ArticleNephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association2023
Real-time prediction of intradialytic hypotension using machine learning and cloud computing infrastructure.
Article in Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.
- Prediction of intradialytic hypotension by machine learning: A systematic review.Journal of nephrology · 2025Pooled it
- Predicting intradialytic exercise intolerance in maintenance hemodialysis patients: an interpretable machine learning approach integrating functional assessments.Renal failure · 2026Observational
- Artificial intelligence in pediatric nephrology: current applications and emerging frameworks for evidence generation.Pediatric nephrology (Berlin, Germany) · 2026Review
- Adequacy of dialysis in children and adolescents: more than just Kt/V.Pediatric nephrology (Berlin, Germany) · 2026Review
- From assistant to collaborator: A systematic review of the evolution of artificial intelligence in end-stage renal disease care and management.PLOS digital health · 2026Article
- Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies.World journal of nephrology · 2026Review
- MI-Net: a dual-stream feature network based on Mamba-convolutional neural network (CNN) fusion to predict intradialytic hypotension using multimodal data.Quantitative imaging in medicine and surgery · 2026Article
- Artificial Intelligence in Dialysis Therapies:Saudi medical journal · 2026Review
- The value of pleth variability index in predicting hypotension during maintenance hemodialysis: a prospective observational multicenter study (PVi-HD study).BMC nephrology · 2026Observational
- Continuous monitoring of relative blood volume allows real-time assessment of intradialytic hypotension risk.Clinical kidney journal · 2026Article
- Accurate prediction of intradialytic hypo- and hypertension in haemodialysis patients: the dual-attention Transformer model.Clinical kidney journal · 2026Article
- Artificial Intelligence in Critical Care Nephrology: Current Applications, Emerging Techniques, and Challenges to Clinical Integration.Kidney360 · 2026Review
- Transforming nephrology through artificial intelligence: a state-of-the-art roadmap for clinical integration.Clinical kidney journal · 2026Review
- Current and Future Applications of AI-Driven Predictive Modeling and a Proposed Framework for AI-Bioprognostics in Kidney Care.Risk management and healthcare policy · 2026Review
- Intra-Dialytic Hypotension and the VExUS Assessment in Hospitalized Patients: A Prospective Cohort Study.Cardiorenal medicine · 2026Article
- Updates in the management of intradialytic hypotension: Emerging strategies and innovations.World journal of nephrology · 2025Review
- Prediction of intradialytic hypotension based on heart rate variability and skin sympathetic nerve activity using LASSO-enabled feature selection: a two-center study.Renal failure · 2025Article
- Predicting blood pressure variability in hemodialysis using an explainable boosting machine model.Clinical kidney journal · 2025Article
- Artificial intelligence, machine learning, telemedicine, and digital transformation in nephrology and transplantation.Renal failure · 2025Article
- Prediction of ultrafiltration volume in maintenance hemodialysis patients using an artificial neural network model based on body composition information.BMC medical informatics and decision making · 2025Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn 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.
methodsWe 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.
resultsWe 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.
conclusionsReal-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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.