Risk of self-harm with all common treatments for bipolar disorder

Описание: AMIA Mental Health Informatics Working Group Presentation given 5/13/2021 by Christophe Lambert: "Drug-dependent risk of self-harm in patients with bipolar disorder: a comparative effectiveness study using machine learning imputed outcomes". Our paper is discussed: Nestsiarovich A, Kumar P, Lauve NR, Hurwitz NG, Mazurie AJ, Cannon DC, Zhu Y, Nelson SJ, Crisanti AS, Kerner B, Tohen M, Perkins DJ, Lambert CG. Using Machine Learning Imputed Outcomes to Assess Drug-Dependent Risk of Self-Harm in Patients with Bipolar Disorder: A Comparative Effectiveness Study. JMIR Ment Health. 2021 Apr 21;8(4):e24522. PMID: 33688834 https://doi.org/10.2196/24522

We dedicate this article to our dear friend and coauthor, Dr Nathaniel G Hurwitz, who passed away during its preparation. He worked on this project as a labor of love, both for us and for all those affected by bipolar disorder.

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=8.76×10-58). 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.