Applications of Data Science Techniques in Predicting Mortality among Pediatric ARDS Patients: A Medico-Statistical Approach - Palavardhan, Peddapalegani; Naidu, M. Bhupathi; Vani, Akkyam; Reshma, Mohammad (Journal of Neonatal Surgery, 2025)
This study explores the application of data science techniques in identifying key predictors of mortality among pediatric patients with Acute Respiratory Distress Syndrome (ARDS). A retrospective analysis was conducted on 200 mechanically ventilated patients admitted to the Pediatric Intensive Care Unit (PICU) of the Department of Pediatrics, Malla Reddy Narayana Multispeciality Hospital. Clinical and ventilatory variables at 0 and 24 hours were analyzed using Python. Statistical methods included descriptive statistics, paired t-tests, point-biserial correlation, and multivariable logistic regression. Variance Inflation Factor (VIF) was applied to address multicollinearity, and ROC curve analysis was used to assess model performance. The Sequential Organ Failure Assessment (SOFA) score and Pediatric Risk of Mortality (PRISM) score emerged as significant independent predictors of mortality. The final logistic model achieved an AUC of 0.85, with a sensitivity of 77.3% and specificity of 84.4%. These findings highlight the potential of integrating data science techniques into clinical prediction modeling to improve early risk stratification in pediatric ARDS.