NDLI: Using machine learning to predict the risk of short-term and long-term death in acute kidney injury patients after commencing CRRT

Content Provider	Springer Nature : BioMed Central
Author	Gu, Menglei Liu, Yalan Sun, Hongbin Sun, Haitong Fang, Yufei Chen, Luping Zhang, Lu
Abstract	Background The mortality rate and prognosis of short-term and long-term acute kidney injury (AKI) patients who undergo continuous renal replacement therapy (CRRT) are different. Setting up risk stratification tools for both short-term and long-term deaths is highly important for clinicians. Method A total of 1535 AKI patients receiving CRRT were included in this study, with 1144 from the training set (the Dryad database) and 391 from the validation set (MIMIC IV database). A model for predicting mortality within 10 and 90 days was built using nine different machine learning (ML) algorithms. AUROC, F1-score, accuracy, sensitivity, specificity, precision, and calibration curves were used to assess the predictive performance of various ML models. Results A total of 420 (31.1%) deaths occurred within 10 days, and 1080 (68.8%) deaths occurred within 90 days. The random forest (RF) model performed best in both predicting 10-day (AUROC: 0.80, 95% CI: 0.74–0.84; accuracy: 0.72, 95% CI: 0.67–0.76; F1-score: 0.59) and 90-day mortality (AUROC: 0.78, 95% CI: 0.73–0.83; accuracy: 0.73, 95% CI: 0.69–0.78; F1-score: 0.80). The importance of the feature shows that SOFA scores are rated as the most important risk factor for both 10-day and 90-day mortality. Conclusion Our study, utilizing multiple machine learning models, estimates the risk of short-term and long-term mortality among AKI patients who commence CRRT. The results demonstrated that the prognostic factors for short-term and long-term mortality are different. The RF model has the best prediction performance and has valuable potential for clinical application.
Related Links	https://bmcnephrol.biomedcentral.com/counter/pdf/10.1186/s12882-024-03676-x.pdf
Ending Page	10
Page Count	10
Starting Page	1
File Format	HTM / HTML
ISSN	14712369
DOI	10.1186/s12882-024-03676-x
Journal	BMC Nephrology
Issue Number	1
Volume Number	25
Language	English
Publisher	BioMed Central
Publisher Date	2024-07-30
Access Restriction	Open
Subject Keyword	Nephrology Internal Medicine Machine learning Acute kidney injury Risk stratification tool Mortality Continuous renal replacement treatment
Content Type	Text
Resource Type	Article
Subject	Nephrology
Journal Impact Factor	2.2/2023
5-Year Journal Impact Factor	2.6/2023

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