Predictive model for initial response to first-line treatment in children with infantile epileptic spasms syndrome

Abstract

Background: Previous studies have suggested that factors such as the treatment interval and aetiology may influence the initial response rate to first-line treatment for infantile epileptic spasms syndrome (IESS). However, few children with IECSS have undergone clinically accessible tests to determine the aetiology.

Methods: Using a dataset from our previously published research, we constructed and tested a predictive model for the initial response to first-line treatment in children with IESS. Random sampling and 5-fold cross-validation were performed, with synthetic minority oversampling technique to correct data imbalance. Machine learning algorithms and evaluation metrics optimised model accuracy and efficacy.

Results: This study included 532 children with IESS who had completed monotherapy first-line treatment, of whom 160 achieved an initial response. The model's accuracy, F1 score, and area under the curve (AUC) in the validation set were 0.7836 ± 0.0229 (ranging from 0.75167 to 0.80536), 0.7833 ± 0.0229 (ranging from 0.75145 to 0.80531), and 0.8516 ± 0.0165 (ranging from 0.82468 to 0.86936), respectively. Factors such as the age of seizure onset, age of spasm onset, lead time, MRI subtype, treatment choice, and age at treatment consistently ranked in the top six for importance in contributing to the model.

Conclusions: The study findings suggest that this model may help effectively predict the initial response to first-line treatment, supporting clinical decision-making for children with IESS. Key predictors such as the age of seizure onset and MRI subtype enable early, data-driven intervention strategies in clinical practice.

Keywords: First-line treatment; Infantile epileptic spasms syndrome (IESS); Initial response; Predictive model.

Fig. 1

T-SNE clustering visualization of original and SMOTE-enhanced data. A. T-SNE clustering visualisation of the original data. B. T-SNE clustering visualisation of the data after the SMOTE algorithm. Each point represents a patient, with blue indicating nonresponders and green indicating responders. The similar distribution intervals of responders and nonresponders confirm that there is no significant change in the data distribution after SMOTE compared with the original data distribution

Fig. 2

Performance of the XGBoost predictive model in the validation set. (A) AUC across 5-fold cross-validation, each color represents each fold; (B) AUC (mean value of 5-flod) comparison with other models, each color represents a different model, and all AUC values are the v of the 5 - fold of a single model. The multiple coloured curves represent the results of fivefold cross-validation, illustrating the relationship between the true positive rate (TPR) and the false-positive rate (FPR) at different thresholds. The closer the curve is to the top left corner, the better the model’s performance; AUC, the area under the curve, is a critical metric for measuring model performance. Values closer to 1 indicate better classification performance; CI, confidence interval, provides a potential range for the AUC value. At a certain confidence level, there is a high probability that the actual AUC value will fall within this interval, helping to assess the uncertainty in the model’s performance

Fig. 3

Interpretability of predictors in the XGBoost predictive model during 5-fold cross-validation. The vertical axis lists a series of features arranged by the average magnitude of their SHAP values. The horizontal axis represents the SHAP value for each feature, indicating the impact of each feature on the model’s prediction. Larger absolute SHAP values indicate a more significant effect. Points represent the SHAP values for individual samples across features. Point position corresponds to the SHAP value on the horizontal axis and the feature on the vertical axis. The colour of the points indicates the impact level: red signifies a high impact (High), meaning that the feature significantly increases the model’s output or response rate; blue represents a low impact (Low), meaning that the feature significantly decreases the model’s output or response rate. This visualisation helps identify which features most influence the model’s predictions and how different feature values (high or low) affect individual predictions. It provides an intuitive understanding of the model’s behaviour, facilitating stakeholder communication