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oai:recercat.cat:11351/143662026-03-21T06:15:40Zcom_2072_378070com_2072_378040col_2072_378092

A predictive model for early intubation in patients with COVID–19–induced acute hypoxemic respiratory failure under awake prone position Morales-Quinteros, Luis Fuentes, Nora Olmos, Matias Exequiel Busico, Marina Vitali, Alejandra Plata Menchaca, Erika Paola Muriel, Alfonso Ferrer, Ricard Institut Català de la Salut [Morales–Quinteros L] Servei de Medicina Intensiva, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain. [Fuentes NA, Olmos M] Intensive Care Unit, Hospital Privado de Comunidad, Universidad Nacional de Mar del Plata, Mar del Plata, Buenos Aires, Argentina. [Muriel A] Clinical Biostatistics Unit, Hospital Universitario Ramón y Cajal, IRYCIS, CIBERESP, Universidad de Alcalá, Madrid, Spain. [Busico M] Intensive Care Unit, Clínica Olivos SMG, Olivos, Buenos Aires, Argentina. [Vitali A] Intensive Care Unit, Sanatorio de la Trinidad Palermo, Ciudad Autónoma de Buenos Aires, Argentina. [Plata-Menchaca EP] Clinic Barcelona University Hospital, Barcelona, Spain. Grup de Recerca de Shock, Disfunció Orgànica i Ressuscitació, Vall d’Hebron Hospital Universitari, Barcelona, Spain. [Ferrer R] Servei de Medicina Intensiva, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain Vall d'Hebron Barcelona Hospital Campus COVID-19 (Malaltia) - Tractament Insuficiència respiratòria - Tractament Tràquea - Intubació Postura humana DISEASES::Respiratory Tract Diseases::Respiration Disorders::Respiratory Insufficiency Other subheadings::Other subheadings::/therapy PHENOMENA AND PROCESSES::Musculoskeletal and Neural Physiological Phenomena::Musculoskeletal Physiological Phenomena::Posture::Prone Position ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Therapeutics::Airway Management::Intubation, Intratracheal DISEASES::Virus Diseases::RNA Virus Infections::Nidovirales Infections::Coronaviridae Infections::Coronavirus Infections Other subheadings::Other subheadings::/therapy ENFERMEDADES::enfermedades respiratorias::trastornos respiratorios::insuficiencia respiratoria Otros calificadores::Otros calificadores::/terapia FENÓMENOS Y PROCESOS::fenómenos fisiológicos nerviosos y musculoesqueléticos::fenómenos fisiológicos musculoesqueléticos::postura::decúbito prono TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::terapéutica::manejo de la via aérea::intubación endotraqueal ENFERMEDADES::virosis::infecciones por virus ARN::infecciones por Nidovirales::infecciones por Coronaviridae::infecciones por Coronavirus Otros calificadores::Otros calificadores::/terapia Acute respiratory distress syndrome; Acute respiratory failure; COVID-19 Síndrome de dificultad respiratoria aguda; Insuficiencia respiratoria aguda; COVID-19 Síndrome de dificultat respiratòria aguda; Insuficiència respiratòria aguda; COVID-19 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. This article received partial economic support from the European Society of Intensive Care Medicine (ESICM). 2026-03-20T11:21:01Z 2026-03-20T11:21:01Z 2025-11-24 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Morales-Quinteros L, Fuentes NA, Muriel A, Olmos M, Busico M, Vitali A, et al. A predictive model for early intubation in patients with COVID–19–induced acute hypoxemic respiratory failure under awake prone position. Ann Intensive Care. 2025 Nov 24;15:188. 2110-5820 http://hdl.handle.net/11351/14366 10.1186/s13613-025-01602-4 41284115 001621615700001 https://hdl.handle.net/11351/14366 eng Annals of Intensive Care;15 https://doi.org/10.1186/s13613-025-01602-4 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess application/pdf Springer Scientia