Innovative Methods for Real-time Risk Modeling of Postoperative Complications

项目摘要 外科手术有术后并发症的风险，这些并发症可能很严重， 价格昂贵，患者的生命处于危险之中。围手术期风险分层 决策支持可以帮助规划和减轻这些复杂情况。研究旨在 支持了解这些并发症的风险因素和开发风险模型 通过高质量的注册数据，如国家外科手术质量改进项目 （NSQIP）注册表。越来越多的研究表明，术中风险因素影响 并发症的风险，但即使在NSQIP中也很少捕获。 在这项工作中，我们建议开发和实施先进的风险模型， 术前和实时流高分辨率术中数据。该系统将具有 能够为患者建立术前基线状态， 手术进展，并提供患者不同风险的最新估计 在手术前、手术中和手术后的任何时间自动监测并发症（无需人工 干预）。这将有助于我们了解术中数据在预测 术后并发症 我们在两个地点开展我们的项目：明尼苏达大学附属的费尔维尤健康中心 服务和马约诊所。我们将开发建模技术， 异质性（如健康差异）考虑在内，但产生的模型，可移植到 这两个网站。我们在两个地点分别建立模型，交叉验证模型， 机构和实施的临床决策支持系统的验证模型 各自的网站。实现的系统形成了未来的交互式实时 围手术期决策支持系统

PROJECT SUMMARY Surgical procedures carry the risk of post-operative complications, which can be severe, expensive and put patients' lives at risk. Risk stratification in the context of perioperative decision support can help plan for and mitigate these complications. Research aimed at understanding the risk factors and developing risk models for these complications is supported by high-quality registry data, such as the National Surgical Quality Improvement Project (NSQIP) registry. A growing body of research indicates that intraoperative risk factors influence the risk of complications, but they are poorly captured even in the NSQIP. In this work, we propose developing and implementing advanced risk models based on preoperative and real-time streaming high-resolution intraoperative data. This system will have the ability to establish a preoperative baseline state for a patient, track his condition as the surgery progresses and provide an up-to-date estimate of the patient's risk of different complications at any time before, during and after surgery automatically (without human intervention). It will help us understand the value of intraoperative data in predicting postoperative complications. We carry out our project at two sites: at the University of Minnesota affiliated Fairview Health Services and Mayo Clinic. We will develop modeling techniques that can take patient heterogeneity (e.g. health disparities) into account, yet produce models that are portable across the two sites. We construct models at the two sites independently, validate the models cross- institutionally and implement the validated models in the clinical decision support systems of the respective sites. The implemented system forms the foundation of a future interactive real-time perioperative decision support system.