ASN's Mission

To create a world without kidney diseases, the ASN Alliance for Kidney Health elevates care by educating and informing, driving breakthroughs and innovation, and advocating for policies that create transformative changes in kidney medicine throughout the world.

learn more

Contact ASN

1401 H St, NW, Ste 900, Washington, DC 20005


The Latest on X

Kidney Week

Please note that you are viewing an archived section from 2022 and some content may be unavailable. To unlock all content for 2022, please visit the archives.

Abstract: SA-PO351

Proof-of-Concept Model for the Prediction of Dry Weight in Hemodialysis Patients

Session Information

Category: Dialysis

  • 701 Dialysis: Hemodialysis and Frequent Dialysis


  • Sandys, Vicki K., Royal College of Surgeons in Ireland, Dublin, Ireland
  • Bhat, Lavleen, Royal College of Surgeons in Ireland, Dublin, Ireland
  • Sexton, Donal J., The University of Dublin Trinity College, Dublin, Ireland
  • O'Seaghdha, Conall M., Royal College of Surgeons in Ireland, Dublin, Ireland

Group or Team Name

  • Haemodialysis Outcomes & Patient Empowerment group

An automated, accurate and periodic assessment of dry weight using hemodialysis data would be a clinically useful, low cost, and rapidly scalable method of assessing fluid status.


A post-hoc analysis of a 3 week observational study in 20 patients (HOPE-02, NCT04623281). 143 features were created using clinical and calculated parameters over 1 or 2 weeks prior to the targeted session including variability measures, differences, confidence intervals and interdialytic calculations. Averaged data was combined with single-session data. Dry weight was defined as the mean of BCM normohydration weights. Data was split 70:30 into training and testing sets.The algorithm was trained to predict overhydration (OH) index pre and post-dialysis as determined by the Fresenius Body Composition Monitor (BCM). Dry weight was derived by subtracting OH status from pre or post-dialysis weight. Model performance was evaluated using adjusted R2. The error metrics used were mean absolute error (MAE) and root mean squared error (RMSE). Accuracy was calculated using categories of fluid status defined as overhydration > 1.1 L, normohydration 1.1 - 1.1 L and underhydration < 1.1 L.


20 subjects involving 44 haemodialysis sessions were used. A linear regression (LR) model combining single-session data with moving averages of data from the preceding 1 or 2 weeks was created. The final model had 9 features (Figure 1). The LR model predicting post-dialysis OH status outperformed a pre-dialysis OH model. Adjusted R2 for the model was 0.719. Training RMSE for post-dialysis OH= 1.12 kg, testing RMSE= 1.21 kg. The training RMSE for dry weight= 1.21 kg, the testing RMSE= 1.13 kg. The model had a classification accuracy of 81.8%.


This model represents an initial step in the creation of an automated assessment of dry weight. Work has begun on external validation and further development of this algorithm in a large dialysis dataset. A final model could potentially be embedded in a clinical decision support system for dry weight management.


  • Commercial Support –