ArticleCritical care (London, England)2025
Machine learning models compared with current clinical indices to predict the outcome of high flow nasal cannula therapy in acute hypoxemic respiratory failure.
Article in Critical care (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based prediction models for noninvasive respiratory support failure in acute respiratory failure: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Early Detection of Clinical Deterioration in ICU Patients With Respiratory Failure Receiving Vasoactive Support: A Machine Learning Approach to Identifying High-Risk Individuals.Health science reports · 2026Article
- Prediction of high-flow nasal cannula failure in patients with sepsis by a preliminary risk stratification model based on the ROX index.BMC infectious diseases · 2026Article
- Computational tools for personalizing treatment of acute respiratory failure, from machine learning to digital twins: a narrative review.Critical care (London, England) · 2026Review
- Associations of Red Blood Cell Distribution Width-Derived Indicators and Their Longitudinal Dynamic Trajectories With Mortality Risk in Critically Ill Patients With Pulmonary Hypertension.Pulmonary circulation · 2026Article
- Prediction of high-flow nasal cannula failure in critically ill patients: a narrative review.Journal of intensive care · 2026Review
- Predicting failure of extubation and non-invasive respiratory support in critically ill patients: clinical complexity, limitations of traditional indices, and machine learning perspectives.Frontiers in medicine · 2026Review
- Analysis and implications of validation results for a clinical data-based early warning model for geriatric sepsis.Frontiers in public health · 2026Article
- Development and validation of an interpretable predictive model for mismatch between endoscopic ultrasonography and histopathological diagnosis in colorectal subepithelial lesions: a retrospective single-center study.Frontiers in medicine · 2026Article
- High-Flow Nasal Cannula Oxygen Therapy in Perioperative Respiratory Care: Application and Prospects.Journal of multidisciplinary healthcare · 2026Review
- Evaluating the effect of heart and respiratory rate measurement errors on the ability to predict the outcome of high flow nasal cannula therapy: a multi-centre study.Critical care (London, England) · 2025Article
- Development and external validation of a machine learning model to predict high flow nasal cannula failure.BMJ open respiratory research · 2025Article
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Authors and funding
11 authors.
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Abstract
backgroundEarly 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.
methodsWe 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
resultsInternal 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%, sensitivity of 73%, specificity of 73%, and AUC of 0.79. The ROX index had an accuracy of 64%, sensitivity of 79%, specificity of 60%, and AUC of 0.74. When arterial blood gasses (ABG's) were also used for model training, the SVM model had an accuracy of 83%, sensitivity of 84%, specificity of 82%, and AUC of 0.82 in external validation on the MIMIC-IV/eICU dataset. The modified ROX index, which requires PaO
conclusionsDecision 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.
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