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Craniotomy for Glioma Resection: A Predictive Model.

Symeon Missios1, Piyush Kalakoti1, Anil Nanda1

  • 1Department of Neurosurgery, Louisiana State University Health Sciences, Shreveport, Lousiana, USA.

World Neurosurgery
|May 7, 2015
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Summary
This summary is machine-generated.

This study developed predictive models for glioma surgery complications. These models help estimate patient risks before surgery, aiding in outcome benchmarking and quality assessment.

Keywords:
CraniotomyGliomaNational Inpatient SampleRisk prediction

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Area of Science:

  • Neurosurgery
  • Oncology
  • Health Services Research

Background:

  • Regulatory agencies are standardizing quality metrics for surgical procedures.
  • There is a need for predictive models to assess perioperative complications in glioma surgery.

Purpose of the Study:

  • To create and validate a predictive model for perioperative complications in patients undergoing craniotomies for glioma resection.

Main Methods:

  • Retrospective cohort study of 21,384 patients from the National Inpatient Sample (NIS) database (2005-2011).
  • Development and validation of predictive models using logistic regression analysis and a bootstrapped sample.

Main Results:

  • Identified inpatient postoperative risks including death (1.6%), discharge to rehabilitation (25.8%), hydrocephalus (4.0%), cardiac complications (0.7%), respiratory complications (0.5%), deep wound infection (0.8%), deep venous thrombosis (0.6%), pulmonary embolus (3.1%), and acute renal failure (1.3%).
  • Models demonstrated good discrimination with Areas Under the Curve (AUC) ranging from 0.64 to 0.81 for individual complications.
  • Model calibration was assessed using the Hosmer-Lemeshow test.

Conclusions:

  • The developed models can assist in the preoperative estimation of complication risk for glioma patients.
  • These models can serve as an adjunct for outcome benchmarking in the glioma patient population.