Development and validation of a predictive machine learning model for postoperative long-term diabetes insipidus following transsphenoidal surgery for sellar lesions.

Journal: Clinical neurology and neurosurgery
PMID: 40253842

Abstract

OBJECTIVE: Diabetes Insipidus (DI) is a common complication that occurs following transsphenoidal surgery for sellar lesions. DI is usually transient but can be permanent in select patients. Prior studies have described preoperative risk factors for developing postoperative DI. However, no predictive risk score has been created to risk stratify these patients.

Authors

  • Simon G Ammanuel
    Department of Neurological Surgery, University of Wisconsin, School of Medicine and Public Health, Madison, WI 53792, United States.
  • Manasa H Kalluri
    Department of Neurological Surgery, University of Wisconsin, School of Medicine and Public Health, Madison, WI 53792, United States.
  • Jesse D Montoure
    Department of Neurological Surgery, University of Wisconsin, School of Medicine and Public Health, Madison, WI 53792, United States.
  • Benjamin Lee
    Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, MA, 02138, USA.
  • Garret P Greeneway
    Department of Neurological Surgery, University of Wisconsin, School of Medicine and Public Health, Madison, WI 53792, United States.
  • Paul S Page
    Department of Neurological Surgery, University of Wisconsin, School of Medicine and Public Health, Madison, WI 53792, United States.
  • Azam S Ahmed
    Department of Neurological Surgery, University of Wisconsin, School of Medicine and Public Health, Madison, WI 53792, United States.
  • Mustafa K Baskaya
    Department of Neurological Surgery, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin, United States.

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