SSRI use during acute COVID-19 and risk of Long COVID among patients with depression

{ 'indexed': { 'date-parts': [[2024, 10, 15]], 'date-time': '2024-10-15T15:40:21Z', 'timestamp': 1729006821903}, 'reference-count': 49, 'publisher': 'Springer Science and Business Media LLC', 'issue': '1', 'license': [ { 'start': { 'date-parts': [[2024, 10, 8]], 'date-time': '2024-10-08T00:00:00Z', 'timestamp': 1728345600000}, 'content-version': 'tdm', 'delay-in-days': 0, 'URL': 'https://creativecommons.org/licenses/by/4.0'}, { 'start': { 'date-parts': [[2024, 10, 8]], 'date-time': '2024-10-08T00:00:00Z', 'timestamp': 1728345600000}, 'content-version': 'vor', 'delay-in-days': 0, 'URL': 'https://creativecommons.org/licenses/by/4.0'}], 'content-domain': {'domain': ['link.springer.com'], 'crossmark-restriction': False}, 'abstract': '<jats:title>Abstract</jats:title><jats:sec>\n' ' <jats:title>Background</jats:title>\n' ' <jats:p>Long COVID, also known as post-acute sequelae of COVID-19 (PASC), is ' 'a poorly understood condition with symptoms across a range of biological domains that often ' 'have debilitating consequences. Some have recently suggested that lingering SARS-CoV-2 ' 'virus\xa0particles in the gut may impede serotonin production and that low serotonin may ' 'drive many Long COVID symptoms across a range of biological systems. Therefore, selective ' 'serotonin reuptake inhibitors (SSRIs), which increase synaptic serotonin availability, may be ' 'used to prevent or treat Long COVID. SSRIs are commonly prescribed for depression, therefore ' 'restricting a study sample to only include patients with depression can reduce the concern of ' 'confounding by indication.</jats:p>\n' ' </jats:sec><jats:sec>\n' ' <jats:title>Methods</jats:title>\n' ' <jats:p>In an observational sample of electronic health records from patients ' 'in the National COVID Cohort Collaborative (N3C) with a COVID-19 diagnosis between September ' '1, 2021, and December 1, 2022, and a comorbid depressive disorder, the leading indication for ' 'SSRI use, we evaluated the relationship between SSRI use during acute COVID-19 and subsequent ' '12-month risk of Long COVID (defined by ICD-10 code U09.9). We defined SSRI use as a ' 'prescription for SSRI medication beginning at least 30\xa0days before acute COVID-19 and not ' 'ending before SARS-CoV-2 infection. To minimize bias, we estimated relationships using ' 'nonparametric targeted maximum likelihood estimation to aggressively adjust for ' 'high-dimensional covariates.</jats:p>\n' ' </jats:sec><jats:sec>\n' ' <jats:title>Results</jats:title>\n' ' <jats:p>We analyzed a sample (<jats:italic>n</jats:italic>\u2009=\u2009' '302,626) of patients with a diagnosis of a depressive condition before COVID-19 diagnosis, ' 'where 100,803 (33%) were using an SSRI. We found that SSRI users had a significantly lower ' 'risk of Long COVID compared to nonusers (adjusted causal relative risk 0.92, 95% CI (0.86, ' '0.99)) and we found a similar relationship comparing new SSRI users (first SSRI prescription ' '1 to 4\xa0months before acute COVID-19 with no prior history of SSRI use) to nonusers ' '(adjusted causal relative risk 0.89, 95% CI (0.80, 0.98)).</jats:p>\n' ' </jats:sec><jats:sec>\n' ' <jats:title>Conclusions</jats:title>\n' ' <jats:p>These findings suggest that SSRI use during acute COVID-19 may be ' 'protective against Long COVID, supporting the hypothesis that serotonin may be a key ' 'mechanistic biomarker of Long COVID.\n' '</jats:p>\n' ' </jats:sec>', 'DOI': '10.1186/s12916-024-03655-x', 'type': 'journal-article', 'created': {'date-parts': [[2024, 10, 8]], 'date-time': '2024-10-08T16:02:11Z', 'timestamp': 1728403331000}, 'update-policy': 'http://dx.doi.org/10.1007/springer_crossmark_policy', 'source': 'Crossref', 'is-referenced-by-count': 0, 'title': 'SSRI use during acute COVID-19 and risk of Long COVID among patients with depression', 'prefix': '10.1186', 'volume': '22', 'author': [ { 'ORCID': 'http://orcid.org/0000-0001-6419-0008', 'authenticated-orcid': False, 'given': 'Zachary', 'family': 'Butzin-Dozier', 'sequence': 'first', 'affiliation': []}, {'given': 'Yunwen', 'family': 'Ji', 'sequence': 'additional', 'affiliation': []}, {'given': 'Sarang', 'family': 'Deshpande', 'sequence': 'additional', 'affiliation': []}, {'given': 'Eric', 'family': 'Hurwitz', 'sequence': 'additional', 'affiliation': []}, {'given': 'A. 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The epidemiology of long coronavirus disease in US adults. Clin ' 'Infect Dis. 2023;76(9):1636–45.', 'journal-title': 'Clin Infect Dis'}, { 'issue': '3', 'key': '3655_CR2', 'doi-asserted-by': 'publisher', 'first-page': '869', 'DOI': '10.1016/j.dsx.2021.04.007', 'volume': '15', 'author': 'AV Raveendran', 'year': '2021', 'unstructured': 'Raveendran AV, Jayadevan R, Sashidharan S. Long COVID: an overview. Diab ' 'Metab Syndr. 2021;15(3):869–75.', 'journal-title': 'Diab Metab Syndr'}, { 'issue': '3', 'key': '3655_CR3', 'doi-asserted-by': 'publisher', 'first-page': '133', 'DOI': '10.1038/s41579-022-00846-2', 'volume': '21', 'author': 'HE Davis', 'year': '2023', 'unstructured': 'Davis HE, McCorkell L, Vogel JM, Topol EJ. Long COVID: major findings, ' 'mechanisms and recommendations. Nat Rev Microbiol. 2023;21(3):133–46.', 'journal-title': 'Nat Rev Microbiol'}, { 'key': '3655_CR4', 'doi-asserted-by': 'publisher', 'unstructured': 'Wong AC, Devason AS, Umana IC, Cox TO, Dohnalová L, Litichevskiy L, et ' 'al. Serotonin reduction in post-acute sequelae of viral infection. Cell. ' '2023. Available from: https://doi.org/10.1016/j.cell.2023.09.013. Cited ' '2023 Oct 19.', 'DOI': '10.1016/j.cell.2023.09.013'}, { 'key': '3655_CR5', 'unstructured': 'Belluck P. Scientists offer a new explanation for long COVID. The New ' 'York Times. 2023. Available from: ' 'https://www.nytimes.com/2023/10/16/health/long-covid-serotonin.html.'}, { 'key': '3655_CR6', 'doi-asserted-by': 'publisher', 'first-page': '104413', 'DOI': '10.1016/j.ebiom.2022.104413', 'volume': '87', 'author': 'JT Reese', 'year': '2023', 'unstructured': 'Reese JT, Blau H, Casiraghi E, Bergquist T, Loomba JJ, Callahan TJ, et ' 'al. Generalisable long COVID subtypes: findings from the NIH N3C and ' 'RECOVER programmes. eBioMedicine. 2023;87:104413 Available from: ' 'https://linkinghub.elsevier.com/retrieve/pii/S2352396422005953.. Cited ' '2023 Oct 26', 'journal-title': 'eBioMedicine'}, { 'key': '3655_CR7', 'unstructured': 'Created with Biorender.com. 2024. Available from: \u200d ' 'https://biorender.com.'}, { 'key': '3655_CR8', 'unstructured': 'Chu A, Wadhwa R. Selective serotonin reuptake inhibitors. In: ' 'StatPearls. Treasure Island (FL): StatPearls Publishing; 2023. Available ' 'from: http://www.ncbi.nlm.nih.gov/books/NBK554406/. Cited 2023 Nov 29.'}, { 'key': '3655_CR9', 'unstructured': 'Preskorn S. Clinical pharmacology of serotonin selective reuptake ' 'inhibitors. Caddo: Professional Communications; 1996.'}, { 'issue': '1', 'key': '3655_CR10', 'doi-asserted-by': 'publisher', 'first-page': 'e42', 'DOI': '10.1016/S2214-109X(21)00448-4', 'volume': '10', 'author': 'G Reis', 'year': '2022', 'unstructured': 'Reis G, Dos Santos Moreira-Silva EA, Silva DCM, Thabane L, Milagres AC, ' 'Ferreira TS, et al. Effect of early treatment with fluvoxamine on risk ' 'of emergency care and hospitalisation among patients with COVID-19: the ' 'TOGETHER randomised, platform clinical trial. Lancet Glob Health. ' '2022;10(1):e42–51.', 'journal-title': 'Lancet Glob Health'}, { 'issue': '10', 'key': '3655_CR11', 'doi-asserted-by': 'publisher', 'first-page': '1119', 'DOI': '10.1016/S1473-3099(23)00299-2', 'volume': '23', 'author': 'CT Bramante', 'year': '2023', 'unstructured': 'Bramante CT, Buse JB, Liebovitz DM, Nicklas JM, Puskarich MA, Cohen K, ' 'et al. Outpatient treatment of COVID-19 and incidence of post-COVID-19 ' 'condition over 10 months (COVID-OUT): a multicentre, randomised, ' 'quadruple-blind, parallel-group, phase 3 trial. Lancet Infect Dis. ' '2023;23(10):1119–29 Available from: ' 'https://linkinghub.elsevier.com/retrieve/pii/S1473309923002992. Cited ' '2023 Oct 13. Cited 2023 Oct 13', 'journal-title': 'Lancet Infect Dis'}, { 'issue': '10', 'key': '3655_CR12', 'doi-asserted-by': 'publisher', 'first-page': '1601', 'DOI': '10.1007/s00228-022-03372-5', 'volume': '78', 'author': 'VS Foletto', 'year': '2022', 'unstructured': 'Foletto VS, da Rosa TF, Serafin MB, Hörner R. Selective serotonin ' 'reuptake inhibitor (SSRI) antidepressants reduce COVID-19 infection: ' 'prospects for use. Eur J Clin Pharmacol. 2022;78(10):1601–11.', 'journal-title': 'Eur J Clin Pharmacol'}, { 'issue': '1', 'key': '3655_CR13', 'doi-asserted-by': 'publisher', 'first-page': 'e20', 'DOI': '10.1192/bjo.2021.1075', 'volume': '8', 'author': 'JC Stingl', 'year': '2021', 'unstructured': 'Stingl JC. Antidepressant drug treatment protecting from COVID-19: one ' 'more piece in the repurposing puzzle. BJPsych Open. 2021;8(1):e20.', 'journal-title': 'BJPsych Open'}, { 'key': '3655_CR14', 'doi-asserted-by': 'crossref', 'unstructured': 'Sidky H, Sahner DK, Girvin AT, Hotaling N, Michael SG, Gersing K. ' 'Assessing the effect of selective serotonin reuptake inhibitors in the ' 'prevention of post-acute sequelae of COVID-19. medRxiv\u202f: the ' 'preprint server for health sciences. United States; 2023. p. ' '2022.11.09.22282142.', 'DOI': '10.1101/2022.11.09.22282142'}, { 'issue': '144', 'key': '3655_CR15', 'doi-asserted-by': 'publisher', 'first-page': '107048', 'DOI': '10.1016/j.ijid.2024.107048', 'volume': '10', 'author': 'IAL Slurink', 'year': '2024', 'unstructured': 'Slurink IAL, van den Houdt SCM, Mertens G. Who develops long COVID? ' 'Longitudinal pre-pandemic predictors of long COVID and symptom clusters ' 'in a representative Dutch population. Int J Infect Dis. ' '2024;10(144):107048.', 'journal-title': 'Int J Infect Dis'}, { 'issue': '1', 'key': '3655_CR16', 'doi-asserted-by': 'publisher', 'first-page': '18599', 'DOI': '10.1038/s41598-023-45072-9', 'volume': '13', 'author': 'CP Rus', 'year': '2023', 'unstructured': 'Rus CP, de Vries BEK, de Vries IEJ, Nutma I, Kooij JJS. Treatment of 95 ' 'post-COVID patients with SSRIs. Sci Rep. 2023;13(1):18599 ' 'https://doi.org/10.1038/s41598-023-45072-9.', 'journal-title': 'Sci Rep'}, { 'issue': '5', 'key': '3655_CR17', 'doi-asserted-by': 'publisher', 'first-page': '404', 'DOI': '10.1111/jnc.15658', 'volume': '162', 'author': 'KE Dunham', 'year': '2022', 'unstructured': 'Dunham KE, Venton BJ. SSRI antidepressants differentially modulate ' 'serotonin reuptake and release in Drosophila. J Neurochem. ' '2022;162(5):404–16.', 'journal-title': 'J Neurochem'}, { 'issue': '1', 'key': '3655_CR18', 'doi-asserted-by': 'publisher', 'first-page': '9', 'DOI': '10.1007/s44192-023-00036-3', 'volume': '3', 'author': 'K Hashimoto', 'year': '2023', 'unstructured': 'Hashimoto K. Overview of the potential use of fluvoxamine for COVID-19 ' 'and long COVID. Discov Ment Health. 2023;3(1):9.', 'journal-title': 'Discov Ment Health'}, { 'key': '3655_CR19', 'doi-asserted-by': 'publisher', 'first-page': '626', 'DOI': '10.1007/978-1-4419-9782-1', 'volume-title': 'Targeted learning: causal inference for observational and experimental ' 'data', 'author': 'MJ van der Laan', 'year': '2011', 'unstructured': 'van der Laan MJ, Rose S. Targeted learning: causal inference for ' 'observational and experimental data. New York: Springer; 2011. p. 626 ' 'Springer series in statistics.'}, { 'key': '3655_CR20', 'unstructured': 'van der Laan M, Coyle J, Hejazi N, Malenica I, Phillips R, Hubbard A. ' 'Targeted learning in R: causal data science with the tlverse software ' 'ecosystem. 2023. Available from: ' 'https://tlverse.org/tlverse-handbook/optimal-individualized-treatment-regimes.html.'}, { 'key': '3655_CR21', 'unstructured': 'National center for advancing translational sciences. N3C dashboards. ' '2023. Available from: https://covid.cd2h.org/dashboard/.'}, { 'key': '3655_CR22', 'doi-asserted-by': 'publisher', 'unstructured': 'Coyle JR, Hejazi NS, Malenica I, Phillips RV, Arnold BF, Mertens A, et ' 'al. Targeted learning. In: Wiley StatsRef: Statistics Reference Online. ' '2023:1–20. Available from: ' 'https://doi.org/10.1002/9781118445112.stat08414. Cited 2023 May 17.', 'DOI': '10.1002/9781118445112.stat08414'}, { 'key': '3655_CR23', 'doi-asserted-by': 'crossref', 'unstructured': 'Van der Laan MJ, Rose S. Targeted learning in data science: causal ' 'inference for complex longitudinal studies. New York, NY: Springer ' 'Berlin Heidelberg; 2017.', 'DOI': '10.1007/978-3-319-65304-4'}, { 'key': '3655_CR24', 'doi-asserted-by': 'publisher', 'first-page': '1', 'DOI': '10.1080/19466315.2022.2116104', 'volume': '23', 'author': 'S Gruber', 'year': '2022', 'unstructured': 'Gruber S, Lee H, Phillips R, Ho M, van der Laan M. Developing a targeted ' 'learning-based statistical analysis plan. Stat Biopharmaceut Res. ' '2022;23:1–8. https://doi.org/10.1080/19466315.2022.2116104.', 'journal-title': 'Stat Biopharmaceut Res'}, { 'key': '3655_CR25', 'unstructured': 'ICD10 Data. 2023 ICD-10-CM diagnosis code U09.9. ICD10data.com. 2023. ' 'Available from: ' 'https://www.icd10data.com/ICD10CM/Codes/U00-U85/U00-U49/U09-/U09.9. ' 'Cited 2023 Sep 12.'}, { 'key': '3655_CR26', 'unstructured': 'Applicable Data Methods & Standards Domain Team. N3C concept set - ' '38249145 (depression). 2024 Jan 30. Available from: ' 'https://zenodo.org/doi/10.5281/zenodo.7685710. Cited 2024 May 14.'}, { 'key': '3655_CR27', 'unstructured': 'Beasley W. Phenotype data acquisition. Github; Available from: ' 'https://github.com/National-COVID-Cohort-Collaborative/Phenotype_Data_Acquisition/wiki/Latest-Phenotype.'}, { 'issue': '10', 'key': '3655_CR28', 'doi-asserted-by': 'publisher', 'first-page': 'e2235089', 'DOI': '10.1001/jamanetworkopen.2022.35089', 'volume': '5', 'author': 'LJ McGrath', 'year': '2022', 'unstructured': 'McGrath LJ, Scott AM, Surinach A, Chambers R, Benigno M, Malhotra D. Use ' 'of the postacute sequelae of COVID-19 diagnosis code in routine clinical ' 'practice in the US. JAMA Netw Open. 2022;5(10):e2235089.', 'journal-title': 'JAMA Netw Open'}, { 'key': '3655_CR29', 'unstructured': 'Zachary Butzin-Dozier, Yunwen Ji, Haodong Li, Jeremy Coyle, Junming ' '(Seraphina) Shi, Rachael V. Philips, et al. Predicting long COVID in the ' 'National COVID Cohort Collaborative using super learner. medRxiv. ' '2023;2023.07.27.23293272. Available from: ' 'http://medrxiv.org/content/early/2023/08/04/2023.07.27.23293272.abstract.'}, { 'key': '3655_CR30', 'unstructured': 'Van Der Laan M. Why we need a statistical revolution. Sense about ' 'Science USA. 2015. Available from: ' 'https://senseaboutscienceusa.org/super-learning-and-the-revolution-in-knowledge/.'}, { 'key': '3655_CR31', 'doi-asserted-by': 'crossref', 'first-page': 'Article25', 'DOI': '10.2202/1544-6115.1309', 'volume': '6', 'author': 'MJ van der Laan', 'year': '2007', 'unstructured': 'van der Laan MJ, Polley EC, Hubbard AE. Super learner. Stat Appl Genet ' 'Mol Biol. 2007;6:Article25.', 'journal-title': 'Stat Appl Genet Mol Biol'}, { 'key': '3655_CR32', 'doi-asserted-by': 'publisher', 'unstructured': 'Phillips RV, van der Laan MJ, Lee H, Gruber S. Practical considerations ' 'for specifying a super learner. Int J Epidemiol. 2023:dyad023. Available ' 'from: https://doi.org/10.1093/ije/dyad023 . Cited 2023 Jun 15.', 'DOI': '10.1093/ije/dyad023'}, { 'key': '3655_CR33', 'doi-asserted-by': 'crossref', 'unstructured': 'Diaz Munoz I, van der Laan MJ. Sensitivity analysis for causal inference ' 'under unmeasured confounding and measurement error problems. Division of ' 'Biostatistics, UC Berkeley; 2012. ' 'http://www.bepress.com/ucbbiostat/paper303.', 'DOI': '10.1515/ijb-2013-0004'}, { 'issue': '9', 'key': '3655_CR34', 'doi-asserted-by': 'publisher', 'first-page': '843', 'DOI': '10.1016/j.amjmed.2009.02.022', 'volume': '122', 'author': 'AL Hassett', 'year': '2009', 'unstructured': 'Hassett AL, Radvanski DC, Buyske S, Savage SV, Sigal LH. Psychiatric ' 'comorbidity and other psychological factors in patients with “chronic ' 'Lyme disease.” Am J Med. 2009;122(9):843–50.', 'journal-title': 'Am J Med'}, { 'issue': '12', 'key': '3655_CR35', 'doi-asserted-by': 'publisher', 'first-page': '1742', 'DOI': '10.1002/art.24314', 'volume': '59', 'author': 'AL Hassett', 'year': '2008', 'unstructured': 'Hassett AL, Radvanski DC, Buyske S, Savage SV, Gara M, Escobar JI, et ' 'al. Role of psychiatric comorbidity in chronic Lyme disease. Arthritis ' 'Care Res. 2008;59(12):1742–9. https://doi.org/10.1002/art.24314. Cited ' '2024 May 9.', 'journal-title': 'Arthritis Care Res'}, { 'key': '3655_CR36', 'doi-asserted-by': 'publisher', 'DOI': '10.3389/fpsyt.2019.00286', 'volume': '10', 'author': 'Y Liu', 'year': '2019', 'unstructured': 'Liu Y, Zhao J, Fan X, Guo W. Dysfunction in serotonergic and ' 'noradrenergic systems and somatic symptoms in psychiatric disorders. ' 'Front Psychiatry. 2019;10: 286.', 'journal-title': 'Front Psychiatry'}, { 'key': '3655_CR37', 'doi-asserted-by': 'publisher', 'first-page': '167', 'DOI': '10.1016/B978-0-12-411512-5.00009-9', 'volume': '68', 'author': 'JG Hensler', 'year': '2013', 'unstructured': 'Hensler JG, Artigas F, Bortolozzi A, Daws LC, De Deurwaerdère P, Milan ' 'L, et al. Catecholamine/serotonin interactions: systems thinking for ' 'brain function and disease. Adv Pharmacol. 2013;68:167–97.', 'journal-title': 'Adv Pharmacol'}, { 'issue': '4', 'key': '3655_CR38', 'doi-asserted-by': 'publisher', 'first-page': 'pyu050', 'DOI': '10.1093/ijnp/pyu050', 'volume': '18', 'author': 'ME Mansari', 'year': '2015', 'unstructured': 'Mansari ME, Manta S, Oosterhof C, El Iskandrani KS, Chenu F, Shim S, et ' 'al. Restoration of serotonin neuronal firing following long-term ' 'administration of bupropion but not paroxetine in olfactory ' 'bulbectomized rats. Int J Neuropsychopharmacol. 2015;18(4):pyu050. ' 'https://doi.org/10.1093/ijnp/pyu050. Cited 2024 May 14.', 'journal-title': 'Int J Neuropsychopharmacol'}, { 'issue': '43', 'key': '3655_CR39', 'doi-asserted-by': 'publisher', 'first-page': '14482', 'DOI': '10.1523/JNEUROSCI.2409-10.2010', 'volume': '30', 'author': 'A Hahn', 'year': '2010', 'unstructured': 'Hahn A, Lanzenberger R, Wadsak W, Spindelegger C, Moser U, Mien LK, et ' 'al. Escitalopram enhances the association of serotonin-1A autoreceptors ' 'to heteroreceptors in anxiety disorders. J Neurosci. ' '2010;30(43):14482–9.', 'journal-title': 'J Neurosci'}, { 'issue': '4', 'key': '3655_CR40', 'doi-asserted-by': 'publisher', 'first-page': '731', 'DOI': '10.1016/j.neuroscience.2009.02.072', 'volume': '160', 'author': 'C Vidal', 'year': '2009', 'unstructured': 'Vidal C, Herzog C, Haeberle AM, Bombarde C, Miquel MC, Carimalo J, et ' 'al. Early dysfunction of central 5-HT system in a murine model of bovine ' 'spongiform encephalopathy. Neuroscience. 2009;160(4):731–43.', 'journal-title': 'Neuroscience'}, { 'key': '3655_CR41', 'doi-asserted-by': 'publisher', 'first-page': '486', 'DOI': '10.3389/fnhum.2013.00486', 'volume': '7', 'author': 'C Wielpuetz', 'year': '2013', 'unstructured': 'Wielpuetz C, Kuepper Y, Grant P, Munk AJL, Hennig J. Acute responsivity ' 'of the serotonergic system to S-citalopram and positive emotionality-the ' 'moderating role of the 5-HTTLPR. Front Hum Neurosci. 2013;7:486.', 'journal-title': 'Front Hum Neurosci'}, { 'issue': '2', 'key': '3655_CR42', 'doi-asserted-by': 'publisher', 'first-page': '75', 'DOI': '10.1097/00004850-200103000-00002', 'volume': '16', 'author': 'SA Montgomery', 'year': '2001', 'unstructured': 'Montgomery SA, Kasper S, Stein DJ, Bang Hedegaard K, Lemming OM. ' 'Citalopram 20 mg, 40 mg and 60 mg are all effective and well tolerated ' 'compared with placebo in obsessive-compulsive disorder. Int Clin ' 'Psychopharmacol. 2001;16(2):75–86.', 'journal-title': 'Int Clin Psychopharmacol'}, { 'issue': '9', 'key': '3655_CR43', 'doi-asserted-by': 'publisher', 'first-page': '796', 'DOI': '10.4088/JCP.v63n0908', 'volume': '63', 'author': 'S Pallanti', 'year': '2002', 'unstructured': 'Pallanti S, Quercioli L, Koran LM. Citalopram intravenous infusion in ' 'resistant obsessive-compulsive disorder: an open trial. J Clin ' 'Psychiatry. 2002;63(9):796–801.', 'journal-title': 'J Clin Psychiatry'}, { 'issue': '4', 'key': '3655_CR44', 'doi-asserted-by': 'publisher', 'first-page': '427', 'DOI': '10.1111/j.1440-1819.2004.01278.x', 'volume': '58', 'author': 'K Kamijima', 'year': '2004', 'unstructured': 'Kamijima K, Murasaki M, Asai M, Higuchi T, Nakajima T, Taga C, et al. ' 'Paroxetine in the treatment of obsessive-compulsive disorder: ' 'randomized, double-blind, placebo-controlled study in Japanese patients. ' 'Psychiatry Clin Neurosci. 2004;58(4):427–33.', 'journal-title': 'Psychiatry Clin Neurosci'}, { 'issue': '1', 'key': '3655_CR45', 'doi-asserted-by': 'publisher', 'first-page': '150', 'DOI': '10.1186/s41983-021-00405-1', 'volume': '57', 'author': 'AA Alkhamees', 'year': '2021', 'unstructured': 'Alkhamees AA. Obsessive-compulsive disorder post-COVID-19: a case ' 'presentation. Egypt J Neurol Psychiatr Neurosurg. 2021;57(1):150.', 'journal-title': 'Egypt J Neurol Psychiatr Neurosurg'}, { 'issue': '1', 'key': '3655_CR46', 'doi-asserted-by': 'publisher', 'first-page': '65', 'DOI': '10.1093/aje/kww165', 'volume': '185', 'author': 'MS Schuler', 'year': '2017', 'unstructured': 'Schuler MS, Rose S. Targeted maximum likelihood estimation for causal ' 'inference in observational studies. Am J Epidemiol. 2017;185(1):65–73.', 'journal-title': 'Am J Epidemiol'}, { 'issue': '7', 'key': '3655_CR47', 'doi-asserted-by': 'publisher', 'first-page': 'e532', 'DOI': '10.1016/S2589-7500(22)00048-6', 'volume': '4', 'author': 'ER Pfaff', 'year': '2022', 'unstructured': 'Pfaff ER, Girvin AT, Bennett TD, Bhatia A, Brooks IM, Deer RR, et al. ' 'Identifying who has long COVID in the USA: a machine learning approach ' 'using N3C data. Lancet Digit Health. 2022;4(7):e532–41.', 'journal-title': 'Lancet Digit Health'}, { 'key': '3655_CR48', 'unstructured': 'Chen DG (Din), Sun J, Peace KE, editors. Interval-censored time-to-event ' 'data: methods and applications. 0 ed. Chapman and Hall/CRC; 2012. ' 'Available from: https://www.taylorfrancis.com/books/9781466504288. Cited ' '2024 Jan 10.'}, { 'issue': '1', 'key': '3655_CR49', 'doi-asserted-by': 'publisher', 'first-page': '58', 'DOI': '10.1186/s12916-023-02737-6', 'volume': '21', 'author': 'ER Pfaff', 'year': '2023', 'unstructured': 'Pfaff ER, Madlock-Brown C, Baratta JM, Bhatia A, Davis H, Girvin A, et ' 'al. Coding long COVID: characterizing a new disease through an ICD-10 ' 'lens. BMC Med. 2023;21(1):58.', 'journal-title': 'BMC Med'}], 'container-title': 'BMC Medicine', 'original-title': [], 'language': 'en', 'link': [ { 'URL': 'https://link.springer.com/content/pdf/10.1186/s12916-024-03655-x.pdf', 'content-type': 'application/pdf', 'content-version': 'vor', 'intended-application': 'text-mining'}, { 'URL': 'https://link.springer.com/article/10.1186/s12916-024-03655-x/fulltext.html', 'content-type': 'text/html', 'content-version': 'vor', 'intended-application': 'text-mining'}, { 'URL': 'https://link.springer.com/content/pdf/10.1186/s12916-024-03655-x.pdf', 'content-type': 'application/pdf', 'content-version': 'vor', 'intended-application': 'similarity-checking'}], 'deposited': { 'date-parts': [[2024, 10, 15]], 'date-time': '2024-10-15T15:12:21Z', 'timestamp': 1729005141000}, 'score': 1, 'resource': { 'primary': { 'URL': 'https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-024-03655-x'}}, 'subtitle': [], 'short-title': [], 'issued': {'date-parts': [[2024, 10, 8]]}, 'references-count': 49, 'journal-issue': {'issue': '1', 'published-online': {'date-parts': [[2024, 12]]}}, 'alternative-id': ['3655'], 'URL': 'http://dx.doi.org/10.1186/s12916-024-03655-x', 'relation': {}, 'ISSN': ['1741-7015'], 'subject': [], 'container-title-short': 'BMC Med', 'published': {'date-parts': [[2024, 10, 8]]}, 'assertion': [ { 'value': '25 March 2024', 'order': 1, 'name': 'received', 'label': 'Received', 'group': {'name': 'ArticleHistory', 'label': 'Article History'}}, { 'value': '25 September 2024', 'order': 2, 'name': 'accepted', 'label': 'Accepted', 'group': {'name': 'ArticleHistory', 'label': 'Article History'}}, { 'value': '8 October 2024', 'order': 3, 'name': 'first_online', 'label': 'First Online', 'group': {'name': 'ArticleHistory', 'label': 'Article History'}}, {'order': 1, 'name': 'Ethics', 'group': {'name': 'EthicsHeading', 'label': 'Declarations'}}, { 'value': 'This study was approved by the UC Berkeley Office for Protection of Human ' 'Subjects (2022–01-14980). The N3C data transfer to NCATS is performed under a ' 'Johns Hopkins University Reliance Protocol # IRB00249128 or individual site ' 'agreements with NIH. N3C received a waiver of consent from the NIH ' 'Institutional Review board and allows the secondary analysis of these data ' 'without additional consent.', 'order': 2, 'name': 'Ethics', 'group': {'name': 'EthicsHeading', 'label': 'Ethics approval and consent to participate'}}, { 'value': 'The authors consent to the publication of this manuscript in its entirety.', 'order': 3, 'name': 'Ethics', 'group': {'name': 'EthicsHeading', 'label': 'Consent for publication'}}, { 'value': 'The authors declare no competing interests related to this study.', 'order': 4, 'name': 'Ethics', 'group': {'name': 'EthicsHeading', 'label': 'Competing interests'}}], 'article-number': '445'}