ReviewCureus2024
Artificial Intelligence and Machine Learning in Predicting Intradialytic Hypotension in Hemodialysis Patients: A Systematic Review.
Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic review and comparison of machine learning and conventional statistical models for predicting cardiovascular events in dialysis patients.Renal failure · 2025Pooled it
- Prediction of intradialytic hypotension by machine learning: A systematic review.Journal of nephrology · 2025Pooled it
- Artificial Intelligence in Dialysis Therapies:Saudi medical journal · 2026Review
- The evolving field of nephrology: what comes next? A report from the European Renal Association Scientific Advisory Board.Clinical kidney journal · 2026Review
- Ask a Doctor a Question: A Clinician's Message to the Industry.Medicina (Kaunas, Lithuania) · 2025Article
- Safety and Efficacy of the First Mobile Intelligent Hemodialysis Platform in Mainland China: Protocol for a Single-Center Prospective Exploratory Study in Maintenance Hemodialysis Patients.Kidney diseases (Basel, Switzerland)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intradialytic hypotension (IDH) is a common and potentially life-threatening complication in hemodialysis patients. Traditional preventive measures have shown limited effectiveness in reducing IDH incidence. This systematic review evaluates the existing literature on the use of artificial intelligence (AI) and machine learning (ML) models for predicting IDH in hemodialysis patients. A comprehensive literature search identified five eligible studies employing diverse AI/ML algorithms, including artificial neural networks, decision trees, support vector machines, XGBoost, random forests, and LightGBM. These models utilized various features such as patient demographics, clinical data, laboratory findings, and dialysis-related parameters. The studies reported promising results, with several models achieving high prediction accuracies, sensitivities, specificities, and area under the receiver operating characteristic curve values for predicting IDH. However, limitations include variations in study populations, retrospective designs, and the need for prospective validation. Future research should focus on multicenter prospective studies, assessing clinical utility, and integrating interpretable AI/ML models into clinical decision support systems.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.