Development of hypertension models for lung cancer screening cohorts using clinical and thoracic aorta imaging factors

• Published in: Scientific reports Vol. 14; no. 1; p. 6862
• Main Authors: Yang, Jinrong, Yu, Jie, Wang, Yaoling, Liao, Man, Ji, Yingying, Li, Xiang, Wang, Xuechun, Chen, Jun, Qi, Benling, Yang, Fan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 22.03.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308; 692/499; 692/700; Aorta; Aorta, Thoracic; Calibration; Cancer screening; Coronary vessels; Early Detection of Cancer; Humanities and Social Sciences; Humans; Hypertension; Hypertension - diagnostic imaging; Lung cancer; Lung Neoplasms - diagnostic imaging; Medical screening; multidisciplinary; Nomograms; Prediction models; Risk assessment; Science; Science (multidisciplinary); Thorax
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-57396-1

This study aims to develop and validate nomogram models utilizing clinical and thoracic aorta imaging factors to assess the risk of hypertension for lung cancer screening cohorts. We included 804 patients and collected baseline clinical data, biochemical indicators, coexisting conditions, and thoracic aorta factors. Patients were randomly divided into a training set (70%) and a validation set (30%). In the training set, variance, t-test/Mann–Whitney U-test and standard least absolute shrinkage and selection operator were used to select thoracic aorta imaging features for constructing the AIScore. Multivariate logistic backward stepwise regression was utilized to analyze the influencing factors of hypertension. Five prediction models (named AIMeasure model, BasicClinical model, TotalClinical model, AIBasicClinical model, AITotalClinical model) were constructed for practical clinical use, tailored to different data scenarios. Additionally, the performance of the models was evaluated using receiver operating characteristic (ROC) curves, calibration curves and decision curve analyses (DCA). The areas under the ROC curve for the five models were 0.73, 0.77, 0.83, 0.78, 0.84 in the training set, and 0.77, 0.78, 0.81, 0.78, 0.82 in the validation set, respectively. Furthermore, the calibration curves and DCAs of both sets performed well on accuracy and clinical practicality. The nomogram models for hypertension risk prediction demonstrate good predictive capability and clinical utility. These models can serve as effective tools for assessing hypertension risk, enabling timely non-pharmacological interventions to preempt or delay the future onset of hypertension.