{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:57:17Z","timestamp":1782842237037,"version":"3.54.5"},"reference-count":43,"publisher":"JMIR Publications Inc.","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Ment Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Incomplete suicidality coding in administrative claims data is a known obstacle for observational studies. With most of the negative outcomes missing from the data, it is challenging to assess the evidence on treatment strategies for the prevention of self-harm in bipolar disorder (BD), including pharmacotherapy and psychotherapy. There are conflicting data from studies on the drug-dependent risk of self-harm, and there is major uncertainty regarding the preventive effect of monotherapy and drug combinations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The aim of this study was to compare all commonly used BD pharmacotherapies, as well as psychotherapy for the risk of self-harm, in a large population of commercially insured individuals, using self-harm imputation to overcome the known limitations of this outcome being underrecorded within US electronic health care records.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The IBM MarketScan administrative claims database was used to compare self-harm risk in patients with BD following 65 drug regimens and drug-free periods. Probable but uncoded self-harm events were imputed via machine learning, with different probability thresholds examined in a sensitivity analysis. Comparators included lithium, mood-stabilizing anticonvulsants (MSAs), second-generation antipsychotics (SGAs), first-generation antipsychotics (FGAs), and five classes of antidepressants. Cox regression models with time-varying covariates were built for individual treatment regimens and for any pharmacotherapy with or without psychosocial interventions (\u201cpsychotherapy\u201d).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 529,359 patients, 1.66% (n=8813 events) had imputed and\/or coded self-harm following the exposure of interest. A higher self-harm risk was observed during adolescence. After multiple testing adjustment (P\u2264.012), the following six regimens had higher risk of self-harm than lithium: tri\/tetracyclic antidepressants + SGA, FGA + MSA, FGA, serotonin-norepinephrine reuptake inhibitor (SNRI) + SGA, lithium + MSA, and lithium + SGA (hazard ratios [HRs] 1.44-2.29), and the following nine had lower risk: lamotrigine, valproate, risperidone, aripiprazole, SNRI, selective serotonin reuptake inhibitor (SSRI), \u201cno drug,\u201d bupropion, and bupropion + SSRI (HRs 0.28-0.74). Psychotherapy alone (without medication) had a lower self-harm risk than no treatment (HR 0.56, 95% CI 0.52-0.60; P=8.76\u00d710-58). The sensitivity analysis showed that the direction of drug-outcome associations did not change as a function of the self-harm probability threshold.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our data support evidence on the effectiveness of antidepressants, MSAs, and psychotherapy for self-harm prevention in BD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial Registration<\/jats:title>\n                    <jats:p>ClinicalTrials.gov NCT02893371; https:\/\/clinicaltrials.gov\/ct2\/show\/NCT02893371<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/24522","type":"journal-article","created":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T10:50:09Z","timestamp":1615373409000},"page":"e24522","source":"Crossref","is-referenced-by-count":11,"title":["Using Machine Learning Imputed Outcomes to Assess Drug-Dependent Risk of Self-Harm in Patients with Bipolar Disorder: A Comparative Effectiveness Study"],"prefix":"10.2196","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5558-2381","authenticated-orcid":false,"given":"Anastasiya","family":"Nestsiarovich","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4981-9020","authenticated-orcid":false,"given":"Praveen","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9348-0319","authenticated-orcid":false,"given":"Nicolas Raymond","family":"Lauve","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nathaniel 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