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JACC-Heart Fail., Oct 31 2025, S2213-1779(25)00683-3 | https://doi.org/10.1016/j.jchf.2025.102753
A Novel Computational Pipeline for Acquiring Pressure-Volume Hemodynamics of the Right Ventricle in Pulmonary Hypertension
Gyeongtae S. Moon BS a, Paul J. Scheel 3rd MD b, Maggie Montovano MD c, Milan Kaushik MD b, Cole Buchanan MD d, Samuel H. Friedman MD e, Rebecca Vanderpool PhD f, Tess Allan MD g, M. Imran Aslam MD g, Ryan J. Tedford MD d, Monica Mukherjee MD, MHS b, Paul M. Hassoun MD c, Vivek P. Jani MS b, Steven Hsu MD b
aDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
bJohns Hopkins University School of Medicine, Baltimore, Maryland, USA
cDivision of Pulmonology, Johns Hopkins University, Baltimore, Maryland, USA
dDivision of Cardiology, Medical University of South Carolina, Charleston, South Carolina, USA
eDivision of Pulmonary and Critical Care, Medical University of South Carolina, Charleston, South Carolina, USA
fOhio State University School of Medicine, Columbus, Ohio, USA
gDivision of Cardiology, Duke University, Durham, North Carolina, USA
Address for correspondence: Mr Vivek P. Jani, Dr Steven Hsu
aDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
bJohns Hopkins University School of Medicine, Baltimore, Maryland, USA
cDivision of Pulmonology, Johns Hopkins University, Baltimore, Maryland, USA
dDivision of Cardiology, Medical University of South Carolina, Charleston, South Carolina, USA
eDivision of Pulmonary and Critical Care, Medical University of South Carolina, Charleston, South Carolina, USA
fOhio State University School of Medicine, Columbus, Ohio, USA
gDivision of Cardiology, Duke University, Durham, North Carolina, USA
Address for correspondence: Mr Vivek P. Jani, Dr Steven Hsu
Abstract
Background: Load-independent indices of right ventricular (RV) dysfunction aid in the prognosis of patients with pulmonary hypertension (PH), but acquisition of these indices remains difficult. Simpler image-based tools could bring these metrics to everyday practice.
Objectives: This study sought to develop a novel, artificial intelligence-based pipeline that estimates load-independent RV functional indices using a pressure-time waveform and stroke volume from clinical right-sided heart catheterization.
Methods: Clinical data and pressure-volume-time data were collected from 76 patients referred for right-sided heart catheterization for known or suspected PH from 3 centers. A computational pipeline was developed to determine the RV pressure-volume loop and extract load-independent RV indices using computer vision image processing and single-beat analysis. Agreement with gold standard single-beat analysis and prognostic value were evaluated.
Results: Strong concordance was observed between both methods for end-systolic elastance (Ees: R = 0.96; concordance correlation coefficient [CCC] = 0.58), effective arterial elastance (Ea: R = 0.97; CCC = 0.88), end-diastolic elastance (Eed: R = 0.87; CCC = 0.47), and Ees/Ea ratio (R = 0.93; CCC = 0.71) in both the validation and external cohorts. Prognostic analyses showed that calculated Ea (HR: 2.09; 95% CI: 1.04-4.20) and Ees/Ea (HR: 0.27; 95% CI: 0.08-0.87) were significant predictors of clinical outcomes. Cluster analysis using single-beat indices identified 2 RV subphenotypes with distinct hemodynamic features that were more predictive of poor outcomes than analysis using standard clinical features.
Conclusions: Study investigators have developed a novel computational pipeline that digitizes and generates single-beat estimates of RV-pulmonary arterial coupling from an image of the RV pressure waveform and stroke volume. Its output correlates with single-beat methods and predicts clinical outcomes.
Objectives: This study sought to develop a novel, artificial intelligence-based pipeline that estimates load-independent RV functional indices using a pressure-time waveform and stroke volume from clinical right-sided heart catheterization.
Methods: Clinical data and pressure-volume-time data were collected from 76 patients referred for right-sided heart catheterization for known or suspected PH from 3 centers. A computational pipeline was developed to determine the RV pressure-volume loop and extract load-independent RV indices using computer vision image processing and single-beat analysis. Agreement with gold standard single-beat analysis and prognostic value were evaluated.
Results: Strong concordance was observed between both methods for end-systolic elastance (Ees: R = 0.96; concordance correlation coefficient [CCC] = 0.58), effective arterial elastance (Ea: R = 0.97; CCC = 0.88), end-diastolic elastance (Eed: R = 0.87; CCC = 0.47), and Ees/Ea ratio (R = 0.93; CCC = 0.71) in both the validation and external cohorts. Prognostic analyses showed that calculated Ea (HR: 2.09; 95% CI: 1.04-4.20) and Ees/Ea (HR: 0.27; 95% CI: 0.08-0.87) were significant predictors of clinical outcomes. Cluster analysis using single-beat indices identified 2 RV subphenotypes with distinct hemodynamic features that were more predictive of poor outcomes than analysis using standard clinical features.
Conclusions: Study investigators have developed a novel computational pipeline that digitizes and generates single-beat estimates of RV-pulmonary arterial coupling from an image of the RV pressure waveform and stroke volume. Its output correlates with single-beat methods and predicts clinical outcomes.
논문정보
- Research article
- 2025년 10월 (BRIC 등록일 2025-11-04)
- 국외 연구진
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