# Innovative Methods for Real-time Risk Modeling of Postoperative Complications

> **NIH NIH R01** · UNIVERSITY OF MINNESOTA · 2020 · $361,079

## Abstract

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.

## Key facts

- **NIH application ID:** 9904738
- **Project number:** 5R01GM120079-04
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** GYORGY SIMON
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $361,079
- **Award type:** 5
- **Project period:** 2017-04-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9904738

## Citation

> US National Institutes of Health, RePORTER application 9904738, Innovative Methods for Real-time Risk Modeling of Postoperative Complications (5R01GM120079-04). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/9904738. Licensed CC0.

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