Personalized Risk Prediction for Prevention and Early Detection of Postoperative Failure to Rescue

摘要 在未来的医院，住院几乎将专门为严重急性呼吸综合征患者保留 疾病，工作人员数量将减少，医院将围绕智能环境建设，以促进 始终如一地提供有效、公平和无差错的护理，重点是以患者为中心，而不是提供商- 以结果为中心。这与外科手术人群尤其相关。而门诊外科中心 是增长最快的提供商，每年有5100多万名住院患者进行手术 美国的医院和住院手术中心正在照顾病情较重和年龄较大的患者。而当 由于手术技术、麻醉管理和治疗的改进，术中死亡率很低。 术中监测，全球术后死亡率仍然是全球第三大死亡原因 美国人民。最近的研究表明，虽然术后主要并发症的发生率 大手术后医院间相似(~25%)，术后死亡率较高 从一家医院到另一家医院的主要并发症可能高出2.5倍。这表明减少 大手术后死亡率的变化将需要策略来提高高死亡率的能力 医院处理术后主要并发症，减少抢救失败。其中一个解决方案是 确定的目标是利用健康信息技术。这项提议的目标是使用机器学习 基于多模式的实时术后风险预测工具的开发、验证和测试方法 使用电子健康记录数据、高保真生理波形特征和基因组的数据源 以确定哪些患者有发生术后主要并发症的风险。vbl.使用 扩展了高保真生理波形的电子健康记录衍生注释 特征和基因组数据并应用最先进的机器学习方法，常见模式 注定要发生术后主要并发症的受试者和那些发生风险非常低的受试者 术后主要并发症将被描述和量化。然后这些输入将是 用于模拟实时床边管理，迭代设计一个原型临床决策支持工具。 这一临床决策支持工具将在麻醉后护理病房出院时使用，以确定 将受益于病房持续远程监控和预警系统的外科患者 防止术后抢救失败。然后将评估这种方法的可行性和可接受性。 在连续10周、13周、10周阶段的小规模前瞻性纵向试点评估中 帮助设计未来的大规模临床试验。

Abstract In the Hospital of the Future hospitalization will be reserved almost exclusively for patients with severe acute illness, staff numbers will be reduced, and hospitals will be built around smart environments that facilitate consistent delivery of effective, equitable, and error-free care focused on patient-centered rather than provider- centered outcomes. This is particularly relevant to the surgical population. While ambulatory surgical centers are the fastest growing providers, more than 51 million inpatients procedures are performed annually in hospitals in the US and inpatient surgery centers are taking care of sicker and older patients. While intraoperative mortality is rare due to improvements in surgical techniques, anesthesia management, and intraoperative monitoring, global postoperative mortality remains the third leading cause of death among American People. Recent studies have shown that while the incidence of postoperative major complications after major surgery is similar between hospitals (~25%), the postoperative mortality following postoperative major complications from one hospital to the other can be up to 2.5-fold higher. This suggests that reducing variations in mortality following major surgery will require strategies to improve the ability of high-mortality hospitals to manage postoperative major complications and decrease failure-to-rescue. One of the solutions identified is to leverage Health Information Technologies. The goal of this proposal is to use machine learning approaches to develop, validate, and test real-time postoperative risk prediction tools based on multi-modal data sources using electronic health record data, high-fidelity physiological waveform features, and genomic data to identify patients who are at risk of developing postoperative major complications after surgery. Using extensive electronic health record derived annotation augmented with high-fidelity physiological waveform features and genomic data and applying state-of-the-art machine learning approaches, common patterns in subjects destined to develop postoperative major complications and those at very low risk of developing postoperative major complications after surgery will be characterized and quantified. These inputs will then be used in simulated real-time bedside management to iteratively design a prototype clinical decision support tool. This clinical decision support tool will be used at discharge from the post anesthesia care unit to identify surgical patients who will benefit from continuous remote monitoring and early warning system on the ward to prevent postoperative failure to rescue. The feasibility and acceptability of this approach will then be assessed in a small-scale prospective, longitudinal pilot evaluation in sequential 10-weeks, 13-weeks, 10-weeks phases at UCLA to help design a future, large-scale clinical trial.