{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T22:54:45Z","timestamp":1768863285036,"version":"3.49.0"},"posted":{"date-parts":[[2025,6,2]]},"group-title":"Cardiovascular Medicine","reference-count":45,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T00:00:00Z","timestamp":1748822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.medrxiv.org\/about\/FAQ#license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,6,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Load-independent indices of right ventricular (RV) dysfunction aid in the prognosis of patients with pulmonary hypertension (PH), but their acquisition remains difficult. This study aimed to develop a novel computer vision artificial intelligence-based pipeline that can estimate load-independent RV functional indices using screenshots of the RV pressure-time waveform from a standard clinical right heart catheterization (RHC).<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>Prospectively collected clinical data and research-grade pressure-volume-time data were collected from 76 patients from three centers. Patients were referred for RHC for known or suspected PH. Thirty-nine patients from one center were used for internal development, with external validation performed on the remaining 37 patients from two independent centers. A MATLAB-based computational pipeline was developed to predict RV pressure-volume (P-V) loop and extract load-independent RV indices using image processing and single-beat analysis. Agreement with gold-standard single-beat analysis was assessed via Bland-Altman analysis, concordance correlation coefficient (CCC), and Pearson correlation coefficient. Kaplan-Meier survival analysis, Cox regression analysis, and K-means clustering were performed to evaluate prognostic value.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The average age of the derivation cohort was 57\u00b112 years. Strong concordance was observed between the novel and gold-standard methods for end-systolic elastance (Ees, R=0.96, 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). Agreement was validated with the external cohort. Prognostic analyses showed that pipeline-derived Ea (HR: 2.09 [1.04, 4.20]) and Ees\/Ea (HR: 0.27 [0.08, 0.87]) were significant predictors of clinical outcomes. Cluster analysis identified two RV sub-phenotypes with distinct hemodynamic features, with the group exhibiting higher Ea (1.05\u00b10.27 vs. 0.41\u00b10.19 mmHg\/mL, p&lt;0.0001) and lower Ees\/Ea (0.37\u00b10.15 vs. 0.76\u00b10.40 mmHg\/mL, p=0.0002) demonstrating worse outcomes.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>We have developed a novel computational pipeline tool that digitizes and generates single-beat estimates of RV-pulmonary arterial coupling from an image of the RV pressure waveform. Its output correlates with single-beat methods and predicts clinical outcomes.<\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2025.06.01.25328762","type":"posted-content","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T14:11:02Z","timestamp":1748873462000},"source":"Crossref","is-referenced-by-count":0,"title":["Development of Computational Pipeline for Right Ventricular Hemodynamic Single-Beat Analysis"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4067-3627","authenticated-orcid":false,"given":"Gyeongtae S.","family":"Moon","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"suffix":"III","given":"Paul J.","family":"Scheel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maggie","family":"Montovano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Milan","family":"Kaushik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cole","family":"Buchanan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samuel H.","family":"Friedman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rebecca","family":"Vanderpool","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. 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