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 aim ed 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.
Surgical procedures carry the risk of post-operative complications, which can be severe, expensive and put patients' lives at risk. In this work, we propose developing and implementing advanced risk models based on preoperative and real-time streaming high-resolution intraoperative data to help plan for and mitigate these complications. This system will have the ability to establish a preoperative baseline state for a patient, tracks the patient's condition as the surgery progresses automatically (without human intervention) providing real-time estimates of the patient's risk of different complications at any time before, during and after surgery. The proposed system forms the basis of a future interactive real-time perioperative decision support system. As part of the proposed work, we will develop novel data modeling techniques that can take patient heterogeneity into account yet construct models that can generalize well across the two participating sites, Mayo Clinic and the Fairview Health Services.
| Pruinelli, Lisiane; Westra, Bonnie L; Yadav, Pranjul et al. (2018) Delay Within the 3-Hour Surviving Sepsis Campaign Guideline on Mortality for Patients With Severe Sepsis and Septic Shock. Crit Care Med 46:500-505 |
| Skube, Steven J; Lindemann, Elizabeth A; Arsoniadis, Elliot G et al. (2018) Characterizing Functional Health Status of Surgical Patients in Clinical Notes. AMIA Jt Summits Transl Sci Proc 2017:379-388 |
| Pruinelli, Lisiane; Simon, György J; Monsen, Karen A et al. (2018) A Holistic Clustering Methodology for Liver Transplantation Survival. Nurs Res 67:331-340 |
| Zhang, Rui; Simon, Gyorgy; Yu, Fang (2017) Advancing Alzheimer's research: A review of big data promises. Int J Med Inform 106:48-56 |
