Center for Healthcare Policy and Research.
Department of Pediatrics, University of California, Davis, Sacramento.
Med Care. 2021 Dec 1;59(12):1051-1058. doi: 10.1097/MLR.0000000000001651.
Tools are needed to aid clinicians in estimating their patients' risk of transitioning to long-term opioid use and to inform prescribing decisions.
The objective of this study was to develop and validate a model that predicts previously opioid-naive patients' risk of transitioning to long-term use.
This was a statewide population-based prognostic study.
Opioid-naive (no prescriptions in previous 2 y) patients aged 12 years old and above who received a pill-form opioid analgesic in 2016-2018 and whose prescriptions were registered in the California Prescription Drug Monitoring Program (PDMP).
A multiple logistic regression approach was used to construct a prediction model with long-term (ie, >90 d) opioid use as the outcome. Models were developed using 2016-2017 data and validated using 2018 data. Discrimination (c-statistic), calibration (calibration slope, intercept, and visual inspection of calibration plots), and clinical utility (decision curve analysis) were evaluated to assess performance.
Development and validation cohorts included 7,175,885 and 2,788,837 opioid-naive patients with outcome rates of 5.0% and 4.7%, respectively. The model showed high discrimination (c-statistic: 0.904 for development, 0.913 for validation), was well-calibrated after intercept adjustment (intercept, -0.006; 95% confidence interval, -0.016 to 0.004; slope, 1.049; 95% confidence interval, 1.045-1.053), and had a net benefit over a wide range of probability thresholds.
A model for the transition from opioid-naive status to long-term use had high discrimination and was well-calibrated. Given its high predictive performance, this model shows promise for future integration into PDMPs to aid clinicians in formulating opioid prescribing decisions at the point of care.
需要工具来帮助临床医生估计患者转为长期使用阿片类药物的风险，并为处方决策提供信息。
本研究旨在开发和验证一种预测先前未使用阿片类药物的患者转为长期使用的风险的模型。
这是一项全州范围内基于人群的预后研究。
2016-2018 年接受过阿片类药物镇痛剂且在加利福尼亚州处方药物监测计划（PDMP）中登记处方的年龄在 12 岁及以上且在过去 2 年内无阿片类药物处方的阿片类药物初治患者。
使用多变量逻辑回归方法构建以长期（即>90 天）阿片类药物使用为结局的预测模型。使用 2016-2017 年的数据进行模型开发，并使用 2018 年的数据进行验证。评估了区分度（c 统计量）、校准（校准斜率、截距和校准图的直观检查）和临床实用性（决策曲线分析），以评估性能。
开发和验证队列分别包括 7175885 名和 2788837 名阿片类药物初治患者，结局发生率分别为 5.0%和 4.7%。该模型具有较高的区分度（c 统计量：开发队列为 0.904，验证队列为 0.913），经过截距调整后校准良好（截距，-0.006；95%置信区间，-0.016 至 0.004；斜率，1.049；95%置信区间，1.045 至 1.053），并且在广泛的概率阈值范围内具有净获益。
从阿片类药物初治状态转为长期使用的模型具有较高的区分度且校准良好。鉴于其出色的预测性能，该模型有望在未来集成到 PDMP 中，以帮助临床医生在护理点制定阿片类药物处方决策。
