Machine learning-based prediction of acute severity in infants hospitalized for bronchiolitis: a multicenter prospective study

Abstract

We aimed to develop machine learning models to accurately predict bronchiolitis severity, and to compare their predictive performance with a conventional scoring (reference) model. In a 17-center prospective study of infants (aged < 1 year) hospitalized for bronchiolitis, by using routinely-available pre-hospitalization data as predictors, we developed four machine learning models: Lasso regression, elastic net regression, random forest, and gradient boosted decision tree. We compared their predictive performance—e.g., area-under-the-curve (AUC), sensitivity, specificity, and net benefit (decision curves)—using a cross-validation method, with that of the reference model. The outcomes were positive pressure ventilation use and intensive treatment (admission to intensive care unit and/or positive pressure ventilation use). Of 1,016 infants, 5.4% underwent positive pressure ventilation and 16.0% had intensive treatment. For the positive pressure ventilation outcome, machine learning models outperformed reference model (e.g., AUC 0.88 [95% CI 0.84–0.93] in gradient boosted decision tree vs 0.62 [95% CI 0.53–0.70] in reference model), with higher sensitivity (0.89 [95% CI 0.80–0.96] vs. 0.62 [95% CI 0.49–0.75]) and specificity (0.77 [95% CI 0.75–0.80] vs. 0.57 [95% CI 0.54–0.60]). The machine learning models also achieved a greater net benefit over ranges of clinical thresholds. Machine learning models consistently demonstrated a superior ability to predict acute severity and achieved greater net benefit.