Feasibility of machine learning based predictive modelling of postoperative hyponatremia after pituitary surgery.

Journal: Pituitary
PMID: 32488759

Abstract

PURPOSE: Hyponatremia after pituitary surgery is a frequent finding with potential severe complications and the most common cause for readmission. Several studies have found parameters associated with postoperative hyponatremia, but no reliable specific predictor was described yet. This pilot study evaluates the feasibility of machine learning (ML) algorithms to predict postoperative hyponatremia after resection of pituitary lesions.

Authors

  • Stefanos Voglis
    Department of Neurosurgery and Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinkstrasse 10, 8091, Zurich, Switzerland. stefanos.voglis@usz.ch.
  • Christiaan H B van Niftrik
    1Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland.
  • Victor E Staartjes
    Department of Neurosurgery, Bergman Clinics, Naarden, The Netherlands; and.
  • Giovanna Brandi
    Neurosurgical Intensive Care Unit, Institute for Intensive Care Medicine, University Hospital and University of Zurich, Zurich, Switzerland.
  • Oliver Tschopp
    Department of Endocrinology, Diabetes, and Clinical Nutrition, University Hospital and University of Zurich, Zurich, Switzerland.
  • Luca Regli
    Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
  • Carlo Serra
    1Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland.

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