Predictive modeling: the role of opioid use in suicide risk

项目总结/摘要： 近年来，自杀死亡和阿片类药物过量死亡都在增加。这两 公共卫生危机有很大的重叠：我们的初步工作表明，22%至37%的 与阿片类药物有关的过量是自杀或企图自杀。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大的病人。 我们开发了预测自杀企图或死亡风险的模型，准确率为83%至86%。然而，在这方面， 这些模型不包括重要的类阿片相关变量。在一系列平行的工作中， 基于编码电子健康记录（EHR）数据的算法，以识别阿片类药物相关过量， 将其归类为非故意或故意自杀。该项目将现有的两个 研究路线。 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 300万患者的就诊;目前正在扩大，以包括通过以下方式获得的额外结果和就诊 2016年，以及其他预测因素，但是，纳入阿片类药物相关变量不是资助的 补充.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、停药或显著减少处方剂量 阿片类药物或先前非致命性阿片类药物相关过量可改善自杀企图或死亡的预测， 门诊医疗访视后90天。我们还将开发专门预测阿片类药物的模型- 在整个样本中以及在处方阿片类药物的人群中， 药物，并确定阿片类药物相关的自杀企图或死亡的预测因素是否一致， 男人和女人 所提出的工作的目标是最大限度地提高我们的模型的性能，以创造最好的 为临床医生提供工具，以帮助减少未来的自杀。我们与 最大的全国EHR供应商，并正在努力开发一种基于EHR的即时临床工具来预测 自杀未遂和死亡的数据因此，这项工作将直接影响到 通过为临床医生提供一个有效的，基于证据的工具来评估自杀风险。的 这项工作还将提供关于未充分研究的与阿片有关的自杀预测因素和调节因素的关键数据。

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.