ORIGINAL RESEARCH article
Front. Cardiovasc. Med.
Sec. Atherosclerosis and Vascular Medicine
Volume 12 - 2025 | doi: 10.3389/fcvm.2025.1581074
This article is part of the Research TopicExploring New Frontiers in Aortic and Carotid Disease ResearchView all articles
Development and validation of a nomogram based on LASSO-logistic regression for predicting carotid atherosclerosis in patients with hypertension
Provisionally accepted
- 1Changzhou Hospital Affiliated to Nanjing University of Chinese Medicine, Changzhou, China
- 2Changzhou Third People's Hospital, Changzhou, Jiangsu Province, China
- 3Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China
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Carotid atherosclerosis (CAS) is increasingly prevalent among hypertensive patients. This study aims to develop a predictive nomogram for CAS in hypertensive population. Methods A total of 930 patients with hypertension were hospitalized in the Department of Cardiology of the Affiliated Hospital of Changzhou, Nanjing University of Chinese Medicine (August 2018 -August 2024) formed the development cohort, categorized into CAS (156 individuals) and non-CAS (774 individuals) groups. Additionally, 398 hypertensive patients from the Department of Cardiology of the Second Affiliated Hospital of Soochow University served as the validation cohort (ratio 7:3), with 72 CAS individuals and 326 non-CAS individuals. LASSO regression initially identified key risk factors, followed by logistic regression for further analysis. The nomogram, constructed using the "rms" package in R 4.2.6, underwent internal validation via the 1000 iterations of Bootstrap resampling. Model performance was evaluated through ROC curves, calibration curves, and decision curve analysis. Results Eight significant risk factors-Age, history of smoking (Smoke), history of diabetes mellitus (DM), course of hypertension (Course), physical activity (PA), body mass index (BMI), low-density lipoprotein (LDL), and uric acid (UA)-were identified (P<0.05), among which DM was the most important influencing factor. The nomogram demonstrated strong predictive accuracy, with AUC values of 0.858 [95% CI (0.798, 0.918)] in the development cohort and 0.808 [95% CI (0.740, 0.876)] in the validation cohort. Calibration curves closely aligned with the ideal model, and decision curve analysis indicated optimal predictive performance within a probability threshold range of 0.050-0.960. Conclusions This study presents a robust nomogram for assessing CAS risk in hypertensive patients, offering a valuable tool for clinical risk evaluation.
Keywords: Hypertension, carotid atherosclerosis, Logistic regression analysis, nomogram, LASSO regression
Received: 21 Feb 2025; Accepted: 17 Oct 2025.
Copyright: © 2025 Cao, Qiu, Gu, Tang, Li and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xiaolong Li, 15650796727@163.com
Daohai Chen, 18101490727@163.com
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