@Article{info:doi/10.2196/24522,
author="Nestsiarovich, Anastasiya
and Kumar, Praveen
and Lauve, Nicolas Raymond
and Hurwitz, Nathaniel G
and Mazurie, Aur{\'e}lien J
and Cannon, Daniel C
and Zhu, Yiliang
and Nelson, Stuart James
and Crisanti, Annette S
and Kerner, Berit
and Tohen, Mauricio
and Perkins, Douglas J
and Lambert, Christophe Gerard",
title="Using Machine Learning Imputed Outcomes to Assess Drug-Dependent Risk of Self-Harm in Patients with Bipolar Disorder: A Comparative Effectiveness Study",
journal="JMIR Ment Health",
year="2021",
month="Apr",
day="21",
volume="8",
number="4",
pages="e24522",
keywords="bipolar; mood; mania; depression; pharmacotherapy; self-harm; suicide; machine learning; psychotherapy",
abstract="Background: 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. Objective: 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. Methods: 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 (``psychotherapy''). Results: 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≤.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{\texttimes}10-58). The sensitivity analysis showed that the direction of drug-outcome associations did not change as a function of the self-harm probability threshold. Conclusions: Our data support evidence on the effectiveness of antidepressants, MSAs, and psychotherapy for self-harm prevention in BD. Trial Registration: ClinicalTrials.gov NCT02893371; https://clinicaltrials.gov/ct2/show/NCT02893371 ",
issn="2368-7959",
doi="10.2196/24522",
url="https://mental.jmir.org/2021/4/e24522",
url="https://doi.org/10.2196/24522",
url="http://www.ncbi.nlm.nih.gov/pubmed/33688834"
}