Center for Healthcare Policy and Research, University of California Davis, Sacramento, California, USA.
Center for Healthcare Policy and Research, University of California Davis, Sacramento, California, USA; Department of Internal Medicine, University of California Davis, Sacramento, California, USA.
Transl Res. 2021 Aug;234:74-87. doi: 10.1016/j.trsl.2021.03.012. Epub 2021 Mar 21.
Drug, and specifically opioid-related, overdoses remain a major public health problem in the United States. Multiple studies have examined individual risk factors associated with overdose risk, but research developing clinical risk prediction tools for overdose has only emerged in the last few years. We conducted a comprehensive review of the literature on patient-level factors associated with opioid-related overdose risk, with an emphasis on clinical risk prediction models for opioid-related overdose in the United States. Studies that developed and/or validated clinical prediction models were closely reviewed and evaluated to determine the state of the field. We identified 12 studies that reported risk prediction models for opioid-related overdose risk. Published models were developed from a variety of data sources, including Veterans Health Administration data, Medicare data, commercial insurance data, and statewide linked datasets. Studies reported model performance using measures of discrimination, usually at good-to-excellent levels, though they did not always assess calibration. C-statistics were better for models that included clinical predictors (c-statistics: 0.75-0.95) compared to models without them (c-statistics: 0.69-0.82). External validation of models was rare, and we found no studies evaluating implementation of models or risk prediction tools into clinical practice. A common feature of these models was a high rate of false positives, largely because opioid-related overdose is rare in the general population. Thus, efforts to implement prediction models into practice should take into account that published models overestimate overdose risk for many low-risk patients. Future prediction models assessing overdose risk should employ external validation and address model calibration. In order to translate findings from prediction models into clinical public health benefit, future studies should focus on developing clinical prediction tools based on prediction models, implementing these tools into clinical practice, and evaluating the impact of these models on treatment decisions, patient outcomes, and, ultimately, opioid overdose rates.
药物，特别是阿片类药物相关的药物过量，仍然是美国的一个主要公共卫生问题。多项研究已经检查了与药物过量风险相关的个体风险因素，但在过去几年中，才刚刚出现用于药物过量的临床风险预测工具的研究。我们对与阿片类药物相关的药物过量风险相关的患者水平因素的文献进行了全面审查，重点是美国与阿片类药物相关的药物过量的临床风险预测模型。我们仔细审查和评估了开发和/或验证临床预测模型的研究，以确定该领域的现状。我们确定了 12 项报告与阿片类药物相关的药物过量风险预测模型的研究。已发表的模型是从各种数据源开发的，包括退伍军人健康管理局数据、医疗保险数据、商业保险数据和全州链接数据集。研究使用区分度测量指标（通常为良好到优秀水平）报告模型性能，但并非总是评估校准度。包含临床预测指标的模型的 C 统计值更好（C 统计值：0.75-0.95），而不包含这些指标的模型的 C 统计值则较差（C 统计值：0.69-0.82）。模型的外部验证很少，我们没有发现任何评估模型或风险预测工具在临床实践中的应用的研究。这些模型的一个共同特征是假阳性率很高，这主要是因为阿片类药物相关的药物过量在普通人群中很少见。因此，将预测模型付诸实践的努力应该考虑到，已发表的模型会过高估计许多低风险患者的药物过量风险。未来评估药物过量风险的预测模型应采用外部验证并解决模型校准问题。为了将预测模型的研究结果转化为临床公共卫生效益，未来的研究应专注于基于预测模型开发临床预测工具，将这些工具应用于临床实践，并评估这些模型对治疗决策、患者结局以及最终阿片类药物过量率的影响。
