Integrating data, algorithms and clinical reasoning for surgical risk assessment

 描述(申请人提供)：主要的术后并发症(PC)是常见的，并导致死亡率和医疗费用的增加。一些及时实施的经济有效的策略可以降低PC的风险，但使用这些策略的能力取决于对那些风险最高的患者的及时和准确的识别。对这种风险的评估需要及时、准确和动态地综合整个围手术期获得的大量临床信息。今天，对于给定的患者，不可能预测和量化PC的个人和实时风险，该风险将术前风险与手术期间对事件的生理反应所产生的风险相结合。可以预防PC的干预措施是在不考虑患者个人风险的情况下应用的，或者由于风险被低估而经常根本不应用。围手术期电子病历(EHR)中包含着丰富的生理、实验室等临床数据，但这些数据的大小和复杂性往往超出了医生对这些信息的理解和及时利用的能力。其目标是开发一个由高性能计算机、算法和医生实时交互组成的智能系统，该系统可以利用复杂的临床数据以更快的速度和更高的精度生成有用的医学知识。我们由医学和工程领域的多学科科学专家组成的团队将解决与实施临床环境中计算机和医生之间的实时数据集成和处理、数据分析和知识交换相关的方法学挑战。有三个具体目标：1.结合术中生理时间序列，使用10,000名手术患者的时间数据库，完善和验证EHR对主要并发症的预测风险模型。2.实施并验证预测风险模型与医生之间的双向知识交换。我们将设计一个交互式知识交流应用程序，向医生展示预测模型背后的知识，同时允许他们将自己的评估输入到模型中。3.实施和评估使用实时EHR数据进行自动风险分析的智能围术期系统。在一项对60名医生进行的前瞻性临床研究中，我们将验证预测性风险模型的诊断性能，将它们与医生的风险评估进行比较，并测量与系统进行知识交流后医生风险认知的变化。这一方法论将为个性化围手术期医学提供重要的一步，通过建模和量化身体对手术的反应，同时使用在常规医疗护理中获得的临床数据。

 DESCRIPTION (provided by applicant): Major postoperative complications (PC) are common and lead to increase in mortality and healthcare cost. Some cost-effective strategies, implemented in a timely fashion, can ameliorate the risk for PC but the ability to use them depends on the timely and accurate identification of those patients at greatest risk. Assessment of that risk requires timely, accurate and dynamic synthesis of the large amount of clinical information obtained throughout the perioperative period. Today it is not possible to predict and quantify, for a given patient, a personal and real-time risk for PC that integrates preoperative risk with the risk incurred by the physiologic response to events during surgery. The interventions that could prevent PC are applied without consideration of a patient's personal risk profile or often not applied at all because the risk is underestimated. There is an abundance of physiologic, laboratory and other clinical data in the perioperative electronic health records (EHR), but their magnitude and complexity often overwhelms a physicians' ability to comprehend and use the information in an optimal and timely way. The objective is to develop an intelligent system, composed of high- performance computers, algorithms and physicians interacting in real time, which can generate usable medical knowledge with both increased speed and accuracy using complex clinical data. Our multidisciplinary team of scientific experts in medicine and engineering will address methodological challenges related to implementation of real-time data integration and processing, data analytics and knowledge exchange between computers and physicians in the clinical environment. There are three specific aims: 1. Refine and validate predictive risk models for major complications using EHR integrated with intraoperative physiologic time series using a temporal database for 10,000 surgical patients. 2. Implement and validate two-way knowledge exchange between predictive risk models and physicians. We will design an interactive knowledge exchange application that presents the knowledge behind predictive models to physicians, while allowing them to input their own assessment into the models. 3. Implement and evaluate an intelligent perioperative system for automated risk analysis using real-time EHR data. In a prospective clinical study of 60 physicians we will validate the diagnostic performance of predictive risk models, compare them with the physicians' risk assessment and measure change in physicians' risk perception after knowledge exchange with the system. This methodology will provide a significant step towards personalized perioperative medicine by modeling and quantifying the body's responses to surgery while using clinical data acquired during routine medical care.