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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),...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2021
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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 |
format | Online Article Text |
id | pubmed-8100888 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
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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