Development of a predictive model and nomogram using CTPA parameters for the identification of group 1 pulmonary arterial hypertension
Abstract
This study aimed to identify computed tomography pulmonary angiography (CTPA)-derived predictors of pulmonary arterial hypertension (PAH) and to develop a prediction model and nomogram for non-invasive diagnosis. We retrospectively analyzed CTPA datasets from 203 patients in the training cohort and 98 patients in the validation cohort, stratified into PAH (confirmed by right heart catheterization) and non-PAH groups. Pulmonary vascular and cardiac structural metrics were measured. The least absolute shrinkage and selection operator (LASSO) and logistic regression identified interventricular septal angle (IVSA), interventricular septal thickness (IVST), and right ventricular outflow tract wall thickness (RVOTW) as independent predictors. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.964 and 0.969 in the training and validation cohorts, respectively, with excellent calibration and clinical utility. The CTPA-based predictive model and nomogram may serve as a useful reference tool, potentially assisting in the identification of PAH and optimizing clinical workflows.
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BibTeX
@article{yuan2026development,
title = {{Development of a predictive model and nomogram using CTPA parameters for the identification of group 1 pulmonary arterial hypertension}},
author = {Huaxin Yuan and Iokfai Cheang and Gengmin Liang and Xu Zhu and Yutong Xing and Wenting Li and Shengen Liao and Jian He and Yunshan Cao and Xinli Li and Yi Xu and Yanli Zhou},
journal = {iScience},
year = {2026},
month = oct,
doi = {10.1016/j.isci.2026.117763},
url = {https://www.cell.com/iscience/fulltext/S2589-0042(26)03144-5},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Development of a predictive model and nomogram using CTPA parameters for the identification of group 1 pulmonary arterial hypertension
AU - Huaxin Yuan
AU - Iokfai Cheang
AU - Gengmin Liang
AU - Xu Zhu
AU - Yutong Xing
AU - Wenting Li
AU - Shengen Liao
AU - Jian He
AU - Yunshan Cao
AU - Xinli Li
AU - Yi Xu
AU - Yanli Zhou
PY - 2026
DA - 2026/10/08
JO - iScience
DO - 10.1016/j.isci.2026.117763
UR - https://www.cell.com/iscience/fulltext/S2589-0042(26)03144-5
AB - This study aimed to identify computed tomography pulmonary angiography (CTPA)-derived predictors of pulmonary arterial hypertension (PAH) and to develop a prediction model and nomogram for non-invasive diagnosis. We retrospectively analyzed CTPA datasets from 203 patients in the training cohort and 98 patients in the validation cohort, stratified into PAH (confirmed by right heart catheterization) and non-PAH groups. Pulmonary vascular and cardiac structural metrics were measured. The least absolute shrinkage and selection operator (LASSO) and logistic regression identified interventricular septal angle (IVSA), interventricular septal thickness (IVST), and right ventricular outflow tract wall thickness (RVOTW) as independent predictors. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.964 and 0.969 in the training and validation cohorts, respectively, with excellent calibration and clinical utility. The CTPA-based predictive model and nomogram may serve as a useful reference tool, potentially assisting in the identification of PAH and optimizing clinical workflows.
SN - 2589-0042
ER -