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

BACKGROUND: 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. METHODS: ratio, sequential organ failure assessment and heart rate, acidosis, consciousness, oxygenation and respiratory rate scores. RESULTS: , achieved 70% accuracy, 63% sensitivity, 74% specificity, and AUC of 0.65. CONCLUSIONS: Decision support tools based on SVM models could provide clinicians with more accurate early predictions of HFNC outcome than currently available clinical indices. If available, ABG measurements could improve the capability to accurately identify patients at risk of failing HFNC therapy.