# Mobile Device-Based Congestion Prediction for Reducing Hospitalizations in Patients with Concomitant Heart Failure and Atrial Fibrillation

> **NIH NIH R21** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2022 · $180,983

## Abstract

PROJECT SUMMARY/ABSTRACT
Atrial fibrillation (AF) and heart failure (HF) are among the most common cardiovascular diseases and are
associated with significant morbidity and mortality, and frequent hospitalizations. Among patients first diagnosed
with either HF or AF, at least 1 in 4 patients subsequently develop both chronic conditions. The primary cause
for hospitalizations is the symptom of severe dyspnea (shortness of breath) caused by pulmonary congestion.
Early recognition of congestion would allow cardiologists to proactively adjust patient therapy in the outpatient
setting, and potentially avoid a hospitalization. Prior non-invasive methods of monitoring for congestion in the
outpatient setting have shown limited utility. Elevated left ventricular filling pressure (LVFP) has been shown to
be an accurate, and early predictor of congestion, but has heretofore required invasive monitoring, and thus
could not be utilized broadly in an outpatient setting.
 Our goal is to leverage a smartphone and smart wearables-based photo-plethysmography (PPG) sensor
to monitor patients at home with concomitant HF and AF, and predict worsening congestion. We will measure
amplitude changes in continuous pulsatile blood volume waveforms acquired via PPG, and derive a novel
congestion prediction index (CPI) that noninvasively tracks changes to LVFP. To achieve our goal, we will exploit
the irregularity of the heart rhythm that is a hallmark of AF. We hypothesize that when a patient is on the steep
part of the Starling curve, LVFP is low and the irregularity of the heart rate in AF will lead to larger beat-to-beat
changes in the pulse amplitude due to differences in diastolic filling times. Conversely, when the patient is on
the flatter part of the Starling curve, LVFP is higher, and thus we hypothesize that patients with impending
congestion will have smaller beat-to-beat amplitude changes in the arterial pulse in response to beat-to-beat
changes in instantaneous heart rate during AF.
 We propose to investigate CPI monitoring via a smartphone and smart wearables in patients with
concomitant AF and HF. The specific aims are: (1) to develop a computational model of the cardiovascular
system to elucidate the detailed mechanisms of AF-induced beat-to-beat changes in arterial pulse amplitude in
patients with concomitant AF and HF, (2) to develop a smartphone and smart wearables (smart wristband and
smart ring) to measure CPI in patients in real-time, and (3) to evaluate device-based CPI correlation with invasive
reference LVFP measurements from patients. Successful completion of this line of research may translate to
reducing hospitalizations and associated healthcare costs in a significant patient population.

## Key facts

- **NIH application ID:** 10120519
- **Project number:** 5R21EB027276-03
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Mohammed Saeed
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $180,983
- **Award type:** 5
- **Project period:** 2019-06-01 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10120519

## Citation

> US National Institutes of Health, RePORTER application 10120519, Mobile Device-Based Congestion Prediction for Reducing Hospitalizations in Patients with Concomitant Heart Failure and Atrial Fibrillation (5R21EB027276-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10120519. Licensed CC0.

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