Real-time prediction of intradialytic relative blood volume: a proof-of-concept for integrated cloud computing infrastructure

S. Chaudhuri*, H. Han, C. Monaghan, J. Larkin, P. Waguespack, B. Shulman, Z.W. Kuang, S. Bellamkonda, J. Brzozowski, J. Hymes, M. Black, P. Kotanko, J.P. Kooman, F.W. Maddux, L. Usvyat

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Background Inadequate refilling from extravascular compartments during hemodialysis can lead to intradialytic symptoms, such as hypotension, nausea, vomiting, and cramping/myalgia. Relative blood volume (RBV) plays an important role in adapting the ultrafiltration rate which in turn has a positive effect on intradialytic symptoms. It has been clinically challenging to identify changes RBV in real time to proactively intervene and reduce potential negative consequences of volume depletion. Leveraging advanced technologies to process large volumes of dialysis and machine data in real time and developing prediction models using machine learning (ML) is critical in identifying these signals. Method We conducted a proof-of-concept analysis to retrospectively assess near real-time dialysis treatment data from in-center patients in six clinics using Optical Sensing Device (OSD), during December 2018 to August 2019. The goal of this analysis was to use real-time OSD data to predict if a patient's relative blood volume (RBV) decreases at a rate of at least - 6.5 % per hour within the next 15 min during a dialysis treatment, based on 10-second windows of data in the previous 15 min. A dashboard application was constructed to demonstrate how reporting structures may be developed to alert clinicians in real time of at-risk cases. Data was derived from three sources: (1) OSDs, (2) hemodialysis machines, and (3) patient electronic health records. Results Treatment data from 616 in-center dialysis patients in the six clinics was curated into a big data store and fed into a Machine Learning (ML) model developed and deployed within the cloud. The threshold for classifying observations as positive or negative was set at 0.08. Precision for the model at this threshold was 0.33 and recall was 0.94. The area under the receiver operating curve (AUROC) for the ML model was 0.89 using test data. Conclusions The findings from our proof-of concept analysis demonstrate the design of a cloud-based framework that can be used for making real-time predictions of events during dialysis treatments. Making real-time predictions has the potential to assist clinicians at the point of care during hemodialysis.
Original languageEnglish
Article number274
Number of pages10
JournalBmc Nephrology
Issue number1
Publication statusPublished - 9 Aug 2021


  • End Stage Kidney Disease
  • Real-Time Prediction
  • Machine Learning


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