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Development and Validation of a Machine Learning Individualized Treatment Rule in First-Episode Schizophrenia

IMPORTANCE: Little guidance exists to date on how to select antipsychotic medications for patients with first-episode schizophrenia. OBJECTIVE: To develop a preliminary individualized treatment rule (ITR) for patients with first-episode schizophrenia. DESIGN, SETTING, AND PARTICIPANTS: This prognost...

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Autores principales: Wu, Chi-Shin, Luedtke, Alex R., Sadikova, Ekaterina, Tsai, Hui-Ju, Liao, Shih-Cheng, Liu, Chen-Chung, Gau, Susan Shur-Fen, VanderWeele, Tyler J., Kessler, Ronald C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7043195/
https://www.ncbi.nlm.nih.gov/pubmed/32083693
http://dx.doi.org/10.1001/jamanetworkopen.2019.21660
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author Wu, Chi-Shin
Luedtke, Alex R.
Sadikova, Ekaterina
Tsai, Hui-Ju
Liao, Shih-Cheng
Liu, Chen-Chung
Gau, Susan Shur-Fen
VanderWeele, Tyler J.
Kessler, Ronald C.
author_facet Wu, Chi-Shin
Luedtke, Alex R.
Sadikova, Ekaterina
Tsai, Hui-Ju
Liao, Shih-Cheng
Liu, Chen-Chung
Gau, Susan Shur-Fen
VanderWeele, Tyler J.
Kessler, Ronald C.
author_sort Wu, Chi-Shin
collection PubMed
description IMPORTANCE: Little guidance exists to date on how to select antipsychotic medications for patients with first-episode schizophrenia. OBJECTIVE: To develop a preliminary individualized treatment rule (ITR) for patients with first-episode schizophrenia. DESIGN, SETTING, AND PARTICIPANTS: This prognostic study obtained data from Taiwan's National Health Insurance Research Database on patients with prescribed antipsychotic medications, ambulatory claims, or discharge diagnoses of a schizophrenic disorder between January 1, 2005, and December 31, 2011. An ITR was developed by applying a targeted minimum loss–based ensemble machine learning method to predict treatment success from baseline clinical and demographic data in a 70% training sample. The model was validated in the remaining 30% of the sample. The probability of treatment success was estimated for each medication for each patient under the model. The analysis was conducted between July 16, 2018, and July 15, 2019. EXPOSURES: Fifteen different antipsychotic medications. MAIN OUTCOMES AND MEASURES: Treatment success was defined as not switching medication and not being hospitalized for 12 months. RESULTS: Among the 32 277 patients in the analysis, the mean (SD) age was 36.7 (14.3) years, and 15 752 (48.8%) were male. In the validation sample, the treatment success rate (SE) was 51.7% (1.0%) under the ITR and was 44.5% (0.5%) in the observed population (Z = 7.1; P < .001). The estimated treatment success if all patients were given a prescription for 1 medication was significantly lower for each of the 13 medications than under the ITR (Z = 4.2-16.8; all P < .001). Aripiprazole (3088 [31.9%]) and amisulpride (2920 [30.2%]) were the medications most often recommended by the ITR. Only 1054 patients (10.9%) received ITR-recommended medications. Observed treatment success, although lower than the success under the ITR, was nonetheless significantly higher than if medications had been randomized (44.5% [SE, 0.55%] vs 41.3% [SE, 0.4%]; Z = 6.9; P < .001), although only marginally higher than if medications had been randomized in their observed population proportions (44.5% [SE, 0.5%] vs 43.5% [SE, 0.4%]; Z = 2.2; P = .03]). CONCLUSIONS AND RELEVANCE: These results suggest that an ITR may be associatded with an increase in the treatment success rate among patients with first-episode schizophrenia, but experimental evaluation is needed to confirm this possibility. If confirmed, model refinement that investigates biomarkers, clinical observations, and patient reports as additional predictors in iterative pragmatic trials would be needed before clinical implementation.
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spelling pubmed-70431952020-03-10 Development and Validation of a Machine Learning Individualized Treatment Rule in First-Episode Schizophrenia Wu, Chi-Shin Luedtke, Alex R. Sadikova, Ekaterina Tsai, Hui-Ju Liao, Shih-Cheng Liu, Chen-Chung Gau, Susan Shur-Fen VanderWeele, Tyler J. Kessler, Ronald C. JAMA Netw Open Original Investigation IMPORTANCE: Little guidance exists to date on how to select antipsychotic medications for patients with first-episode schizophrenia. OBJECTIVE: To develop a preliminary individualized treatment rule (ITR) for patients with first-episode schizophrenia. DESIGN, SETTING, AND PARTICIPANTS: This prognostic study obtained data from Taiwan's National Health Insurance Research Database on patients with prescribed antipsychotic medications, ambulatory claims, or discharge diagnoses of a schizophrenic disorder between January 1, 2005, and December 31, 2011. An ITR was developed by applying a targeted minimum loss–based ensemble machine learning method to predict treatment success from baseline clinical and demographic data in a 70% training sample. The model was validated in the remaining 30% of the sample. The probability of treatment success was estimated for each medication for each patient under the model. The analysis was conducted between July 16, 2018, and July 15, 2019. EXPOSURES: Fifteen different antipsychotic medications. MAIN OUTCOMES AND MEASURES: Treatment success was defined as not switching medication and not being hospitalized for 12 months. RESULTS: Among the 32 277 patients in the analysis, the mean (SD) age was 36.7 (14.3) years, and 15 752 (48.8%) were male. In the validation sample, the treatment success rate (SE) was 51.7% (1.0%) under the ITR and was 44.5% (0.5%) in the observed population (Z = 7.1; P < .001). The estimated treatment success if all patients were given a prescription for 1 medication was significantly lower for each of the 13 medications than under the ITR (Z = 4.2-16.8; all P < .001). Aripiprazole (3088 [31.9%]) and amisulpride (2920 [30.2%]) were the medications most often recommended by the ITR. Only 1054 patients (10.9%) received ITR-recommended medications. Observed treatment success, although lower than the success under the ITR, was nonetheless significantly higher than if medications had been randomized (44.5% [SE, 0.55%] vs 41.3% [SE, 0.4%]; Z = 6.9; P < .001), although only marginally higher than if medications had been randomized in their observed population proportions (44.5% [SE, 0.5%] vs 43.5% [SE, 0.4%]; Z = 2.2; P = .03]). CONCLUSIONS AND RELEVANCE: These results suggest that an ITR may be associatded with an increase in the treatment success rate among patients with first-episode schizophrenia, but experimental evaluation is needed to confirm this possibility. If confirmed, model refinement that investigates biomarkers, clinical observations, and patient reports as additional predictors in iterative pragmatic trials would be needed before clinical implementation. American Medical Association 2020-02-21 /pmc/articles/PMC7043195/ /pubmed/32083693 http://dx.doi.org/10.1001/jamanetworkopen.2019.21660 Text en Copyright 2020 Wu C-S et al. JAMA Network Open. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the CC-BY License.
spellingShingle Original Investigation
Wu, Chi-Shin
Luedtke, Alex R.
Sadikova, Ekaterina
Tsai, Hui-Ju
Liao, Shih-Cheng
Liu, Chen-Chung
Gau, Susan Shur-Fen
VanderWeele, Tyler J.
Kessler, Ronald C.
Development and Validation of a Machine Learning Individualized Treatment Rule in First-Episode Schizophrenia
title Development and Validation of a Machine Learning Individualized Treatment Rule in First-Episode Schizophrenia
title_full Development and Validation of a Machine Learning Individualized Treatment Rule in First-Episode Schizophrenia
title_fullStr Development and Validation of a Machine Learning Individualized Treatment Rule in First-Episode Schizophrenia
title_full_unstemmed Development and Validation of a Machine Learning Individualized Treatment Rule in First-Episode Schizophrenia
title_short Development and Validation of a Machine Learning Individualized Treatment Rule in First-Episode Schizophrenia
title_sort development and validation of a machine learning individualized treatment rule in first-episode schizophrenia
topic Original Investigation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7043195/
https://www.ncbi.nlm.nih.gov/pubmed/32083693
http://dx.doi.org/10.1001/jamanetworkopen.2019.21660
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