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Using Machine Learning Imputed Outcomes to Assess Drug-Dependent Risk of Self-Harm in Patients with Bipolar Disorder: A Comparative Effectiveness Study

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),...

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Autores principales: Nestsiarovich, Anastasiya, Kumar, Praveen, Lauve, Nicolas Raymond, Hurwitz, Nathaniel G, Mazurie, Aurélien J, Cannon, Daniel C, Zhu, Yiliang, Nelson, Stuart James, Crisanti, Annette S, Kerner, Berit, Tohen, Mauricio, Perkins, Douglas J, Lambert, Christophe Gerard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8100888/
https://www.ncbi.nlm.nih.gov/pubmed/33688834
http://dx.doi.org/10.2196/24522
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author Nestsiarovich, Anastasiya
Kumar, Praveen
Lauve, Nicolas Raymond
Hurwitz, Nathaniel G
Mazurie, Aurélien J
Cannon, Daniel C
Zhu, Yiliang
Nelson, Stuart James
Crisanti, Annette S
Kerner, Berit
Tohen, Mauricio
Perkins, Douglas J
Lambert, Christophe Gerard
author_facet Nestsiarovich, Anastasiya
Kumar, Praveen
Lauve, Nicolas Raymond
Hurwitz, Nathaniel G
Mazurie, Aurélien J
Cannon, Daniel C
Zhu, Yiliang
Nelson, Stuart James
Crisanti, Annette S
Kerner, Berit
Tohen, Mauricio
Perkins, Douglas J
Lambert, Christophe Gerard
author_sort Nestsiarovich, Anastasiya
collection PubMed
description 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
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spelling pubmed-81008882021-05-07 Using Machine Learning Imputed Outcomes to Assess Drug-Dependent Risk of Self-Harm in Patients with Bipolar Disorder: A Comparative Effectiveness Study Nestsiarovich, Anastasiya Kumar, Praveen Lauve, Nicolas Raymond Hurwitz, Nathaniel G Mazurie, Aurélien J Cannon, Daniel C Zhu, Yiliang Nelson, Stuart James Crisanti, Annette S Kerner, Berit Tohen, Mauricio Perkins, Douglas J Lambert, Christophe Gerard JMIR Ment Health Original Paper 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 JMIR Publications 2021-04-21 /pmc/articles/PMC8100888/ /pubmed/33688834 http://dx.doi.org/10.2196/24522 Text en ©Anastasiya Nestsiarovich, Praveen Kumar, Nicolas Raymond Lauve, Nathaniel G Hurwitz, Aurélien J Mazurie, Daniel C Cannon, Yiliang Zhu, Stuart James Nelson, Annette S Crisanti, Berit Kerner, Mauricio Tohen, Douglas J Perkins, Christophe Gerard Lambert. Originally published in JMIR Mental Health (https://mental.jmir.org), 21.04.2021. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on http://mental.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Nestsiarovich, Anastasiya
Kumar, Praveen
Lauve, Nicolas Raymond
Hurwitz, Nathaniel G
Mazurie, Aurélien J
Cannon, Daniel C
Zhu, Yiliang
Nelson, Stuart James
Crisanti, Annette S
Kerner, Berit
Tohen, Mauricio
Perkins, Douglas J
Lambert, Christophe Gerard
Using Machine Learning Imputed Outcomes to Assess Drug-Dependent Risk of Self-Harm in Patients with Bipolar Disorder: A Comparative Effectiveness Study
title Using Machine Learning Imputed Outcomes to Assess Drug-Dependent Risk of Self-Harm in Patients with Bipolar Disorder: A Comparative Effectiveness Study
title_full Using Machine Learning Imputed Outcomes to Assess Drug-Dependent Risk of Self-Harm in Patients with Bipolar Disorder: A Comparative Effectiveness Study
title_fullStr Using Machine Learning Imputed Outcomes to Assess Drug-Dependent Risk of Self-Harm in Patients with Bipolar Disorder: A Comparative Effectiveness Study
title_full_unstemmed Using Machine Learning Imputed Outcomes to Assess Drug-Dependent Risk of Self-Harm in Patients with Bipolar Disorder: A Comparative Effectiveness Study
title_short Using Machine Learning Imputed Outcomes to Assess Drug-Dependent Risk of Self-Harm in Patients with Bipolar Disorder: A Comparative Effectiveness Study
title_sort using machine learning imputed outcomes to assess drug-dependent risk of self-harm in patients with bipolar disorder: a comparative effectiveness study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8100888/
https://www.ncbi.nlm.nih.gov/pubmed/33688834
http://dx.doi.org/10.2196/24522
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