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

机器学习对缺血性卒中后1年死亡率和复发的预测 丰富的电子病历数据 项目摘要/摘要 中风是世界范围内导致死亡和残疾的主要原因。据估计，一年的风险 中风后的死亡率和复发率分别约为15%和10%。此外，最近一份来自 全球疾病负担(GBD)显示，每年中风和 继发性死亡，特别是在低收入群体中。复发性中风有增加的趋势，有更高的 死亡率和致残率。因此，确定高危患者的复发和死亡是当务之急。 并及时评估、分配资源、有针对性地预防。调查人员最近发表的评论 提示─预测卒中复发的多项临床评分仅有有限的临床应用价值 实用程序。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 受限于它们的验证队列大小、临床变量的广度和总体有效性。调查人员 最近开发了基于机器学习的中风后全原因死亡率和复发模型 电子健康记录(EHR)数据。尽管结果很有希望，但我们目前的试点预测模型是有限的。 对于单一的保健系统，由于隐性偏见，可能具有不充分的概括性。 该提案寻求通过创造性地使用经过审查的电子病历数据来扩展和改进预测模型 适用于来自三大不同医疗系统(宾夕法尼亚州立大学健康、盖辛格和 约翰斯·霍普金斯大学)，照顾农村和城市地区的800多万人。该项目将进一步探索 将健康的社会决定因素(SDoH)添加到临床数据中时的预测价值。调查人员 提出一种综合的方法来设计参数优化且可解释的模型，利用丰富的 EHR以确定缺血性中风复发和全因死亡的风险。目标1：基于电子病历的标准化 跨卫生保健中心的数据，以确定具有共同特征的缺血性中风患者群。目标 2：开发最佳的可解释集合模型以预测脑缺血后1年的死亡率和复发 卒中。目标3：前瞻性和外部性验证1年死亡率和中风的整体模型 复发。 该建议包括具有内部、外部和时间验证的模型开发，并为 为提供临床实用证据的影响研究奠定基础。调查人员设想，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.