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Application of decision tree model in diagnosis of mycoplasma pneumoniae pneumonia with plastic bronchitis
Lin Li1, Dong Wang1, Rongrong Yang1
1Department of Infectious Diseases, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Children's Hospital (Fujian Branch of Shanghai Children's Medical Center), National Regional Medical Center, Fujian Medical University, Fuzhou, 350014, PR China.
Insights
A decision tree model aids in diagnosing pediatric plastic bronchitis (PB) in children with Mycoplasma pneumoniae pneumonia (MPP). This model, using key indicators, shows good discriminative ability for clinical application.
Area of Science:
- Pediatric Pulmonology
- Infectious Diseases
- Medical Informatics
Background:
- Mycoplasma pneumoniae pneumonia (MPP) can be complicated by plastic bronchitis (PB) in children.
- Accurate auxiliary diagnosis is crucial for effective management of these complex cases.
Purpose of the Study:
- To establish a decision tree model for diagnosing pediatric MPP complicated with PB.
- To evaluate the clinical application value of this decision tree model in auxiliary diagnosis.
Main Methods:
- A retrospective study analyzed clinical data from 214 children (66 with PB, 148 without).
- A decision tree algorithm was used to train a model on 70% of the data, employing indicators like D-dimer and C-reactive protein.
- The model was validated on a separate dataset.
Main Results:
- A decision tree model was constructed using D-dimer, C-reactive protein, drug resistance, and serum ferritin levels.
- The model achieved a sensitivity of 0.884 and specificity of 0.727.
- The area under the receiver operating characteristic curve was 0.831, indicating good discriminative ability.
Conclusions:
- The developed decision tree model can serve as a valuable tool for auxiliary diagnosis in children with MPP complicated by PB.
- The model demonstrates good discriminative capability and warrants further clinical investigation and application.
Background:
To establish a decision tree model of Mycoplasma pneumoniae pneumonia(MPP) complicated with plastic bronchitis(PB) in children, and to explore the application value of decision tree model in the auxiliary diagnosis of children.
Methods:
A retrospective study was conducted to collect the clinical data of 214 children who met the admission criteria in Fujian Children's Hospital from June 2022 to June 2024, and they were divided into plastic bronchitis group (n = 66) and non-plastic bronchitis group (n = 148). Using R language, 70% of the data from each group of patients was randomly selected for training the model using decision tree algorithm analysis, thus generating a clinical diagnostic decision tree for Mycoplasma pneumoniae (MP) combined with PB. The generated decision tree model was validated on the validation sample dataset and the detection effect value of the model was calculated.
Result:
In this study, a total of 22 indicators were employed to build the decision tree diagnostic model. Univariate statistical analysis was carried out prior to the model construction, and it was discovered that the differences of 13 indicators between the molded group and the non-molded group were statistically significant. A decision tree model with D-dimer ≥ 1.7ug/mL, C-reactive protein ≥ 15 mg/L, drug resistance or not, and serum ferritin<137 mg/L was constructed in the training sample dataset of the molded group and the non-molded group. The sensitivity of the decision tree model was 0.884, which was verified in the dataset of the remolded group and the non-molded group. The specificity was 0.727, and the area under the receiver operating characteristic curve was 0.831.
Conclusion:
Decision tree model can provide reference for the application of auxiliary diagnosis in children with mycoplasma pneumoniae pneumonia complicated with plastic bronchitis. The model has good discriminative ability in general, and is worthy of clinical application and further study.
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