A machine learning approach using multi-site photoplethysmography successfully estimated the ankle-brachial index (r = 0.79) and identified pathological cardiovascular status (AUC = 0.85).
Observational
Does a machine learning approach using photoplethysmography accurately predict the ankle-brachial index compared to standard measurement?
A machine learning approach using photoplethysmography can accurately estimate the ankle-brachial index and identify pathological cardiovascular status, offering a simple and operator-independent screening tool.
Effect estimate: r = 0.79, AUC = 0.85
Cardiovascular disease is a leading cause of death. Several markers have been proposed to predict cardiovascular morbidity. The ankle-brachial index (ABI) marker is defined as the ratio between the ankle and the arm systolic blood pressures, and it is generally assessed through sphygmomanometers. An alternative tool for cardiovascular status assessment is Photoplethysmography (PPG). PPG is a non-invasive optical technique that measures volumetric blood changes induced by pulse pressure propagation within arteries. However, PPG does not provide absolute pressure estimation, making assessment of cardiovascular status less direct. The capability of a multivariate data-driven approach to predict ABI from peculiar PPG features was investigated here. ABI was measured using a commercial instrument (Enverdis Vascular Explorer, VE-ABI), and it was then used for a General Linear Model estimation of ABI from multi-site PPG in a supervised learning framework (PPG-ABI). A Receiver Operating Characteristic (ROC) analysis allowed to investigate the capability of PPG-ABI to discriminate cardiovascular impairment as defined by VE-ABI. Findings suggested that ABI can be estimated form PPG (r = 0.79) and can identify pathological cardiovascular status (AUC = 0.85). The advantages of PPG are simplicity, speed and operator-independency, allowing extensive screening of cardiovascular status and associated cardiovascular risks.
Perpetuini et al. (Sat,) conducted a observational in Cardiovascular disease. Photoplethysmography (PPG) with machine learning (PPG-ABI) vs. Commercial instrument (Enverdis Vascular Explorer, VE-ABI) was evaluated on Estimation of ABI and identification of pathological cardiovascular status (r = 0.79, AUC = 0.85). A machine learning approach using multi-site photoplethysmography successfully estimated the ankle-brachial index (r = 0.79) and identified pathological cardiovascular status (AUC = 0.85).