本研究旨在利用行政数据开发机器学习（ML）模型，用于估计老年人苯二氮䓬类（BZRA）用药后30天内不良结果的风险，以供卫生部门/监管机构使用。该研究在加拿大阿尔伯塔省于2018年至2019年期间进行，并针对65岁及以上的阿尔伯塔居民进行。排除了任何恶性肿瘤或临终关怀的病例。每个社区药房的BZRA发药作为分析单位。使用2018年行政数据开发ML算法，以预测BZRA发药后30天内的任何原因住院、急诊就诊或死亡风险。使用XGBoost在2019年行政数据上进行验证，评估排名预测的区分度、校准度和其他相关指标。在2019年数据上模拟了每日和季度的预测。研究纳入了65,063名参与者，代表2018年至2019年间的633,333次BZRA发药。验证集中有314,615次发药与55,928种全因素结果相关联，表示先验概率为17.8%。XGBoost模型的C统计量为0.75。在2019年末测量风险时，预测风险前0.1百分位的LR+为40.31，对应的后验概率为90%。每日和每季度的分类模拟结果显示，在所有风险预测类别中，正似然比都小于10，预测信息不具备参考价值。先前的住院历史在变量重要性中排名最高。仅利用行政卫生数据开发的ML模型可能无法为卫生监管机构提供足够的信息预测，以作为潜在干预的决策辅助工具，尤其是在考虑老年人BZRA风险的每日或每季分类时。如果首选年度分类，则ML模型可能对这种情景具有参考意义。卫生监管机构应该能够获取其他类型的数据来改善ML预测。版权所有 © 2023作者。由Elsevier B.V.出版。保留所有权利。

To develop a machine-learning (ML) model using administrative data to estimate risk of adverse outcomes within 30-days of a benzodiazepine (BZRA) dispensation in older adults for use by health departments/regulators.This study was conducted in Alberta, Canada during 2018-2019 in Albertans 65 years of age and older. Those with any history of malignancy or palliative care were excluded.Each BZRA dispensation from a community pharmacy served as the unit of analysis.ML algorithms were developed on 2018 administrative data to predict risk of any-cause hospitalization, emergency department visit or death within 30-days of a BZRA dispensation. Validation on 2019 administrative data was done using XGBoost to evaluate discrimination, calibration and other relevant metrics on ranked predictions. Daily and quarterly predictions were simulated on 2019 data.65,063 study participants were included which represented 633,333 BZRA dispensation during 2018-2019. The validation set had 314,615 dispensations linked to 55,928 all-cause outcomes representing a pre-test probability of 17.8%. C-statistic for the XGBoost model was 0.75. Measuring risk at the end of 2019, the top 0.1 percentile of predicted risk had a LR + of 40.31 translating to a post-test probability of 90%. Daily and quarterly classification simulations resulted in uninformative predictions with positive likelihood ratios less than 10 in all risk prediction categories. Previous history of admissions was ranked highest in variable importance.Developing ML models using only administrative health data may not provide health regulators with sufficient informative predictions to use as decision aids for potential interventions, especially if considering daily or quarterly classifications of BZRA risks in older adults. ML models may be informative for this context if yearly classifications are preferred. Health regulators should have access to other types of data to improve ML prediction.Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.