Machine learning for the early prediction of acute respiratory distress syndrome (ARDS) in patients with sepsis in the ICU based on clinical data

Abstract

Background: Acute respiratory distress syndrome (ARDS) is a fatal outcome of severe sepsis. Machine learning models are helpful for accurately predicting ARDS in patients with sepsis at an early stage.

Objective: We aim to develop a machine-learning model for predicting ARDS in patients with sepsis in the intensive care unit (ICU).

Methods: The initial clinical data of patients with sepsis admitted to the hospital (including population characteristics, clinical diagnosis, complications, and laboratory tests) were used to predict ARDS, and screen out the crucial variables. After comparing eight different algorithms, namely, XG boost, logistic regression, light GBM, random forest, GaussianNB, complement NB, support vector machine (SVM), and K nearest neighbors (KNN), rebuilding a prediction model with the best one. When remodeling with the best algorithm, 10% was randomly selected to test, and the remaining was trained for cross-validation. Using the area under the curve (AUC), sensitivity, accuracy, specificity, positive and negative predictive value, F1 score, kappa value, and clinical decision curve to evaluate the model's performance. Eventually, the application in the model illustrated by the SHAP package.

Results: Ten critical features were screened utilizing the lasso method, namely, PaO₂/PAO₂, A-aDO₂, PO₂(T), CRP, gender, PO₂, RDW, MCH, SG, and chlorine. The prior ranking of variables demonstrated that PaO₂/PAO₂ was the most significant variable. Among the eight algorithms, the performance of the Gaussian NB algorithm was significantly better than that of the others. After remodeling with the best algorithm, the AUC in the training and validation sets were 0.777 and 0.770, respectively, and the algorithm performed well in the test set (AUC = 0.781, accuracy = 78.6%, sensitivity = 82.4%, F1 score = 0.824). A comparison of the overlap factors with those of previous models revealed that the model we developed performs better.

Conclusion: Sepsis-associated ARDS can be accurately predicted early via a machine learning model based on existing clinical data. These findings are helpful for accurate identification and improvement of the prognosis in patients with sepsis-associated ARDS.

Keywords: ARDS; Acute respiratory distress syndrome; Algorithm; ICU; Machine learning; Sepsis.

Fig. 1

Workflow diagram of this study. (A) Data collection process. (B) Establishment of machine learning model and comparison of eight models.

Fig. 2

Comparison of eight machine learning algorithms. (A, B) The ROC results of the models were established by eight machine learning algorithms in the training set and validation set. (C) A forest plot of each model AUC score built by eight machine learning algorithms. (D) Calibration plots of models built by eight machine learning algorithms.

Fig. 3

The performance of the model is built by the Gaussian NB algorithm. (A, B, C) The ROC result of the model was established by the Gaussian NB algorithm in the training set, validation set, and testing set. (D) The ROC result of the model was established by the Gaussian NB algorithm in the training set and the validation set according to the change in sample size.

Fig. 4

Interpretation of the model. (A) SHAP plot of 10 key variables. (B) Importance ranking chart of 10 key variables. (C, D) Show patients with positive (ARDS) and negative (NO-ARDS) predictions, respectively.