# Early Diagnosis of Heart Failure: A Perioperative Data-Driven Approach

> **NIH NIH K01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2021 · $172,800

## Abstract

PROJECT SUMMARY / ABSTRACT
Candidate: Dr. Michael Mathis is a cardiothoracic anesthesiologist with board certification in anesthesiology
and advanced perioperative echocardiography at the University of Michigan. Through completion of a T32
Research Training Grant, Dr. Mathis has developed expertise in perioperative outcomes research for patients
with advanced cardiovascular disease. His long-term career goal is to improve care for patients with heart
failure (HF) through harnessing perioperative electronic healthcare record (EHR) data for early diagnosis and
management. This proposal builds on Dr. Mathis's expertise, providing protected time for training in data
science methods necessary to drive forward the analytic techniques proposed for improving HF diagnosis.
Environment: The University of Michigan is the coordinating center for the Multicenter Perioperative
Outcomes Group (MPOG), an international consortium of over 50 anesthesiology and surgical departments
with perioperative information systems. Dr. Sachin Kheterpal, MD, MBA is the primary mentor for Dr. Mathis,
and is the Director for MPOG and member of the NIH Precision Medicine Initiative Advisory Panel. The
proposed research will be completed under the guidance of Dr. Kheterpal, as well as co-mentors Milo Engoren,
MD, Daniel Clauw, MD, and Kayvan Najarian, PhD. An advisory panel of experts in HF diagnosis and data
science methodologies will provide Dr. Mathis with additional guidance.
Background: HF is among the most common chronic conditions requiring hospitalization and carries high
rates of mortality. In the perioperative period, HF is a risk factor for major cardiac complications. Despite
advances in care, little progress has been made to reduce HF healthcare burden, with difficulties attributable to
a lack of inexpensive, reliable diagnostic measures. Consequently, patients with HF can go unrecognized in
early stages and do not receive treatments to reduce mortality. The perioperative period is an underutilized
opportunity to improve HF diagnosis. Beyond the wealth of preoperative data available, the intraoperative
period serves as a cardiac stress test through which hemodynamic responses to surgical and anesthetic
stimuli are recorded with high resolution. Yet, this data remains an untapped resource for HF evaluation.
Research: The goal of the proposed research is to incorporate the perioperative period as an opportunity for
early diagnosis of HF. The two specific Aims are to develop a data-driven diagnostic algorithm for HF using
preoperative EHR data (Aim 1) as well as intraoperative EHR data (Aim 2). Both aims will use automated
techniques to extract features of HF from the perioperative EHR, developed at UM and scalable to multiple
centers via the MPOG infrastructure. This work represents a paradigm shift in perioperative evaluation, using
perioperative data as a diagnostic tool rather than a risk-assessment tool. The proposed research and training
will provide Dr. Mathis wit...

## Key facts

- **NIH application ID:** 10136697
- **Project number:** 5K01HL141701-04
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Michael Robert Mathis
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $172,800
- **Award type:** 5
- **Project period:** 2018-04-05 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10136697

## Citation

> US National Institutes of Health, RePORTER application 10136697, Early Diagnosis of Heart Failure: A Perioperative Data-Driven Approach (5K01HL141701-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10136697. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*