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
• acute pancreatitis
• machine learning
• prediction model
• MIMIC- IV database

Acknowledgements

The authors gratefully acknowledge the study participants who have been involved and contributed to the procedure of data collection. The authors gratefully acknowledge the financial supports by the Natural Science Foundation of China.

Ethics approval and consent to participate

MIMIC IV database used in the present study was approved by the Institutional Review Boards (IRB) of the Massachusetts Institute of Technology and does not contain protected health information.

Authors’ contributions

Study design: JJB, PPJ, SWL.

Interpretation of data: SWL, WBL, PPJ.

Drafting of the manuscript: SWL, WBL.

Data collection: XQL, TW, YW, LDC.

Data analysis: SWL, WBL.

All authors gave approval for the final version of the manuscript.

Consent for publication

Not applicable.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The co-first author of this study, Wenbin Lu, has been granted authorization for data extraction from the MIMIC-IV database, specifically for research purposes (certification number: 50992435). The datasets used in the present study were downloaded from the following website: https://physionet.org/content/mimiciv/2.2/.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (grant numbers 82272205).