Association between antidepressant use and reduced risk of intubation or death in hospitalized patients with COVID-19: results from an observational study
Open accessPublished version of a preprint
Abstract
A prior meta-analysis showed that antidepressant use in major depressive disorder was associated with reduced plasma levels of several pro-inflammatory mediators, which have been associated with severe COVID-19. Recent studies also suggest that several antidepressants may inhibit acid sphingomyelinase activity, which may prevent the infection of epithelial cells with SARS-CoV-2, and that the SSRI fluoxetine may exert in-vitro antiviral effects on SARS-CoV-2. We examined the potential usefulness of antidepressant use in patients hospitalized for COVID-19 in an observational multicenter retrospective cohort study conducted at AP-HP Greater Paris University hospitals. Of 7230 adults hospitalized for COVID-19, 345 patients (4.8%) received an antidepressant within 48 h of hospital admission. The primary endpoint was a composite of intubation or death. We compared this endpoint between patients who received antidepressants and those who did not in time-to-event analyses adjusted for patient characteristics, clinical and biological markers of disease severity, and other psychotropic medications. The primary analysis was a multivariable Cox model with inverse probability weighting. This analysis showed a significant association between antidepressant use and reduced risk of intubation or death (HR, 0.56; 95% CI, 0.43-0.73, p < 0.001). This association remained significant in multiple sensitivity analyses. Exploratory analyses suggest that this association was also significant for SSRI and non-SSRI antidepressants, and for fluoxetine, paroxetine, escitalopram, venlafaxine, and mirtazapine (all p < 0.05). These results suggest that antidepressant use could be associated with lower risk of death or intubation in patients hospitalized for COVID-19. Double-blind controlled randomized clinical trials of antidepressant medications for COVID-19 are needed.
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- Findingown data
Among adults hospitalized for COVID-19 in AP-HP hospitals, 345 of 7230 (4.8%) received an antidepressant within 48 hours of admission.
“Of the remaining 7230 adult inpatients, 345 patients (4.8%) received an antidepressant within 48 h of hospital admission at a mean fluoxetine-equivalent dose of 21.6 mg (SD = 14.1) per day (Fig. 1).”
Quote in the paperNot a substantial claim - Findingown data
SSRI antidepressant use was associated with a lower risk of intubation or death (HR 0.51; 95% CI 0.316-0.72; p<0.001).
“Following adjustments, the primary multivariable analyses with inverse probability weighting showed significant associations between use of any antidepressant (HR, 0.56; 95% CI, 0.43-0.73,p < 0.001) (Fig. 2), SSRI antidepressants (HR, 0.51; 95% CI, 0.316-0.72,p < 0.001), and non-SSRI antidepressants (HR, 0.65; 95% CI, 0.45-0.93,p = 0.018) (Fig. 3) with reduced risk of intubation or death (Table 2)”
Quote in the paperSubstantial claim - Findingown data
In the primary multivariable Cox model with inverse probability weighting, any antidepressant use was associated with a lower risk of intubation or death (HR 0.56; 95% CI 0.43-0.73; p<0.001).
“Following adjustments, the primary multivariable analyses with inverse probability weighting showed significant associations between use of any antidepressant (HR, 0.56; 95% CI, 0.43-0.73,p < 0.001) (Fig. 2), SSRI antidepressants (HR, 0.51; 95% CI, 0.316-0.72,p < 0.001), and non-SSRI antidepressants (HR, 0.65; 95% CI, 0.45-0.93,p = 0.018) (Fig. 3) with reduced risk of intubation or death (Table 2)”
Quote in the paperSubstantial claim - Findingown data
The link between antidepressant use and the composite endpoint was seen only in patients who received an antidepressant during the visit, not in those who received it only within the 3 months before hospital admission.
“Furthermore, our findings indicate that the association between antidepressant use and the endpoint was only observed for patients receiving an antidepressant during the visit and not in those who received it only within the 3 months before hospital admission.”
Quote in the paperSubstantial claim - Findingreasoning
Hospitalized COVID-19 patients with psychiatric disorders had a higher likelihood of intubation or death.
“We found that individuals with psychiatric disorders and hospitalized for COVID-19 would be at higher risk of intubation or death, in line with findings of recent studies [33,34].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [33] Wang QQ, Kaelber DC, Xu R, Volkow ND. COVID-19 risk and outcomes in patients with substance use disorders: analyses from electronic health records in the United States. Mol Psychiatry. 2021;26:30–9. source
Not checked in the free run.
- cites [34] Li L, Li F, Fortunati F, Krystal JH. Association of a prior psychiatric diagnosis with mortality among hospitalized patients with coronavirus disease 2019 (COVID-19) Infection. JAMA Netw Open. 2020;3:e2023282–e2023282. source
Not checked in the free run.
- Findingown data
There was a significant link between antidepressant use and the endpoint among ICU patients.
“Fifth, the low number of patients with antidepressants admitted to ICUs in our study (12 of 577 patients, i.e. 2.1%) and the significant association between antidepressant use and the endpoint in these patients further give strength to our conclusion.”
Quote in the paperSubstantial claim - Findingown data
In exploratory analyses, escitalopram, fluoxetine, paroxetine, and venlafaxine were linked to a lower likelihood of intubation or death.
“Exploratory analyses suggest that SSRI and non-SSRI antidepressants, and specifically the SSRIs escitalopram, fluoxetine, and paroxetine, and the SNRI venlafaxine, were significantly associated with reduced risk of intubation or death.”
Quote in the paperNot a substantial claim - Findingown data
Patients prescribed an antidepressant in the three months before but not during the visit had a significantly higher risk of intubation or death than those who received an antidepressant only during the visit.
“Patients who were prescribed an antidepressant in the three months before but not during the visit were significantly at higher risk of intubation or death than those who received an antidepressant only during the visit (Supplementary Table 18).”
Quote in the paperSubstantial claim - Findingown data
Among patients admitted to ICUs, 12 of 577 (2.1%) were receiving antidepressants.
“Fifth, the low number of patients with antidepressants admitted to ICUs in our study (12 of 577 patients, i.e. 2.1%) and the significant association between antidepressant use and the endpoint in these patients further give strength to our conclusion.”
Quote in the paperSubstantial claim - Findingown data
When diazepam was used as an active comparator, similar results were found.
“Finally, when considering diazepam as an active comparator, we found similar results.”
Quote in the paperNot a substantial claim - Findingown data
Participants receiving diazepam did not have a significantly different risk of intubation or death compared to those not receiving either medication.
“Participants receiving diazepam did not have a significantly different risk of intubation or death compared to those not receiving either medication.”
Quote in the paperNot a substantial claim - Findingown data
The association between α2-antagonist antidepressants and the outcome was not significant in the matched analytic sample.
“that between α2-antagonist antidepressants and the outcome, which was not significant in the matched analytic sample”
Quote in the paperNot a substantial claim - Findingown data
The association between antidepressant use and the endpoint did not significantly differ across subgroups defined by baseline characteristics.
“This association did not significantly differ across subgroups defined by baseline characteristics (Supplementary Table 17).”
Quote in the paperNot a substantial claim - Findingown data
Exposure to a combination of antidepressants was not significantly associated with the endpoint compared to exposure to only one antidepressant.
“Exposure to a combination of antidepressants was not significantly associated with the endpoint compared to exposure to only one antidepressant (Supplementary Table 21).”
Quote in the paperNot a substantial claim - Findingown data
The main results remained unchanged after additional adjustment for the hospitalization criteria.
“However, its length was relatively limited (i.e., from January 24th until April 1st) and our main results remained unchanged following additional adjustment for it (Supplementary Tables 242526).”
Quote in the paperNot a substantial claim - Findingown data
Among 577 patients admitted to ICUs, antidepressant use was significantly associated with reduced risk of intubation or death.
“Finally, among the 577 patients admitted to ICUs, antidepressant use was significantly associated with reduced risk of intubation or death (Supplementary Table 23).”
Quote in the paperSubstantial claim - Findingown data
In exploratory analyses, SSRI and non-SSRI antidepressants were linked to a lower likelihood of intubation or death.
“Exploratory analyses suggest that SSRI and non-SSRI antidepressants, and specifically the SSRIs escitalopram, fluoxetine, and paroxetine, and the SNRI venlafaxine, were significantly associated with reduced risk of intubation or death.”
Quote in the paperSubstantial claim - Findingown data
The link between antidepressant use and the endpoint remained significant when death and intubation were analyzed as separate endpoints.
“Fourth, this association remained significant when using death and intubation as separate endpoints.”
Quote in the paperSubstantial claim - Findingown data
No significant difference in risk of intubation or death was found for exposure to any other antidepressant molecule.
“There were no significant differences in this risk according to exposure to any other molecule (Table 2; Supplementary Figs.12345).”
Quote in the paperNot a substantial claim - Findingown data
Unadjusted age-stratified hazard ratio estimates of the association between antidepressant use and the endpoint were non-significant (all p>0.060).
“Unadjusted hazard ratio age-stratified estimates of the association between antidepressant use and the endpoint were non-significant (all p > 0.060) (Supplementary Table 14).”
Quote in the paperNot a substantial claim - Findingown data
Non-SSRI antidepressant use was associated with a lower risk of intubation or death (HR 0.65; 95% CI 0.45-0.93; p=0.018).
“Following adjustments, the primary multivariable analyses with inverse probability weighting showed significant associations between use of any antidepressant (HR, 0.56; 95% CI, 0.43-0.73,p < 0.001) (Fig. 2), SSRI antidepressants (HR, 0.51; 95% CI, 0.316-0.72,p < 0.001), and non-SSRI antidepressants (HR, 0.65; 95% CI, 0.45-0.93,p = 0.018) (Fig. 3) with reduced risk of intubation or death (Table 2)”
Quote in the paperSubstantial claim - Findingown data
In hospitalized COVID-19 patients, antidepressant use at a mean dose of 21.6 fluoxetine-equivalent mg (SD 14.1) was linked to a markedly lower likelihood of intubation or death, after adjustment for patient characteristics, clinical and biological severity markers, and other psychotropic medications.
“In this multicenter retrospective observational study involving a large sample of patients hospitalized for COVID-19, we found that antidepressant use, at a mean dosage of 21.6 (SD = 14.1) fluoxetine-equivalent milligrams, was significantly and substantially associated with reduced risk of intubation or death, independently of patient characteristics, clinical and biological markers of disease sev”
- Also stated in: § Discussion · plain
“In this multicenter observational retrospective study, antidepressant use at usual antidepressant doses was significantly and substantially associated with lower risk of intubation or death among adult patients hospitalized for COVID-19.”
Quote in the paperSubstantial claim - Also stated in: § Discussion · plain
- Study design and conductcitation
The study used multivariable regression models with inverse probability weighting to reduce confounding by indication.
“First, we used multivariable regression models with inverse probability weighting to minimize the effects of confounding by indication [27,28].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [27] Robins JM, Hernán MÁ, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11:550–60. source
Not checked in the free run.
- cites [28] Geleris J, Sun Y, Platt J, Zucker J, Baldwin M, Hripcsak G, et al. Observational study of hydroxychloroquine in hospitalized patients with Covid-19. N. Engl J Med. 2020;382:2411–8. source
Not checked in the free run.
- Study design and conductown data
After applying propensity score weights, differences in baseline characteristics between exposed and non-exposed patients were substantially reduced.
“After applying the propensity score weights, these differences were substantially reduced (Supplementary Tables 345678910111213).”
Quote in the paperSubstantial claim - Premisecitation
COVID-19 and SARS are characterized by an overexuberant inflammatory response.
“Both COVID-19 and severe acute respiratory syndrome (SARS) are characterized by an overexuberant inflammatory response [5]”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [5] Stebbing J, Phelan A, Griffin I, Tucker C, Oechsle O, Smith D, et al. COVID-19: combining antiviral and anti-inflammatory treatments. Lancet Infect Dis. 2020;20:400–2. source
Not checked in the free run.
- Study design and conductown data
The association between antidepressant use and the endpoint was significant in both genders.
“Additional analyses indicated that the association between antidepressant use and the endpoint was significant in both genders (Supplementary Table 15)”
Quote in the paperNot a substantial claim - Study design and conductown data
The analyses adjusted for numerous potential confounders.
“Third, although some amount of unmeasured confounding may remain, our analyses adjusted for numerous potential confounders.”
Quote in the paperSubstantial claim - Premisecitation
Numerous antidepressants are S1R agonists.
“Second, numerous antidepressants are S1R agonists [13], which might prevent the cytokine storm observed in severe COVID-19 [3].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [13] Lenze EJ, Mattar C, Zorumski CF, Stevens A, Schweiger J, Nicol GE, et al. Fluvoxamine vs placebo and clinical deterioration in outpatients with symptomatic COVID-19: a randomized clinical trial. JAMA. 2020;324:2292–2300. source
Not checked in the free run.
- Premisecitation
Certain antidepressants, such as fluoxetine, may have antiviral effects on SARS-CoV-2.
“Finally, certain antidepressants may exert antiviral effects on SARS-CoV-2, such as fluoxetine [12].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [12] Zimniak M, Kirschner L, Hilpert H, Seibel J, Bodem J. The serotonin reuptake inhibitor Fluoxetine inhibits SARS-CoV-2. BioRxiv. 2020. https://www.biorxiv.org/content/10.1101/2020.06.14.150490v2. source
Not checked in the free run.
- Premisecitation
Severe COVID-19 is associated with increased plasma levels of inflammatory mediators including cytokines and chemokines.
“Accumulating evidence suggests that severe COVID-19 is associated with an increased plasma level of inflammatory mediators including cytokines and chemokines”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [7] Hojyo S, Uchida M, Tanaka K, Hasebe R, Tanaka Y, Murakami M, et al. How COVID-19 induces cytokine storm with high mortality. Inflamm Regen. 2020;40:1–7. source
Not checked in the free run.
- Study design and conductreasoning
Some unmeasured confounding may remain.
“Third, although some amount of unmeasured confounding may remain, our analyses adjusted for numerous potential confounders.”
Quote in the paperNot a substantial claim - Interpretationreasoning
The higher likelihood of intubation or death in hospitalized COVID-19 patients with psychiatric disorders may be explained by their greater prevalence of clinical characteristics linked to severe COVID-19, including medical comorbidity and clinical and biological severity markers.
“Our results suggest that this association may be explained by the greater prevalence of clinical characteristics associated with severe COVID-19 in individuals with psychiatric disorders than their counterparts, including medical comorbidity, and clinical and biological markers of disease severity.”
Quote in the paperSubstantial claim - Premisecitation
A prior meta-analysis of antidepressant treatment in major depressive disorder found that antidepressants may be associated with decreased plasma levels of four of sixteen tested inflammatory mediators, including IL-10, TNFα, and CCL-2.
“A recent meta-analysis [8] of studies conducted in individuals with major depressive disorder following antidepressant treatment, mostly including selective serotonin reuptake inhibitors (SSRIs), supports that, overall, antidepressants may be associated with decreased plasma levels of 4 of 16 tested inflammatory mediators, including IL-10, TNFα, and CCL-2”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [8] Köhler CA, Freitas TH, Stubbs B, Maes M, Solmi M, Veronese N, et al. Peripheral alterations in cytokine and chemokine levels after antidepressant drug treatment for major depressive disorder: systematic review and meta-analysis. Mol Neurobiol. 2018;55:4195–206.
Not checked in the free run.
- Premisecitation
Antidepressant use may be associated with reduced plasma levels of several inflammatory mediators linked to severe COVID-19, including IL-6, IL-10, TNF-α, and CCL-2.
“Third, antidepressant use may be associated with reduced plasma levels of several inflammatory mediators associated with severe COVID-19 [7], including IL-6, IL-10, TNF-α, and CCL-2 [8].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [7] Hojyo S, Uchida M, Tanaka K, Hasebe R, Tanaka Y, Murakami M, et al. How COVID-19 induces cytokine storm with high mortality. Inflamm Regen. 2020;40:1–7. source
Not checked in the free run.
- cites [8] Köhler CA, Freitas TH, Stubbs B, Maes M, Solmi M, Veronese N, et al. Peripheral alterations in cytokine and chemokine levels after antidepressant drug treatment for major depressive disorder: systematic review and meta-analysis. Mol Neurobiol. 2018;55:4195–206.
Not checked in the free run.
- Premisecitation
Inhibition of acid sphingomyelinase may prevent infection of epithelial cells by SARS-CoV-2.
“First, prior studies suggest that several antidepressants may inhibit acid sphingomyelinase activity [10], which may prevent the infection of epithelial cells with SARS-CoV-2 [11].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [11] Carpinteiro A, Edwards MJ, Hoffmann M, Kochs G, Gripp B, Weigang S, et al. Pharmacological inhibition of acid sphingomyelinase prevents uptake of SARS-CoV-2 by epithelial cells. Cell Reports Med. 2020;1:100142. source
Not checked in the free run.
- Study design and conductown data
In sensitivity analyses, multivariable Cox regression models in the full sample yielded results similar to the primary analysis.
“In sensitivity analyses, multivariable Cox regression models in the full sample yielded similar results”
Quote in the paperNot a substantial claim - Study design and conductcitation
The study cannot show that antidepressant use causes a lower likelihood of intubation or death.
“Second, our study cannot establish a causal relationship between antidepressant use and reduced risk of intubation or death [35].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [35] Le Strat Y, Hoertel N. Correlation is no causation: gymnasium proliferation and the risk of obesity. Addiction. 2011;106:1871–2. source
Not checked in the free run.
- Premisecitation
S1R agonism might prevent the cytokine storm seen in severe COVID-19.
“Second, numerous antidepressants are S1R agonists [13], which might prevent the cytokine storm observed in severe COVID-19 [3].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [3] Gordon DE, Jang GM, Bouhaddou M, Xu J, Obernier K, White KM, et al. A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature. 2020;583:459–68. source
Not checked in the free run.
- Study design and conductcitation
The primary analyses used propensity score analysis with inverse probability weighting to account for nonrandomized antidepressant prescription and reduce confounding.
“To help account for the nonrandomized prescription of antidepressants and reduce the effects of confounding, the primary analyses used a propensity score analysis with inverse probability weighting [27,28].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [27] Robins JM, Hernán MÁ, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11:550–60. source
Not checked in the free run.
- cites [28] Geleris J, Sun Y, Platt J, Zucker J, Baldwin M, Hripcsak G, et al. Observational study of hydroxychloroquine in hospitalized patients with Covid-19. N. Engl J Med. 2020;382:2411–8. source
Not checked in the free run.
- Study design and conductreasoning
Missing data existed for some baseline characteristic variables, including baseline clinical and biological markers of COVID-19 severity.
“Other limitations include missing data for some baseline characteristic variables, including baseline clinical and biological markers of severity of COVID-19, which may be explained by the overwhelming of all hospital units during the COVID-19 peak incidence, and potential for inaccuracies in electronic health records in this context, especially for hand-written medical prescriptions.”
Quote in the paperNot a substantial claim
- May combine two claims. 16
- Classified as probably not a substantial claim. 22
- The quote only partly states this claim. 3
May combine two claims.
Findingown dataThe composite endpoint of intubation or death occurred in 24.3% of patients receiving any antidepressant, 23.1% of those receiving SSRIs, and 26.0% of those receiving non-SSRI antidepressants, compared with 17.3% of non-exposed patients.
“Among patients receiving any antidepressant, SSRIs, and non-SSRI antidepressants, the primary endpoint of intubation or death occurred respectively in 84 patients (24.3%), 45 patients (23.1%) and 39 patients (26.0%), while 1188 non-exposed patients (17.3%) had this outcome (Table 2).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataWhen intubation and death were analyzed as separate endpoints, antidepressant use was significantly associated with lower risks of death and intubation.
“When using intubation and death as separate endpoints, we found that antidepressant use was significantly associated with lower risks of death and intubation (Supplementary Table 22).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenThe study period was relatively short, from January 24 to April 1.
“However, its length was relatively limited (i.e., from January 24th until April 1st) and our main results remained unchanged following additional adjustment for it (Supplementary Tables 242526).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataExposure to escitalopram, fluoxetine, paroxetine, venlafaxine, or mirtazapine was significantly associated with reduced risk of intubation or death (all p<0.05).
“Finally, exposure to escitalopram, fluoxetine, paroxetine, venlafaxine or mirtazapine was significantly associated with reduced risk of intubation or death (all p < 0.05).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataAll baseline characteristics, when examined independently, were significantly associated with the composite endpoint.
“All baseline characteristics, when examined independently, were significantly associated with the endpoint (Table 1).”
Quote in the paperNot a substantial claimThe quote only partly states this claim.
Findingown dataThe link between antidepressant use and intubation or death remained significant across multiple sensitivity analyses.
“This association remained significant in multiple sensitivity analyses.”
- Also stated in: § Discussion · plain
“Second, we performed sensitivity analyses that showed similar results.”
Quote in the paperSubstantial claim- Also stated in: § Discussion · plain
Probably not a substantial claim: may not pass S3 (load-bearing).
FindingcitationMost associations observed between baseline characteristics and mortality or intubation were consistent with prior epidemiological studies.
“However, most associations observed between baseline characteristics and mortality or intubation are in line with prior epidemiological studies [17,19].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [17] Williamson E, Walker AJ, Bhaskaran KJ, Bacon S, Bates C, Morton CE, et al. OpenSAFELY: factors associated with COVID-19-related hospital death in the linked electronic health records of 17 million adult NHS patients. MedRxiv. 2020. https://doi.org/10.1101/2020.05.06.20092999. source
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- cites [19] Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395:1054–62. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataOver a mean follow-up of 18.4 days, 1272 patients (18.1%) had a primary endpoint event by the data cutoff.
“Over a mean follow-up of 18.4 days (SD = 27.2;median = 5 days; range: 1-117 days), 1272 patients (18.1%) had a primary end-point event at the time of data cutoff on May 20th.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataWhen diazepam was used as an active comparator, antidepressant use was significantly associated with reduced risk of intubation or death in crude analysis (HR 0.50; 95% CI 0.32-0.77; p=0.002) and main analysis (HR 0.47; 95% CI 0.25-0.88; p=0.018), as well as in sensitivity analyses.
“When considering diazepam as an active comparator, we found that antidepressant use was significantly associated with reduced risk of intubation or death in the crude (HR, 0.50; 95% CI, 0.32-0.77,p = 0.002) and in the main (HR, 0.47; 95% CI, 0.25-0.88,p = 0.018) analyses, as well as in sensitivity analyses (Supplementary Table 19).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataExposure to higher rather than lower doses of antidepressants was not significantly associated with the endpoint, except for tetracyclic antidepressants, for which higher doses were associated with an increased risk.
“Exposure to higher rather than lower doses of antidepressants was not significantly associated with the endpoint, except for tetracyclic antidepressants, for which higher doses were associated with an increased risk (Supplementary Table 20).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataIn multivariable analysis, baseline characteristics remained significantly associated with the endpoint except current smoking, compassionate-use or clinical-trial medication, current mood or anxiety disorder, other current psychiatric disorder, mood stabilizer, and antipsychotic medication.
“In the multivariable analysis, these associations remained significant, except for current smoking, any medication prescribed according to compassionate use or as part of a clinical trial, any current mood or anxiety disorder, any other current psychiatric disorder, any mood stabilizer medication, and any antipsychotic medication (Table 1).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataAmong non-SSRI antidepressants, no class was significantly associated with the endpoint except α2-antagonist antidepressants (HR 0.22; 95% CI 0.05-0.93; p=0.040).
“Among non-SSRI antidepressants, there were no significant associations between the endpoint and exposures to each class of antidepressants, except for α2-antagonist antidepressants (HR, 0.22; 95% CI, 0.05-0.93,p = 0.040) (Table 2; Fig. 3).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenResidual analyses were performed to assess data fit, check assumptions including proportional hazards, and examine the influence of outliers.
“For all associations, we performed residual analyses to assess the fit of the data, check assumptions, including proportional hazards assumptions, and examined the potential influence of outliers.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationFunctional inhibitors of acid sphingomyelinase activity, including several SSRI and non-SSRI antidepressants, may prevent infection of epithelial cells with SARS-CoV-2.
“Furthermore, a recent study [10] suggests that functional inhibitors of acid sphingomyelinase activity (FIASMA), including several SSRI and non-SSRI antidepressants, may prevent the infection of epithelial cells with SARS-CoV-2 [11].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [10] Gulbins E, Palmada M, Reichel M, Lüth A, Böhmer C, Amato D, et al. Acid sphingomyelinase–ceramide system mediates effects of antidepressant drugs. Nat Med. 2013;19:934–8. source
Not checked in the free run.
- cites [11] Carpinteiro A, Edwards MJ, Hoffmann M, Kochs G, Gripp B, Weigang S, et al. Pharmacological inhibition of acid sphingomyelinase prevents uptake of SARS-CoV-2 by epithelial cells. Cell Reports Med. 2020;1:100142. source
Not checked in the free run.
May combine two claims.
Study design and conductown dataUnivariate Cox regression models in matched analytic samples yielded similar results, except that the SNRI association was significant in both sensitivity analyses.
“as did univariate Cox regression models in the matched analytic samples, except for the association between SNRI antidepressants and the endpoint, which was significant in both sensitivity analyses”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe criteria for deciding whether a COVID-19 patient should be hospitalized might have varied during the study period.
“Fifth, the criteria used to determine whether a patient with COVID-19 should be hospitalized or not might have varied during the study period.”
Quote in the paperNot a substantial claimThe quote only partly states this claim.
Study design and conductown dataIn a subsample excluding patients receiving antipsychotic medication, antidepressant use was significantly associated with the endpoint.
“and in a subsample of patients excluding those receiving an antipsychotic medication, who might be at increased risk for immune dysregulation linked to a psychotic disorder (Supplementary Table 16)”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationFluvoxamine, an SSRI, sigma-1 receptor agonist and FIASMA, may prevent clinical deterioration in outpatients with acute COVID-19 compared to placebo.
“Finally, a recent randomized trial suggests that fluvoxamine, a SSRI, sigma-1 receptor (S1R) agonist and FIASMA, may prevent clinical deterioration in outpatients with acute COVID-19 compared to placebo [13].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [13] Lenze EJ, Mattar C, Zorumski CF, Stevens A, Schweiger J, Nicol GE, et al. Fluvoxamine vs placebo and clinical deterioration in outpatients with symptomatic COVID-19: a randomized clinical trial. JAMA. 2020;324:2292–2300. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationFor COVID-19, viral load is associated with worsening of symptoms.
“for COVID-19, viral load is associated with the worsening of symptoms [6]”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [6] Liu Y, Yan L-M, Wan L, Xiang T-X, Le A, Liu J-M, et al. Viral dynamics in mild and severe cases of COVID-19. The Lancet Infectious Diseases. 2020;20:656–7. source
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationPrior studies suggest that several antidepressants may inhibit acid sphingomyelinase activity.
“First, prior studies suggest that several antidepressants may inhibit acid sphingomyelinase activity [10], which may prevent the infection of epithelial cells with SARS-CoV-2 [11].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [10] Gulbins E, Palmada M, Reichel M, Lüth A, Böhmer C, Amato D, et al. Acid sphingomyelinase–ceramide system mediates effects of antidepressant drugs. Nat Med. 2013;19:934–8. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningElectronic health records may contain inaccuracies, especially for hand-written medical prescriptions.
“Other limitations include missing data for some baseline characteristic variables, including baseline clinical and biological markers of severity of COVID-19, which may be explained by the overwhelming of all hospital units during the COVID-19 peak incidence, and potential for inaccuracies in electronic health records in this context, especially for hand-written medical prescriptions.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductown dataIn matched analytic samples, there were no significant differences in patient characteristics across exposures, except sex ratio differed for any antidepressant, non-SSRI antidepressants, and SNRI antidepressants.
“In the matched analytic samples, there were no significant differences in patient characteristics across the different exposures, except for exposures to any antidepressant, non-SSRI antidepressants and SNRI antidepressants, for which sex ratio significantly differed (Supplementary Tables 345678910111213).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningThe missing baseline data may be explained by hospital units being overwhelmed during the COVID-19 peak.
“Other limitations include missing data for some baseline characteristic variables, including baseline clinical and biological markers of severity of COVID-19, which may be explained by the overwhelming of all hospital units during the COVID-19 peak incidence, and potential for inaccuracies in electronic health records in this context, especially for hand-written medical prescriptions.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenInformation on the reason for prescribing the antidepressant, duration and adherence to prescription, prescription records before admission, and date of COVID-19 symptom onset was unavailable.
“Fourth, information on the reason for prescribing the antidepressant, the duration and adherence to its prescription, the prescription record of patients before the admission, and the date that COVID-19 symptoms appeared, was not available.”
Quote in the paperNot a substantial claimMay combine two claims. The quote only partly states this claim.
Study design and conductown dataIn the full sample, antidepressant use and exposure to each antidepressant class and individual medication significantly differed according to most baseline characteristics, with exposed patients being older and having greater medical severity.
“In the full sample, antidepressant use and exposure to each antidepressant class and each individual medication significantly differed according to most baseline characteristics.”
- Also stated in: § Characteristics of the cohort · hedged
“The direction of these associations indicated older age [mean age of patients exposed to antidepressants was 73.7 (SD = 15.8)years, whereas it was 56.8 (SD = 19.3)years in those who were not] and overall greater medical severity of patients who received antidepressants than those who did not.”
Quote in the paperNot a substantial claim- Also stated in: § Characteristics of the cohort · hedged
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningType I error inflation might have occurred in exploratory analyses because of multiple testing.
“Third, inflation of type I error might have occurred in exploratory analyses due to multiple testing.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenFor matched analyses, one control per exposed case was selected a priori for any antidepressant and each class, and two controls per exposed case for individual antidepressant medications, based on the same variables used in the weighted and multivariable Cox analyses.
“For these latter analyses, we decided a priori to select one control for each exposed case for exposures to any antidepressant and each class of antidepressants, and two controls for each exposed case for individual antidepressant medications, based on the same variables used for both the inverse-probability-weighted and the multivariable Cox regression analyses.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationShort-term use of low to moderate doses of antidepressants, particularly SSRIs, is generally well tolerated, including in older adults.
“Short-term use of low to moderate doses of antidepressants, and particularly of SSRIs, is generally well tolerated [14], and notably in older adults [15][16][17].”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [14] Cipriani A, Furukawa TA, Salanti G, Chaimani A, Atkinson LZ, Ogawa Y, et al. Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis. Focus. 2018;16:420–9. source
Not checked in the free run.
- cites [15] Tham A, Jonsson U, Andersson G, Söderlund A, Allard P, Bertilsson G. Efficacy and tolerability of antidepressants in people aged 65 years or older with major depressive disorder–a systematic review and a meta-analysis. J Affect Disord. 2016;205:1–12. source
Not checked in the free run.
- cites [16] Krause M, Gutsmiedl K, Bighelli I, Schneider-Thoma J, Chaimani A, Leucht S. Efficacy and tolerability of pharmacological and non-pharmacological interventions in older patients with major depressive disorder: A systematic review, pairwise and network meta-analysis. Eur Neuropsychopharmacol. 2019;29:1003–22. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitationThe study followed the STROBE reporting recommendations.
“We followed the recommendations of The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative [32].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [32] Von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med. 2007;147:573–7. source
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
PremisecitationThe SSRI fluoxetine may have in-vitro antiviral effects on SARS-CoV-2, although this effect was not observed for paroxetine and escitalopram.
“A recent in-vitro study [12] also supports antiviral effects of the SSRI fluoxetine on SARS-CoV-2, although this effect was not observed for other SSRIs, including paroxetine and escitalopram.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [12] Zimniak M, Kirschner L, Hilpert H, Seibel J, Bodem J. The serotonin reuptake inhibitor Fluoxetine inhibits SARS-CoV-2. BioRxiv. 2020. https://www.biorxiv.org/content/10.1101/2020.06.14.150490v2. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenDespite the multicenter design, the results may not be generalizable to outpatients or other countries.
“Finally, despite the multicenter design, our results may not be generalizable to outpatients and other countries.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductreasoningOptimal matching was used to minimize the average absolute distance across clinical characteristics between exposed and non-exposed matched controls.
“To reduce the effects of confounding, optimal matching was used in order to obtain the smallest average absolute distance across all clinical characteristics between exposed patients and non-exposed matched controls.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductnone givenStatistical significance was fixed a priori at a two-sided p value below 0.05.
“Statistical significance was fixed a priori at two-sided p value < 0.05.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataSensitivity analyses included multivariable Cox regression with the same covariates as the inverse-probability-weighted analyses and univariate Cox regression in matched analytic samples.
“We conducted sensitivity analyses, including multivariable Cox regression models comprising as covariates the same variables as the inverse-probability-weighted analyses, and univariate Cox regression models in matched analytic samples.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
At the time of the study, no clinical study had examined the potential usefulness of antidepressants in patients hospitalized for COVID-19.
“To our knowledge, no clinical study has examined to date the potential usefulness of antidepressants in patients hospitalized for COVID-19.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Taking fluoxetine lowers the risks of intubation and death by reducing the cytokine storm.
“Taking fluoxetine lowers the risks of intubation and death by reducing the “cytokine storm” [29,30].”
From Antimicrobial Properties of Antidepressants and Antipsychotics—Possibilities and Implications (2021) · cites this paper as [29]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
SSRI confidence-interval lower bound differs between results and table
MinorThe results give 0.316 as the lower bound of the SSRI hazard-ratio confidence interval, whereas Table 2 gives 0.36 for the same estimate.
“SSRI antidepressants (HR, 0.51; 95% CI, 0.316-0.72,p < 0.001)”
“0.51 (0.36-0.72; <0.001*)”
Matched-sample size conflicts with the table's control count
MinorFigure 2 describes a 1:1 matched sample of 648 patients, which would contain 324 controls. Table 2 instead reports 345 patients in the matched control group for any antidepressant use.
“in the matched analytic sample using a 1:1 ratio (n = 648)”
“Number of events/Number | of patients in the matched | control groups | Events/N (%) | Ref. | | 136/345 (39.4)”
Percentage receiving multiple antidepressants is overstated
MinorThe 47 patients receiving at least two antidepressants represent 13.6% of the 345 exposed patients, not 15.8%.
Reported 15.8 · recomputed 13.623188
“Among patients exposed to antidepressants, 298 (86.4%) were exposed to only one antidepressant, and 47 (15.8%) were exposed to at least two antidepressants.”
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
Nonparametric Estimation from Incomplete Observations
Kaplan, E. L., & Meier, P. (1992) · Springer series in statistics · doi:10.1007/978-1-4612-4380-9_25 · Cited by 45,232 · see citing works
Nonparametric Estimation from Incomplete Observations
Kaplan, E. L., & Meier, P. (1958) · Journal of the American Statistical Association · doi:10.1080/01621459.1958.10501452 · Cited by 39,158 · see citing works
Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., Xiang, J., Wang, Y., Song, B., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30566-3 · Cited by 27,263 · see citing works
von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., Vandenbroucke, J. P., & Initiative, F. T. S. (2007) · PLoS Medicine · doi:10.1371/journal.pmed.0040296 · Cited by 23,020 · see citing works
von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., Vandenbroucke, J. P., & Initiative, F. T. S. (2007) · Annals of Internal Medicine · doi:10.7326/0003-4819-147-8-200710160-00010 · Cited by 17,796 · see citing works
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz682 · Cited by 7,218 · see citing works
Collaborative, T. O., Williamson, E., Walker, A. J., Bhaskaran, K., Bacon, S., Bates, C., Morton, C. E., Curtis, H. J., Mehrkar, A., Evans, D., et al. (2020) · doi:10.1101/2020.05.06.20092999 · Cited by 6,674 · cited 1× · see citing works
“However, most associations observed between baseline characteristics and mortality or intubation are in line with prior epidemiological studies [17, 19].”
ResultFactors associated with COVID-19-related death using OpenSAFELY
Williamson, E. J., Walker, A. J., Bhaskaran, K., Bacon, S., Bates, C., Morton, C. E., Curtis, H. J., Mehrkar, A., Evans, D., Inglesby, P., et al. (2020) · Nature · doi:10.1038/s41586-020-2521-4 · Cited by 6,674 · see citing works
Marginal Structural Models and Causal Inference in Epidemiology
Robins, J. M., Hernán, M. Á., & Brumback, B. (2000) · Epidemiology · doi:10.1097/00001648-200009000-00011 · Cited by 5,581 · see citing works
Ruan, Q., Yang, K., Wang, W., Jiang, L., & Song, J. (2020) · Intensive Care Medicine · doi:10.1007/s00134-020-05991-x · Cited by 4,898 · see citing works
A SARS-CoV-2 protein interaction map reveals targets for drug repurposing
Gordon, D. E., Jang, G. M., Bouhaddou, M., Xu, J., Obernier, K., White, K. M., O’Meara, M. J., Rezelj, V. V., Guo, J. Z., Swaney, D. L., et al. (2020) · Nature · doi:10.1038/s41586-020-2286-9 · Cited by 4,405 · see citing works
Cipriani, A., Furukawa, T. A., Salanti, G., Chaimani, A., Atkinson, L. Z., Ogawa, Y., Leucht, S., Ruhe, H. G., Turner, E. H., Higgins, J. P. T., et al. (2018) · The Lancet · doi:10.1016/s0140-6736(17)32802-7 · Cited by 3,541 · see citing works
The pathogenesis and treatment of the `Cytokine Storm' in COVID-19
Ye, Q., Wang, B., & Mao, J. (2020) · Journal of Infection · doi:10.1016/j.jinf.2020.03.037 · Cited by 2,761 · see citing works
Viral dynamics in mild and severe cases of COVID-19
Liu, Y., Yan, L.-M., Wan, L., Xiang, T.-X., Le, A., Liu, J.-M., Peiris, M., Poon, L. L. M., & Zhang, W. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30232-2 · Cited by 1,727 · see citing works
Observational Study of Hydroxychloroquine in Hospitalized Patients with Covid-19
Geleris, J., Sun, Y., Platt, J., Zucker, J., Baldwin, M., Hripcsak, G., Labella, A., Manson, D. K., Kubin, C., Barr, R. G., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2012410 · Cited by 1,625 · see citing works
COVID-19: combining antiviral and anti-inflammatory treatments
Stebbing, J., Phelan, A., Griffin, I., Tucker, C., Oechsle, O., Smith, D., & Richardson, P. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30132-8 · Cited by 1,117 · see citing works
Nonparametric standard errors and confidence intervals
Efron, B. (1981) · Canadian Journal of Statistics · doi:10.2307/3314608 · Cited by 683 · see citing works
How COVID-19 induces cytokine storm with high mortality
Hojyo, S., Uchida, M., Tanaka, K., Hasebe, R., Tanaka, Y., Murakami, M., & Hirano, T. (2020) · Inflammation and Regeneration · doi:10.1186/s41232-020-00146-3 · Cited by 663 · see citing works
Wang, Q. Q., Kaelber, D. C., Xu, R., & Volkow, N. D. (2021) · Molecular Psychiatry · doi:10.1038/s41380-020-00880-7 · Cited by 634 · see citing works
Lagunas‐Rangel, F. A. (2020) · Journal of Medical Virology · doi:10.1002/jmv.25819 · Cited by 606 · see citing works
Lenze, E. J., Mattar, C., Zorumski, C. F., Stevens, A., Schweiger, J., Nicol, G. E., Miller, J. P., Yang, L., Yingling, M., Avidan, M. S., et al. (2020) · JAMA · doi:10.1001/jama.2020.22760 · Cited by 495 · see citing works
Köhler, C. A., Freitas, T. H., Stubbs, B., Maes, M., Solmi, M., Veronese, N., de Andrade, N. Q., Morris, G., Fernandes, B. S., Brunoni, A. R., et al. (2017) · Molecular Neurobiology · doi:10.1007/s12035-017-0632-1 · Cited by 449 · see citing works
Ruan, Q., YANG, K. Y., Wang, W., Jiang, L., & Song, J. (2020) · Intensive Care Medicine · doi:10.1007/s00134-020-06028-z · Cited by 421 · see citing works
Hayasaka, Y., Purgato, M., Magni, L. R., Ogawa, Y., Takeshima, N., Cipriani, A., Barbui, C., Leucht, S., & Furukawa, T. A. (2015) · Journal of Affective Disorders · doi:10.1016/j.jad.2015.03.021 · Cited by 401 · see citing works
Acid sphingomyelinase–ceramide system mediates effects of antidepressant drugs
Gulbins, E., Palmada, M., Reichel, M., Lüth, A., Böhmer, C., Amato, D., Müller, C. P., Tischbirek, C. H., Groemer, T. W., Tabatabai, G., et al. (2013) · Nature Medicine · doi:10.1038/nm.3214 · Cited by 384 · see citing works
Tynan, R. J., Weidenhofer, J., Hinwood, M., Cairns, M. J., Day, T. A., & Walker, F. R. (2012) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2011.12.011 · Cited by 382 · see citing works
A stochastic agent-based model of the SARS-CoV-2 epidemic in France
Hoertel, N., Blachier, M., Blanco, C., Olfson, M., Massetti, M., Rico, M. S., Limosin, F., & Leleu, H. (2020) · Nature Medicine · doi:10.1038/s41591-020-1001-6 · Cited by 345 · see citing works
Self-citationCipriani, A., Furukawa, T. A., Salanti, G., Chaimani, A., Atkinson, L. Z., Ogawa, Y., Leucht, S., Ruhe, H. G., Turner, E. H., Higgins, J. P. T., et al. (2018) · Focus · doi:10.1176/appi.focus.16407 · Cited by 328 · see citing works
Ensuring mental health care during the SARS-CoV-2 epidemic in France: A narrative review
Chevance, A., Gourion, D., Hoertel, N., Llorca, P., Thomas, P., Bocher, R., Moro, M. R., Laprévote, V., Benyamina, A., Fossati, P., et al. (2020) · L Encéphale · doi:10.1016/j.encep.2020.04.005 · Cited by 274 · see citing works
Self-citationChevance, A., Gourion, D., Hoertel, N., Llorca, P., Thomas, P., Bocher, R., Moro, M. R., Laprévote, V., Benyamina, A., Fossati, P., et al. (2020) · L Encéphale · doi:10.1016/j.encep.2020.03.001 · Cited by 218 · see citing works
Self-citationHiles, S. A., Baker, A., de Malmanche, T., & Attia, J. (2012) · Psychological Medicine · doi:10.1017/s0033291712000128 · Cited by 218 · see citing works
Factors Associated With Intubation and Prolonged Intubation in Hospitalized Patients With COVID‐19
Hur, K., Price, C. P. E., Gray, E., Gulati, R. K., Maksimoski, M. T., Racette, S. D., Schneider, A. L., & Khanwalkar, A. R. (2020) · Otolaryngology · doi:10.1177/0194599820929640 · Cited by 187 · see citing works
Carpinteiro, A., Edwards, M. J., Hoffmann, M., Kochs, G., Gripp, B., Weigang, S., Adams, C., Carpinteiro, E., Gulbins, A., Keitsch, S., et al. (2020) · Cell Reports Medicine · doi:10.1016/j.xcrm.2020.100142 · Cited by 184 · see citing works
Li, L., Li, F., Fortunati, F., & Krystal, J. H. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.23282 · Cited by 154 · see citing works
The serotonin reuptake inhibitor Fluoxetine inhibits SARS-CoV-2
Zimniak, M., Kirschner, L., Hilpert, H., Seibel, J., & Bodem, J. (2020) · doi:10.1101/2020.06.14.150490 · Cited by 139 · cited 2× · see citing works
“Finally, certain antidepressants may exert antiviral effects on SARS-CoV-2, such as fluoxetine [12].”
BackgroundResultThe serotonin reuptake inhibitor Fluoxetine inhibits SARS-CoV-2 in human lung tissue
Zimniak, M., Kirschner, L., Hilpert, H., Geiger, N., Danov, O., Oberwinkler, H., Steinke, M. R., Sewald, K., Seibel, J., & Bodem, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-85049-0 · Cited by 139 · see citing works
Tham, A., Jonsson, U., Andersson, G., Söderlund, A., Allard, P., & Bertilsson, G. (2016) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.06.013 · Cited by 104 · see citing works
Krause, M., Gutsmiedl, K., Bighelli, I., Schneider-Thoma, J., Chaimani, A., & Leucht, S. (2019) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2019.07.130 · Cited by 65 · see citing works
Hoertel, N., Blachier, M., Blanco, C., Olfson, M., Massetti, M., Limosin, F., & Leleu, H. (2020) · medRxiv · doi:10.1101/2020.04.23.20076885 · Cited by 60 · see citing works
Self-citationObservational study of haloperidol in hospitalized patients with COVID-19
Hoertel, N., Rico, M. S., Vernet, R., Jannot, A., Neuraz, A., Blanco, C., Lemogne, C., Airagnes, G., Paris, N., Daniel, C., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0247122 · Cited by 56 · see citing works
Self-citationCORRELATION IS NO CAUSATION: GYMNASIUM PROLIFERATION AND THE RISK OF OBESITY
LE STRAT, Y., & HOERTEL, N. (2011) · Addiction · doi:10.1111/j.1360-0443.2011.03547.x · Cited by 44 · see citing works
Self-citationHoertel, N., Rico, M. S., Vernet, R., Beeker, N., Neuraz, A., Alvarado, J. M., Daniel, C., Paris, N., Gramfort, A., Lemaitre, G., et al. (2021) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.14784 · Cited by 43 · see citing works
Self-citationObservational Study of Chlorpromazine in Hospitalized Patients with COVID-19
Hoertel, N., Rico, M. S., Vernet, R., Jannot, A., Neuraz, A., Blanco, C., Lemogne, C., Airagnes, G., Paris, N., Daniel, C., et al. (2021) · Clinical Drug Investigation · doi:10.1007/s40261-021-01001-0 · Cited by 43 · see citing works
Self-citationStrategies for immune regulation in iPS cell-based cardiac regenerative medicine
Murata, K., Ikegawa, M., Minatoya, K., & Masumoto, H. (2020) · Inflammation and Regeneration · doi:10.1186/s41232-020-00145-4 · Cited by 20 · see citing works
Observational Study of Haloperidol in Hospitalized Patients with Covid-19Preprint
Hoertel, N., Rico, M. S., Vernet, R., et al. (2020) · medRxiv · doi:10.1101/2020.07.15.20150490 · Cited by 16 · see citing works
Self-citationHoertel, N., Sánchez, M., Vernet, R., et al. (2020) · medRxiv · doi:10.1101/2020.10.23.20218172 · Cited by 12 · see citing works
Self-citationObservational Study of Chlorpromazine in Hospitalized Patients with Covid-19Preprint
Hoertel, N., Rico, M. S., Vernet, R., et al. (2020) · medRxiv · doi:10.1101/2020.07.15.20154310 · Cited by 7 · see citing works
Self-citationBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805
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Crook, H., Raza, S., Nowell, J., Young, M., & Edison, P. (2021) · BMJ · doi:10.1136/bmj.n1648 · Cited by 1,655 · see citing works
Simons, D., Shahab, L., Brown, J., & Perski, O. (2020) · Addiction · doi:10.1111/add.15276 · Cited by 377 · see citing works
Reis, G., Moreira-Silva, E. A. D. S., Silva, D. C. M., Thabane, L., Milagres, A. C., Ferreira, T. S., dos Santos, C. V. Q., Campos, V. H. D. S., Nogueira, A. M. R., Almeida, A. P. F. G., et al. (2021) · The Lancet Global Health · doi:10.1016/s2214-109x(21)00448-4 · Cited by 372 · see citing works
Vai, B., Mazza, M. G., Colli, C. D., Foiselle, M., Allen, B., Benedetti, F., Borsini, A., Dias, M. C., Tamouza, R., Leboyer, M., et al. (2021) · The Lancet Psychiatry · doi:10.1016/s2215-0366(21)00232-7 · Cited by 306 · see citing works
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Ceban, F., Nogo, D., Carvalho, I. P., Lee, Y. L., Nasri, F., Xiong, J., Lui, L. M. W., Subramaniapillai, M., Gill, H., Liu, R. N., et al. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2021.1818 · Cited by 201 · see citing works
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Sukhatme, V. P., Reiersen, A. M., Vayttaden, S. J., & Sukhatme, V. V. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.652688 · Cited by 187 · see citing works
Carpinteiro, A., Edwards, M. J., Hoffmann, M., Kochs, G., Gripp, B., Weigang, S., Adams, C., Carpinteiro, E., Gulbins, A., Keitsch, S., et al. (2020) · Cell Reports Medicine · doi:10.1016/j.xcrm.2020.100142 · Cited by 184 · see citing works
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Oskotsky, T., Marić, I., Tang, A., Oskotsky, B., Wong, R. J., Aghaeepour, N., Sirota, M., & Stevenson, D. K. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.33090 · Cited by 131 · see citing works
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Hashimoto, Y., Suzuki, T., & Hashimoto, K. (2022) · Molecular Psychiatry · doi:10.1038/s41380-021-01432-3 · Cited by 125 · see citing works
Shohan, M., Nashibi, R., Mahmoudian‐Sani, M., Abolnezhadian, F., Ghafourian, M., Alavi, S. M., Sharhani, A., & Khodadadi, A. (2021) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2021.174615 · Cited by 110 · see citing works
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Rapid response to selective serotonin reuptake inhibitors in post-COVID depression
Mazza, M. G., Zanardi, R., Palladini, M., Rovere-Querini, P., & Benedetti, F. (2022) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2021.09.009 · Cited by 75 · see citing works
Hoertel, N., Rico, M. S., Gulbins, E., Kornhuber, J., Carpinteiro, A., Lenze, E. J., Reiersen, A. M., Abellán, M., de la Muela, P., Vernet, R., et al. (2021) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2317 · Cited by 72 · see citing works
Self-citationA scoping review of the pathophysiology of COVID-19
Marik, P. E., Iglesias, J. I., Varón, J., & Kory, P. D. (2021) · International Journal of Immunopathology and Pharmacology · doi:10.1177/20587384211048026 · Cited by 70 · see citing works
An overview on the treatments and prevention against COVID-19
Panahi, Y., Gorabi, A. M., Talaei, S., Beiraghdar, F., Akbarzadeh, A., Tarhriz, V., & Mellatyar, H. (2023) · Virology Journal · doi:10.1186/s12985-023-01973-9 · Cited by 70 · see citing works
Binder, M. D., Mazereel, V., Stroobants, M., Picker, L. D., Assche, K. V., & Detraux, J. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.798554 · Cited by 69 · see citing works
Fred, S. M., Kuivanen, S. H., Uğurlu, H., Casarotto, P. C., Levanov, L., Saksela, K., Vapalahti, O. P., & Ċastrén, E. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.755600 · Cited by 67 · see citing works
Reis, G., Silva, E. A. D. S. M., Silva, D. C. M., Thabane, L., Milagres, A. C., Ferreira, T. S., dos Santos, C. V. Q., Neto, A. D. D. F., Callegari, E. D., Savassi, L. C. M., et al. (2021) · The Lancet Regional Health - Americas · doi:10.1016/j.lana.2021.100142 · Cited by 64 · see citing works
RetractedBreiden, B., & Sandhoff, K. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22169001 · Cited by 62 · see citing works
Fluoxetine as an anti-inflammatory therapy in SARS-CoV-2 infection
Creeden, J. F., Imami, A. S., Eby, H. M., Gillman, C., Becker, K. N., Reigle, J., Andari, E., Pan, Z. K., O’Donovan, S. M., McCullumsmith, R. E., et al. (2021) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2021.111437 · Cited by 61 · see citing works
Sphingolipids as Modulators of SARS-CoV-2 Infection
Törnquist, K., Asghar, M. Y., Srinivasan, V., Korhonen, L., & Lindholm, D. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.689854 · Cited by 60 · see citing works
Hoertel, N., Rico, M. S., Cougoule, C., Gulbins, E., Kornhuber, J., Carpinteiro, A., Becker, K. A., Reiersen, A. M., Lenze, E. J., Seftel, D., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01254-3 · Cited by 59 · see citing works
Self-citationObservational study of haloperidol in hospitalized patients with COVID-19
Hoertel, N., Rico, M. S., Vernet, R., Jannot, A., Neuraz, A., Blanco, C., Lemogne, C., Airagnes, G., Paris, N., Daniel, C., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0247122 · Cited by 56 · see citing works
Self-citationAntimicrobial Properties of Antidepressants and Antipsychotics—Possibilities and Implications
Caldara, M., & Marmiroli, N. (2021) · Pharmaceuticals · doi:10.3390/ph14090915 · Cited by 54 · see citing works
Ceramide and Related Molecules in Viral Infections
Beckmann, N., & Becker, K. A. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22115676 · Cited by 53 · see citing works
Mahmud, N., Anik, M. I., Hossain, M. K., Khan, M. I., Uddin, S., Ashrafuzzaman, M., & Rahaman, M. M. (2022) · ACS Applied Bio Materials · doi:10.1021/acsabm.2c00123 · Cited by 53 · see citing works
Hoertel, N., Rico, M. S., Gulbins, E., Kornhuber, J., Carpinteiro, A., Lenze, E. J., Reiersen, A. M., Abellán, M., de la Muela, P., Vernet, R., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-01804-5 · Cited by 51 · see citing works
Self-citationDepressive and Neurocognitive Disorders in the Context of the Inflammatory Background of COVID-19
Dąbrowska, E., Galińska-Skok, B., & Waszkiewicz, N. (2021) · Life · doi:10.3390/life11101056 · Cited by 47 · see citing works
Pashaei, Y. (2021) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2021.03.010 · Cited by 46 · see citing works
Hoertel, N. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.36510 · Cited by 45 · see citing works
Self-citationClelland, C. L. T., Ramiah, K., Steinberg, L. J., & Clelland, J. D. (2021) · BJPsych Open · doi:10.1192/bjo.2021.1053 · Cited by 45 · see citing works
Hoertel, N., Rico, M. S., Vernet, R., Beeker, N., Neuraz, A., Alvarado, J. M., Daniel, C., Paris, N., Gramfort, A., Lemaitre, G., et al. (2021) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.14784 · Cited by 43 · see citing works
Self-citationObservational Study of Chlorpromazine in Hospitalized Patients with COVID-19
Hoertel, N., Rico, M. S., Vernet, R., Jannot, A., Neuraz, A., Blanco, C., Lemogne, C., Airagnes, G., Paris, N., Daniel, C., et al. (2021) · Clinical Drug Investigation · doi:10.1007/s40261-021-01001-0 · Cited by 43 · see citing works
Self-citationFluoxetine Can Inhibit SARS-CoV-2 In Vitro
Dechaumes, A., Nekoua, M. P., Belouzard, S., Sané, F., Engelmann, I., Dubuisson, J., Alidjinou, E. K., & Hober, D. (2021) · Microorganisms · doi:10.3390/microorganisms9020339 · Cited by 42 · see citing works
Recent trends in the management of depression in persons with cancer
Panjwani, A. A., & Li, M. (2021) · Current Opinion in Psychiatry · doi:10.1097/yco.0000000000000727 · Cited by 41 · see citing works
Khater, S. E., El‐Khouly, A., Abdel‐Bar, H. M., Al-mahallawi, A. M., & Ghorab, D. M. (2021) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2021.121023 · Cited by 37 · see citing works
Brimson, J. M., Prasanth, M. I., Malar, D. S., Brimson, S. J., Thitilertdecha, P., & Tencomnao, T. (2021) · Expert Opinion on Therapeutic Targets · doi:10.1080/14728222.2021.1952987 · Cited by 36 · see citing works
Németh, Z., Szűcs, A., Vitrai, J., Juhász, D., Németh, J. P., & Holló, A. (2021) · Ideggyógyászati Szemle · doi:10.18071/isz.74.0389 · Cited by 35 · see citing works
Long COVID: Complications, Underlying Mechanisms, and Treatment Strategies
Zadeh, F. H. (2023) · Archives of Microbiology & Immunology · doi:10.26502/ami.93650103 · Cited by 34 · see citing works
Blocking TNF signaling may save lives in COVID-19 infection
Ablamunits, V. G., & Lepsy, C. S. (2022) · Molecular Biology Reports · doi:10.1007/s11033-022-07166-x · Cited by 34 · see citing works
Nemani, K., Williams, S. Z., Olfson, M., Leckman‐Westin, E., Finnerty, M. T., Kammer, J., Smith, T. E., Silverman, D. J., Lindenmayer, J., Capichioni, G., et al. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.10743 · Cited by 33 · see citing works
Simons, D., Shahab, L., Brown, J., & Perski, O. (2020) · doi:10.32388/ujr2aw.8 · Cited by 33 · see citing works
Hernández‐Bello, J., Morales‐Núñez, J. J., Machado-Sulbarán, A. C., Díaz-Pérez, S. A., Torres-Hernández, P. C., Balcázar-Félix, P., Gutiérrez-Brito, J. A., Lomelí-Nieto, J. Á., & Muñóz-Valle, J. F. (2021) · Vaccines · doi:10.3390/vaccines9091047 · Cited by 32 · see citing works
Hoertel, N., Boulware, D. R., Rico, M. S., Burgun, A., & Limosin, F. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.42140 · Cited by 32 · see citing works
Self-citationSen, A. (2021) · Medical Hypotheses · doi:10.1016/j.mehy.2021.110627 · Cited by 30 · see citing works
Nykamp, M. J., Zorumski, C. F., Reiersen, A. M., Nicol, G. E., Cirrito, J. R., & Lenze, E. J. (2021) · Pharmacopsychiatry · doi:10.1055/a-1686-9620 · Cited by 30 · see citing works
Seo, H., Kim, H., Bae, S., Park, S., Chung, H., Sung, H., Jung, J., Kim, M. J., Kim, S., Lee, S., et al. (2022) · Infection and Chemotherapy · doi:10.3947/ic.2021.0142 · Cited by 30 · see citing works
Hoertel, N., Rico, M. S., Kornhuber, J., Gulbins, E., Reiersen, A. M., Lenze, E. J., Fritz, B. A., Jalali, F., Mills, E. J., Cougoule, C., et al. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11195882 · Cited by 29 · see citing works
Self-citationIn silico drug repurposing in COVID-19: A network-based analysis
Sibilio, P., Bini, S., Fiscon, G., Sponziello, M., Conte, F., Pecce, V., Durante, C., Paci, P., Falcone, R., Norata, G. D., et al. (2021) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2021.111954 · Cited by 29 · see citing works
Association between antidepressant use and ED or hospital visits in outpatients with SARS-CoV-2
Fritz, B. A., Hoertel, N., Lenze, E. J., Jalali, F., & Reiersen, A. M. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02109-3 · Cited by 29 · see citing works
Self-citationSidky, H., Hansen, K., Girvin, A. T., Hotaling, N. A., Michael, S. G., Gersing, K. R., & Sahner, D. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.12.045 · Cited by 29 · see citing works
Beyond Vaccines: Clinical Status of Prospective COVID-19 Therapeutics
Kumar, S., Çalışkan, D. M., Janowski, J., Faist, A., Conrad, B. C. G., Lange, J., Ludwig, S., & Brunotte, L. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.752227 · Cited by 28 · see citing works
Darquennes, G., Corre, P. L., Moine, O. L., & Loas, G. (2021) · Pharmaceuticals · doi:10.3390/ph14030226 · Cited by 26 · see citing works
5-Hydroxytryptamine, Glutamate, and ATP: Much More Than Neurotransmitters
Franco, R., Rivas‐Santisteban, R., Lillo, J., Camps, J., Navarro, G., & Reyes‐Resina, I. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.667815 · Cited by 26 · see citing works
Rico, M. S., de la Muela, P., Herrera-Morueco, J. J., Geoffroy, P. A., Limosin, F., & Hoertel, N. (2022) · Journal of Travel Medicine · doi:10.1093/jtm/taab195 · Cited by 26 · see citing works
Self-citationAntiviral and Anti-Inflammatory Activities of Fluoxetine in a SARS-CoV-2 Infection Mouse Model
Péricat, D., Leon‐Icaza, S. A., Rico, M. S., Mühle, C., Zoicas, I., Schumacher, F., Planès, R., Mazars, R., Gros, G., Carpinteiro, A., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232113623 · Cited by 26 · see citing works
Self-citationAntidepressants for COVID-19: A systematic review
Zheng, W., Sun, H., Cai, H. X., Zhang, Q., Ng, C. H., & Xiang, Y. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.03.059 · Cited by 25 · see citing works
Nakhaee, H., Bayati, R., Rahmanian, M., Jolfayi, A. G., Zangiabadian, M., & Rakhshanderou, S. (2022) · PLoS ONE · doi:10.1371/journal.pone.0267423 · Cited by 25 · see citing works
Overview of the potential use of fluvoxamine for COVID-19 and long COVID
Hashimoto, K. (2023) · Discover Mental Health · doi:10.1007/s44192-023-00036-3 · Cited by 25 · see citing works
Abusukhun, M., Winkler, M. S., Pöhlmann, S., Moerer, O., Meissner, K., Tampe, B., Hofmann-Winkler, H., Bauer, M. W., Gräler, M. H., & Claus, R. A. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.784989 · Cited by 24 · see citing works
The Acid Ceramidase Is a SARS-CoV-2 Host Factor
Geiger, N., Kersting, L., Schlegel, J. A., Stelz, L., Fähr, S., Diesendorf, V., Roll, V., Sostmann, M., König, E., Reinhard, S., et al. (2022) · Cells · doi:10.3390/cells11162532 · Cited by 24 · see citing works
DrugWAS: Drug‐wide Association Studies for COVID‐19 Drug Repurposing
Bejan, C. A., Cahill, K. N., Staso, P. J., Choi, L., Peterson, J. F., & Phillips, E. J. (2021) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2376 · Cited by 23 · see citing works
Assessment and Management of Long COVID
Rivas-Vazquez, R. A., Rey, G. J. F. D., Quintana, A., & Rivas-Vazquez, A. A. (2022) · Journal of Health Service Psychology · doi:10.1007/s42843-022-00055-8 · Cited by 23 · see citing works
Selective serotonin reuptake inhibitors and preeclampsia: A quality assessment and meta-analysis
Gumusoglu, S. B., Schickling, B., Vignato, J. A., Santillan, D. A., & Santillan, M. K. O. (2022) · Pregnancy Hypertension · doi:10.1016/j.preghy.2022.08.001 · Cited by 23 · see citing works
Psychotropic drug repurposing for COVID-19: A Systematic Review and Meta-Analysis
Fico, G., Isayeva, U., Prisco, M. D., Oliva, V., Solé, B., Montejo, L., Grande, I., Arbelo, N., Gómez-Ramiro, M., Pintor, L., et al. (2022) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2022.10.004 · Cited by 23 · see citing works
Eugene, A. R. (2021) · F1000Research · doi:10.12688/f1000research.53275.1 · Cited by 22 · see citing works
Alam, M. S. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e13285 · Cited by 22 · see citing works
Corre, P. L., & Loas, G. (2021) · Journal of Clinical Pharmacy and Therapeutics · doi:10.1111/jcpt.13390 · Cited by 21 · see citing works
Nazerian, Y., Vakili, K. G., Ebrahimi, A., & Niknejad, H. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.717587 · Cited by 21 · see citing works
Rauchman, S. H., Mendelson, S. G., Rauchman, C., Kasselman, L. J., Pinkhasov, A., & Reiss, A. B. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm11010070 · Cited by 21 · see citing works
Bonnet, U., & Juckel, G. (2022) · Journal of Clinical Psychopharmacology · doi:10.1097/jcp.0000000000001543 · Cited by 21 · see citing works
Ivrigh, Z. J.-N., Bigdeli, A., Jafarinejad, S., & Hormozi-Nezhad, M. R. (2022) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2022.131855 · Cited by 21 · see citing works
Modulating neuroinflammation in COVID-19 patients with obsessive-compulsive disorder
Nezgovorova, V., Ferretti, C. J., Pallanti, S., & Hollander, E. (2021) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2021.11.025 · Cited by 19 · see citing works
Hoertel, N., Rico, M. S., de la Muela, P., Abellán, M., Blanco, C., Leboyer, M., Cougoule, C., Gulbins, E., Kornhuber, J., Carpinteiro, A., et al. (2022) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2021.12.007 · Cited by 19 · see citing works
Self-citationLoucera, C., Carmona, R., Esteban‐Medina, M., Bostelmann, G., Muñoyerro-Muñiz, D., Villegas, R., Peña-Chilet, M., & Dopazo, J. (2023) · Virology Journal · doi:10.1186/s12985-023-02195-9 · Cited by 19 · see citing works
Psychosis and substance abuse increase the COVID-19 mortality risk
Catalán, A., Aymerich, C., Bilbao, A., Pedruzo, B., Pérez, J. L., Aranguren, N., de Pablo, G. S., Hedges, E. P., Gil, P., Segarra, R., et al. (2022) · Psychological Medicine · doi:10.1017/s0033291722000976 · Cited by 19 · see citing works
Interactions between COVID-19 and Lung Cancer: Lessons Learned during the Pandemic
Bian, D. J. H., Sabri, S., & Abdulkarim, B. (2022) · Cancers · doi:10.3390/cancers14153598 · Cited by 19 · see citing works
Update on Functional Inhibitors of Acid Sphingomyelinase (FIASMAs) in SARS-CoV-2 Infection
Loas, G., & Corre, P. L. (2021) · Pharmaceuticals · doi:10.3390/ph14070691 · Cited by 18 · see citing works
Hoertel, N., Rico, M. S., Herrera-Morueco, J. J., de la Muela, P., Gulbins, E., Kornhuber, J., Carpinteiro, A., Becker, K. A., Cougoule, C., & Limosin, F. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01393-7 · Cited by 18 · see citing works
Self-citationPiotrowicz, K., Ryś, M., Perera, I., Gryglewska, B., Fedyk‐Łukasik, M., Michel, J., Wizner, B., Sydor, W., Olszanecka, A., Grodzicki, T. K., et al. (2022) · BMC Geriatrics · doi:10.1186/s12877-022-03571-w · Cited by 18 · see citing works
Rico, M. S., Limosin, F., Vernet, R., Beeker, N., Neuraz, A., Blanco, C., Olfson, M., Lemogne, C., Meneton, P., Daniel, C., et al. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10245891 · Cited by 18 · see citing works
Self-citationAntidepressant Drugs and COVID-19: A Review of Basic and Clinical Evidence
Más, M., García-Vicente, J. A., Estrada-Gelonch, A., Pérez‐Mañá, C., Papaseit, E., Torrens, M., & Farré, M. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11144038 · Cited by 18 · see citing works
Lakbar, I., Léone, M., Pauly, V., Orléans, V., Srougbo, K. J., Diao, S., Llorca, P., Solmi, M., Correll, C. U., Fernandes, S., et al. (2023) · PLoS Medicine · doi:10.1371/journal.pmed.1004202 · Cited by 18 · see citing works
Laguna-Muñoz, D., Pata, M. P., Jiménez-Peinado, A., Jaén‐Moreno, M. J., Camacho-Rodríguez, C., del Pozo, G. I., Vieta, E., Correll, C. U., Solmi, M., Caballero‐Villarraso, J., et al. (2025) · The Lancet Psychiatry · doi:10.1016/s2215-0366(25)00262-7 · Cited by 17 · see citing works
Aree, T. (2021) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2021.121113 · Cited by 17 · see citing works
Bartoletti, M., Azap, Ö. K., Barac, A. M., Bussini, L., Ergönül, Ö., Krause, R. G. E., Martín‐Quirós, A., Paño‐Pardo, J. R., Power, N., Sibani, M., et al. (2022) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2022.08.013 · Cited by 17 · see citing works
Fei, L., Santarelli, G., D’Anna, G., Moretti, S., MIROSSI, G., Patti, A., Sanfilippo, G., Almerigogna, F., Berni, A., Caldini, E., et al. (2021) · Panminerva Medica · doi:10.23736/s0031-0808.21.04436-0 · Cited by 16 · see citing works
Hoertel, N., Rico, M. S., Gulbins, E., Kornhuber, J., Vernet, R., Beeker, N., Neuraz, A., Blanco, C., Olfson, M., Airagnes, G., et al. (2022) · Epidemiology and Psychiatric Sciences · doi:10.1017/s2045796021000743 · Cited by 16 · see citing works
Self-citationQuantum tunnelling in the context of SARS-CoV-2 infection
Adams, B., Sinayskiy, I., van Grondelle, R., & Petruccione, F. (2022) · Scientific Reports · doi:10.1038/s41598-022-21321-1 · Cited by 16 · see citing works
Visos‐Varela, I., Zapata‐Cachafeiro, M., Piñeiro‐Lamas, M., Carracedo‐Martínez, E., Sáez, M., Herdeiro, M. T., Figueiras, A., & Salgado‐Barreira, Á. (2023) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2023.03.011 · Cited by 16 · see citing works
Schultebraucks, K., Blekić, W., Basaraba, C. N., Corbeil, T., Khan, Z. A., Henry, B. F., Krawczyk, N., Rivera, B. D., Allen, B., Arout, C. A., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02049-4 · Cited by 16 · see citing works
Wang, T., Zheng, R., & Sun, S. Y. (2022) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2022.02.015 · Cited by 16 · see citing works
Hromić‐Jahjefendić, A., Barh, D., Pinto, C. H. R., Gomes, L. G. R., Machado, J. L. P., Afolabi, O. O., Tiwari, S., Aljabali, A. A. A., Tambuwala, M. M., Serrano‐Aroca, Á., et al. (2022) · Viruses · doi:10.3390/v14050910 · Cited by 16 · see citing works
Foletto, V. S., da Rosa, T. F., Serafin, M. B., & Hörner, R. (2022) · European Journal of Clinical Pharmacology · doi:10.1007/s00228-022-03372-5 · Cited by 16 · see citing works
Exploring the causal effects of depression and antidepressants on COVID-19
Fu, L., Baranova, A. V., Cao, H., & Zhang, F. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.05.122 · Cited by 15 · see citing works
Villoutreix, B. O., Krishnamoorthy, R., Tamouza, R., Leboyer, M., & Beaune, P. H. (2021) · Advances and Applications in Bioinformatics and Chemistry · doi:10.2147/aabc.s304649 · Cited by 15 · see citing works
- Authors
- Hoertel, N., Sánchez-Rico, M., Vernet, R., Beeker, N., Jannot, A.-S., Neuraz, A., Salamanca, E., Paris, N., Daniel, C., Gramfort, A., Lemaitre, G., Bernaux, M., Bellamine, A., Lemogne, C., Airagnes, G., Burgun, A., Limosin, F., & On behalf of AP-HP / Universities / INSERM COVID-19 Research Collaboration and AP-HP COVID CDR Initiative
- Affiliations
- Inserm (FR)
- Assistance Publique – Hôpitaux de Paris (FR)
- Hôpital Cochin (FR)
- Hôpital Necker-Enfants Malades (FR)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Corresponding author
- Hoertel, Nicolas
- ORCID
- Venue
- Molecular Psychiatry
- Published
- 2021-02-04
- Volume
- 26
- Issue
- 9
- Pages
- 5199-5212
- ISSN
- 1359-4184 (ISSN-L)
- 1476-5578
- OpenAlex
- W3128622946
- Semantic Scholar
- a2067d6c851793d8f54fbca350ac5f77f6bd4f16
- Cited by
- 269
- References
- 47
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 23 · 2023: 42 · 2022: 86 · 2021: 94 · 2020: 6
- FWCI
- 19.34
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Molecular Psychiatry (publishedVersion)
- medRxiv (acceptedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Funders
- Assistance publique-Hôpitaux de Paris
- Topics
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Social Sciences › Psychology › Clinical Psychology › COVID-19 and Mental Health
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Keywords
- antidepressants, COVID-19 pandemic, intubation, mortality, hospitalized patients, major depressive disorder, fluoxetine, selective serotonin reuptake inhibitors, SARS-CoV-2, antiviral activity, pro-inflammatory mediators, mirtazapine, paroxetine, escitalopram, venlafaxine
- MeSH terms
- COVID-19, SARS-CoV-2, Antidepressive Agents, Major Depressive Disorder, Humans, Intubation, Intratracheal, Retrospective Studies, Multicenter Studies as Topic, Observational Studies as Topic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-06.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-06.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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