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Review
. 2024 Jul 25;16(7):e65334.
doi: 10.7759/cureus.65334. eCollection 2024 Jul.

Artificial Intelligence and Machine Learning in Predicting Intradialytic Hypotension in Hemodialysis Patients: A Systematic Review

Taha Zahid Chaudhry  1 , Mansi Yadav  2 , Syed Faqeer Hussain Bokhari  3 , Syeda Rubab Fatimah  4 , Abdur Rehman  5 , Muhammad Kamran  6 , Aiman Asim  7 , Mohamed Elhefyan  8 , Osman Yousif  8
Affiliations
Review

Artificial Intelligence and Machine Learning in Predicting Intradialytic Hypotension in Hemodialysis Patients: A Systematic Review

Taha Zahid Chaudhry et al. Cureus. .

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.

Keywords: ai; artificial intelligence; dialysis; hemodialysis; intradialytic hypotension; machine learning; ml; nephrology; renal; systematic review.

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Conflict of interest statement

Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.

Figures

Figure 1
Figure 1. PRISMA diagram illustrating the study selection process.
PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
See this image and copyright information in PMC

References

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