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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Real-time prediction of intradialytic hypotension using machine learning and cloud computing infrastructure.

Hanjie Zhang1, Lin-Chun Wang1, Sheetal Chaudhuri2,3

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Nephrology, Dialysis, Transplantation : Official Publication of the European Dialysis and Transplant Association - European Renal Association
|April 13, 2023
PubMed
Summary

Machine learning accurately predicts intradialytic hypotension (IDH) in hemodialysis patients up to 75 minutes beforehand. This early warning system shows promise for reducing complications and improving patient outcomes during dialysis.

Keywords:
end-stage kidney diseaseintradialytic hypotensionmachine learningreal-time prediction

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Area of Science:

  • Nephrology
  • Artificial Intelligence
  • Clinical Informatics

Background:

  • Intradialytic hypotension (IDH) is a common and serious complication in maintenance hemodialysis patients, linked to adverse clinical outcomes.
  • Predicting IDH allows for timely interventions, potentially reducing its incidence and improving patient safety.

Purpose of the Study:

  • To develop and validate a machine learning model for the early prediction of intradialytic hypotension (IDH) in hemodialysis patients.
  • To assess the predictive performance of the model using real-time clinical and machine data.

Main Methods:

  • A machine learning model was developed using data from 693 hemodialysis patients, including demographics, clinical factors, and real-time intradialytic machine data.
  • The model predicted IDH (defined as systolic blood pressure <90 mmHg) 15-75 minutes in advance.
  • Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC).

Main Results:

  • The study analyzed 42,656 hemodialysis sessions and over 355,000 blood pressure measurements.
  • IDH occurred in 16.2% of the analyzed treatments.
  • The developed model achieved an AUROC of 0.89 in predicting IDH, identifying recent SBP, IDH rate, and prior nadir SBP as key predictors.

Conclusions:

  • Real-time prediction of IDH during hemodialysis is feasible with a machine learning model.
  • The model demonstrates clinically actionable predictive performance.
  • Prospective studies are needed to confirm if this predictive capability leads to reduced IDH rates and better patient outcomes.