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Article

Predicting the Length of Mechanical Ventilation in Acute Respiratory Disease Syndrome Using Machine Learning: The PIONEER Study

by
Jesús Villar
1,2,3,4,*,
Jesús M. González-Martín
1,2,
Cristina Fernández
2,
Juan A. Soler
5,
Alfonso Ambrós
6,
Lidia Pita-García
7,
Lorena Fernández
8,
Carlos Ferrando
1,9,
Blanca Arocas
10,
Myriam González-Vaquero
11,
José M. Añón
1,12,
Elena González-Higueras
13,
Dácil Parrilla
14,
Anxela Vidal
15,
M. Mar Fernández
16,
Pedro Rodríguez-Suárez
1,17,
Rosa L. Fernández
1,2,
Estrella Gómez-Bentolila
2,
Karen E. A. Burns
3,18,19,
Tamas Szakmany
20,
Ewout W. Steyerberg
21 and
the PredictION of Duration of mEchanical vEntilation in ARDS (PIONEER) Network
†add Show full author list remove Hide full author list
1
CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, 28029 Madrid, Spain
2
Research Unit, Hospital Universitario Dr. Negrín, 35019 Las Palmas de Gran Canaria, Spain
3
Li Ka Shing Knowledge Institute at St. Michael’s Hospital, Toronto, ON M5B 1W8, Canada
4
Faculty of Health Sciences, Universidad del Atlántico Medio (UNAM), 35017 Tafira Baja, Gran Canaria, Canary Islands, Spain
5
Intensive Care Unit, Hospital Universitario Virgen de Arrixaca, 30120 Murcia, Spain
6
Intensive Care Unit, Hospital General Universitario de Ciudad Real, 13005 Ciudad Real, Spain
7
Intensive Care Unit, Hospital Universitario de A Coruña, 15006 La Coruña, Spain
8
Intensive Care Unit, Hospital Universitario Río Hortega, 47012 Valladolid, Spain
9
Surgical Intensive Care Unit, Department Anesthesia, Hospital Clinic, IDIBAPS, 08036 Barcelona, Spain
10
Department of Anesthesia, Hospital Clínico Universitario de Valencia, 46010 Valenci, Spain
11
Intensive Care Unit, Complejo Asistencial Universitario de León, 24001 León, Spain
12
Intensive Care Unit, Hospital Universitario La Paz, IdiPaz, 28046 Madrid, Spain
13
Intensive Care Unit, Hospital Virgen de La Luz, 16002 Cuenca, Spain
14
Intensive Care Unit, Hospital Universitario NS de Candelaria, 38010 Santa Cruz de Tenerife, Spain
15
Intensive Care Unit, Hospital Universitario Fundación Jiménez Díaz, 28040 Madrid, Spain
16
Intensive Care Unit, Hospital Universitario Mutua Terrassa, 08221 Terrassa, Spain
17
Thoracic Surgery, Hospital Universitario Dr. Negrín, 35019 Las Palmas de Gran Canaria, Spain
18
Critical Care Medicine, Unity Health Toronto-St. Michael’s Hospital, Toronto, ON M5B 1W8, Canada
19
Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada
20
Intensive Care, Cardiff University, Cardiff CF14 4XW, UK
21
Department Biomedical Data Sciences, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
 
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*
Author to whom correspondence should be addressed.
†
Members of the PIONEER Project are listed in the Supplementary Materials.
J. Clin. Med. 2024, 13(6), 1811; https://doi.org/10.3390/jcm13061811
Submission received: 2 February 2024 / Revised: 19 March 2024 / Accepted: 19 March 2024 / Published: 21 March 2024
(This article belongs to the Topic The Use of New Technologies for Health and Clinical Practice)
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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).
Keywords: acute respiratory distress syndrome; lung-protective ventilation; duration of mechanical ventilation; machine learning; prediction models; observational studies; clinical trials acute respiratory distress syndrome; lung-protective ventilation; duration of mechanical ventilation; machine learning; prediction models; observational studies; clinical trials

Share and Cite

MDPI and ACS Style

Villar, J.; González-Martín, J.M.; Fernández, C.; Soler, J.A.; Ambrós, A.; Pita-García, L.; Fernández, L.; Ferrando, C.; Arocas, B.; González-Vaquero, M.; et al. Predicting the Length of Mechanical Ventilation in Acute Respiratory Disease Syndrome Using Machine Learning: The PIONEER Study. J. Clin. Med. 2024, 13, 1811. https://doi.org/10.3390/jcm13061811

AMA Style

Villar J, González-Martín JM, Fernández C, Soler JA, Ambrós A, Pita-García L, Fernández L, Ferrando C, Arocas B, González-Vaquero M, et al. Predicting the Length of Mechanical Ventilation in Acute Respiratory Disease Syndrome Using Machine Learning: The PIONEER Study. Journal of Clinical Medicine. 2024; 13(6):1811. https://doi.org/10.3390/jcm13061811

Chicago/Turabian Style

Villar, Jesús, Jesús M. González-Martín, Cristina Fernández, Juan A. Soler, Alfonso Ambrós, Lidia Pita-García, Lorena Fernández, Carlos Ferrando, Blanca Arocas, Myriam González-Vaquero, and et al. 2024. "Predicting the Length of Mechanical Ventilation in Acute Respiratory Disease Syndrome Using Machine Learning: The PIONEER Study" Journal of Clinical Medicine 13, no. 6: 1811. https://doi.org/10.3390/jcm13061811

APA Style

Villar, J., González-Martín, J. M., Fernández, C., Soler, J. A., Ambrós, A., Pita-García, L., Fernández, L., Ferrando, C., Arocas, B., González-Vaquero, M., Añón, J. M., González-Higueras, E., Parrilla, D., Vidal, A., Fernández, M. M., Rodríguez-Suárez, P., Fernández, R. L., Gómez-Bentolila, E., Burns, K. E. A., ... the PredictION of Duration of mEchanical vEntilation in ARDS (PIONEER) Network. (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. https://doi.org/10.3390/jcm13061811

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