Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV database

Abstract

Background: Acute kidney injury (AKI) is a common and serious complication in severe acute pancreatitis (AP), associated with high mortality rate. Early detection of AKI is crucial for prompt intervention and better outcomes. This study aims to develop and validate predictive models using machine learning (ML) to identify the onset of AKI in patients with AP.

Methods: Patients with AP were extracted from the MIMIC-IV database. We performed feature selection using the random forest method. Model construction involved an ensemble of ML, including random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN), naive Bayes (NB), neural network (NNET), generalized linear model (GLM), and gradient boosting machine (GBM). The best-performing model was fine-tuned and evaluated through split-set validation.

Results: We analyzed 1,235 critically ill patients with AP, of which 667 cases (54%) experienced AKI during hospitalization. We used 49 variables to construct models, including GBM, GLM, KNN, NB, NNET, RF, and SVM. The AUC for these models was 0.814 (95% CI, 0.763 to 0.865), 0.812 (95% CI, 0.769 to 0.854), 0.671 (95% CI, 0.622 to 0.719), 0.812 (95% CI, 0.780 to 0.864), 0.688 (95% CI, 0.624 to 0.752), 0.809 (95% CI, 0.766 to 0.851), and 0.810 (95% CI, 0.763 to 0.856) respectively. In the test set, the GBM's performance was consistent, with an area of 0.867 (95% CI, 0.831 to 0.903).

Conclusions: The GBM model's precision is crucial, aiding clinicians in identifying high-risk patients and enabling timely interventions to reduce mortality rates in critical care.

Keywords: Acute kidney injury; MIMIC- IV database; acute pancreatitis; machine learning; prediction model.

Figure 1.

Diagram of methods. The complete data set was split into training and test sets. The machine-learning methods were trained on the training set and the best performer selected for additional parameter tuning before being applied to the test set for validation.

Figure 2.

Reduction of dimensionality by recursive feature elimination on the training data set. The number of features used for training was reduced from the list of features on the left to the list of features on the right.

Figure 3.

Variable importance of features included in gradient boosting machine algorithm for prediction of AKI. Variable importance is computed based on how important any given feature is to aid in the classification process when the classifier is built, determined by its effect on the performance measure. The greater the importance, the more essential the variable is to the performance of the model. Assumptions about effect size cannot be drawn directly about the relationship of variable importance to the primary outcome.

Figure 4.

Receiver operating characteristic curves of machine-learning methods for prediction of AKI in the test data set. A greater area under the receiver operating characteristic curve represents higher discriminative ability of the model. Area under the receiver operative characteristics curves, as well as specificity and sensitivity of each machine learning model for prediction of AKI at “best” threshold are presented with 95% CIs. “best” threshold refers to the threshold at which specificity and sensitivity are both maximized.