity outcomes of COVID-19 228 229 Four studies have discussed about the relation of using fluvoxamine and COVID-19 severity. 230 One cohort study (20) and three clinical trials (24-26). Lenze et al. (25) represented that patients 231 treated with fluvoxamine, compared with placebo, had a lower likelihood of clinical deterioration 232 over 15 days (absolute difference:8.7% 95% CI:1.8%-16.4%). Also serious adverse events in 233 fluvoxamine group were less than placebo group (1.3% vs 6.9%). They suggested that this effect 234 is because of the influence of fluvoxamine on the S1R-IRE1 pathway and anti-inflammatory 235 (Cytokine reduction) actions resulting from S1R activation. Also according to Seftel et al. (20) 236 study the incidence of subsequent hospitalization was lower in fluvoxamine group (0% vs 237 12.5%) and elevated respiratory rates were improved faster by day 7 in this group so 238 fluvoxamine seems to be promising as early treatment for COVID-19 to prevent clinical 239 deterioration requiring hospitalization and to prevent possible long haul symptoms persisting 240 beyond 2 weeks. On the other hand, Reis et al. (26) suggested that There were no significant 241 differences between fluvoxamine and placebo for viral clearance at day 7 (RR:0·67; 95% 242 CI:0·42–1·06), hospitalizations due to COVID-19 (RR:0·77; 95% CI:0·55–1·05), time to 243 hospitalization (RR:0·79; 95% CI:0·58–1·06), mechanical ventilation (RR:0·77; CI:0·45–1·30), 244 time on mechanical ventilation (RR:1·03; 95% CI: 0·64–1·67), death (RR:0·69; 95% CI:0·36– 245 1·27) and time to death (RR:0·80; 95%CI:0·43–1·51). However, according to Calusic et al. (24) 246 study, there was no statistically significant differences between groups were observed regarding 247 the number of days on ventilator support, duration of ICU or total hospital stay. But overall 248 mortality was lower in the fluvoxamine group (HR: 0.58; 95% CI: 0.36–0.94). Although only 249 mortality in women were significantly reduced in fluvoxamine group (HR: 0.40; 95% CI: 0.16– 250 0.99) and there were no significant differences in men mortality (HR: 0.69; 95% CI: 0.39–1.24). 251 . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint 7 252 Statistical focus on fluvoxamine 253 254 The meta-analysis on clinical trials showed that fluvoxamine could significantly decrease the 255 severity outcomes of COVID-19 (RR: 0.745; 95% CI: 0.580-0.956) (figure 2). There was no 256 evidence of publication bias (p > 0.05) (figure 3). 257 258 259 260 Discussion 261 262 Our systematic review study included cohort, case-control, and clinical trials and showed 263 contradictory findings regarding the effect of antidepressant drugs on severity and mortality in 264 covid-19 patients. Although various subgroups of antidepressants mainly reduce the risk of ARDS 265 and mortality in hospitalized covid-19 patients compared to control groups, some studies reported 266 no difference between case and control groups. On the other hand, one study proposed that 267 antidepressants such as SSRIs and TCAs increase the risk of severe COVID-19 due to their 268 anticholinergic effect that is likely to cause pneumonia. 269 Our meta-analysis on clinical trials also demonstrated that fluvoxamine could significantly 270 decrease the severity outcomes of COVID-19 and as Seftel et al. (20) study proposed that it can 271 be a therapeutic choice to prevent clinical deterioration requiring hospitalization for COVID-19 272 patients. 273 It has been proven that patients with clinical depression have lower immunity and thus more 274 vulnerability to infection than healthy controls. (5) The suggested mechanism of action is fewer 275 circulating CD4+ T-cells and reduction in natural killer cell cytotoxic responses of lymphocyte 276 proliferation in elderly patients with clinical depression. There is also an abnormal regulation of 277 the hypothalamus–pituitary–adrenal axis, sympathetic–adrenal–medullary axis, and 278 hypothalamic-pituitary ovarian axis in patients with clinical depression (6). On the other hand, 279 severe cases of COVID-19 infection are associated with an increase in the production of anti- 280 inflammatory cytokines such as IL-6, IL-8, IL-10, and proliferation of CD8+ cytotoxic T-cells 281 derived from CD4+ T-cells, which act in neutralizing the virus and inflammation of the lung (27). 282 Thus it is justified to consider clinical depression and the subsequent low immunity as risk factors 283 for the severity of COVID-19 cases. 284 In covid-19 patients with clinical depression, anti-depression treatment could improve immune 285 responses by neutralizing the comorbidity (28). Still, there are several other mechanisms through 286 which antidepressants might lower the risk of severe outcomes. 287 288 Inhibition of serotonin transporter 289 SSRI and SNRIs are shown to have anti-inflammatory effects in both preclinical and clinical 290 studies.(11, 29, 30) this might be owing to serotonin transporter inhibition.(31) Serotonin is 291 involved in anti-inflammatory cytokine production. (32) For example, it can increase the release 292 of the IL-10, which is a potent cytokine with reputable anti-inflammatory assets (33) IL-10 also 293 moderates the levels of TNF-α and IL-6(34) . Which are both responsible for the inflammatory 294 response in covid-19 infection. Serotonin also reduces the production of inflammatory cytokines 295 such as TNF-α and interferon-gamma (IFN-γ) by human blood leucocytes (35, 36). High serotonin 296 transporter expression in the lungs(37) brings up its possible function in lung inflammation. 297 Serotonin inhibits IL-12 and TNF-α release in human alveolar macrophages, but it increases IL- . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint 8 298 10 production via 5-HT2 receptors (38). Therefore, SSRIs and SNRIs may influence COVID-19 299 patients’ lung function. Considering that other antidepressants with the ability to block serotonin 300 transporters did not show similar beneficial effects for COVID-19 patients, it is unlikely that 301 serotonin transporter inhibition may play a significant role in SSRIs’ beneficial effects for COVID- 302 19 patients. However, the anti-inflammatory effects of serotonin transporter inhibition may be a 303 factor for the beneficial effects of SSRIs such as fluoxetine and fluvoxamine (31). 304 305 306 307 Acid sphingomyelinase (ASM) 308 Many antidepressants, such as fluoxetine, fluvoxamine, sertraline, paroxetine, and amitriptyline, 309 directly inhibit acid sphingomyelinase activity (11, 39, 40)which is necessary for epithelial cells 310 to uptake SARS-CoV-2 (41). This could explain the findings of Hoertel et al. study (8) , although 311 considering that there are other drugs such as chlorpromazine with ASM inhibition activity that 312 didn’t show the same protective effects on mortality in COVID-19 patients (42), this mechanism 313 is unlikely a major factor for said effect. 314 315 Sigma-1 receptor 316 sigma-1 receptor, an endoplasmic reticulum (ER) protein, is a cellular factor mediating the early 317 steps of viral RNA replication, thus essential for virus replication at the early stage of infection 318 (43, 44). A recent study showed that several compounds for sigma-1 and sigma-2 receptors are 319 active in protein-protein interactions between SARS-CoV-2 and human proteins and could be 320 promising inhibitors for SARS-CoV-2 replication (45). 321 On the other hand, animal studies indicated that Some SSRIs, such as fluvoxamine, sertraline, 322 fluoxetine, and citalopram, have a high to moderate affinity for sigma-1 receptors in the rat brain. 323 (45). Binding of Sigma-1 receptor agonists (i.e., fluvoxamine and fluoxetine) can result in sigma- 324 1 receptor chaperone activity in the cells (46, 47). Another study demonstrated that binding of 325 fluoxetine to the sigma-1 receptor in the endoplasmic reticulum decreases cytokine activity and 326 enhances survival in preclinical models of sepsis and inflammation (48). Given the crucial role of 327 the chaperone activity by sigma-1 receptor agonist in SARS-CoV-2 replication, it is possible that 328 the SSRIs (i.e., fluvoxamine, fluoxetine, escitalopram) with sigma-1 receptor agonisms could be 329 COVID-19 prophylactic drugs (8). 330 331 Another mechanism proposed to explain the anti-inflammatory effects of SSRIs is increasing 332 melatonin levels due to cytochrome P450 enzyme CYP1A2 inhibition by fluvoxamine (49, 50). 333 Melatonin which is naturally synthesized in the pineal gland and immune cells from the amino 334 acid tryptophan, has anti-inflammatory, immunomodulatory, and antioxidant effects and is a 335 therapeutic candidate to treat covid-19 (51). 336 337 338 Among the articles indicating no significant effect for antidepressants, Fei et al. (17) and Calusic 339 et al. (24) studies were limited by small sample size. Calusic et al. (24) and Reis et al. (26) 340 studies couldn’t entirely rule out selection bias. Fei et al. also stated that control group showed 341 higher mean age and high size of medical comorbidities. Patients with antidepressant therapy 342 have worse health conditions and immune responses than general population. This issue can have 343 an impact on prognosis of covid-19 (17). . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint 9 344 Another important clinical issue is to either continue or discontinue SSRIs when a COVID-19 345 patient is hospitalized or admitted to an ICU. There is evidence showing adverse ICU outcomes 346 after discontinuation of SSRIs. This is probably due to increased agitation and need for sedation 347 among these patients that can result in respiratory depression (52, 53) . SSRI use may sometimes 348 be contraindicated in ICU patients due to ECG changes and abnormal coagulation effects(54). A 349 systematic review regarding the use of antidepressants in Critical Care, suggested that there may 350 be excess morbidity in critically ill SSRI/SNRI users, but whether this is due to chronic effects, 351 ongoing use, or drug withdrawal is unclear (53). 352 Reis et al (26) study suggested the modulatory effect of fluvoxamine on systemic inflammation 353 as lower respiratory tract infections and hospital admissions were reported less frequently in 354 patients in the fluvoxamine group compared to those in the placebo group. 355 356 Finally, efficacy of fluvoxamine in COVID-19 patients could be dependent on the timing of 357 treatment, where increased effectiveness could be achieved if treatment is initiated earlier during 358 SARSCoV-2 infection. 359 360 Limitations 361 362 Most of the included articles in this study were not specific about antidepressant drugs and didn’t 363 evaluate each drug separately. They mainly studied antidepressants in groups like SSRIs, SNRIs, 364 and TCAs. We reviewed antidepressants but could only arrange a meta-analysis for fluvoxamine 365 which was studied in three clinical trial which two of them had a high risk of bias. Our meta- 366 analysis was limited to a small population of about 2000 persons. We also generally expressed our 367 results about severity and outcome and couldn’t arrange a subgroup analysis for each outcome or 368 sex group. 369 370 Suggestions 371 As there is strong evidence of the link between antidepressant use and improving outcomes of 372 covid-19, it seems legible to conduct more research on the subject, aiming to find more therapeutic 373 options to treat covid-19. Future studies should focus on antidepressants separately and be more 374 specific about the outcome in different patient groups. We also need to arrange more clinical trials 375 with larger populations to confirm the efficacy of a candidate certainly. SSRIs such as Fluoxetine 376 and Fluvoxamine are supported by stronger evidence and could be favorable options for future 377 research programs. 378 379 Conclusion 380 381 Among the studied experiment, seven studies showed anti-depressant ability to reduce the severity 382 of covid-19, and five studies didn’t mention any significant effect on it. The covid-19 reducing 383 effect of the mentioned medications can be concluded from the decline in the risk of intubation 384 and death, clinical outcomes, and biological markers demonstrating the disease’s severity. Among 385 the SSRI medication, the most common studied drug was fluvoxamine, which has a less 386 subsequent hospitalization rate, less likelihood of 15-day-deterioration, and fewer adverse events 387 than placebo, attributable to the S1R-IRE1 pathway and anti-inflammatory actions. Other SSRI 388 medications, including Escitalopram, Fluoxetine, and Paroxetine and also among the SNRI drugs 389 Venlafaxine are also reasonably associated with reduced risk of intubation or death. Still, they 390 didn’t study as much as fluvoxamine. Although most of the evidence supports the effectiveness . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint 10 391 hypothesis of anti-depressant drugs, a single study proposed that some anti-depressants can 392 intensify the severity of COVID-19 via anticholinergic effects that induce pneumonia. This issue 393 should be investigated more precisely in future studies. 394 395 396 397 398 399 400 DATA AVAILABILITY STATEMENT 401 402 The original contributions presented in the study are included in the article/supplementary 403 material. Further inquiries can be directed to the corresponding authors. 404 405 AUTHOR CONTRIBUTIONS 406 407 MZ, HN: designed the study. MZ, RB, MR, and AG: performed the search, study selection, and 408 data synthesis. MZ, HN: wrote the first draft of the manuscript. SR: revised the article. All 409 authors contributed to the article and approved the submitted version. 410 411 CONFILICT OF INTREST: 412 The mentioned in this article, have no involvement in any organization or entity with any 413 financial interest nor non-financial interest such as personal or professional relationships, 414 affiliations, knowledge or beliefs) in the subject matter discussed in this article. 415 416 ACKNOWLEDGMENTS 417 418 This study is related to the MPH project From the Department of Public Health, School of Public 419 Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 420 . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint 11 421 422 Figure1. Flow chart of study selection for inclusion in the systematic review and meta-analysis. 423 Records identified through databases (n=2041) PubMed/Medline: 91 Embase: 1528 Scopus: 422 Records after duplicates removed (n=1572) Title and abstract of records screened (n=1572) Excluded irrelevant (n=1556) Full-text articles assessed for eligibility (n=16) Studies included (n=12) Excluded irrelevant (n=3) Reason for exclusion: Focus on infection risk (n=1) Without specific data for anti- depressants (n=1) Full text not found (n=1) Without data for severity outcomes (n=1) ScreeningIncluded Eligibility Identification . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint 12 424 Table 1. Study characteristics 425 426 427 ABBREVIATIONS 428 PCR: polymerase chain reaction/ RT-PCR: reverse transcription polymerase chain reaction/ ICD 10: 429 International Statistical Classification of Diseases and Related Health Problems, Tenth Revision/ N/R: not reported 430 431 432 First author Study design Publication year country Study duration detection test of COVID-19 Lenze et al. (25) Randomized Clinical Trial 2020 USA April 10, 2020, to August 5, 2020 PCR Reis et al. (26) Randomized Clinical Trial 2021 Brazil June 2, 2020 to Aug 5, 2021 RT-PCR Calusic et al. (24) cohort trial 2021 Croatia April and May 2021, PCR Israel et al. (21) case-control 2021 Israel November 30, 2020 to December 31, 2020. RT-PCR McKeigue et al. (22) case-control 2021 Scotland June 6, 2020 to June 14, 2020 N/R Nemeth et al. (23) case-control 2021 Hungary March 17, 2021 to April 22, 2021 antigen or polymerase chain reaction test Rauchman et al. (19) retrospective cohort 2021 USA March 2020 to March 2021 N/R Hoertel et al. (8) retrospective cohort 2021 France January 24, 2020 to April 1, 2020 RT-PCR FEI et al. (17) Prospective Cohort 2021 Italy N/R N/R Oskotsky et al. (18) retrospective cohort 2020 USA January to September 2020 laboratory test for SARS- CoV-2 (nucleic acid amplification tests and immunoassays) and/or by ICD-10 (for COVID-19 confirmed by laboratory testing) Seftel et al. (20) Prospective Cohort 2021 USA November– December 2020 PCR Bora et al. (16) retrospective cohort 2021 Turkey October 1, 2020 to January 1, 2021 the ICD-10 classification confirmed by the test results . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint 13 433 434 Table 2. Quality assessment of cohort studies 435 436 1. Were the two groups similar and recruited from the same population? 437 2. Were the exposures measured similarly to assign people to both exposed and unexposed groups? 438 3. Was the exposure measured in a valid and reliable way? 439 4. Were confounding factors identified? 440 5. Were strategies to deal with confounding factors stated? 441 6. Were the groups/participants free of the outcome at the start of the study? 442 7. Were the outcomes measured in a valid and reliable way? 443 8. Was the follow-up time reported and sufficient to be long enough for outcomes to occur? 444 9. Was follow-up complete, and, if not, were the reasons to loss to follow-up described and explored? 445 10. Were strategies to address incomplete follow-up utilized? 446 11. Was appropriate statistical analysis used 447 Table 3. Quality assessment of case-control studies author 1 2 3 4 5 6 7 8 9 10 Israel (Cohort1) yes yes yes yes yes yes yes yes yes yes Israel (Cohort2) yes yes yes yes yes yes yes yes yes yes McKeigue yes yes yes yes yes yes yes yes yes yes Nemeth yes yes yes yes yes no no yes yes yes 448 1. Were the groups comparable other than the presence of disease in cases or the absence of disease in controls? 2. Were cases and controls matched appropriately? 3. Were the same criteria used for identification of cases and controls? 4. Was exposure measured in a standard, valid and reliable way? 5. Was exposure measured in the same way for cases and controls? 6. Were confounding factors identified? 7. Were strategies to deal with confounding factors stated? 8. Were outcomes assessed in a standard, valid and reliable way for cases and controls? 9. Was the exposure period of interest long enough to be meaningful? 10. Was appropriate statistical analysis used? 449 author 1 2 3 4 5 6 7 8 9 10 11 Rauchman yes yes yes no no yes yes yes yes yes yes Hoertel yes yes yes no yes yes yes yes yes no yes FEI yes yes yes no no yes yes yes yes yes yes Oskotsky yes yes yes no yes unclear yes yes yes yes yes Seftel yes yes yes no yes yes yes no yes yes yes Bora yes yes yes no no yes yes unclear unclear unclear yes . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint 14 450 Table 4. Quality assessment of the experimental studies author 1 2 3 4 5 6 7 8 9 10 11 12 13 Lenze yes yes yes Yes yes yes yes yes yes yes yes yes yes Calusic unclear unclear yes Unclear unclear unclear yes yes yes yes yes yes yes Reis yes yes yes Unclear unclear unclear yes yes yes yes yes yes yes 451 452 1. Was true randomization used for assignment of participants to treatment groups? 453 2. Was allocation to treatment groups concealed? 454 3. Were treatment groups similar at baseline? 455 4. Were participants blind to treatment assignment? 456 5. Were those delivering treatment blind to treatment assignment? 457 6. Were outcome assessors blind to treatment assignment? 458 7. Were treatment groups treated identically other than the intervention of interest? 459 8. Was follow-up complete, and, if not, were differences between groups in terms of their follow-up adequately described and 460 analyzed? 461 9. Were participants analyzed in the groups to which they were randomized? 462 10. Were outcomes measured in the same way for treatment groups? 463 11. Were outcomes measured in a reliable way? 464 12. Was appropriate statistical analysis used? 465 13. Was the trial design appropriate and were any deviations from the standard randomized controlled trial design accounted for 466 in the conduct and analysis of the trial? 467 468 . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint 15 469 Table 5. Patient characteristics Age Gender (F/M %) First author case contr ol case Control matching Case definition Control definition Severity indices Mean:46 71.7/28.3Lenze et al. (25) 46 45 70/30 74/26 1:1 Age, sex Adults with SARS CoV- 2 infection who received Fluvoxamine and were symptomatic within 7 days of the first dose of study medication Adults with SARS CoV- 2 infection who received placebo capsules and were symptomatic within 7 days of the first dose of study medication oxygen saturation <92% plus supplemental oxygen needed and hospitalization related to dyspnea or hypoxia and/or ventilator support needed for ≥3 days 57.5/42.5Reis et al. (26) Median:50 55/45 60/40 1:1 BMI, Age, co- morbidities patients were allocated to fluvoxamine patients were allocated to placebo Hospitalized for COVID-19, need to mechanical ventilation and/or mortality Mean:65.7 33.3/66.7Calusic et al. (24) 65.6 65.9 33/67 33/67 1:1 Age, sex, co- morbidities, vaccination status, disease severity patients over the age of 18 with positive SARS-CoV-2 PCR test and acute clinical condition consistent with COVID-19 requiring ICU admission who received fluvoxamine + standard therapy patients over the age of 18 with positive SARS-CoV-2 PCR test and acute clinical condition consistent with COVID-19 requiring ICU admission who received standard therapy Days of hospital stay, Days of ICU stay, Days on ventilator support and/or mortality Mean: 64.7 49.9/50.1Israel et al. (21) Group 1 64.6 64.8 50/50 50/50 1:5 Age, sex, co- morbidities, BMI, smoking status Hospitalized COVID-19 patients patients were chosen among the general population overall risk for hospitalization due to COVID-19 Mean: 65.7 48.6/51.4Israel et al. (21) Group 2 65.7 65.7 49/51 49/51 1:2 Age, sex, co- morbidities, BMI, smoking status Hospitalized COVID-19 patients patients who had a positive test for SARS-CoV-2 but had not been hospitalized Risk for COVID- 19 hospitalization in patients who had a proven infection with the virus. McKeig ue et al. (22) Mean:61.2 N/R 1:10 sex A positive nucleic acid test followed by entry to critical care or death within 28 days or a death certificate with COVID-19 as underlying cause. Controls were alive and had not yet tested positive on the date that the case first tested positive. entry to critical care and/or mortality . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint 16 470 471 ABBREVIATIONS 472 BMI: body mass index/ ICU: intensive care unit/ PCR: polymerase chain reaction/ N/R: not reported 473 474 475 476 477 478 Mean: 66 54.6/45.4Nemeth et al. (23) 65 67 53/47 44/56 1:1.5 N/R adult patients hospitalized with moderate or severe COVID-19 pneumonia who received fluoxetine as an adjuvant medication in combination with antiviral drugs adult patients hospitalized with moderate or severe COVID-19 pneumonia who received antiviral drugs mortality between hospital days 2 and 28 45.9/54.1Rauchm an et al. (19) >=18 61/39 44/56 1:10 Age, sex, primary race adult patients 18 and over admitted with a diagnosis of COVID-19 and on an antidepressant drug during admission adult patients 18 and over admitted with a diagnosis of COVID-19 and not on an antidepressant drug during admission mortality Mean:57.6Hoertel et al. (8) 73.7 56.8 49.2/50.8 1:14.5 N/R Patients who receiving any antidepressant during the first 48 h of hospital admission and before the end of the index hospitalization or intubation or death Patients not receiving any antidepressant during the first 48 h of hospital admission and before the end of the index hospitalization or intubation or death intubation or death Mean:70 40.3/59.7FEI et al. (17) 80.1 69.1 56/44 39/61 1:11 N/R patient treated with an antidepressant before admission until discharge from hospital patient not treated with antidepressant noninvasive ventilation (NIV), intubation, ICU admission, mortality Mean:52 50.1/49.9Oskotsk y et al. (18) 63.1 51.6 59/41 50/50 1:21 Age, sex, race & ethnicity, co- morbidities Patients with COVID-19 and a medication order for an SSRI at least once within a period of 10 days before and 7 days after their first recorded COVID-19 diagnosis patients with COVID-19 and no SSRI orders mortality Median:42 24.8/75.2Seftel et al. (20) Mean :44 Mea n:43 23/77 27/73 1.5:1 Demographic features patients treated with fluvoxamine for 14 days patients not treated with fluvoxamine Hospitalization, ICU care and /or mortality Bora et al. (16) Mean:47.6 46.6/53.4 1:9 patients treated by antidepressants with no limit to the types of these medications patients not treated by antidepressants mortality . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint 17 479 Table 6. Interventions and exposures Characteristics First author Anti-depressant type Dosage Frequency Follow-up time Lenze et al. (25) Fluvoxamine 50 mg ( in the evening immediately after the baseline assessment and confirmation of eligibility) 100 mg (for 2 days) 100 mg ( through day 15 then stopped) --- twice daily 3 times daily 30 days Reis et al. (26) Fluvoxamine 100mg (for 10 days) twice daily 28days Calusic et al. (24) Fluvoxamine 100 mg (for 15 days) 50 mg (After Day15) 3 times daily twice daily 22 days Israel et al. (21) Escitalopram N/R N/R 35 days McKeig ue et al. (22) TCAs & SSRIs & other anti-depressants N/R N/R N/R Nemeth et al. (23) Fluoxetine 20 mg once daily N/R Rauchm an et al. (19) Citalopram, Desvenlafaxine, Duloxetine, Escitalopram, Fluoxetine, Paroxetine, Sertraline, Venlafaxine N/R N/R N/R Hoertel et al. (8) Citalopram Escitalopram Citalopram or Escitalopram Flouxetine Fluvoxamine Paroxetine Sertraline Vortioxetine Venlafaxine Duloxetine N/R N/R 18.4 days FEI et al. (17) Sertraline Escitalopram Citalopram Paroxetine Venlafaxine Duloxetine Escitalopram + Venlafaxine N/R N/R 3 months Oskotsk y et al. (18) Fluoxetine Flouxetine or Fluvoxamine SSRI other than Flouxetine or Fluvoxamine 30.2mg/d 29mg/d 30.4mg/d N/R 8 months Seftel et al. (20) Fluvoxamine 50- to 100-mg loading dose 50 mg --- twice daily 14 days . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint 18 480 481 ABBREVIATIONS 482 SSRI: Selective serotonin re-uptake inhibitors/ TCA: Tricyclic antidepressant 483 Bora et al. (16) Duloxetine, Escitalopram, Fluoxetine, Fluvoxamine, Mirtazapine, Paroxetine, Sertraline, Venlafaxine, tricyclic antidepressants N/R N/R N/R . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted April 16, 2022. ; https://doi.org/10.1101/2022.04.11.22273709doi: medRxiv preprint 19 484 485 486 487 Figure 2. Pooled RR for clinical trials 488 489 490 Figure 3. the funnel plot of analysis 491 . 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