Machine learning models compared with current clinical indices to predict the outcome of high flow nasal cannula therapy in acute hypoxemic respiratory failure

作者：Hang Yu, Sina Saffaran, Roberto Tonelli, John G. Laffey, António M. Esquinas, Lucas Martins de Lima, Letícia Kawano-Dourado, Israel Silva Maia, Alexandre Biasi Cavalcanti, Enrico Clini, Declan G. Bates · 发表于：Critical Care · 年份：2025 · DOI：10.1186/s13054-025-05336-4 · 被引用次数：17 · 研究领域：Respiratory Support and Mechanisms、Sepsis Diagnosis and Treatment、Obstructive Sleep Apnea Research

Early identification of patients with acute hypoxemic respiratory failure (AHRF) who are at risk of failing high-flow nasal cannula (HFNC) therapy could facilitate closer monitoring, and timely adjustment/escalation of treatment. We aimed to establish whether machine learning (ML) models could predict HFNC outcome, early in the course of treatment, with greater accuracy than currently used clinical indices. We developed ML models trained using measurements made within the first 2 h of treatment from 184 AHRF patients (37% HFNC failures) treated at the respiratory ICU of the University Hospital of Modena between 2018 and 2023. For external validation, we used a dataset on 567 AHRF patients (22% failures) comprising 510 patients from the recent RENOVATE trial in Brazil and 57 from the MIMIC-IV and eICU databases in the US. Predictive performance of the ML models was benchmarked against optimized thresholds of the following clinical indices: respiratory rate oxygenation index (ROX) and variants, heart rate to saturation of pulse oxygen (SpO 2 ) ratio, SpO 2 /FiO 2 ratio, PaO 2 /FiO 2 ratio, sequential organ failure assessment and heart rate, acidosis, consciousness, oxygenation and respiratory rate scores. Internal and external predictive performance of a Support Vector Machine (SVM) ML model was superior to all clinical indices across all scenarios tested. In external validation on the 567-patient dataset, a SVM model trained on non-invasive measurements had an accuracy of 73%,...