Construction of Risk Prediction Model for Frequent Acute Exacerbations of Chronic Obstructive Pulmonary Disease Under Disease-syndrome Combination
10.13422/j.cnki.syfjx.20251998
- VernacularTitle:病证结合模式下慢性阻塞性肺疾病频繁急性加重风险预测模型的构建
- Author:
Jing ZHOU
1
;
Gang TENG
1
;
Nianzhi ZHANG
1
;
Yuanyuan WANG
1
;
Qianqian ZHANG
1
;
He HUANG
1
;
Ling LIU
1
;
Mei DONG
1
;
Juan JI
1
Author Information
1. The First Clinical Medical College of Anhui University of Traditional Chinese Medicine， The First Affiliated Hospital of Anhui University of Chinese Medicine，Hefei 230031，China
- Publication Type:Journal Article
- Keywords:
acute exacerbations of chronic obstructive pulmonary disease;
frequent acute exacerbations;
nomogram;
clinical prediction model;
disease-syndrome combination
- From:
Chinese Journal of Experimental Traditional Medical Formulae
2026;32(6):143-151
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo construct a risk prediction model for frequent acute exacerbations of chronic obstructive pulmonary disease （COPD） under disease-syndrome combination， thus providing decision support for precise clinical intervention. MethodsA total of 2 029 patients with acute exacerbations of COPD admitted to the First Affiliated Hospital of Anhui University of Chinese Medicine from January 2020 to August 2024 were retrospectively included. These patients were classified into groups of frequent acute exacerbations （≥2 times/year） and infrequent acute exacerbations （<2 times/year） according to the hospitalization times per year. Risk factors were screened by LASSO regression combined with logistic regression， and a nomogram model was constructed. The model performance was assessed based on the area under the curve （AUC）， calibration curves， and decision curve analysis （DCA）. ResultsThe differences in baseline characteristics between the frequent acute exacerbations group （1 196 cases） and infrequent acute exacerbations group （833 cases） were not statistically significant. LASSO regression combined with multivariate logistic regression screened the following independent risk factors： body mass index （BMI）， hospitalization days， number of smoking years， place of residence， use of noninvasive ventilators， oxygen-demanding therapy， liver cirrhosis， use of systemic glucocorticosteroids， and traditional Chinese medicine syndrome （phlegm and stasis obstructing the lung）. The nomogram model showed good discrimination and calibration in both the training set （AUC=0.748） and validation set （AUC=0.774）. ConclusionThe risk prediction model for frequent acute exacerbations of COPD， integrating traditional Chinese medicine syndrome， constructed in this study has high accuracy. It can provide a scientific basis for early clinical identification of high-risk patients and individualized intervention.
