Predictive modeling: the role of opioid use in suicide risk

项目摘要/摘要： 自杀死亡和阿片类药物相关的过量死亡近年来都在增加。这两个 公共卫生危机有很大的重叠：我们的初步工作表明，22%到37%的 与阿片类药物相关的过量是自杀或自杀未遂。医疗机构是进行干预的理想场所 然而，为了防止自杀，临床医生需要更好的工具来识别风险最高的患者。 我们开发了预测自杀未遂或死亡风险的模型，准确率为83%至86%。然而， 这些模型不包括重要的阿片类药物相关变量。在一项平行的工作中，我们开发了 基于编码的电子健康记录(EHR)数据的算法以识别与阿片类药物相关的过量和 将其归类为非故意自杀或故意自杀。拟议的项目将这两个现有的 研究领域。 我们的自杀风险预测数据集包括七个大型医疗保健系统和大约2000万 300万患者就诊；目前正在扩大范围，包括通过以下途径获得更多成果和就诊 2016年，以及其他预测因素，然而，纳入阿片类药物相关变量不是供资的一部分 副刊。在拟议的研究中，我们将确定是否包括与非法和 处方阿片类药物使用、阿片类药物使用障碍、停用或大幅减少处方剂量 阿片类药物或先前非致命性阿片类药物相关过量提高了自杀未遂或死亡的预测 在门诊就诊后90天。我们还将开发专门预测阿片类药物的模型- 整个样本和服用阿片类药物的人群中与自杀未遂和死亡相关的情况 药物，并确定与阿片类药物相关的自杀企图或死亡的预测因素是否与 男人和女人。 拟议工作的目标是最大限度地提高我们的模型的性能，以便创建最佳 临床医生可以使用的工具来帮助减少未来的自杀率。我们已经与 最大的全国性电子病历供应商，正在努力开发一种基于电子病历的护理点临床工具，以预测 根据我们的研究结果，自杀未遂和死亡人数。因此，这项工作将对 通过为临床医生提供一种有效的、循证的工具来评估自杀风险。这个 这项工作还将提供有关自杀的阿片类药物相关预测因素和调节因素的研究不足的关键数据。

PROJECT SUMMARY/ABSTRACT: Suicide deaths and opioid-related overdose deaths have both been increasing in recent years. These two public health crises have substantial overlap: our preliminary work suggests that between 22% and 37% of opioid-related overdoses are suicides or suicide attempts. Healthcare settings are ideal places to intervene to prevent suicides, however clinicians need better tools to recognize the patients at greatest risk. We developed models that predict risk of suicide attempt or death with 83% to 86% accuracy. However, these models do not include important opioid-related variables. In a parallel body of work, we developed algorithms based on coded electronic health record (EHR) data to identify opioid-related overdoses and classify them as unintentional or intentional suicides. The proposed project integrates these two existing lines of research. Our suicide risk prediction dataset includes seven large healthcare systems and approximately 20 million visits by 3 million patients; it is currently being expanded to include additional outcomes and visits through 2016, and additional predictors, however inclusion of opioid-related variables was not part of the funded supplement. In the proposed study, we will determine whether including variables related to illicit and prescribed opioid use, opioid use disorder, discontinuation or significant dose reductions of prescription opioids, or prior non-fatal opioid-related overdoses improves predictions of suicide attempts or death within 90 days following an outpatient healthcare visit. We will also develop models that specifically predict opioid- related suicide attempts and deaths in the sample as a whole and among people prescribed opioid medications, and determine if the predictors of opioid-related suicide attempts or deaths are consistent for men and women. The goal of the proposed work is to maximize the performance of our models in order to create the best available tools for clinicians to help reduce future suicides. We have an established collaboration with the largest national EHR vendor and are working to develop an EHR-based, point-of-care clinical tool to predict suicide attempts and deaths based on our research findings. This work will therefore have a direct impact on clinical practice by providing clinicians with an efficient, evidence-based tool to evaluate suicide risk. The work will also provide critical data on understudied opioid-related predictors and moderators of suicide.