Predicting High-Flow Nasal Cannula Failure in Acute Hypoxemic Respiratory Failure Using Metabolomics and Clinical Data

Overview

The goal of this observational study is to determine whether metabolomic profiles combined with clinical data can predict high-flow nasal cannula (HFNC) failure and help optimize respiratory support in adult patients with acute hypoxemic respiratory failure (AHRF). The main questions it aims to answer are: Can metabolomic biomarkers identify patients at higher risk of HFNC failure? Does combining metabolomic and clinical data improve the prediction of respiratory support escalation and clinical outcomes? Participants will: Receive standard HFNC treatment according to clinical practice. Undergo collection of clinical, physiological, and laboratory data. Provide blood samples for metabolomic analysis during respiratory support.

This observational study aims to evaluate whether metabolomic signatures combined with routinely collected clinical and physiological data can improve the prediction of high-flow nasal cannula (HFNC) failure in patients with acute hypoxemic respiratory failure (AHRF). HFNC is widely used as first-line non-invasive respiratory support in AHRF; however, delayed recognition of treatment failure may lead to worse clinical outcomes, including delayed intubation and increased morbidity and mortality. The study will prospectively enroll adult patients with AHRF treated with HFNC. Clinical variables, respiratory parameters, laboratory results, and patient outcomes will be collected during routine clinical care. Blood samples will also be obtained for metabolomic analysis to identify molecular profiles associated with HFNC success or failure. The primary objective is to identify metabolomic and clinical predictors associated with HFNC failure and escalation of respiratory support. Secondary objectives include evaluating the association between metabolomic patterns and relevant clinical outcomes such as endotracheal intubation, duration of respiratory support, intensive care unit (ICU) length of stay, and mortality. The study also aims to develop predictive models integrating biological and clinical data to support personalized respiratory management strategies in AHRF.

Conditions