{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:01:45Z","timestamp":1782846105913,"version":"3.54.5"},"reference-count":33,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Inform"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Pulmonary hypertension (PH) is a progressive disorder characterized by elevated pulmonary artery pressure and increased pulmonary vascular resistance, ultimately leading to right heart failure. Early detection is critical for improving patient outcomes.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n The diagnosis of PH primarily relies on right heart catheterization, but its invasive nature significantly limits its clinical use. Echocardiography, as the most common noninvasive screening and diagnostic tool for PH, provides valuable patient information. This study aims to identify key PH predictors from echocardiographic parameters, laboratory tests, and demographic data using machine learning, ultimately constructing a predictive model to support early noninvasive diagnosis of PH.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n This study compiled comprehensive datasets comprising echocardiography measurements, clinical laboratory data, and fundamental demographic information from patients with PH and matched controls. The final analytical cohort consisted of 895 participants with 85 evaluated variables. Recursive feature elimination was used to select the most relevant echocardiographic variables, which were subsequently integrated into a composite ultrasound index using machine learning techniques, XGBoost (Extreme Gradient Boosting). LASSO (least absolute shrinkage and selection operator) regression was applied to select the potential predictive variable from laboratory tests. Then, the ultrasound index variables and selected laboratory tests were combined to construct a logistic regression model for the predictive diagnosis of PH. The model\u2019s performance was rigorously evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis to ensure its clinical relevance and accuracy. Both internal and external validation were used to assess the performance of the constructed model.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n A total of 16 echocardiographic parameters (right atrium diameter, pulmonary artery diameter, left atrium diameter, tricuspid valve reflux degree, right ventricular diameter, E\/E\u2019 [ratio of mitral valve early diastolic inflow velocity (E) to mitral annulus early diastolic velocity (E\u2019)], interventricular septal thickness, left ventricular diameter, ascending aortic diameter, left ventricular ejection fraction, left ventricular outflow tract velocity, mitral valve reflux degree, pulmonary valve outflow velocity, mitral valve inflow velocity, aortic valve reflux degree, and left ventricular posterior wall thickness) combined with 2 laboratory biomarkers (prothrombin time activity and cystatin C) were identified as optimal predictors, forming a high-performance PH prediction model. The diagnostic model demonstrated high predictive accuracy, with an area under the receiver operating characteristic curve of 0.997 in the internal validation and 0.974 in the external validation. Both calibration plots and decision curve analysis validated the model\u2019s predictive accuracy and clinical applicability, with optimal performance observed at higher risk stratification cutoffs.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n This model enhances early PH diagnosis through a noninvasive approach and demonstrates strong predictive accuracy. It facilitates early intervention and personalized treatment, with potential applications in broader cardiovascular disease management.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/74117","type":"journal-article","created":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T14:35:08Z","timestamp":1755268508000},"page":"e74117-e74117","source":"Crossref","is-referenced-by-count":3,"title":["Interpretable Machine Learning Model for Pulmonary Hypertension Risk Prediction: Retrospective Cohort 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