Abstract

Purpose

To re-assess cardiovascular metrics on computed tomography pulmonary angiography (CTPA) in predicting pulmonary hypertension (PH) under the 2022 ESC/ERS guidelines.

Materials and methods

This observational study retrospectively included 272 patients (female 143, mean age = 54.9 ± 12.5 years old) with suspected PH. 218 patients were grouped to evaluate cardiovascular metrics on CTPA and develop a binary logistic regression model. The other 54 patients were grouped into the validation group to assess the performance of the prediction model under the updated criteria. Based on mean pulmonary artery pressure (mPAP), patients were divided into three groups: group A consisted of patients with mPAP ≤ 20 mmHg, group B included patients with 20 mmHg < mPAP < 25 mmHg, and group C comprised patients with mPAP ≥ 25 mmHg. Cardiovascular metrics among the three groups were compared, and receiver operating characteristic curves (ROCs) were used to evaluate the performance of cardiovascular metrics in predicting mPAP > 20 mmHg.

Results

The main pulmonary arterial diameter (MPAd), MPAd/ascending aorta diameter ratio (MPAd/AAd ratio), and right ventricular free wall thickness (RVFWT) showed significant differences among the three groups (p < 0.05). The area under curve (AUC) of MPAd was larger than MPAd/AAd ratio and RVFWT. A MPAd cutoff value of 30.0 mm has a sensitivity of 83.1% and a specificity of 90.4%. The AUC of the binary logistic regression model (Z =  − 12.98187 + 0.31053 MPAd + 1.04863 RVFWT) was 0.938 ± 0.018. In the validation group, the AUC, sensitivity, specificity, and accuracy of the prediction model were 0.878, 92.7%, 76.9%, and 88.9%, respectively.

Conclusion

Under the updated criteria, MPAd with a threshold value of 30.0 mm has better sensitivity and specificity in predicting PH. The binary logistic regression model may improve the diagnostic accuracy.

Critical relevance statement

Under the updated criteria, the main pulmonary arterial diameter with a threshold value of 30.0 mm has better sensitivity and specificity in predicting pulmonary hypertension. The binary logistic regression model may improve diagnostic accuracy.

Key points

• According to 2022 ESC/ERS guidelines, a MPAd cutoff value of 30.0 mm has better sensitivity and specificity in predicting mPAP > 20 mmHg

• A binary logistic regression model (Z = − 12.98187 + 0.31053 MPAd + 1.04863 RVFWT) was developed and had a sensitivity, specificity, and accuracy of 92.7%, 76.9%, and 88.9% in predicting mPAP > 20 mmHg.

• A binary logistic regression prediction model outperforms MPAd in predicting mPAP > 20 mmHg.

Details

Title
Cardiovascular metrics on CT pulmonary angiography in patients with pulmonary hypertension — re-evaluation under the updated guidelines of pulmonary hypertension
Author
Liu, Anqi 1 ; Xu, Wenqing 2 ; Xi, Linfeng 3 ; Deng, Mei 4 ; Yang, Haoyu 2 ; Huang, Qiang 5 ; Gao, Qian 5 ; Zhang, Peiyao 6 ; Xie, Wanmu 5 ; Huang, Zhenguo 7 ; Liu, Min 7   VIAFID ORCID Logo 

 China-Japan Friendship Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China 
 Peking University China-Japan Friendship School of Clinical Medicine, Department of Radiology, Beijing, China (GRID:grid.11135.37) (ISNI:0000 0001 2256 9319) 
 China-Japan Friendship Hospital, Department of Pulmonary and Critical Care Medicine, Beijing, China (GRID:grid.415954.8) (ISNI:0000 0004 1771 3349); Capital Medical University, Beijing, China (GRID:grid.24696.3f) (ISNI:0000 0004 0369 153X) 
 China-Japan Friendship Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China (GRID:grid.24696.3f) 
 China-Japan Friendship Hospital, Department of Pulmonary and Critical Care Medicine, Beijing, China (GRID:grid.415954.8) (ISNI:0000 0004 1771 3349) 
 Capital Medical University, Beijing, China (GRID:grid.24696.3f) (ISNI:0000 0004 0369 153X) 
 China-Japan Friendship Hospital, Department of Radiology, Beijing, China (GRID:grid.415954.8) (ISNI:0000 0004 1771 3349) 
Pages
179
Publication year
2023
Publication date
Dec 2023
Publisher
Springer Nature B.V.
e-ISSN
18694101
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2880587436
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.