Early prediction of ICU mortality in patients with acute hypoxemic respiratory failure using machine learning: The MEMORIAL Study

Villar, Jesús, González-Martín, Jesús M., Fernández, Cristina, Añón, José M., Ferrando, Carlos, Mora-Ordoñez, Juan M., Martínez, Domingo, Mosteiro, Fernando, Ambrós, Alfonso, Fernández, Lorena, Murcia, Isabel, Vidal, Anxela, Pestaña, David, Romera, Miguel A., Montiel, Raquel, Domínguez-Berrot, Ana M., Soler, Juan A., Gómez-Bentolila, Estrella, Steyerberg, Ewout W. and Szakmany, Tamas ORCID: https://orcid.org/0000-0003-3632-8844 2025. Early prediction of ICU mortality in patients with acute hypoxemic respiratory failure using machine learning: The MEMORIAL Study. Journal of Clinical Medicine 14 (5) , p. 1711. 10.3390/jcm14051711

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Abstract

Background: Early prediction of ICU death in acute hypoxemic respiratory failure (AHRF) could inform clinicians for targeting therapies to reduce harm and increase survival. We sought to determine clinical modifiable and non-modifiable features during the first 24 h of AHRF associated with ICU death. Methods: This is a development, testing, and validation study using data from a prospective, multicenter, nation-based, observational cohort of 1241 patients with AHRF (defined as PaO2/FiO2 ≤ 300 mmHg on mechanical ventilation [MV] with positive end-expiratory pressure [PEEP] ≥ 5 cmH2O and FiO2 ≥ 0.3) from any etiology. Using relevant features captured at AHRF diagnosis and within 24 h, we developed a logistic regression model following variable selection by genetic algorithm and machine learning (ML) approaches. Results: We analyzed 1193 patients, after excluding 48 patients with no data at 24 h after AHRF diagnosis. Using repeated random sampling, we selected 75% (n = 900) for model development and testing, and 25% (n = 293) for final validation. Risk modeling identified six major predictors of ICU death, including patient’s age, and values at 24 h of PEEP, FiO2, plateau pressure, tidal volume, and number of extrapulmonary organ failures. Performance with ML methods was similar to logistic regression and achieved a high area under the receiver operating characteristic curve (AUROC) of 0.88, 95%CI 0.86–0.90. Validation confirmed adequate model performance (AUROC 0.83, 95%CI 0.78–0.88). Conclusions: ML and traditional methods led to an encouraging model to predict ICU death in ventilated AHRF as early as 24 h after diagnosis. More research is needed to identify modifiable factors to prevent ICU deaths.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Schools > Medicine
Additional Information:	License information from Publisher: LICENSE 1: URL: https://creativecommons.org/licenses/by/4.0/, Start Date: 2025-03-04
Publisher:	MDPI
Date of First Compliant Deposit:	17 March 2025
Date of Acceptance:	26 February 2025
Last Modified:	17 Mar 2025 12:00
URI:	https://orca.cardiff.ac.uk/id/eprint/176918

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