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Machine Learning Models to Predict 30-Day Mortality in Mechanically Ventilated Patients

Jong Ho Kim1,2, Young Suk Kwon1,2, Moon Seong Baek3

  • 1Department of Anaesthesiology and Pain Medicine, College of Medicine, Hallym University, Chuncheon Sacred Heart Hospital, Chuncheon 24253, Korea.

Journal of Clinical Medicine
|June 2, 2021
PubMed

Insights

Machine learning models significantly improve 30-day mortality prediction for mechanically ventilated patients, outperforming traditional scoring systems like APACHE II. This advancement offers better patient outcome forecasting in intensive care units.

Area of Science:

  • Critical Care Medicine
  • Health Informatics
  • Machine Learning

Background:

  • Conventional scoring models, such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II), exhibit limitations in accurately predicting mortality for mechanically ventilated patients.
  • Accurate mortality prediction is crucial for optimizing intensive care unit (ICU) management and patient care strategies.

Purpose of the Study:

  • To enhance the prediction accuracy of 30-day mortality in mechanically ventilated patients by applying machine learning algorithms.
  • To compare the performance of various machine learning models against established scoring systems like APACHE II and ProVent.

Main Methods:

  • Utilized a dataset of 16,940 mechanically ventilated patients, split into training-validation (83%) and test (17%) sets.
  • Applied and evaluated several machine learning algorithms: balanced random forest, light gradient boosting machine, extreme gradient boost, multilayer perceptron, and logistic regression.
  • Compared the area under the receiver operating characteristic curves (AUCs) of machine learning models against APACHE II and ProVent scores.

Main Results:

  • The extreme gradient boost model achieved the highest AUC (0.79) for 30-day mortality prediction, followed closely by the balanced random forest model (0.78).
  • Machine learning models demonstrated superior predictive performance compared to APACHE II (AUC 0.67) and ProVent (AUC 0.69) scores.
  • Key variables identified for model development included APACHE II score, Charlson comorbidity index, and norepinephrine administration.

Conclusions:

  • Machine learning models offer superior accuracy in predicting 30-day mortality for mechanically ventilated patients compared to conventional scoring systems.
  • These advanced models can aid clinicians in better risk stratification and management of critically ill patients requiring mechanical ventilation.
Keywords:
machine learningmechanical ventilationmortalityprediction

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