# Personalizing Preoperative Stress Testing Using Machine Learning

> **NIH NIH K08** · CLEVELAND CLINIC LERNER COM-CWRU · 2021 · $161,047

## Abstract

Project Summary
Dr. Pappas is a junior faculty member in the Medicine Institute at the Cleveland Clinic, with appointments in
the Center for Value-Based Care Research and the Department of Hospital Medicine (Cleveland, OH). This
career development award aims to provide training in propensity methods, deep learning techniques, and pilot
intervention development, ultimately seeking to identify personalized approaches to cardiac stress testing
before surgery. Noncardiac surgery carries risk of mortality and morbidity, and cardiac complications account
for the largest share of perioperative mortality. Meanwhile, current approaches are expensive and may not
effectively reduce cardiac risk. This proposal uses machine learning techniques to define the value of
information provided by each different kind of stress test and the expected benefit of different therapeutic
interventions through which preoperative risks might be modified. It then seeks to identify the most helpful
cardiac stress test, if any. In this career development award, Dr. Pappas proposes three phases of investigation,
and in so doing will acquire new skills critical to achieving his goal of becoming an expert in perioperative risk
mitigation. In Aim 1, Dr. Pappas will use propensity matching techniques to evaluate prior associations
between preoperative stress testing and improved postoperative mortality, when including rich clinical data
not available to previous large studies. In Aim 2, he will use machine learning techniques to estimate the value
of information provided by each modality of stress testing, and the impact on the risk of each event from each
intervention. In Aim 3, Dr. Pappas will pilot an intervention presenting personalized estimates to physicians in
the preoperative clinic. In addition to advanced training through formal coursework, this career development
award is supported by an extraordinary mentorship team, including internationally-recognized experts in
perioperative outcomes research, cardiovascular disease, use of observational healthcare data, and machine
learning. The combination of formal training and mentored research outlined in this application is designed to
ensure that Dr. Pappas will emerge from this award as an independent investigator and expert in personalized
perioperative decision-making.

## Key facts

- **NIH application ID:** 10080366
- **Project number:** 5K08HL141598-03
- **Recipient organization:** CLEVELAND CLINIC LERNER COM-CWRU
- **Principal Investigator:** MATTHEW PAPPAS
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $161,047
- **Award type:** 5
- **Project period:** 2019-01-15 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10080366

## Citation

> US National Institutes of Health, RePORTER application 10080366, Personalizing Preoperative Stress Testing Using Machine Learning (5K08HL141598-03). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/10080366. Licensed CC0.

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