Fig 1.
The study flowchart for developing predictive models of mechanical ventilation in children with dengue shock syndrome.
Table 1.
Baseline clinical and laboratory characteristics of study participants on PICU admission and clinical outcomes at discharge (N = 1,278).
Table 2.
Performance of supervised models to estimate the risk of mechanical ventilation in patients with dengue shock syndrome (N = 1,278).
Fig 2.
The ten most significant features of the Random Forest model based on classification accuracy.
Fig 3.
The SHAP interpretation of the clinically important variables in patients with dengue shock syndrome on mechanical ventilation.
All ten features identified by the SVM model showed high significance in the SHAP model. However, the remaining variables had less impact on mechanical ventilation.
Fig 4.
Calibration plot showing high consistency between the predicted values (x-axis) and actual observation data (y-axis).
Fig 5.
A simplified risk score chart for estimating the risk of mechanical ventilation in children with dengue shock syndrome.
The total points ranged from -2 to 46, corresponding to a probability of mechanical ventilation ranging from 0% to 100%.
Table 3.
Risk score to predict mechanical ventilation among children with dengue shock syndrome during the first 24 hours of PICU admission.