Clinical trial NCT06695442: Predictive Model for High-Flow Nasal Cannula Failure in COVID-19-related Acute Hypoxemic Respiratory Failure Based on Metabolomics and Clinical Data
Official title: Predictive Model for High-Flow Nasal Cannula Failure Based on Metabolomics and Clinical Data: COVID-19 As a Use Case
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Acute Hypoxemic Respiratory Failure
COVID 19
Keywords: Acute Hypoxemic Respiratory Failure
COVID-19
High-Flow Nasal Cannula
Predictive Model
Machine Learning
Metabolomics
Lead sponsor: Hospital del Mar Research Institute (IMIM)
Sponsor class: OTHER
Collaborators: name: Fundacio La Marató de TV3
name: Instituto de Salud Carlos III
name: European Union
First posted: 2024-11-19
Enrollment: 111
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients admitted to Hospital del Mar in Barcelona, Spain

Brief summary: The aim of this prospective observational cohort study is to develop a predictive model for high-flow nasal cannula (HFNC) failure in patients with acute hypoxemic respiratory failure (AHRF) caused by SARS-CoV-2. By combining clinical data and metabolomic profiles, the study seeks to answer:

- Can integrating clinical parameters with metabolomic markers enhance the prediction of HFNC failure in AHRF patients?

Participants with AHRF due to SARS-CoV-2 receiving HFNC under standard-of-care protocols will be monitored until hospital discharge or death. Blood samples for metabolomic analysis will be collected at inclusion.

Detailed description: This prospective observational cohort study focuses on developing and validating a predictive model for HFNC failure in patients with SARS-CoV-2-related AHRF, combining clinical parameters and metabolomic profiling. Patients received standard-of-care therapy based on hospital protocols, with clinical data and plasma samples collected at inclusion (T0) for laboratory and metabolomic analyses.

The study consists of two cohorts: a Training Cohort (March 2020 to April 2021) for model development and a Validation Cohort (May 2021 to October 2021) for model validation and external testing. HFNC outcomes were classified as success or failure, with failure defined as requiring orotracheal intubation (OTI). Advanced machine-learning techniques were employed for feature selection and model optimization, targeting precise integration of clinical and metabolomic data.

This model seeks to enhance early identification of HFNC failure risk, enabling timely interventions and personalized management in critically ill AHRF patients.

Eligibility criteria: Inclusion Criteria:

* Adult patients (≥18 years old) with acute hypoxemic respiratory failure (AHRF) confirmed as secondary to SARS-CoV-2 infection via PCR testing.
* Requirement for high-flow nasal cannula (HFNC) initiated within 48 hours of hospital admission.

Exclusion Criteria:

* Patients under the age of 18
* Patients with active "do not resuscitate" (DNR) orders at the time of inclusion.

Primary outcomes:
measure: Predictive Model Development
timeFrame: From the initiation of HFNC until the need for intubation, hospital discharge, or death, whichever occurs first (up to 28 days). Completed using the Training Cohort (March 2020 to April 2021).
Secondary outcomes:
measure: Identification of Key Metabolites
timeFrame: At study inclusion (T0).
measure: Validation of the Predictive Model
timeFrame: From the initiation of HFNC until the need for intubation, hospital discharge, or death, whichever occurs first (up to 28 days). Completed during the Validation Cohort phase (May to October 2021).
Other outcomes:
Locations:
city: Barcelona
country: Spain
facility: Hospital del Mar
Study officials:
affiliation: Hospital del Mar Research Institute
name: Óscar J Pozo, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Hospital del Mar
name: Joan Ramon Masclans Enviz, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References: