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

未能抢救(FTR)是一种对护士敏感的国家卫生保健质量指标，指的是一名 因可治疗的并发症而住院的患者，并因未能认识到和适当地 对并发症的早期迹象作出反应。研究病人特征的研究很少。 预测FTR并发症。这样的信息可能会将目前的护理监督模式转变为 及早识别、预防和治疗FTR并发症，从而挽救生命。新发静脉 血栓栓塞症(VTE)，一种FTR并发症，发生于深静脉血栓形成(DVT)或 肺栓塞(PE)是可预防的医院死亡的主要原因，具有很高的死亡风险 以及每年70亿美元的国家成本负担。静脉血栓形成是一个复杂的疾病过程，涉及相互作用。 临床危险因素与后天和/或遗传的血栓易感性之间的关系。尽管生物标志物 与VTE相关的临床因素已经确定，临床表现微妙，呈现 逐渐从几个小时到几天。当前的VTE风险评估模型(RAM)是预防的基石，具有 由于其复杂性以及缺乏可靠性、概括性和外部验证，实用性有限。严重的差距 在VTE风险建模研究中，虽然新发的VTE病理在 住院，目前没有一个模型包含动态患者数据和模式的累进累加 他们的建模方法随着时间的推移而演变。常规收集的电子健康记录的总和 (EHR)数据在数量、种类和产量方面都是海量的，并以快速的速度实时进行。如此之大的数据 可用于机器学习(ML)分析方法，以处理时间序列临床数据以识别 微妙的、不断演变的特征模式预测了新的VTE，并解决了这一差距。这项研究建议 汇集大规模、多源、多维的VTE研究数据集，并串联、系统地 为可计算的表型定义与新发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进行基线和应计 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 成人住院患者动态演变新发静脉血栓栓塞症的预测特征。这项建议符合 NINR的战略愿景是让护士科学家采用新的策略来收集和分析复合体 大数据集允许更好地了解健康的生物学基础，并改进护士的方式 预防和管理疾病。这种创新的学习和个性化的训练计划下的强大和良好- 成熟的团队，代表着申请者专注于数据科学的研究轨迹的第一步 预测FTR并发症风险的方法，以及开发、实施和测试动态RAM以告知 有针对性的预防和治疗决策。发现通知实时决策的新知识， 护理监督实践和护理提供系统可以改善护士敏感患者的预后。

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.