Evidence map›Paper›PMID 42898097›Full record

ArticleiScience2026

Development of a predictive model and nomogram using CTPA parameters for the identification of group 1 pulmonary arterial hypertension.

Huaxin Yuan, Iokfai Cheang, Gengmin Liang, Xu Zhu, Yutong Xing, Wenting Li, Shengen Liao, Jian He, Yunshan Cao, Xinli Li and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Huaxin YuanState Key Laboratory for Innovation and Transformation of Luobing Theory, Department of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China.
Iokfai CheangState Key Laboratory for Innovation and Transformation of Luobing Theory, Department of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China.
Gengmin LiangState Key Laboratory for Innovation and Transformation of Luobing Theory, Department of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China.
Xu ZhuState Key Laboratory for Innovation and Transformation of Luobing Theory, Department of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China.
Yutong XingState Key Laboratory for Innovation and Transformation of Luobing Theory, Department of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China.
Wenting LiState Key Laboratory for Innovation and Transformation of Luobing Theory, Department of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China.
Shengen LiaoState Key Laboratory for Innovation and Transformation of Luobing Theory, Department of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China.
Jian HeDepartment of Radiology, the First Affiliated Hospital with Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China.
Yunshan CaoHeart, Lung and Vessels Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610072, China.
Xinli LiState Key Laboratory for Innovation and Transformation of Luobing Theory, Department of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China.
Yi XuDepartment of Radiology, the First Affiliated Hospital with Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China.
Yanli ZhouState Key Laboratory for Innovation and Transformation of Luobing Theory, Department of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

computed tomography pulmonary angiographyCTPAnomogrampredictive modelpulmonary hypertensionRHCright heart catheterization

Identifiers

PMID42898097
PMCPMC13677309

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.