Constructing a predictive model for tsutsugamushi disease complicated with sepsis based on decision tree and logistic regression
- Author:
LIU Xing
;
LI Weixian
;
ZHANG Le
- Publication Type:Journal Article
- Keywords:
Tsutsugamushi disease;
sepsis;
nomogram;
decision tree;
prediction model
- From:
China Tropical Medicine
2024;24(9):1066-
- CountryChina
- Language:Chinese
-
Abstract:
Abstract: Objective To explore the optimal prediction model for tsutsugamushi disease complicated with sepsis through decision tree models and logistic regression models. Methods A total of 235 cases of tsutsugamushi disease patients admitted to the Third People's Hospital of Kunming from June 2012 to December 2023 were selected as the research subjects, including 138 cases of tsutsugamushi disease patients with sepsis as the experimental group and 97 cases of tsutsugamushi disease patients without sepsis as the control group. Decision tree models and logistic regression models were established, and the discriminability of two prediction models was compared using receiver operating curve (ROC) to assess their predictive abilities. Results Among 235 patients, Han ethnicity accounted for 91.1%, farmers accounted for 66.8%, and those residing in suburban areas accounted for 57.4%. The clinical manifestations were mainly fever, headache, and chills, with an incidence rate of scab of 100.0%. Factors that were statistically significant between the two groups included age, IL-1β, IL-2, IL-17, IFN-γ, and TNF-α (P<0.05). The above factors were brought into the logistic regression model and decision tree model, respectively. The multivariate analysis results of logistic regression showed that age (OR=1.039, 95%CI:1.017-1.061) and IFN-γ (OR=1.009, 95%CI:0.999-1.018) were independent influencing factors for tsutsugamushi disease complicated with sepsis. The area under the ROC curve (AUC) was 0.669 (95CI:0.599-0.738, P<0.001), with an accuracy of 63.8%, sensitivity of 71.1%, and specificity of 57.2%. The decision tree model results showed that the root node was IL-1β, and the sub-nodes were age and IL-2. The area under the ROC curve (AUC) was 0.714 (95CI:0.646-0.782, P<0.001), with an accuracy of 68.9%, sensitivity of 58.8%, and specificity of 76.1%. Comparing the two models, the AUC difference was 0.045, the standard error was 0.042, Z=1.074, P=0.283, 95%CI: -0.037-0.128. In terms of treatment and prognosis, doxycycline was used most frequently (78.3%), and the combination of doxycycline and moxifloxacin was the most common (87.7%) in combined treatments. There was a strong positive correlation between the duration of medication and the frequency of combined treatments (τ=0.119, P=0.021), as well as between the duration of medication and hospitalization time (τ=0.725, P<0.001). The clinical diagnostic rate was 94.5%, and the mortality rate was 1.7%. Conclusions Both models have certain predictive values, among which the decision tree model has better predictive ability and is worthy of clinical promotion.
- Full text:20251110103020772758.Constructing a predictive model for tsutsugamushi disease complicated with sepsis based on decision tree and logistic regression.pdf
