Multicenter validation of a machine learning phase space electro-mechanical pulse wave analysis to predict elevated left ventricular end diastolic pressure at the point-of-care

• Published in: PloS one Vol. 17; no. 11; p. e0277300
• Main Authors: Bhavnani, Sanjeev P., Khedraki, Rola, Cohoon, Travis J., Meine, Frederick J., Stuckey, Thomas D., McMinn, Thomas, Depta, Jeremiah P., Bennett, Brett, McGarry, Thomas, Carroll, William, Suh, David, Steuter, John A., Roberts, Michael, Gillins, Horace R., Shadforth, Ian, Lange, Emmanuel, Doomra, Abhinav, Firouzi, Mohammad, Fathieh, Farhad, Burton, Timothy, Khosousi, Ali, Ramchandani, Shyam, Sanders, William E., Smart, Frank
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 15.11.2022; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Analysis; Angiography; Bayesian analysis; Biology and Life Sciences; Blood Pressure; Cardiovascular disease; Computer and Information Sciences; Coronary artery disease; Data acquisition; Data collection; Data entry; Data processing; Diastolic pressure; Ejection fraction; Evaluation; Feature extraction; Heart diseases; Humans; Intubation; Learning algorithms; Machine Learning; Mechanical systems; Medical diagnosis; Medical imaging; Medicine and Health Sciences; Phase space (Statistical physics); Physical Sciences; Point-of-Care Systems; Population; Predictions; Pulse Wave Analysis; Research and Analysis Methods; Signs and symptoms; Stroke Volume; System theory; Ventricle; Ventricular Dysfunction, Left - diagnosis; Ventricular Function, Left; Ventricular Pressure; Wave analysis; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0277300

Phase space is a mechanical systems approach and large-scale data representation of an object in 3-dimensional space. Whether such techniques can be applied to predict left ventricular pressures non-invasively and at the point-of-care is unknown. This study prospectively validated a phase space machine-learned approach based on a novel electro-mechanical pulse wave method of data collection through orthogonal voltage gradient (OVG) and photoplethysmography (PPG) for the prediction of elevated left ventricular end diastolic pressure (LVEDP). Consecutive outpatients across 15 US-based healthcare centers with symptoms suggestive of coronary artery disease were enrolled at the time of elective cardiac catheterization and underwent OVG and PPG data acquisition immediately prior to angiography with signals paired with LVEDP (IDENTIFY; NCT #03864081). The primary objective was to validate a ML algorithm for prediction of elevated LVEDP using a definition of ≥25 mmHg (study cohort) and normal LVEDP ≤ 12 mmHg (control cohort), using AUC as the measure of diagnostic accuracy. Secondary objectives included performance of the ML predictor in a propensity matched cohort (age and gender) and performance for an elevated LVEDP across a spectrum of comparative LVEDP (<12 through 24 at 1 mmHg increments). Features were extracted from the OVG and PPG datasets and were analyzed using machine-learning approaches. The study cohort consisted of 684 subjects stratified into three LVEDP categories, ≤12 mmHg (N = 258), LVEDP 13-24 mmHg (N = 347), and LVEDP ≥25 mmHg (N = 79). Testing of the ML predictor demonstrated an AUC of 0.81 (95% CI 0.76-0.86) for the prediction of an elevated LVEDP with a sensitivity of 82% and specificity of 68%, respectively. Among a propensity matched cohort (N = 79) the ML predictor demonstrated a similar result AUC 0.79 (95% CI: 0.72-0.8). Using a constant definition of elevated LVEDP and varying the lower threshold across LVEDP the ML predictor demonstrated and AUC ranging from 0.79-0.82. The phase space ML analysis provides a robust prediction for an elevated LVEDP at the point-of-care. These data suggest a potential role for an OVG and PPG derived electro-mechanical pulse wave strategy to determine if LVEDP is elevated in patients with symptoms suggestive of cardiac disease.