A nomogram model based on LASSO-Firth logistic regression to predict constipation risk in hospitalized adult patients with cardiovascular disease
10.12025/j.issn.1008-6358.2026.20260678
- VernacularTitle:基于LASSO-Firth logistic回归构建列线图模型预测住院成人心血管疾病患者的便秘风险
- Author:
Xiaoyan CHU
1
;
Ying LIN
2
;
Zhenzhen LU
3
;
Zhixin ZHOU
3
;
Xinyue DONG
1
Author Information
1. Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
2. Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai 200032, China;Department of Nursing, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
3. Department of Biostatistics, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
- Publication Type:Originalarticle
- Keywords:
cardiovascular disease;
constipation;
adult;
nomogram;
LASSO regression
- From:
Chinese Journal of Clinical Medicine
2026;33(4):599-606
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct a nomogram model for prediction of constipation risk in adult patients with cardiovascular disease (CVD). Methods A total of 780 adult patients with CVD admitted to Zhongshan Hospital, Fudan University from June 15, 2025 to August 15, 2025 were prospectively enrolled and randomly divided into a training set and a validation set at a 7∶3 ratio. Risk factors for constipation were identified using the least absolute shrinkage and selection operator (LASSO) and Firth logistic regressions, and a nomogram model was subsequently constructed in the training set. Receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to evaluate the discrimination ability of the model. Calibration curve and decision curve analysis (DCA) were used to assess calibration and clinical applicability. The value of model was evaluated in the validation set. Results Among 780 adult patients with CVD, 391 patients (50.1%) developed constipation. Eleven variables were selected by LASSO regression and Firth logistic regression, and a nomogram model was constructed. The ROC curve showed that the AUCs of the model to predict constipation in adult patients with CVD in training and validation sets were 0.881 and 0.883, respectively. Calibration curves and DCA demonstrated good calibration and predictive performance of the model in training and validation sets. Conclusions The nomogram model based on LASSO-Firth logistic regression demonstrates good predictive performance and may serve as a useful tool for identifying adult patients with CVD at high risk of constipation.
