Real-time prediction of HFNC treatment failure in acute hypoxemic respiratory failure using machine learning.

ABSTRACT: Accurate and timely prediction of high-flow nasal cannula (HFNC) treatment failure in patients with acute hypoxemic respiratory failure (AHRF) can lower patient mortality. Previous studies have highlighted inconsistencies in the predictive performance of existing indices, such as ROX and mROX, which are limited by their reliance on oxygenation parameters alone. To address this, we developed a machine learning-based predictive model using temporal data from AHRF patients, aimed at facilitating quicker development of individualized treatment plans and intervention strategies for healthcare professionals. We extracted 15 non-invasive and 15 laboratory features, including patient demographic characteristics, Glasgow Coma Scale, blood gas analysis, chemical assay, and complete blood cell count

SUBMITTER: Li X 

PROVIDER: S-EPMC12361419 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature