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.

我们的目标是开发机器学习模型来准确预测细支气管炎的严重程度，并将其预测性能与传统评分（参考）模型进行比较。在一项针对因细支气管炎住院的婴儿（年龄 <1 岁）的 17 中心前瞻性研究中，通过使用常规可用的住院前数据作为预测因子，我们开发了四种机器学习模型：Lasso 回归、弹性网络回归、随机森林和梯度提升决策树。我们使用交叉验证方法将它们的预测性能（例如曲线下面积 (AUC)、敏感性、特异性和净效益（决策曲线））与参考模型的预测性能进行了比较。结果是正压通气的使用和强化治疗（入住重症监护室和/或正压通气的使用）。在 1,016 名婴儿中，5.4% 接受正压通气，16.0% 接受强化治疗。对于正压通气结果，机器学习模型优于参考模型（例如，梯度提升决策树中的 AUC 0.88 [95% CI 0.84–0.93]，而参考模型中的 AUC 0.62 [95% CI 0.53–0.70]），具有更高的灵敏度（ 0.89 [95% CI 0.80–0.96] vs. 0.62 [95% CI 0.49–0.75]）和特异性（0.77 [95% CI 0.75–0.80] vs. 0.57 [95% CI 0.54–0.60]）。机器学习模型还在临床阈值范围内实现了更大的净效益。机器学习模型始终表现出预测急性严重程度的卓越能力，并取得了更大的净效益。