Risk model for predicting severe dengue and dengue with warning signs by early indications in border areas in Yunnan province：based on LASSO-logistic regression
- Author:
FU Hanwen
;
SHEN Jiayuan
;
WU Chao
- Publication Type:Journal Article
- Keywords:
Severe dengue fever;
clinical features;
predictive model;
LASSO regression;
logistic regression;
border regions;
Yunnan Province
- From:
China Tropical Medicine
2025;25(3):309-
- CountryChina
- Language:Chinese
-
Abstract:
Objective A predictive model should be established during the early stages of dengue progression to evaluate the likelihood of severe dengue and dengue with warning signs, thereby preventing delayed clinical management and reducing dengue-related mortality. Methods Clinical and laboratory examination data of 831 patients admitted to Ruili People's Hospital of Yunnan Province during 2019-2023 were retrospectively collected. The dataset was divided into a training set and a validation set in a 7∶3 ratio. Statistical description and univariate analysis were performed on the training set, with LASSO regression employed to screen variables, followed by logistic regression to develop a risk prediction model for severe dengue. Model performance was validated using ROC curves on both the training set and validation set. Results A total of 831 dengue patients were included in the study, with a mean age of (44.20±15.02) years. Among them, 52.59% were male and 5.42% were Myanmar nationality. In total, 122 cases (14.68%) exhibited severe dengue or dengue with warning signs, predominantly female (58.20%). LASSO regression was used in the training set to screen 11 variables related to the risk of severe dengue and dengue with warning signs: Age, dizziness, vomiting, prothrombin time, partial activated thromboplastin time, hematocrit, platelet, monocyte percentage, absolute value of monocytes, hemoglobin, C-reactive protein (λmin= 0.011 59); Logistic regression identified statistically significant variables for the risk model of severe dengue and dengue with warning signs as follows: age [OR=1.034 (95%CI: 1.016-1.053)], red blood cells deposited [OR=1.258 (95%CI: 1.143-1.519)], platelet [OR=0.991 (95%CI: 0.985-0.997)], hemoglobin (OR=0.919 (95%CI: 0.873-0.950)], C-reactive protein [OR=1.019 (95%CI:1.004-1.034)]. The model achieved an AUC of 0.894 (95%CI: 0.796-0.867) in the training set and 0.862 (95%CI: 0.709-0.827) in the validation set. At a cut-off threshold of 0.197, sensitivity and specificity were 0.850 and 0.743, respectively. Conclusion This study established a LASSO-logistic regression model, which can predict the risk of severe dengue and dengue with warning signs. The model enhances the capability of hospitals to prevent and manage severe dengue and provides valuable guidance for clinical decision-making.
- Full text:20251117171803338449.Risk model for predicting severe dengue and dengue with warning signs by early indications in border areas in Yunnan province：based on LASSO-logistic regre.pdf
