Descarga la app Emergencing

A predictive model for early intubation in patients with COVID-19-induced acute hypoxemic respiratory failure under awake prone position

Revista

Annals of Intensive Care

Fecha de publicación

24 de noviembre de 2025

Ann Intensive Care. 2025 Nov 24;15(1):188. Revista: 10.1186/s13613-025-01602-4.

BACKGROUND: Awake prone positioning (APP) reduces the risk of endotracheal intubation and mortality in COVID-19-related acute respiratory failure (ARF) receiving high-flow nasal oxygen (HFNO). However, a significant proportion of patients undergoing APP are ultimately intubated, and mortality in this subgroup remains high. We aimed to develop a predictive model to be applied within the first 24 h of APP to identify patients at higher risk of progressing to intubation within 72 h of APP initiation.

METHODS: We conducted a secondary analysis of a prospective multicenter cohort including adult patients with COVID-19-related ARF admitted to six intensive care units in Argentina between June 2020 and January 2021. Eligible patients received HFNO and APP for at least 6 h per day. Physiological variables were collected at ICU admission (baseline) and 24 h after APP initiation. Two multivariable logistic regression models were developed using baseline and 24-hour variables, respectively. Predictors were selected based on clinical relevance and univariable associations. A final model was constructed by integrating variables retained from both time points.

RESULTS: Of 400 patients included, 136 (34%) required intubation within the first 72 h. Patients who required intubation were older, had lower PaO₂ and PaO₂/FiO₂ ratios, and higher respiratory rates both at baseline and after 24 h. The final predictive model included five variables: age, respiratory rate, PaO₂, FiO₂, and SaO₂/FiO₂ ratio, all measured 24 h after APP initiation. A nomogram was developed based on this model to estimate the individual risk of early intubation.

CONCLUSION: In patients with COVID-19-related ARF treated with HFNO and APP, a model combining baseline characteristics and early physiological response can help predict the need for intubation within 72 h. This tool may support clinicians in identifying high-risk patients and making timely, individualized decisions about escalation of care.

PubMed:41284115 | Revista:10.1186/s13613-025-01602-4

Descarga la app Emergencing!

Accede a los abstracts en español de las revistas científicas más importantes en medicina de urgencias, emergencias y paciente crítico.

Descargo de responsabilidad
El idioma original es este artículo es el inglés. Mediante el sistema de traducción automático de la IA de emergencing, el contenido se ha traducido al español. Esta es una traducción no supervisada por lo que puede que alguna parte del contenido no refleje con exactitud la publicación original del autor/autores.