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Background <p>Intradialytic hypotension is associated with increased morbidity, and mortality. Several machine learning (ML) algorithms have been recently developed to predict intradialytic hypotension. We systematically reviewed ML models employed to predict intradialytic hypotension, their performance, methodological integrity, and clinical applicability.</p> Methods <p>We conducted this systematic review with a pre-established protocol registered at the International Prospective Register of Systematic Reviews (PROSPERO ID: CRD42022362194). Six databases, from their inception to July 20, 2023, were comprehensively searched. Two independent investigators reviewed the articles, extracted data, and evaluated the risk of bias using the Prediction model Risk of Bias Assessment Tool (PROBAST).</p> Results <p>Out of 84 screened articles, 16 studies with 14,500 adult patients on hemodialysis were included in the review. Fourteen studies (87.5%) were found to have a high risk of bias. The intradialytic hypotension prevalence in the population investigated was between 1.2 and 51%. A diverse range of predictive ML tools were used to predict intradialytic hypotension, with various neural networking models being the most frequent, appearing in 13 studies (AUROC ranges: 0.684–0.978). One study performed both internal and external validation.</p> Conclusions <p>Researchers have made a concerted effort to develop ML tools to predict intradialytic hypotension. Despite their significant efforts, the lack of thorough external and clinical validation, and heterogeneity among the models and settings have resulted in a substantial challenge to offering ML tools as a global intradialytic hypotension prevention and management solution. Future studies should focus on external and clinical validation of these models to enhance the chances of clinically relevant changes in clinical practices.</p> Graphical abstract <p></p>

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Prediction of intradialytic hypotension by machine learning: A systematic review

  • 期刊论文

  • 发表日期：

    2025年5月3日

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  • Jacob Ninan,
  • Nasrin Nikravangolsefid,
  • Hong Hieu Truong,
  • Mariam Charkviani,
  • Larry J. Prokop,
  • Raghavan Murugan,
  • Gilles Clermont,
  • Kianoush B. Kashani,
  • Juan Pablo Domecq Garces

摘要

Background

Intradialytic hypotension is associated with increased morbidity, and mortality. Several machine learning (ML) algorithms have been recently developed to predict intradialytic hypotension. We systematically reviewed ML models employed to predict intradialytic hypotension, their performance, methodological integrity, and clinical applicability.

Methods

We conducted this systematic review with a pre-established protocol registered at the International Prospective Register of Systematic Reviews (PROSPERO ID: CRD42022362194). Six databases, from their inception to July 20, 2023, were comprehensively searched. Two independent investigators reviewed the articles, extracted data, and evaluated the risk of bias using the Prediction model Risk of Bias Assessment Tool (PROBAST).

Results

Out of 84 screened articles, 16 studies with 14,500 adult patients on hemodialysis were included in the review. Fourteen studies (87.5%) were found to have a high risk of bias. The intradialytic hypotension prevalence in the population investigated was between 1.2 and 51%. A diverse range of predictive ML tools were used to predict intradialytic hypotension, with various neural networking models being the most frequent, appearing in 13 studies (AUROC ranges: 0.684–0.978). One study performed both internal and external validation.

Conclusions

Researchers have made a concerted effort to develop ML tools to predict intradialytic hypotension. Despite their significant efforts, the lack of thorough external and clinical validation, and heterogeneity among the models and settings have resulted in a substantial challenge to offering ML tools as a global intradialytic hypotension prevention and management solution. Future studies should focus on external and clinical validation of these models to enhance the chances of clinically relevant changes in clinical practices.

Graphical abstract