Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, 13199 E Montview Blvd, Suite 300, Aurora, CO, 80045, USA; Institute for Health Research, Kaiser Permanente Colorado, 2550 S. Parker Road, Suite 200, Aurora, CO, 80014, USA.
Institute for Health Research, Kaiser Permanente Colorado, 2550 S. Parker Road, Suite 200, Aurora, CO, 80014, USA; Colorado Permanente Medical Group, P.C., 10350 E. Dakota Ave., Denver, CO, 80247, USA; Division of General Internal Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, 12631 E 17thAve., Aurora, CO, 80045, USA.
Drug Alcohol Depend. 2021 Oct 1;227:108980. doi: 10.1016/j.drugalcdep.2021.108980. Epub 2021 Aug 28.
Youth are vulnerable to opioid use initiation and its complications. With growing rates of opioid overdose, strategies to identify youth at risk of opioid use disorder (OUD) to efficiently focus prevention interventions are needed. This study developed and validated a prediction model of OUD in youth aged 14-18 years.
The model was developed in a Colorado healthcare system (derivation site) using Cox proportional hazards regression analysis. Model predictors and outcomes were identified using electronic health record data. The model was externally validated in a separate Denver safety net health system (validation site). Youth were followed for up to 3.5 years. We evaluated internal and external validity using discrimination and calibration.
The derivation cohort included 76,603 youth, of whom 108 developed an OUD diagnosis. The model contained 3 predictors (smoking status, mental health diagnosis, and non-opioid substance use or disorder) and demonstrated good calibration (p = 0.90) and discrimination (bootstrap-corrected C-statistic = 0.76: 95 % CI = 0.70, 0.82). Sensitivity and specificity were 57 % and 84 % respectively with a positive predictive value (PPV) of 0.49 %. The validation cohort included 45,790 youth of whom, 74 developed an OUD diagnoses. The model demonstrated poorer calibration (p < 0.001) but good discrimination (C-statistic = 0.89; 95 % CI = 0.84, 0.95), sensitivity of 87.8 % specificity of 68.6 %, and PPV of 0.45 %.
In two Colorado healthcare systems, the prediction model identified 57-88 % of subsequent OUD diagnoses in youth. However, PPV < 1% suggests universal prevention strategies for opioid use in youth may be the best health system approach.
青少年易受阿片类药物使用和相关并发症的影响。随着阿片类药物过量的发生率不断上升，需要制定策略识别有阿片类药物使用障碍（OUD）风险的青少年，以便有效开展预防干预。本研究旨在建立并验证一个针对 14-18 岁青少年 OUD 的预测模型。
该模型在科罗拉多州的医疗系统（推导站点）中，通过 Cox 比例风险回归分析建立。通过电子病历数据确定模型预测因素和结局。该模型在丹佛市另一家社区卫生系统（验证站点）中进行外部验证。对青少年进行了长达 3.5 年的随访。通过区分度和校准度评估内部和外部有效性。
推导队列纳入了 76603 名青少年，其中 108 人被诊断为 OUD。该模型包含 3 个预测因素（吸烟状况、精神健康诊断、非阿片类物质使用或障碍），校准度良好（p=0.90），区分度良好（bootstrap-corrected C 统计量=0.76：95%CI=0.70，0.82）。灵敏度和特异度分别为 57%和 84%，阳性预测值（PPV）为 0.49%。验证队列纳入了 45790 名青少年，其中 74 人被诊断为 OUD。该模型的校准度较差（p<0.001），但区分度较好（C 统计量=0.89；95%CI=0.84，0.95），灵敏度为 87.8%，特异度为 68.6%，PPV 为 0.45%。
在科罗拉多州的两个医疗系统中，该预测模型能识别出青少年中 57%-88%的后续 OUD 诊断。然而，PPV<1%提示针对青少年阿片类药物使用的普遍预防策略可能是最佳的卫生系统方法。
