Innovative Methods for Real-time Risk Modeling of Postoperative Complications

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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.