Abstract 4368010: Noninvasive Assessment of Left Ventricular Pulsatile Workload Using Smartphone-Measured Carotid Waveforms

Abstract

Introduction: Left ventricular pulsatile workload (LVPW) is a clinically established marker of cardiac afterload and function, and it is strongly associated with cardiovascular morbidity and mortality. Elevated LVPW contributes to adverse ventricular remodeling, impaired cardiac performance, and the development of heart failure (HF) (Eur Heart J. PMID: 29947746). However, clinical adoption of LVPW assessment remains limited due to the requirement for simultaneous pressure and flow measurements. This study introduces a smartphone-based approach for estimating LVPW noninvasively from only carotid pressure waveforms, enabling accessible and scalable cardiovascular monitoring using only smartphone camera-derived signals. Methods: A clinical cohort of 115 participants (41% women, BMI 25.9 ± 5.5, age range 20–92 years, mean 53 ± 18) was studied, including 43 individuals with cardiovascular disease (17 ambulatory HF patients). Reference LVPW values were calculated using ascending aorta flow from phase-contrast MRI combined with carotid pressure waveforms acquired via applanation tonometry. Carotid pressure waveforms were also recorded using a custom iPhone 5S (Apple Inc.) application by placing the camera against the neck, over the carotid artery (Crit Care Med. PMID: 28441235). These waveforms were calibrated using cuff-based brachial pressures, and their intrinsic frequency (IF) parameters were extracted. Using these IF metrics, a physics-based machine learning model was trained on 80% of the dataset to approximate LVPW and evaluated on the remaining 20% in a blinded test. Results: Smartphone-derived LVPW estimates showed a strong correlation with the gold standard reference computed from pressure-flow values. The Pearson correlation coefficient was 0.83 for the blind test set and 0.86 among the HF patient subgroup, as shown in Fig. 1. Conclusions: LVPW can be reliably and non-invasively estimated using only carotid pressure waveforms captured with an unmodified smartphone camera (iPhone in this study). This noninvasive, low-cost approach may enable routine assessment of pulsatile afterload for both clinical and at-home cardiac monitoring. This may facilitate the delivery of precision medicine with timely treatment plan modifications in patients with HF whose outcomes are highly sensitive to increases in pulsatile afterload. This technique may also expand access to early cardiovascular risk stratification in other diseases.