Real-time prediction of intradialytic hypotension using machine learning and cloud computing infrastructure

Hanjie Zhang*, Lin-Chun Wang, Sheetal Chaudhuri, Aaron Pickering, Len Usvyat, John Larkin, Pete Waguespack, Zuwen Kuang, Jeroen P. Kooman, Franklin W. Maddux, Peter Kotanko

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Background In maintenance hemodialysis patients, intradialytic hypotension (IDH) is a frequent complication that has been associated with poor clinical outcomes. Prediction of IDH may facilitate timely interventions and eventually reduce IDH rates. Methods We developed a machine learning model to predict IDH in in-center hemodialysis patients 15-75 min in advance. IDH was defined as systolic blood pressure (SBP) <90 mmHg. Demographic, clinical, treatment-related and laboratory data were retrieved from electronic health records and merged with intradialytic machine data that were sent in real-time to the cloud. For model development, dialysis sessions were randomly split into training (80%) and testing (20%) sets. The area under the receiver operating characteristic curve (AUROC) was used as a measure of the model's predictive performance. Results We utilized data from 693 patients who contributed 42 656 hemodialysis sessions and 355 693 intradialytic SBP measurements. IDH occurred in 16.2% of hemodialysis treatments. Our model predicted IDH 15-75 min in advance with an AUROC of 0.89. Top IDH predictors were the most recent intradialytic SBP and IDH rate, as well as mean nadir SBP of the previous 10 dialysis sessions. Conclusions Real-time prediction of IDH during an ongoing hemodialysis session is feasible and has a clinically actionable predictive performance. If and to what degree this predictive information facilitates the timely deployment of preventive interventions and translates into lower IDH rates and improved patient outcomes warrants prospective studies.
Original languageEnglish
Pages (from-to)1761-1769
Number of pages9
JournalNephrology Dialysis Transplantation
Issue number7
Early online date1 Apr 2023
Publication statusPublished - 30 Jun 2023


  • end-stage kidney disease
  • intradialytic hypotension
  • machine learning
  • real-time prediction
  • FLOW


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