Abstract
BackgroundAcute respiratory distress syndrome (ARDS) is associated with significantly increased risk of death, and early risk stratification may help to choose the appropriate treatment. The study aimed to develop a neural network model by using a genetic algorithm (GA) for the prediction of mortality in patients with ARDS.MethodsThis was a secondary analysis of two multicenter randomized controlled trials conducted in forty-four hospitals that are members of the National Heart, Lung, and Blood Institute, founded to create an acute respiratory distress syndrome Clinical Trials Network. Model training and validation were performed using the SAILS and OMEGA studies, respectively. A GA was employed to screen variables in order to predict 90-day mortality, and a neural network model was trained for the prediction. This machine learning model was compared to the logistic regression model and APACHE III score in the validation cohort.ResultsA total number of 1,071 ARDS patients were included for analysis. The GA search identified seven important variables, which were age, AIDS, leukemia, metastatic tumor, hepatic failure, lowest albumin, and FiO2. A representative neural network model was constructed using the forward selection procedure. The area under the curve (AUC) of the neural network model evaluated with the validation cohort was 0.821 (95% CI [0.753–0.888]), which was greater than the APACHE III score (0.665; 95% CI [0.590–0.739]; p = 0.002 by Delong’s test) and logistic regression model, albeit not statistically significant (0.743; 95% CI [0.669–0.817], p = 0.130 by Delong’s test).ConclusionsThe study developed a neural network model using a GA, which outperformed conventional scoring systems for the prediction of mortality in ARDS patients.
Highlights
Acute respiratory distress syndrome (ARDS) is associated with significantly increased risk of death, and early risk stratification may help to choose the appropriate treatment
Risk stratification for ARDS can be a useful tool in medical decision making and the design of clinical trials, strenuous efforts have been made to derive a model for the prediction of ARDS mortality (Cooke et al, 2009; Frenzel et al, 2011; Balzer et al, 2016; Zhao et al, 2017)
The SAILS trial was used for model development and the OMEGA trial was used for model validation
Summary
Acute respiratory distress syndrome (ARDS) is associated with significantly increased risk of death, and early risk stratification may help to choose the appropriate treatment. A GA was employed to screen variables in order to predict 90-day mortality, and a neural network model was trained for the prediction This machine learning model was compared to the logistic regression model and APACHE III score in the validation cohort. Prediction model for patients with acute respiratory distress syndrome: use of a genetic algorithm to develop a neural network model. APACHE III has been widely used for the prediction of ARDS mortality (Knaus et al, 1991) Most of these studies employed conventional regression methods to develop prediction models, which requires preexisting domain knowledge for model interactions and/or higher-order terms; while sophisticated machine learning methods can capture these complex relationships automatically based on the data
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