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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

BACKGROUND: 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. OBJECTIVE: This study prospectively valid...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9665374/
https://www.ncbi.nlm.nih.gov/pubmed/36378672
http://dx.doi.org/10.1371/journal.pone.0277300
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author 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
author_facet 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
author_sort Bhavnani, Sanjeev P.
collection PubMed
description BACKGROUND: 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. OBJECTIVE: 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). METHODS: 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. RESULTS: 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. CONCLUSION: 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.
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spelling pubmed-96653742022-11-15 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 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 PLoS One Research Article BACKGROUND: 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. OBJECTIVE: 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). METHODS: 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. RESULTS: 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. CONCLUSION: 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. Public Library of Science 2022-11-15 /pmc/articles/PMC9665374/ /pubmed/36378672 http://dx.doi.org/10.1371/journal.pone.0277300 Text en © 2022 Bhavnani et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
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
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
title 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9665374/
https://www.ncbi.nlm.nih.gov/pubmed/36378672
http://dx.doi.org/10.1371/journal.pone.0277300
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