Machine Learning Prediction of 1-Year Mortality and Recurrence after Ischemic Stroke Using Enriched EHR data

使用机器学习预测缺血性卒中后1年死亡率和复发率 丰富的EHR数据 项目总结/摘要 中风是世界范围内死亡和残疾的主要原因。据估计，1年的风险 中风后的死亡率和复发率分别约为15%和10%。此外，最近的一份报告， 全球疾病负担（GBD）显示每年中风人数大幅增加， 二次死亡，特别是低收入群体。复发性中风有增加的趋势， 死亡率和残疾率。因此，必须确定复发和死亡的风险患者， 及时评估，资源分配和有针对性的预防。调查人员最近发表的评论 表明，用于预测中风复发的多种临床评分仅具有有限的临床意义， 效用同样，目前的中风预后模型在质量上差异很大;中风后死亡率的预测模型 受限于其验证队列规模、临床变量的广度和总体有用性。调查人员 最近开发了基于机器学习的中风后全因死亡率和复发模型， 电子健康记录（EHR）。尽管取得了可喜的成果，但我们目前的试点预测模型是有限的 单一的卫生系统，并可能由于隐含的偏见而具有不充分的普遍性。 该提案旨在通过创造性地使用经过审查的EHR数据来扩展和改进预测模型 对于来自三个大型和不同的卫生系统的缺血性中风患者（宾夕法尼亚州卫生，盖辛格， 约翰霍普金斯），照顾超过八百万人在农村和城市地区。该项目将进一步探索 社会健康决定因素（SDoH）的预测价值，当添加到临床数据。调查人员 提出一种综合方法来设计参数优化和可解释的模型，利用丰富的 EHR用于确定缺血性卒中复发和全因死亡率的风险。目标1：基于EHR的标准化 数据，以确定具有共同特征的缺血性中风患者群。目的 2：开发最佳可解释的整体模型，以预测缺血性脑卒中后1年的死亡率和复发率。 中风目标3：前瞻性和外部的1年死亡率和卒中的总体模型 复发 该提案包括模型开发与内部，外部和时间验证，并奠定了 影响研究的基础，以提供临床效用的证据。研究人员预计，这项研究将 导致基于EHR的筛查工具，可以标记高危中风患者，以进行更有针对性的二级预防。

Machine Learning Prediction of 1-Year Mortality and Recurrence after Ischemic Stroke Using Enriched EHR data PROJECT SUMMARY / ABSTRACT Stroke is the leading cause of death and disability worldwide. It has been estimated that the 1-year risk of death and recurrence after a stroke is around 15% and 10%, respectively. Furthermore, a recent report from the Global Burden of Diseases (GBD) has shown a substantial increase in the annual number of strokes and secondary deaths, especially in low-income groups. Recurrent strokes, with an increasing trend, have a higher rate of death and disability. Thus, it is imperative to identify at-risk patients for recurrence and death for proper and timely evaluation, resource allocation, and targeted prevention. The investigators’ recently published review indicates that ─the multiple clinical scores developed for predicting stroke recurrence have only limited clinical utility. Similarly, current stroke prognostic models vary widely in quality; prediction models of post-stroke mortality are limited by their validation cohort size, breadth of clinical variables, and overall usefulness. The investigators have recently developed machine learning-based models of post-stroke all-cause mortality and recurrence using electronic health records (EHR) data. Despite promising results, our current pilot predictive models are limited to a single health system and may have inadequate generalizability due to implicit bias. This proposal seeks to expand and improve predictive models through the creative use of vetted EHR data for ischemic stroke patients from three large and different health systems (Penn State Health, Geisinger, and Johns Hopkins), caring for more than eight million people in rural and urban areas. This project will further explore the predictive value of social determinants of health (SDoH) when added to the clinical data. The investigators propose an integrative approach to design parameter-optimized and interpretable models, leveraging enriched EHR to identify the risk of ischemic stroke recurrence and all-cause mortality. Aim 1: Standardize EHR-based data across health care centers to identify clusters of ischemic stroke patients with common traits. Aim 2: Develop optimal interpretable ensemble models to predict 1-year mortality and recurrence after an ischemic stroke. Aim 3: Validate, prospectively and externally, ensemble models for 1-year mortality and stroke recurrence. This proposal includes model development with internal, external, and temporal validation and lays the foundation for an impact study to provide evidence of clinical utility. The investigators envision that this study will lead to EHR-based screening tools that can flag high-risk stroke patients for more targeted secondary prevention.