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.
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. Patients with concomitant AF and HF are at increased risk of hospitalizations, and frequently present to the hospital with symptoms of severe dyspnea (shortness of breath) caused by pulmonary congestion. Development of a mobile-device based congestion prediction application that allows for patients and their clinicians to recognize impending congestion earlier in the process may allow cardiologists to proactively adjust patient therapy in the outpatient setting, and avoid hospitalizations.
