Dialysis adequacy predictions using a machine learning method

Abstract

Dialysis adequacy is an important survival indicator in patients with chronic hemodialysis. However, there are inconveniences and disadvantages to measuring dialysis adequacy by blood samples. This study used machine learning models to predict dialysis adequacy in chronic hemodialysis patients using repeatedly measured data during hemodialysis. This study included 1333 hemodialysis sessions corresponding to the monthly examination dates of 61 patients. Patient demographics and clinical parameters were continuously measured from the hemodialysis machine; 240 measurements were collected from each hemodialysis session. Machine learning models (random forest and extreme gradient boosting [XGBoost]) and deep learning models (convolutional neural network and gated recurrent unit) were compared with multivariable linear regression models. The mean absolute percentage error (MAPE), root mean square error (RMSE), and Spearman's rank correlation coefficient (Corr) for each model using fivefold cross-validation were calculated as performance measurements. The XGBoost model had the best performance among all methods (MAPE = 2.500; RMSE = 2.906; Corr = 0.873). The deep learning models with convolutional neural network (MAPE = 2.835; RMSE = 3.125; Corr = 0.833) and gated recurrent unit (MAPE = 2.974; RMSE = 3.230; Corr = 0.824) had similar performances. The linear regression models had the lowest performance (MAPE = 3.284; RMSE = 3.586; Corr = 0.770) compared with other models. Machine learning methods can accurately infer hemodialysis adequacy using continuously measured data from hemodialysis machines.

Figure 1

Scatter plots of URR and the predicted value for each model using Spearman’s rank correlation coefficient and the best fit line. (A) Linear regression (R = 0.771). (B) Random forest (R = 0.864). (C) XGBoost (R = 0.873). (D) Deep learning with convolutional neural network (R = 0.834). URR urea reduction ratio, XGBoost extreme gradient boosting.

Figure 2

Random forest and XGBoost feature importance plots. XGBoost extreme gradient boosting, MSE mean squared error, SD standard deviation, BFR blood flow rate, DFR dialysate flow rate, UV ultrafiltration volume.