Deep Learning on Electrocardiograms for Prediction of In-hospital Intradialytic Hypotension in Patients with ESKD.

Journal: Kidney360
PMID: 37418626

Abstract

Intradialytic hypotension is common in patients who are on hemodialysis. We applied deep learning techniques to ECGs to predict patients at risk of IDH. The performance of the model was good with an AUC of 0.763 and AUPRC of 0.35.

Authors

  • Akhil Vaid
    Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA.
  • Kullaya Takkavatakarn
    Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY; Division of Nephrology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
  • Jasmin Divers
    Division of Health Services, Department of Medicine, NYU (New York University) Long Island School of Medicine, Mineola, New York.
  • David M Charytan
    Division of Nephrology, Department of Medicine, New York University Grossman School of Medicine, New York, New York.
  • Lili Chan
    Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, USA.
  • Girish N Nadkarni
    Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.

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