Abstract
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.