Jesús Villar1,2, Alfonso Ambrós3, Fernando Mosteiro4, Domingo Martínez5, Lorena Fernández6, Carlos Ferrando1,7, Demetrio Carriedo8, Juan A Soler5, Dácil Parrilla9, Mónica Hernández10, David Andaluz-Ojeda11, José M Añón1,10, Anxela Vidal12, Elena González-Higueras13, Carmen Martín-Rodríguez3, Ana M Díaz-Lamas4, Jesús Blanco1,6, Javier Belda7, Francisco J Díaz-Domínguez8, Jesús Rico-Feijoó14, Carmen Martín-Delgado15, Miguel A Romera16, Jesús M González-Martín17, Rosa L Fernández1,2, Robert M Kacmarek18,19. 1. CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain. 2. Research Unit, Hospital Universitario Dr. Negrín, Las Palmas de Gran Canaria, Spain. 3. Intensive Care Unit, Hospital General Universitario de Ciudad Real, Ciudad Real, Spain. 4. Intensive Care Unit, Hospital Universitario A Coruña, La Coruña, Spain. 5. Intensive Care Unit, Hospital Universitario Virgen de Arrixaca, Murcia, Spain. 6. Intensive Care Unit, Hospital Universitario Río Hortega, Valladolid, Spain. 7. Department of Anesthesiology, Hospital Clínico Universitario de Valencia, Valencia, Spain. 8. Intensive Care Unit, Complejo Asistencial Universitario de León, León, Spain. 9. Intensive Care Unit, Hospital Universitario NS de Candelaria, Santa Cruz de Tenerife, Spain. 10. Intensive Care Unit, Hospital Universitario La Paz, IdiPAZ, Madrid, Spain. 11. Intensive Care Unit, Hospital Clínico Universitario de Valladolid, Valladolid, Spain. 12. Intensive Care Unit, Hospital Universitario Fundación Jiménez Díaz, Madrid, Spain. 13. Intensive Care Unit, Hospital Virgen de la Luz, Cuenca, Spain. 14. Department of Anesthesiology, Hospital Universitario Río Hortega, Valladolid, Spain. 15. Intensive Care Unit, Hospital La Mancha Centro, Alcázar de San Juan, Ciudad Real, Spain. 16. Intensive Care Unit, Hospital Universitario Puerta de Hierro Majadahonda, Majadahonda, Madrid, Spain. 17. Biostatistics Division, Research Unit, Hospital Universitario Dr. Negrín, Las Palmas de Gran Canaria, Spain. 18. Department of Respiratory Care, Massachusetts General Hospital, Boston, MA. 19. Department of Anesthesiology, Harvard University, Boston, MA.
Abstract
OBJECTIVES: Incomplete or ambiguous evidence for identifying high-risk patients with acute respiratory distress syndrome for enrollment into randomized controlled trials has come at the cost of an unreasonable number of negative trials. We examined a set of selected variables early in acute respiratory distress syndrome to determine accurate prognostic predictors for selecting high-risk patients for randomized controlled trials. DESIGN: A training and testing study using a secondary analysis of data from four prospective, multicenter, observational studies. SETTING: A network of multidisciplinary ICUs. PATIENTS: We studied 1,200 patients with moderate-to-severe acute respiratory distress syndrome managed with lung-protective ventilation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We evaluated different thresholds for patient's age, PaO2/FIO2, plateau pressure, and number of extrapulmonary organ failures to predict ICU outcome at 24 hours of acute respiratory distress syndrome diagnosis. We generated 1,000 random scenarios as training (n = 900, 75% of population) and testing (n = 300, 25% of population) datasets and averaged the logistic coefficients for each scenario. Thresholds for age (< 50, 50-70, > 70 yr), PaO2/FIO2 (≤ 100, 101-150, > 150 mm Hg), plateau pressure (< 29, 29-30, > 30 cm H2O), and number of extrapulmonary organ failure (< 2, 2, > 2) stratified accurately acute respiratory distress syndrome patients into categories of risk. The model that included all four variables proved best to identify patients with the highest or lowest risk of death (area under the receiver operating characteristic curve, 0.86; 95% CI, 0.84-0.88). Decision tree analyses confirmed the accuracy and robustness of this enrichment model. CONCLUSIONS: Combined thresholds for patient's age, PaO2/FIO2, plateau pressure, and extrapulmonary organ failure provides prognostic enrichment accuracy for stratifying and selecting acute respiratory distress syndrome patients for randomized controlled trials.
OBJECTIVES: Incomplete or ambiguous evidence for identifying high-risk patients with acute respiratory distress syndrome for enrollment into randomized controlled trials has come at the cost of an unreasonable number of negative trials. We examined a set of selected variables early in acute respiratory distress syndrome to determine accurate prognostic predictors for selecting high-risk patients for randomized controlled trials. DESIGN: A training and testing study using a secondary analysis of data from four prospective, multicenter, observational studies. SETTING: A network of multidisciplinary ICUs. PATIENTS: We studied 1,200 patients with moderate-to-severe acute respiratory distress syndrome managed with lung-protective ventilation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We evaluated different thresholds for patient's age, PaO2/FIO2, plateau pressure, and number of extrapulmonary organ failures to predict ICU outcome at 24 hours of acute respiratory distress syndrome diagnosis. We generated 1,000 random scenarios as training (n = 900, 75% of population) and testing (n = 300, 25% of population) datasets and averaged the logistic coefficients for each scenario. Thresholds for age (< 50, 50-70, > 70 yr), PaO2/FIO2 (≤ 100, 101-150, > 150 mm Hg), plateau pressure (< 29, 29-30, > 30 cm H2O), and number of extrapulmonary organ failure (< 2, 2, > 2) stratified accurately acute respiratory distress syndromepatients into categories of risk. The model that included all four variables proved best to identify patients with the highest or lowest risk of death (area under the receiver operating characteristic curve, 0.86; 95% CI, 0.84-0.88). Decision tree analyses confirmed the accuracy and robustness of this enrichment model. CONCLUSIONS: Combined thresholds for patient's age, PaO2/FIO2, plateau pressure, and extrapulmonary organ failure provides prognostic enrichment accuracy for stratifying and selecting acute respiratory distress syndromepatients for randomized controlled trials.
Authors: Michael O Harhay; Jonathan D Casey; Marina Clement; Sean P Collins; Étienne Gayat; Michelle Ng Gong; Samir Jaber; Pierre-François Laterre; John C Marshall; Michael A Matthay; Rhonda E Monroe; Todd W Rice; Eileen Rubin; Wesley H Self; Alexandre Mebazaa Journal: Intensive Care Med Date: 2020-02-18 Impact factor: 17.440
Authors: Jesús Villar; Carlos Ferrando; Gerardo Tusman; Lorenzo Berra; Pedro Rodríguez-Suárez; Fernando Suárez-Sipmann Journal: Front Physiol Date: 2021-11-30 Impact factor: 4.566
Authors: Jesús Villar; Juan M Mora-Ordoñez; Juan A Soler; Fernando Mosteiro; Anxela Vidal; Alfonso Ambrós; Lorena Fernández; Isabel Murcia; Belén Civantos; Miguel A Romera; Adrián Mira; Francisco J Díaz-Domínguez; Dácil Parrilla; J Francisco Martínez-Carmona; Domingo Martínez; Lidia Pita-García; Denis Robaglia; Ana Bueno-González; Jesús Sánchez-Ballesteros; Ángel E Pereyra; Mónica Hernández; Carlos Chamorro-Jambrina; Pilar Cobeta; Raúl I González-Luengo; Raquel Montiel; Leonor Nogales; M Mar Fernández; Blanca Arocas; Álvaro Valverde-Montoro; Ana M Del Saz-Ortiz; Victoria Olea-Jiménez; José M Añón; Pedro Rodríguez-Suárez; Rosa L Fernández; Cristina Fernández; Tamas Szakmany; Jesús M González-Martín; Carlos Ferrando; Robert M Kacmarek; Arthur S Slutsky Journal: Crit Care Explor Date: 2022-04-29
Authors: Jesús Villar; Cristina Fernández; Jesús M González-Martín; Carlos Ferrando; José M Añón; Ana M Del Saz-Ortíz; Ana Díaz-Lamas; Ana Bueno-González; Lorena Fernández; Ana M Domínguez-Berrot; Eduardo Peinado; David Andaluz-Ojeda; Elena González-Higueras; Anxela Vidal; M Mar Fernández; Juan M Mora-Ordoñez; Isabel Murcia; Concepción Tarancón; Eleuterio Merayo; Alba Pérez; Miguel A Romera; Francisco Alba; David Pestaña; Pedro Rodríguez-Suárez; Rosa L Fernández; Ewout W Steyerberg; Lorenzo Berra; Arthur S Slutsky Journal: J Clin Med Date: 2022-09-27 Impact factor: 4.964
Authors: Pratik Sinha; Kevin L Delucchi; Daniel F McAuley; Cecilia M O'Kane; Michael A Matthay; Carolyn S Calfee Journal: Lancet Respir Med Date: 2020-01-13 Impact factor: 30.700