Machine Learning to Determine Dynamically Evolving New-Onset Venous Thromboembolic (VTE) Event Risk in Hospitalized Patients

抢救失败（FTR）是一项对护士敏感的国家卫生保健质量指标，指的是 住院患者的可治疗并发症，并加强了未能认识和适当 对并发症的早期症状作出反应。缺乏对患者特征的研究 预测FTR并发症。这些信息可以改变目前的护理监督模式， 早期识别、预防和治疗FTR并发症，从而挽救生命。新发静脉 血栓栓塞（VTE），一种FTR并发症，发生为深静脉血栓形成（DVT）或血栓栓塞（VTE）。 肺栓塞（PE）是可预防的医院死亡的主要原因，具有高死亡风险 每年的国家成本负担为70亿美元。静脉血栓栓塞是一种复杂的疾病过程， 临床危险因素与获得性和/或遗传性血栓形成易感性之间的关系。虽然生物标志物 与VTE相关的临床因素已被确定，临床表现微妙， 逐渐地持续数小时到数天。目前的VTE风险评估模型（RAM）是预防的基石， 由于其复杂性和缺乏可靠性、普遍性和外部验证，实用性有限。的重大差距 在静脉血栓栓塞风险建模研究中， 住院，目前没有模型纳入动态患者数据和模式的逐步增加 随着时间的推移，他们的建模方法。常规收集的电子健康记录总数 (EHR)数据在数量、种类和快速实时生产方面是巨大的。这样的大数据 可用于机器学习（ML）分析方法，以处理时间序列临床数据， 预测新发VTE的微妙、不断变化的特征模式，并解决这一差距。本研究建议， 收集大规模、多来源、多维VTE研究数据集，并串联、系统地 为可计算表型定义与新发VTE诊断相关的EHR数据元素 算法开发然后，我们将应用机器学习分析方法来基线和累积 精选数据集中的密集时间序列临床数据，以开发识别数据模式的模型， 预测成人住院患者新发VTE动态演变的特征。该提案与 与NINR的战略眼光，护士科学家采用新的战略，收集和分析复杂的 大数据集，以更好地了解健康的生物基础，并改善护士 预防和管理疾病。这种创新的学习和个性化的培训计划下一个强大的和良好的- 已建立的团队，代表了申请人专注于数据科学的研究轨迹的初步步骤 预测FTR并发症风险的方法，并开发、实施和测试动态RAM， 有针对性的预防和治疗决定。发现新知识，为实时决策提供信息， 护理监督实践和护理提供系统可以改善护士敏感的患者结果。

Failure to rescue (FTR), a nurse-sensitive national metric of health care quality, refers to death of a hospitalized patient from a treatable complication, and is potentiated by failure to recognize and appropriately respond to early signs of complications. There is a paucity of research examining patient features predictive of FTR complications. Such information could shift the current paradigm of nursing surveillance to earlier recognition, prevention and treatment of FTR complications, thereby saving lives. New-onset venous thromboembolism (VTE), an FTR complication occurring as either a deep vein thrombosis (DVT) or a pulmonary embolism (PE), is the leading cause of preventable hospital death, carrying a high risk of mortality and a national cost burden of $7 billion annually. VTE is a complex disease process involving interactions between clinical risk factors and acquired and/or inherited susceptibilities to thrombosis. Although biomarkers and clinical factors associated with VTE have been identified, clinical manifestations are subtle, presenting gradually over hours to days. Current VTE risk assessment models (RAM), the cornerstone of prevention, have limited utility due to their complexity and lack of reliability, generalizability and external validation. A critical gap in VTE risk modeling research is that while new-onset VTE pathology evolves over the course of hospitalization, no current models incorporate the progressive accrual of dynamic patient data and pattern evolution over time in their modeling approaches. The totality of routinely collected electronic health record (EHR) data is massive in terms of volume, variety, and production at a rapid velocity in real-time. Such big data could be used in machine learning (ML) analytic approaches to process time series clinical data to identify subtle, evolving feature patterns predictive of new-onset VTE and address this gap. This study proposes to assemble a large scale, multi-source, multi-dimensional VTE study dataset, and in tandem, systematically define the EHR data elements associated with a new-onset VTE diagnosis for computable phenotype algorithm development. We will then apply machine learning analytic approaches to baseline and accruing intensive time series clinical data in the curated dataset to develop models identifying data patterns and features predictive of dynamically evolving new-onset VTE in adult hospitalized patients. This proposal aligns with NINR’s strategic vision for nurse scientists to employ new strategies for collecting and analyzing complex big data sets to permit better understanding of the biological underpinnings of health, and improve ways nurses prevent and manage illness. This innovative study and individualized training plan under a strong and well- established team, represents initial steps in the applicant’s research trajectory focused on data science approaches to predict FTR complication risk, and develop, implement and test dynamic RAMs to inform targeted prevention and treatment decisions. Discovering new knowledge informing real-time decision making, nursing surveillance practices and care delivery systems can improve nurse sensitive patient outcomes.