Development and external validation of a machine learning model to predict high flow nasal cannula failure.

ABSTRACT:

Introduction

High-flow nasal cannula (HFNC) is an important treatment option for acute hypoxic respiratory failure and can improve outcomes. However, patients on a prolonged duration of HFNC have worse clinical outcomes and increased mortality. It is difficult to determine which patients will fail HFNC support at the time of initiation.

Research question

Does an externally validated machine learning model predict HFNC failure with greater discrimination compared with the ROX Index?

Study design and methods

Adult inpatients hospitalised at seven hospitals in four health systems who received HFNC were eligible for inclusion. Patients were excluded if they were intubated before first HFNC initiation, experienced the primary outcome <1 hour after HFNC initiation, unable t

SUBMITTER: Temte B 

PROVIDER: S-EPMC12636983 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature