Joshua Parreco1, Antonio Hidalgo1, Jonathan J Parks1, Robert Kozol1, Rishi Rattan2. 1. DeWitt Daughtry Family Department of Surgery, University of Miami, Miller School of Medicine, Miami, Florida. 2. Division of Trauma Surgery and Surgical Critical Care, DeWitt Daughtry Family Department of Surgery, University of Miami, Miller School of Medicine, Miami, Florida. Electronic address: rrattan@med.miami.edu.
Abstract
BACKGROUND: Early identification of critically ill patients who will require prolonged mechanical ventilation (PMV) has proven to be difficult. The purpose of this study was to use machine learning to identify patients at risk for PMV and tracheostomy placement. MATERIALS AND METHODS: The Multiparameter Intelligent Monitoring in Intensive Care III database was queried for all intensive care unit (ICU) stays with mechanical ventilation. PMV was defined as ventilation >7 d. Classifiers with a gradient-boosted decision trees algorithm were created for the outcomes of PMV and tracheostomy placement. The variables used were six different severity-of-illness scores calculated on the first day of ICU admission including their components and 30 comorbidities. Mean receiver operating characteristic curves were calculated for the outcomes, and variable importance was quantified. RESULTS: There were 20,262 ICU stays identified. PMV was required in 13.6%, and tracheostomy was performed in 6.6% of patients. The classifier for predicting PMV was able to achieve a mean area under the curve (AUC) of 0.820 ± 0.016, and tracheostomy was predicted with an AUC of 0.830 ± 0.011. There were 60.7% patients admitted to a surgical ICU, and the classifiers for these patients predicted PMV with an AUC of 0.852 ± 0.017 and tracheostomy with an AUC of 0.869 ± 0.015. The variable with the highest importance for predicting PMV was the logistic organ dysfunction score pulmonary component (13%), and the most important comorbidity in predicting tracheostomy was cardiac arrhythmia (12%). CONCLUSIONS: This study demonstrates the use of artificial intelligence through machine-learning classifiers for the early identification of patients at risk for PMV and tracheostomy. Application of these identification techniques could lead to improved outcomes by allowing for early intervention.
BACKGROUND: Early identification of critically illpatients who will require prolonged mechanical ventilation (PMV) has proven to be difficult. The purpose of this study was to use machine learning to identify patients at risk for PMV and tracheostomy placement. MATERIALS AND METHODS: The Multiparameter Intelligent Monitoring in Intensive Care III database was queried for all intensive care unit (ICU) stays with mechanical ventilation. PMV was defined as ventilation >7 d. Classifiers with a gradient-boosted decision trees algorithm were created for the outcomes of PMV and tracheostomy placement. The variables used were six different severity-of-illness scores calculated on the first day of ICU admission including their components and 30 comorbidities. Mean receiver operating characteristic curves were calculated for the outcomes, and variable importance was quantified. RESULTS: There were 20,262 ICU stays identified. PMV was required in 13.6%, and tracheostomy was performed in 6.6% of patients. The classifier for predicting PMV was able to achieve a mean area under the curve (AUC) of 0.820 ± 0.016, and tracheostomy was predicted with an AUC of 0.830 ± 0.011. There were 60.7% patients admitted to a surgical ICU, and the classifiers for these patients predicted PMV with an AUC of 0.852 ± 0.017 and tracheostomy with an AUC of 0.869 ± 0.015. The variable with the highest importance for predicting PMV was the logistic organ dysfunction score pulmonary component (13%), and the most important comorbidity in predicting tracheostomy was cardiac arrhythmia (12%). CONCLUSIONS: This study demonstrates the use of artificial intelligence through machine-learning classifiers for the early identification of patients at risk for PMV and tracheostomy. Application of these identification techniques could lead to improved outcomes by allowing for early intervention.
Authors: 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 Journal: BMC Med Inform Decis Mak Date: 2021-05-07 Impact factor: 2.796