ArticleCritical care (London, England)2026
In-hospital testing of NIVPredict - an AI tool for early prediction of non-invasive ventilation outcome in acute respiratory failure.
Article in Critical care (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Computational tools for personalizing treatment of acute respiratory failure, from machine learning to digital twins: a narrative review.Critical care (London, England) · 2026Review
- Prediction of noninvasive ventilation failure: a narrative review.Annals of intensive care · 2026Review
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Authors and funding
26 authors.
Funding
Abstract
backgroundSuccessful non-invasive ventilation (NIV) reduces ICU length of stay, the need for intubation and the risk of death. However, patients who fail NIV and require intubation have a higher risk of death. We developed NIVPredict, an easy-to-use web-based AI tool to predict NIV outcome within two hours of initiation in patients with acute respiratory failure (ARF) from diverse aetiologies and tested its useability in a hospital setting.
methodsThis study included data from immunocompromised and immunocompetent patients with hypoxemic ARF due to pneumonia, sepsis or COVID-19, and hypercapnic ARF due to acute exacerbation of chronic obstructive pulmonary disease or obesity hypoventilation syndrome. The tool uses the recently proposed Tabular Prior-Data Fitted Network (TabPFN) machine learning model and was trained using a dataset of routinely collected measurements taken within one hour after NIV initiation in 665 ARF patients from the recent RENOVATE trial in Brazil. Initial external validation of the model was conducted on a dataset of 422 ARF patients from Italy, Spain, and the USA. Subsequently, the useability of a web-based tool based on the model was tested by clinicians at the University Hospitals of North Midlands NHS Trust in the UK between December 2024 and November 2025, who applied it to data collected from 57 eligible ARF patients.
resultsThe AI tool provided accurate and robust prediction of NIV outcomes and consistently outperformed conventional clinical indices across all validation settings. In internal repeated cross-validation, external validation, and in-hospital testing, the tool achieved AUCs of 0.793, 0.772, and 0.858, vs. 0.717, 0.709, and 0.693 for the best clinical index (Updated HACOR score), and balanced accuracies of 78.9%, 74.5%, and 85.0%, vs. 68.7%, 63.7%, and 67.6% for the best clinical index (HACOR or Updated HACOR score), respectively.
conclusionsThis study demonstrates superior predictive performance, compared to current clinical indices, of an AI-based tool for NIV outcome prediction on a cohort of patients with overt-acute and acute-on-chronic respiratory failure. Clinical useability of the tool was confirmed via testing by clinicians in a hospital setting, motivating its future evaluation in prospective multi-centre studies.
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