作者
Wei-Hsuan Lo-Ciganic, James L Huang, Hao H Zhang, Jeremy C Weiss, Yonghui Wu, C Kent Kwoh, Julie M Donohue, Gerald Cochran, Adam J Gordon, Daniel C Malone, Courtney C Kuza, Walid F Gellad
发表日期
2019/3/1
期刊
JAMA network open
卷号
2
期号
3
页码范围
e190968-e190968
出版商
American Medical Association
简介
Importance
Current approaches to identifying individuals at high risk for opioid overdose target many patients who are not truly at high risk.
Objective
To develop and validate a machine-learning algorithm to predict opioid overdose risk among Medicare beneficiaries with at least 1 opioid prescription.
Design, Setting, and Participants
A prognostic study was conducted between September 1, 2017, and December 31, 2018. Participants (n = 560 057) included fee-for-service Medicare beneficiaries without cancer who filled 1 or more opioid prescriptions from January 1, 2011, to December 31, 2015. Beneficiaries were randomly and equally divided into training, testing, and validation samples.
Exposures
Potential predictors (n = 268), including sociodemographics, health status, patterns of opioid use, and practitioner-level and regional-level factors, were measured in 3-month windows, starting 3 months before …
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WH Lo-Ciganic, JL Huang, HH Zhang, JC Weiss, Y Wu… - JAMA network open, 2019