Predictive models of epilepsy outcomes.
Journal
Current opinion in neurology
ISSN: 1473-6551
Titre abrégé: Curr Opin Neurol
Pays: England
ID NLM: 9319162
Informations de publication
Date de publication:
16 Jan 2024
16 Jan 2024
Historique:
medline:
15
1
2024
pubmed:
15
1
2024
entrez:
15
1
2024
Statut:
aheadofprint
Résumé
Multiple complex medical decisions are necessary in the course of a chronic disease like epilepsy. Predictive tools to assist physicians and patients in navigating this complexity have emerged as a necessity and are summarized in this review. Nomograms and online risk calculators are user-friendly and offer individualized predictions for outcomes ranging from safety of antiseizure medication withdrawal (accuracy 65-73%) to seizure-freedom, naming, mood, and language outcomes of resective epilepsy surgery (accuracy 72-81%). Improving their predictive performance is limited by the nomograms' inability to ingest complex data inputs. Conversely, machine learning offers the potential of multimodal and expansive model inputs achieving human-expert level accuracy in automated scalp electroencephalogram (EEG) interpretation but lagging in predictive performance or requiring validation for other applications. Good to excellent predictive models are now available to guide medical and surgical epilepsy decision-making with nomograms offering individualized predictions and user-friendly tools, and machine learning approaches offering the potential of improved performance. Future research is necessary to bridge the two approaches for optimal translation to clinical care.
Identifiants
pubmed: 38224138
doi: 10.1097/WCO.0000000000001241
pii: 00019052-990000000-00133
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.
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