Developing and validating a machine-learning algorithm to predict opioid overdose in Medicaid beneficiaries in two US states: a prognostic modelling study

• Published in: The Lancet. Digital health 2022-06, Vol.4 (6), p.e455-e465
• Main Authors: Lo-Ciganic, Wei-Hsuan, Donohue, Julie M, Yang, Qingnan, Huang, James L, Chang, Ching-Yuan, Weiss, Jeremy C, Guo, Jingchuan, Zhang, Hao H, Cochran, Gerald, Gordon, Adam J, Malone, Daniel C, Kwoh, Chian K, Wilson, Debbie L, Kuza, Courtney C, Gellad, Walid F
• Format: Article
• Language: English
• Subjects: Algorithms; Analgesics, Opioid; Drug Overdose; Humans; Machine Learning; Medicaid; Opiate Overdose; Prognosis; United States
• ISSN: 2589-7500; 2589-7500
• DOI: 10.1016/S2589-7500(22)00062-0

Little is known about whether machine-learning algorithms developed to predict opioid overdose using earlier years and from a single state will perform as well when applied to other populations. We aimed to develop a machine-learning algorithm to predict 3-month risk of opioid overdose using Pennsylvania Medicaid data and externally validated it in two data sources (ie, later years of Pennsylvania Medicaid data and data from a different state). This prognostic modelling study developed and validated a machine-learning algorithm to predict overdose in Medicaid beneficiaries with one or more opioid prescription in Pennsylvania and Arizona, USA. To predict risk of hospital or emergency department visits for overdose in the subsequent 3 months, we measured 284 potential predictors from pharmaceutical and health-care encounter claims data in 3-month periods, starting 3 months before the first opioid prescription and continuing until loss to follow-up or study end. We developed and internally validated a gradient-boosting machine algorithm to predict overdose using 2013–16 Pennsylvania Medicaid data (n=639 693). We externally validated the model using (1) 2017–18 Pennsylvania Medicaid data (n=318 585) and (2) 2015–17 Arizona Medicaid data (n=391 959). We reported several prediction performance metrics (eg, C-statistic, positive predictive value). Beneficiaries were stratified into risk-score subgroups to support clinical use. A total of 8641 (1·35%) 2013–16 Pennsylvania Medicaid beneficiaries, 2705 (0·85%) 2017–18 Pennsylvania Medicaid beneficiaries, and 2410 (0·61%) 2015–17 Arizona beneficiaries had one or more overdose during the study period. C-statistics for the algorithm predicting 3-month overdoses developed from the 2013–16 Pennsylvania training dataset and validated on the 2013–16 Pennsylvania internal validation dataset, 2017–18 Pennsylvania external validation dataset, and 2015–17 Arizona external validation dataset were 0·841 (95% CI 0·835–0·847), 0·828 (0·822–0·834), and 0·817 (0·807–0·826), respectively. In external validation datasets, 71 361 (22·4%) of 318 585 2017–18 Pennsylvania beneficiaries were in high-risk subgroups (positive predictive value of 0·38–4·08%; capturing 73% of overdoses in the subsequent 3 months) and 40 041 (10%) of 391 959 2015–17 Arizona beneficiaries were in high-risk subgroups (positive predictive value of 0·19–1·97%; capturing 55% of overdoses). Lower risk subgroups in both validation datasets had few individuals (≤0·2%)