Development and validation of a dampness constitution prediction model based on clinical laboratory indicators
10.3760/cma.j.cn114452-20241129-00650
- VernacularTitle:基于临床实验室指标湿性体质预测模型的构建及验证
- Author:
Xixi XIE
1
;
Chunmin KANG
;
Xinyan CHEN
;
Haibiao LIN
;
Xiaobin WU
;
Xianzhang HUANG
Author Information
1. 广州中医药大学第二临床医学院检验教研室，广州 510405
- Publication Type:Journal Article
- Keywords:
Body constitution;
Sub health;
Laboratory testing;
Predictive model
- From:
Chinese Journal of Laboratory Medicine
2025;48(7):930-937
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop a clinical predictive model for dampness constitution based on laboratory testing indicators.Methods:A retrospective cohort study was conducted on 1 355 healthy individuals who underwent physical examinations at the Health Examination Center of Guangdong Provincial Hospital of Traditional Chinese Medicine from October 1 st, 2022 to October 31 st, 2023. Basic information and blood routine, blood glucose, liver function, lipid metabolism, and kidney function test results of 1 355 apparently healthy individuals were collected. According to the diagnostic criteria for dampness constitution in traditional Chinese medicine, they were divided into a dampness constitution group (683 cases, including 394 with phlegm-dampness constitution and 289 with damp-heat constitution) and a non-dampness constitution group (672 cases). Among them, there were 547 males and 136 females in the dampness constitution group, with an age of 38.0 (32.0, 45.0) years; and there were 355 males and 317 females in the non-dampness constitution group, with an age of 33.0 (27.0, 41.0) years. A total of 1 355 apparently healthy individuals were randomly divided into a training set ( n=948) and a validation set ( n=407) using computer-generated random numbers in a 7∶3 ratio. Logistic regression analysis was employed to identify risk factors associated with dampness constitution. Utilizing these identified risk factors, a predictive model was constructed and subsequently visualized. The model′s predictive accuracy, consistency, and clinical utility were assessed using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), respectively. Results:Among 1 355 subjects, there were statistically significant differences ( P<0.05) in gender, age, body mass index (BMI), blood glucose, some indicators of renal function, some indicators of blood routine, liver function, and four indicators of lipid metabolism between the dampness constitution group and the non-dampness constitution group. Gender ( OR=0.434,95 %CI 0.253-0.738), Cr ( OR=0.981,95 %CI 0.967-0.996), BMI ( OR=1.366,95 %CI 1.290-1.450), and LDL-C ( OR=1.388,95 %CI 1.014-1.897) were independent risk factors for dampness constitution ( P<0.05). A nomogram was subsequently developed based on these identified risk factors. The areas under the ROC curves (AUC) of the training set and validation set were 0.810 (95 %CI 0.783-0.837) and 0.804 (95 %CI 0.762-0.846), respectively. Conclusion:Gender,BMI,Cr and LDL-C were risk factors for the development of dampness constitution, and the clinical predictive model has clinical application value in predicting the risk of dampness constitution.
