Publication:
Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation

Issued Date

2021-12-01

Resource Type

Article

ISSN

14726947

DOI

10.1186/s12911-021-01506-w

Other identifier(s)

2-s2.0-85105462293

Rights

Mahidol University

Rights Holder(s)

SCOPUS

Bibliographic Citation

BMC Medical Informatics and Decision Making. Vol.21, No.1 (2021)

Suggested Citation

Behrooz Mamandipoor, Fernando Frutos-Vivar, Oscar Peñuelas, Richard Rezar, Konstantinos Raymondos, Alfonso Muriel, Bin Du, Arnaud W. Thille, Fernando Ríos, Marco González, Lorenzo del-Sorbo, Maria del Carmen Marín, Bruno Valle Pinheiro, Marco Antonio Soares, Nicolas Nin, Salvatore M. Maggiore, Andrew Bersten, Malte Kelm, Raphael Romano Bruno, Pravin Amin, Nahit Cakar, Gee Young Suh, Fekri Abroug, Manuel Jibaja, Dimitros Matamis, Amine Ali Zeggwagh, Yuda Sutherasan, Antonio Anzueto, Bernhard Wernly, Andrés Esteban, Christian Jung, Venet Osmani Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation. BMC Medical Informatics and Decision Making. Vol.21, No.1 (2021). doi:10.1186/s12911-021-01506-w Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/76628

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Title

Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation

Author(s)

Behrooz Mamandipoor
 Fernando Frutos-Vivar
 Oscar Peñuelas
 Richard Rezar
 Konstantinos Raymondos
 Alfonso Muriel
 Bin Du
 Arnaud W. Thille
 Fernando Ríos
 Marco González
 Lorenzo del-Sorbo
 Maria del Carmen Marín
 Bruno Valle Pinheiro
 Marco Antonio Soares
 Nicolas Nin
 Salvatore M. Maggiore
 Andrew Bersten
 Malte Kelm
 Raphael Romano Bruno
 Pravin Amin
 Nahit Cakar
 Gee Young Suh
 Fekri Abroug
 Manuel Jibaja
 Dimitros Matamis
 Amine Ali Zeggwagh
 Yuda Sutherasan
 Antonio Anzueto
 Bernhard Wernly
 Andrés Esteban
 Christian Jung
 Venet Osmani

Other Contributor(s)

South Texas Veterans Health Care System
 Centro de Investigación Biomédica en Red de Enfermedades Respiratorias
 Hospital Regional 1° de Octubre ISSSTE
 Bruno Kessler Foundation
 CHU Fattouma-Bourguiba
 Universidad Pontificia Bolivariana
 Heinrich-Heine-Universität Düsseldorf
 Medizinische Hochschule Hannover (MHH)
 Hospital Ramon y Cajal
 Centre Hospitalier Universitaire de Poitiers
 Flinders University
 Mohammed V University in Rabat
 SKKU School of Medicine
 Faculty of Medicine Ramathibodi Hospital, Mahidol University
 Paracelsus Medizinische Privatuniversitat
 Papageorgiou General Hospital
 İstanbul Tıp Fakültesi
 Bombay Hospital and Medical Research Centre
 Peking Union Medical College Hospital
 Hospital Nacional Professor Dr. Alejandro Posadas
 University of G. d'Annunzio Chieti and Pescara
 Universidade Federal de Juiz de Fora
 Hospital de Especialidades Eugenio Espejo
 Interdepartmental Division of Critical Care Medicine
 Hospital Español
 Hospital Universitário São José

Abstract

Background: Mechanical Ventilation (MV) is a complex and central treatment process in the care of critically ill patients. It influences acid–base balance and can also cause prognostically relevant biotrauma by generating forces and liberating reactive oxygen species, negatively affecting outcomes. In this work we evaluate the use of a Recurrent Neural Network (RNN) modelling to predict outcomes of mechanically ventilated patients, using standard mechanical ventilation parameters. Methods: We performed our analysis on VENTILA dataset, an observational, prospective, international, multi-centre study, performed to investigate the effect of baseline characteristics and management changes over time on the all-cause mortality rate in mechanically ventilated patients in ICU. Our cohort includes 12,596 adult patients older than 18, associated with 12,755 distinct admissions in ICUs across 37 countries and receiving invasive and non-invasive mechanical ventilation. We carry out four different analysis. Initially we select typical mechanical ventilation parameters and evaluate the machine learning model on both, the overall cohort and a subgroup of patients admitted with respiratory disorders. Furthermore, we carry out sensitivity analysis to evaluate whether inclusion of variables related to the function of other organs, improve the predictive performance of the model for both the overall cohort as well as the subgroup of patients with respiratory disorders. Results: Predictive performance of RNN-based model was higher with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.72 (± 0.01) and Average Precision (AP) of 0.57 (± 0.01) in comparison to RF and LR for the overall patient dataset. Higher predictive performance was recorded in the subgroup of patients admitted with respiratory disorders with AUC of 0.75 (± 0.02) and AP of 0.65 (± 0.03). Inclusion of function of other organs further improved the performance to AUC of 0.79 (± 0.01) and AP 0.68 (± 0.02) for the overall patient dataset and AUC of 0.79 (± 0.01) and AP 0.72 (± 0.02) for the subgroup with respiratory disorders. Conclusion: The RNN-based model demonstrated better performance than RF and LR in patients in mechanical ventilation and its subgroup admitted with respiratory disorders. Clinical studies are needed to evaluate whether it impacts decision-making and patient outcomes. Trial registration: NCT02731898 (https://clinicaltrials.gov/ct2/show/NCT02731898), prospectively registered on April 8, 2016.

Keyword(s)

Computer Science
 Medicine

Availability

URI

https://repository.li.mahidol.ac.th/handle/20.500.14594/76628

Collections

Scopus 2021