Predicting the length of mechanical ventilation in acute respiratory disease syndrome using machine learning: The PIONEER Study

Villar, Jesús, González-Martín, Jesús M., Fernández, Cristina, Soler, Juan A., Ambrós, Alfonso, Pita-García, Lidia, Fernández, Lorena, Ferrando, Carlos, Arocas, Blanca, González-Vaquero, Myriam, Añón, José M., González-Higueras, Elena, Parrilla, Dácil, Vidal, Anxela, Fernández, M. Mar, Rodríguez-Suárez, Pedro, Fernández, Rosa L., Gómez-Bentolila, Estrella, Burns, Karen E. A., Szakmany, Tamas ORCID: https://orcid.org/0000-0003-3632-8844, Steyerberg, Ewout W. and the PredictION of Duration of mEchanical vEntilation in ARDS (PI 2024. Predicting the length of mechanical ventilation in acute respiratory disease syndrome using machine learning: The PIONEER Study. Journal of Clinical Medicine 13 (6) , 1811. 10.3390/jcm13061811

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Abstract

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. For external validation, we used an independent cohort of 253 patients on MV ≥ 3 days with moderate/severe ARDS. Results: A total of 441 patients (48%) from the derivation cohort (n = 920) and 100 patients (40%) from the validation cohort (n = 253) were mechanically ventilated for >14 days [median 14 days (IQR 8–25) vs. 13 days (IQR 7–21), respectively]. The best early prediction model was obtained with data collected at 72 h after moderate/severe ARDS diagnosis. Multilayer Perceptron risk modeling identified major prognostic factors for the duration of MV > 14 days, including PaO2/FiO2, PaCO2, pH, and positive end-expiratory pressure. Predictions of the duration of MV > 14 days showed modest discrimination [AUC 0.71 (95%CI 0.65–0.76)]. Conclusions: Prolonged MV duration in moderate/severe ARDS patients remains difficult to predict early even with ML techniques such as Multilayer Perceptron and using data at 72 h of diagnosis. More research is needed to identify markers for predicting the length of MV. This study was registered on 14 August 2023 at ClinicalTrials.gov (NCT NCT05993377).

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Medicine
Additional Information:	License information from Publisher: LICENSE 1: URL: https://creativecommons.org/licenses/by/4.0/, Type: open-access
Publisher:	MDPI
Date of First Compliant Deposit:	8 April 2024
Date of Acceptance:	19 March 2024
Last Modified:	08 Apr 2024 10:00
URI:	https://orca.cardiff.ac.uk/id/eprint/167794

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