#2714 An AI based tool for hydration management in dialysis patients.
Authors: Botler Max , Carioni Paola , Bellocchio Francesco , Croft Kaitlyn , Sandys Vicki , Bhat Lavleen , O'seaghdha Conall , Usvyat Len , Neri Luca 
Published: May 2024

Background and Aims

Hemodialysis patients often experience over-hydration (OH), a critical factor derived from excess extracellular water and associated with cardiovascular complications and overall morbidity. Accurate estimation of pre-dialysis OH is essential for optimizing fluid management and improving patient outcomes. Traditional methods for assessing OH, such as the Body Composition Monitor (BCM) are, in general, time-consuming for clinical staff and costly for healthcare providers. In this study, we present a machine learning model combining data automatically recorded by dialysis machine sensors and medical information captured in electronic chart records to estimate pre-dialysis OH. The aim of this study is to evaluate the possibility of a tool that predicts OH from patient's data without, or less frequently using, a bioimpedance device for OH measurements.

Method

We collected a comprehensive dataset from a cohort of CKD patients undergoing hemodialysis, including demographic information, clinical parameters, and hydration status parameters. The value of OH was assessed using BCM device.

A feature selection approach, guided by a balance between prediction accuracy and model complexity, was used to identify the optimal set of input variables. All variables utilized in the model are either regularly gathered in clinical practice or automatically recorded from the dialysis machine.

We tried different combinations of features using an XGBoost regressor algorithm. We focused the study on the comparison of the prediction of OH with the input variable of the average BCM OH value in the past year (Model 1) and without this variable (Model 2).

Mean absolute error (MAE), Root mean square error (RMSE), R2, calibration and residuals plots were used to assess the models’ accuracy. The measurement unit is in litres. The models were validated with nested 5-fold cross-validation, dividing the data repeatedly into disjunct train, validation and test folds and 5 outer loops, totalling 25 folds.

Results

The dataset contains the observations from 37,011 patients for a total of 934,386 BCM assessments collected in the EuCliD database by Fresenius Medical Care clinics in Cech Republic, Italy, Portugal, Slovakia, Spain, and the United Kingdom between January 2016 to September 2023. The average value of OH is 2.06 (std 1.88). Model 1 generalizes on unseen data samples with an MAE of 0.74, RMSE of 1.02 and R2 of 0.57 after evaluating the performance on the test folds. Model 2 MAE is 0.93, RMSE is 1.25 and R2 is 0.36. The degradation of the accuracy between the two models indicates the importance of patients’ past BCM information for the prediction of the next OH. This degradation is also clear in the calibration and residuals plots (Fig. 1). Nevertheless, model 2 is able to predict OH with an acceptable level of accuracy.

Conclusion

Fluid management is a critical task for physicians and nurses. We present an AI-based tool that can be utilized to support clinicians in their daily decisions about fluid management, with and without the use of a device to measure OH.

The results, as expected, show an important degradation of the accuracy between model 1 and 2, however, model 2 has sufficient overall accuracy to consider the approach valuable, meriting further exploration. In general, this approach can be used to reduce the number of direct OH assessments.