ArticleJMIR mental health2021
Using Machine Learning Imputed Outcomes to Assess Drug-Dependent Risk of Self-Harm in Patients with Bipolar Disorder: A Comparative Effectiveness Study.
Article in JMIR mental health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02893371 (Longitudinal Comparative Effectiveness of Bipolar Disorder Therapies), which is not on this map. Cited by 8 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Longitudinal Comparative Effectiveness of Bipolar Disorder Therapies
Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 9 citations in OpenAlex.
- Optimizing Treatment Strategies in the Bipolar Disorder Spectrum With Classical AI Approaches: Systematic Review of Performance, Bias, and Clinical Applicability.JMIR mental health · 2026Pooled it
- Machine learning and the prediction of suicide in psychiatric populations: a systematic review.Translational psychiatry · 2024Pooled it
- Distinct Patterns of Intentional and Unintentional Overdose Among Psychiatric Patients With Bipolar and Substance Use Disorders.Rhode Island medical journal (2013) · 2026Article
- Detecting Uncoded Self-Harm in Veterans' Electronic Health Records Using Positive and Unlabeled Learning: Retrospective Cohort Study.Journal of medical Internet research · 2026Article
- Examining valproate's potential anticancer effect among patients with bipolar disorder: a territory-wide active-comparator new user study spanning two decades.BMC psychiatry · 2025Article
- Detecting Opioid Use Disorder in Health Claims Data With Positive Unlabeled Learning.IEEE journal of biomedical and health informatics · 2025Article
- Machine Learning in Causal Inference: Application in Pharmacovigilance.Drug safety · 2022Review
- Investigating the Role of Leukocyte Telomere Length in Treatment-Resistant Depression and in Response to Electroconvulsive Therapy.Journal of personalized medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIncomplete 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.
objectiveThe 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.
methodsThe 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 ("psychotherapy").
resultsAmong 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≤.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), "no drug," 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=8.76×10
conclusionsOur data support evidence on the effectiveness of antidepressants, MSAs, and psychotherapy for self-harm prevention in BD.
trial registrationClinicalTrials.gov NCT02893371; https://clinicaltrials.gov/ct2/show/NCT02893371.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.