Jesús Villar, Jesús M González-Martín, Cristina Fernández, Juan A Soler, Alfonso Ambrós, Lidia Pita-García, Lorena Fernández, Carlos Ferrando, Blanca Arocas, Myriam González-Vaquero, José M Añón, Elena González-Higueras, Dácil Parrilla, Anxela Vidal, M Mar Fernández, Pedro Rodríguez-Suárez, Rosa L Fernández, Estrella Gómez-Bentolila, Karen E A Burns, Tamas Szakmany, Ewout W Steyerberg, The PredictION Of Duration Of mEchanical vEntilation In Ards Pioneer Network
Background : The ability to predict a long duration of mechanical ventilation (MV) by clinicians is very limited. We assessed the value of machine learning (ML) for early prediction of the duration of MV > 14 days in patients with moderate-to-severe acute respiratory distress syndrome (ARDS). Methods : This is a development, testing, and external validation study using data from 1173 patients on MV ≥ 3 days with moderate-to-severe ARDS. We first developed and tested prediction models in 920 ARDS patients using relevant features captured at the time of moderate/severe ARDS diagnosis, at 24 h and 72 h after diagnosis with logistic regression, and Multilayer Perceptron, Support Vector Machine, and Random Forest ML techniques...
March 21, 2024: Journal of Clinical Medicine