Diagnostic Evaluation of the STEPSS Score and Machine Learning Models in Predicting Outcomes in Pediatric Status Epilepticus: A Prospective Observational Study

<b>Background:</b><br /> Pediatric status epilepticus (SE) is a neurological emergency associated with significant morbidity. The <b>Status Epilepticus Pediatric Severity Score (STEPSS)</b> offers rapid bedside prognostication, but its performance relative to machine learning (ML) models has not been well studied in resource-limited settings.<br /> <b>Methods:</b><br /> We prospectively enrolled <b>100 children</b> with SE (median age 3.4 years; 52 % male) admitted to a tertiary center in South India (2023–2024). Clinical features, investigations, and outcomes were recorded. Functional outcome was assessed at discharge using the <b>Pediatric Overall Performance Category (POPC)</b>, with unfavorable outcome defined as POPC ≥3. Prognostic accuracy of STEPSS and three ML models—<b>Logistic Regression (LR), Random Forest (RF), and XGBoost</b>—was evaluated using sensitivity, specificity, predictive values, accuracy, and area under the ROC curve (AUC).<br /> <b>Results:</b><br /> Overall, <b>30 % of children had unfavorable POPC outcomes</b>. At presentation, <b>40 % had altered consciousness</b> and <b>45 % had high-risk seizure types</b>. <b>STEPSS ≥3</b> was associated with ICU admission, mechanical ventilation, and poor outcome. STEPSS demonstrated good discrimination (<b>sensitivity 79 %, specificity 78 %, NPV 84 %, AUC 0.83</b>). Additional predictors of poor outcome included <b>low SpO₂, hyperglycemia, CT abnormalities (present in 41 % of those imaged), and delay to first AED</b> EEG was performed in 53 % of patients, with abnormalities in 38 % <b>.</b>Among ML models, LR achieved performance similar to STEPSS (AUC 0.82), while RF (AUC 0.88) and XGBoost (AUC 0.91) outperformed it, with XGBoost achieving the highest accuracy (90 %) and the fewest misclassifications. Feature importance analysis highlighted CT abnormalities, treatment delay, blood glucose, and SpO₂ as dominant predictors, with STEPSS also contributing significantly.<br /> <b>Conclusion:</b><br /> STEPSS remains a practical and reliable bedside triage tool in pediatric SE, particularly in low-resource emergency settings. However, integrating additional clinical indicators into ensemble ML models, especially XGBoost, provides superior prognostic accuracy. A <b>hybrid strategy—STEPSS combined with ML augmentation—offers both interpretability and precision</b>, supporting early risk stratification and informed critical care decisions.