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Preoperative Electroencephalography-Based Machine Learning Predicts Cognitive Deterioration After Subthalamic Deep Brain Stimulation.
Geraedts, Victor J; Koch, Milan; Kuiper, Roy; Kefalas, Marios; Bäck, Thomas H W; van Hilten, Jacobus J; Wang, Hao; Middelkoop, Huub A M; van der Gaag, Niels A; Contarino, Maria Fiorella; Tannemaat, Martijn R.
Afiliação
  • Geraedts VJ; Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands.
  • Koch M; Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
  • Kuiper R; Leiden Institute of Advanced Computer Science, Leiden, The Netherlands.
  • Kefalas M; Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands.
  • Bäck THW; Department of Neurology, Haga Teaching Hospital, Den Haag, The Netherlands.
  • van Hilten JJ; Leiden Institute of Advanced Computer Science, Leiden, The Netherlands.
  • Wang H; Leiden Institute of Advanced Computer Science, Leiden, The Netherlands.
  • Middelkoop HAM; Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands.
  • van der Gaag NA; Leiden Institute of Advanced Computer Science, Leiden, The Netherlands.
  • Contarino MF; Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands.
  • Tannemaat MR; Neuropsychology Unit, Leiden University Institute of Psychology, Leiden, The Netherlands.
Mov Disord ; 36(10): 2324-2334, 2021 10.
Article em En | MEDLINE | ID: mdl-34080712
ABSTRACT

BACKGROUND:

Subthalamic deep brain stimulation (STN DBS) may relieve refractory motor complications in Parkinson's disease (PD) patients. Despite careful screening, it remains difficult to determine severity of alpha-synucleinopathy involvement which influences the risk of postoperative complications including cognitive deterioration. Quantitative electroencephalography (qEEG) reflects cognitive dysfunction in PD and may provide biomarkers of postoperative cognitive decline.

OBJECTIVE:

To develop an automated machine learning model based on preoperative EEG data to predict cognitive deterioration 1 year after STN DBS.

METHODS:

Sixty DBS candidates were included; 42 patients had available preoperative EEGs to compute a fully automated machine learning model. Movement Disorder Society criteria classified patients as cognitively stable or deteriorated at 1-year follow-up. A total of 16,674 EEG-features were extracted per patient; a Boruta algorithm selected EEG-features to reflect representative neurophysiological signatures for each class. A random forest classifier with 10-fold cross-validation with Bayesian optimization provided class-differentiation.

RESULTS:

Tweny-five patients were classified as cognitively stable and 17 patients demonstrated cognitive decline. The model differentiated classes with a mean (SD) accuracy of 0.88 (0.05), with a positive predictive value of 91.4% (95% CI 82.9, 95.9) and negative predictive value of 85.0% (95% CI 81.9, 91.4). Predicted probabilities between classes were highly differential (hazard ratio 11.14 [95% CI 7.25, 17.12]); the risk of cognitive decline in patients with high probabilities of being prognosticated as cognitively stable (>0.5) was very limited.

CONCLUSIONS:

Preoperative EEGs can predict cognitive deterioration after STN DBS with high accuracy. Cortical neurophysiological alterations may indicate future cognitive decline and can be used as biomarkers during the DBS screening. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Núcleo Subtalâmico / Estimulação Encefálica Profunda Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Núcleo Subtalâmico / Estimulação Encefálica Profunda Idioma: En Ano de publicação: 2021 Tipo de documento: Article