Assessing opioid overdose risk: a review of clinical prediction models utilizing patient-level data.
Assessing opioid overdose risk: a review of clinical prediction models utilizing patient-level data.
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DOI：
10.1016/j.trsl.2021.03.012
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发表时间：
2021-08
期刊：
影响因子：
--
通讯作者：
Henry SG
中科院分区：
文献类型：
--
作者：
Tseregounis IE;Henry SG
在美国，药物，特别是与阿片类药物有关的过量用药仍然是一个主要的公共卫生问题。多项研究已经检查了与过量风险相关的个体风险因素，但开发过量临床风险预测工具的研究仅在过去几年才出现。我们对与阿片类药物过量风险相关的患者层面因素的文献进行了全面的回顾，重点是美国阿片类药物过量的临床风险预测模型。对开发和/或验证临床预测模型的研究进行了仔细审查和评估，以确定该领域的状态。我们确定了12项研究报告了阿片类药物过量风险的风险预测模型。已发布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关联数据集。研究报告了模型的性能使用的区别措施，通常在良好到优秀的水平，虽然他们并不总是评估校准。与不包含临床预测因子的模型（C-statistics: 0.69-0.82）相比，包含临床预测因子的模型的C-statistics更好（C-statistics: 0.75-0.95）。模型的外部验证很少，我们没有发现评估模型实施或风险预测工具在临床实践中的研究。这些模型的一个共同特征是假阳性率高，主要是因为阿片类药物过量在一般人群中很少见。因此，在实践中实施预测模型的努力应考虑到已发表的模型高估了许多低风险患者的过量风险。未来评估用药过量风险的预测模型应采用外部验证和解决模型校准问题。为了将预测模型的发现转化为临床公共卫生效益，未来的研究应侧重于开发基于预测模型的临床预测工具，将这些工具应用于临床实践，并评估这些模型对治疗决策、患者预后以及最终阿片类药物过量率的影响。
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.
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