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

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 mach...

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Published in:PloS one 2022-11, Vol.17 (11), p.e0277300-e0277300
Main Authors: Bhavnani, Sanjeev P, Khedraki, Rola, Cohoon, Travis J, Meine, 3rd, 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, Jr, William E, Smart, Frank
Format: Article
Language:English
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
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Summary: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 (
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0277300