Using Machine Learning Imputed Outcomes to Assess Drug-Dependent Risk of Self-Harm in Patients with Bipolar Disorder: A Comparative Effectiveness Study
| Autor: | Anastasiya Nestsiarovich, Yiliang Zhu, Nicolas Lauve, Stuart J. Nelson, Daniel C. Cannon, Berit Kerner, Douglas J Perkins, Praveen Kumar, Nathaniel G. Hurwitz, Annette S. Crisanti, Mauricio Tohen, Christophe G. Lambert, Aurélien J. Mazurie |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Serotonin reuptake inhibitor
mood Machine learning computer.software_genre Lower risk self-harm 03 medical and health sciences pharmacotherapy 0302 clinical medicine mania medicine Psychology Bipolar disorder suicide Bupropion Original Paper Risperidone business.industry Hazard ratio medicine.disease bipolar BF1-990 030227 psychiatry psychotherapy Psychiatry and Mental health machine learning depression Aripiprazole Artificial intelligence medicine.symptom business computer Mania 030217 neurology & neurosurgery medicine.drug |
| Zdroj: | JMIR Mental Health JMIR Mental Health, Vol 8, Iss 4, p e24522 (2021) |
| ISSN: | 2368-7959 0289-3371 |
| Popis: | 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. Trial Registration ClinicalTrials.gov NCT02893371; https://clinicaltrials.gov/ct2/show/NCT02893371 |
| Databáze: | OpenAIRE |
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