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Predictive nomogram for deep brain stimulation-related infections.

Feng Chen1, Xiankun Meng2, Tong Li1

  • 11Department of Neurosurgery, Qingdao Municipal Hospital (Headquarters), Qingdao, Shandong Province.

Neurosurgical Focus
|December 1, 2022
PubMed
Summary
This summary is machine-generated.

Deep brain stimulation (DBS) infections are common. This study identified body mass index (BMI), blood glucose, and albumin as key predictors of DBS infection, developing a reliable nomogram for risk assessment.

Keywords:
Parkinson diseasedeep brain stimulationinfectionlogistic regressionnomogram

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Area of Science:

  • Neurosurgery
  • Infectious Disease Epidemiology
  • Medical Statistics

Background:

  • Infections following implantable pulse generator and deep brain stimulation (DBS) placement are frequent complications.
  • Identifying risk factors for DBS infections is crucial for improving patient outcomes and surgical safety.

Purpose of the Study:

  • To retrospectively evaluate and identify potential risk factors for deep brain stimulation (DBS) infections.
  • To develop and validate a predictive model for DBS infections.

Main Methods:

  • Patients undergoing primary DBS placement or IPG replacement were enrolled in training and validation cohorts.
  • Logistic regression models were used to identify predictors of infection, leading to the development of a nomogram.
  • Nomogram reliability was assessed using calibration curves, ROC analysis, and decision curves.

Main Results:

  • Body Mass Index (BMI), blood glucose, and albumin were identified as significant predictors of DBS infection.
  • The developed nomogram demonstrated good calibration and predictive accuracy in both training (AUC=0.93) and validation (AUC=0.83) cohorts.
  • The nomogram showed clinical utility, offering benefits over standard assessment schemes.

Conclusions:

  • BMI, blood glucose, and albumin are significant predictors of infection risk in patients undergoing DBS surgery.
  • The developed nomogram is a reliable tool for clinical application in assessing DBS infection risk.
  • This predictive model can aid in patient selection and perioperative management to mitigate infection risk.