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Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement.
- Source :
-
The Journal of surgical research [J Surg Res] 2018 Aug; Vol. 228, pp. 179-187. Date of Electronic Publication: 2018 Apr 11. - Publication Year :
- 2018
-
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.<br />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.<br />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%).<br />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.<br /> (Copyright © 2018 Elsevier Inc. All rights reserved.)
- Subjects :
- Critical Care methods
Critical Care statistics & numerical data
Databases, Factual statistics & numerical data
Feasibility Studies
Humans
Intensive Care Units statistics & numerical data
Length of Stay statistics & numerical data
Patient Selection
Risk Assessment methods
Severity of Illness Index
Time Factors
Treatment Outcome
Critical Illness therapy
Decision Support Techniques
Respiration, Artificial statistics & numerical data
Supervised Machine Learning
Tracheostomy statistics & numerical data
Subjects
Details
- Language :
- English
- ISSN :
- 1095-8673
- Volume :
- 228
- Database :
- MEDLINE
- Journal :
- The Journal of surgical research
- Publication Type :
- Academic Journal
- Accession number :
- 29907209
- Full Text :
- https://doi.org/10.1016/j.jss.2018.03.028