Integrating data, algorithms and clinical reasoning for surgical risk assessment

整合数据、算法和临床推理进行手术风险评估

• 批准号: 9233163
• 负责人: Azra Bihorac
• 金额: $ 53.14万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9233163
• 关键词: Active Learning; Acute; Address; Affect; Algorithms; American; Anesthesia procedures; Caring; Cessation of life; Clinical; Clinical Data; Clinical Research; Complex; Computational algorithm; Computers; Computing Methodologies; Data; Data Analytics; Data Engineering; Data Science; Data Set; Databases; Decision Making; Development; Diagnostic; Electronic Health Record; Engineering; Environment; Event; Expert Systems; Health; Health Care Costs; Healthcare; High Performance Computing; Intervention; Knowledge; Laboratories; Lead; Measures; Medical; Medicine; Methodology; Mission; Modeling; Operative Surgical Procedures; Outcome; Patients; Performance; Perioperative; Perioperative Care; Physicians; Physiological; Play; Postoperative Complications; Postoperative Period; Prevention strategy; Preventive Intervention; Risk; Risk Assessment; Series; Speed; Stream; Stress; System; Technology; Testing; Time; United States; United States National Institutes of Health; Validation; advanced disease; clinical decision-making; computerized data processing; cost effective; data integration; design; disease diagnosis; high end computer; improved; improved outcome; innovation; medical complication; mortality; multidisciplinary; open source; operation; personalized decision; personalized diagnostics; predictive modeling; prevent; prospective; prototype; public health relevance; response; risk perception; satisfaction; tool; usability

 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.

 描述（由申请人提供）：主要术后并发症（PC）很常见，会导致死亡率和医疗费用增加。及时实施一些具有成本效益的策略可以降低 PC 风险，但使用这些策略的能力取决于及时、准确地识别风险最大的患者。评估该风险需要及时、准确和动态地综合整个围手术期获得的大量临床信息。如今，对于特定患者来说，无法预测和量化 PC 的个人实时风险，无法将术前风险与手术期间事件的生理反应所产生的风险结合起来。可以预防 PC 的干预措施的应用没有考虑患者的个人风险状况，或者通常因为风险被低估而根本不应用。围手术期电子健康记录 (EHR) 中有大量的生理、实验室和其他临床数据，但其数量和复杂性往往超出了医生以最佳和及时的方式理解和使用这些信息的能力。目标是开发一个由高性能计算机、算法和实时交互的医生组成的智能系统，该系统可以使用复杂的临床数据以更高的速度和准确性生成可用的医学知识。我们的医学和工程学多学科科学专家团队将解决与在临床环境中计算机和医生之间实施实时数据集成和处理、数据分析以及知识交换相关的方法学挑战。共有三个具体目标： 1. 使用 EHR 与术中生理时间序列相结合，使用 10,000 名手术患者的时间数据库来完善和验证主要并发症的预测风险模型。 2. 实施并验证预测风险模型和医生之间的双向知识交换。我们将设计一个交互式知识交换应用程序，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. 实施和评估智能围手术期系统，以使用实时 EHR 数据进行自动风险分析。在一项针对 60 名医生的前瞻性临床研究中，我们将验证预测风险模型的诊断性能，将其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感知的变化。该方法将通过建模和量化身体对手术的反应，同时使用常规医疗护理期间获得的临床数据，为个性化围手术期医学迈出重要一步。