Prediction of dialysis adequacy using data-driven machine learning algorithms.

Background: Adequate delivery of hemodialysis (HD), measured by the spKt/V derived from urea reduction, is an important determinant of clinical outcomes in chronic hemodialysis patients. However, the need for pre- and postdialysis blood samples prevented the assessment of spKt/V in every session.

Methods: This retrospective single-center study was performed on end-stage renal disease (ESKD) patients aged ≥ 18 years who received standard thrice-weekly chronic HD therapy. Eighty-seven variables, including general, intradialytic, and laboratory variables, were collected from the medical records for analysis. Five steps of preprocessing procedure were deployed to select only the most relevant variables. Six binary classification models were developed to predict whether spKt/V was higher than 1.4.

Results: A total of 1869 HD sessions from 373 ESKD patients were included in this study. The Random Forest model showed the best prediction for dialysis adequacy, with AUROC scores of 0.860 in the validation dataset and 0.873 in the testing dataset. Notably, an accessible model that solely relied on noninvasively collected general and dialysis-related variables maintained high prediction accuracy, with AUROC scores of 0.854 and 0.868 in the validation and testing datasets, respectively. The five most significant predictive variables were vascular access, gender, body mass index, ultrafiltration volume, and dialysis duration.

Conclusion: The study results suggest that the development of ML models for accurately predicting dialysis adequacy based on general and intradialytic variables is feasible. These models have the potential to be utilized for noninvasive clinical assessments of dialysis adequacy.