Prediction of successful weaning from renal replacement therapy in critically ill patients based on machine learning

Abstract

Background

Predicting the successful weaning of acute kidney injury (AKI) patients from renal replacement therapy (RRT) has emerged as a research focus, and we successfully built predictive models for RRT withdrawal in patients with severe AKI by machine learning.

Methods

This retrospective single-center study utilized data from our general intensive care unit (ICU) Database, focusing on patients diagnosed with severe AKI who underwent RRT. We evaluated RRT weaning success based on patients being free of RRT in the subsequent week and their overall survival. Multiple logistic regression (MLR) and machine learning algorithms were adopted to construct the prediction models.

Results

A total of 976 patients were included, with 349 patients successfully weaned off RRT. Longer RRT duration (7.0 vs. 9.6 d, p = 0.002, OR = 0.94), higher serum cystatin C levels (1.2 vs. 3.2 mg/L, p < 0.001, OR = 0.46), and the presence of septic shock (28.1% vs. 41.5%, p < 0.001, OR = 0.63) were associated with reduced likelihood of RRT weaning. Conversely, a positive furosemide stress test (FST) (60.2% vs. 40.7%, p < 0.001, OR = 2.75) and higher total urine volume 3 d before RRT withdrawal (755 vs. 125 mL/d, p < 0.001, OR = 2.12) were associated with an increased likelihood of successful weaning from RRT. Next, we demonstrated that machine learning models, especially Random Forest and XGBoost, achieving an AUROC of 0.95. The XGBoost model exhibited superior accuracy, yielding an AUROC of 0.849.

Conclusion

High-risk factors for unsuccessful RRT weaning in severe AKI patients include prolonged RRT duration. Machine learning prediction models, when compared to models based on multivariate logistic regression using these indicators, offer distinct advantages in predictive accuracy.

Keywords:

• RRT weaning
• AKI
• machine learning
• multivariate logic regression
• prediction model

Acknowledgements

Thanks for all the medical staff of ICU for their contribution to nosocomial infection prevention and control. Thanks for the nosocomial infection department for providing professional reference to the details of the study.

Ethics approval and consent to participate

Our research was approved by the ethics committee of the Second Affiliated Hospital of Zhejiang University School of Medicine, and the approval number is IRB-2016-1511. Our data are derived from the special database and had the informed consent form of the subjects, and sensitive information including name, ID card, contact number and so on are deleted through desensitization program. The informed consent we provide can be found in the Supplementary files.

Author contributions

Qiqiang Leung designed the research scheme, analyzed the characteristics of the data, constructed the prediction model, and drafted the manuscript. Xin Xu extracted and collated clinical data through the database, built models, and collected prospective research data. Jin Wu and Shuo Ding determined clinical variables, analyzed the importance of model variables, and completed clinical data in prospective studies. Man Huang supervised the research process, provided clinical reference, and revised the article. All authors read and approved the final manuscript.

Consent for publication

Not applicable.

Disclosure statement

None.

Availability of data and materials

The datasets used and/or analyzed during this study are available from the corresponding author on reasonable request.

Additional information

Funding

This work was supported by Young Innovative Talents Support Program with No. 2022485651 in the Health Department of Zhejiang Province.