A machine learning-based approach for predicting renal function recovery in general ward patients with acute kidney injury

• Main Authors: Nam-Jun Cho (Author), Inyong Jeong (Author), Yeongmin Kim (Author), Dong Ok Kim (Author), Se-Jin Ahn (Author), Sang-Hee Kang (Author), Hyo-Wook Gil (Author), Hwamin Lee (Author)
• Format: Book
• Published: The Korean Society of Nephrology, 2024-07-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2211-9132; 2211-9140; 10.23876/j.krcp.23.330
• Keywords: acute kidney injury; hospital records; machine learning; recovery of function; creatinine; Internal medicine; RC31-1245; Specialties of internal medicine; RC581-951
• Source: Kidney Research and Clinical Practice, Vol 43, Iss 4, Pp 538-547 (2024)

Background Acute kidney injury (AKI) is a significant challenge in healthcare. While there are considerable researches dedicated to AKI patients, a crucial factor in their renal function recovery, is often overlooked. Thus, our study aims to address this issue through the development of a machine learning model to predict restoration of kidney function in patients with AKI. Methods Our study encompassed data from 350,345 cases, derived from three hospitals. AKI was classified in accordance with the Kidney Disease: Improving Global Outcomes. Criteria for recovery were established as either a 33% decrease in serum creatinine levels at AKI onset, which was initially employed for the diagnosis of AKI. We employed various machine learning models, selecting 43 pertinent features for analysis. Results Our analysis contained 7,041 and 2,929 patients' data from internal cohort and external cohort respectively. The Categorical Boosting Model demonstrated significant predictive accuracy, as evidenced by an internal area under the receiver operating characteristic (AUROC) of 0.7860, and an external AUROC score of 0.7316, thereby confirming its robustness in predictive performance. SHapley Additive exPlanations (SHAP) values were employed to explain key factors impacting recovery of renal function in AKI patients. Conclusion This study presented a machine learning approach for predicting renal function recovery in patients with AKI. The model performance was assessed across distinct hospital settings, which revealed its efficacy. Although the model exhibited favorable outcomes, the necessity for further enhancements and the incorporation of more diverse datasets is imperative for its application in real-world.