# REVISED CAROL ACT II

> **NIH NIH U01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2024 · $2,601,866

## Abstract

Project Summary
Patients with valvular heart disease (VHD) requiring surgical intervention are at a high risk of adverse events. Notably, in
the days following surgery, post-operative delirium is common, extends hospitalization, and may lead to long-term
cognitive decline. These surgical patients are also at high risk for atrial and ventricular arrhythmias during and after
hospitalization, although knowledge about the time course and burden of arrhythmias in the post-hospital setting is
limited. Over time, the Cardiothoracic Surgical Trials Network (CTSN) has developed a unique platform of research in
VHD that includes cutting-edge trials in patients with functional and degenerative mitral regurgitation, including mitral
valve prolapse, as well as aortic stenosis and tricuspid valve disease.
In this application, the Network proposes to build on this research agenda in several ways. First, we plan to incorporate
rhythm monitoring into the soon to be launched EPIC trial, which evaluates the impact of left posterior pericardiotomy on
preventing POAF during hospitalization (<5 days of surgery) in patients undergoing surgery for VHD and/or coronary
artery disease. The EPIC trial offers an opportunity to capture 30-day post-hospital discharge arrhythmia burden in
patients with VHD, both those with atrial fibrillation in the immediate post-operative period and those without but who
may be at risk for developing de novo arrhythmias. The proposed research should give new insights into the duration of
the risk period for these VHD patients, and whether the post-hospitalization care of these patients needs to be modified.
Second, we will leverage our embolic protection device trial (the EMPRO trial) of adults undergoing high-risk mitral and
aortic valve surgery by focusing on novel risk factors, including circadian rhythm and sleep disruption and accelerated
epigenetic age, for post-operative delirium. Third, we will identify mitral regurgitation (MR) phenogroups applying
machine learning and artificial intelligence to clinical and imaging data from hundreds of patients followed in our several
mitral valve (MV) trials. Categorization of patients into high and low risk phenogroups will help to identify individuals
for MV intervention at earlier stages of their disease than is currently recommended in clinical practice guidelines.
Finally, we will use national datasets, Use national datasets to evaluate annual procedural volumes and outcomes of TEER
and MVr in patients with MVP, to assess predictors of post-interventional outcomes (death, HF hospitalization, valve re-
interventions, ventricular arrhythmias, sudden death) and identify whether historically under-represented patient
subgroups are at risk of worse clinical outcomes. We will characterize pre-operative variations in diagnostic testing,
cardiovascular referrals, and electrophysiological procedures among patients who undergo surgical MVr for MVP and
identify disparities in use among different groups ...

## Key facts

- **NIH application ID:** 11119107
- **Project number:** 3U01HL088942-17S1
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Anne Christine Gelijns
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $2,601,866
- **Award type:** 3
- **Project period:** 2007-07-01 → 2027-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11119107

## Citation

> US National Institutes of Health, RePORTER application 11119107, REVISED CAROL ACT II (3U01HL088942-17S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/11119107. Licensed CC0.

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