Table_3_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.docx

Background: High flow nasal cannula (HFNC) is commonly used as non-invasive respiratory support in critically ill children. There are limited data to inform consensus on optimal device parameters, determinants of successful patient response, and indications for escalation of support. Clinical scores, such as the respiratory rate-oxygenation (ROX) index, have been described as a means to predict HFNC non-response, but are limited to evaluating for escalations to invasive mechanical ventilation (MV). In the presence of apparent HFNC non-response, a clinician may choose to increase the HFNC flow rate to hypothetically prevent further respiratory deterioration, transition to an alternative non-invasive interface, or intubation for MV. To date, no models have been assessed to predict subsequent escalations of HFNC flow rates after HFNC initiation.Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.Methods: We performed a retrospective, cohort study assessing children admitted for acute respiratory failure under 24 months of age placed on HFNC in the Johns Hopkins Children's Center pediatric intensive care unit from January 2019 through January 2020. We excluded encounters with gaps in recorded clinical data, encounters in which MV treatment occurred prior to HFNC, and cases electively intubated in the operating room. The primary study outcome was discriminatory capacity of generated machine learning algorithms to predict HFNC flow rate escalations as compared to each other and ROX indices using area under the receiver operating characteristic (AUROC) analyses. In an exploratory fashion, model feature importance rankings were assessed by comparing Shapley values.Results: Our gradient boosting model with a time window of 8 h and lead time of 1 h before HFNC flow rate escalation achieved an AUROC with a 95% confidence interval of 0.810 ± 0.003. In comparison, the ROX index achieved an AUROC of 0.525 ± 0.000.Conclusion: In this single-center, retrospective cohort study assessing children under 24 months of age receiving HFNC for acute respiratory failure, tree-based machine learning models outperformed the ROX index in predicting subsequent flow rate escalations. Further validation studies are needed to ensure generalizability for bedside application.

背景：高流量鼻导管（HFNC）在重症儿童的非侵入性呼吸支持中应用广泛。然而，关于最佳设备参数、患者成功响应的决定因素以及支持升级的指征，现有数据有限。临床评分，如呼吸频率-氧合（ROX）指数，已被描述为预测HFNC无反应的手段，但其局限性在于评估升级至侵入性机械通气（MV）。在HFNC无反应的情况下，临床医生可能会选择增加HFNC的流量以防止呼吸进一步恶化、过渡至其他非侵入性接口或进行气管插管以实施MV。迄今为止，尚无模型被评估以预测HFNC启动后的后续流量升级。目标：评估基于树的机器学习算法预测HFNC流量升级的能力。方法：我们进行了一项回顾性队列研究，评估了2019年1月至2020年1月期间在约翰霍普金斯儿童中心儿科重症监护室接受HFNC治疗的24个月以下急性呼吸衰竭儿童的病例。我们排除了记录临床数据存在缺失的病例、MV治疗发生在HFNC之前的情况以及手术室中择期气管插管的病例。主要研究结果是生成的机器学习算法预测HFNC流量升级的区分能力，通过与ROX指数的比较以及使用受试者工作特征曲线下面积（AUROC）分析。以探索性的方式，通过比较Shapley值来评估模型特征的重要性排名。结果：我们的梯度提升模型在HFNC流量升级前8小时的时间窗口和1小时的领先时间内实现了AUROC，其95%置信区间为0.810 ± 0.003。相比之下，ROX指数实现了AUROC 0.525 ± 0.000。结论：在本项针对24个月以下接受HFNC治疗急性呼吸衰竭儿童的单一中心回顾性队列研究中，基于树的机器学习模型在预测后续流量升级方面优于ROX指数。需要进一步验证研究以确保其在床旁应用中的普适性。