All in the numbers? Computational prediction of optimal anaesthetic weaning in status epilepticus.
JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)
 
ID 4597131
Author(s) Rüegg, Stephan; Sutter, Raoul
Author(s) at UniBasel Sutter, Raoul Christian
Year 2020
Title All in the numbers? Computational prediction of optimal anaesthetic weaning in status epilepticus.
Journal Brain : a journal of neurology
Volume 143
Number 4
Pages / Article-Number 1048-1050
Abstract

Intravenous anaesthetics are the agents of last resort for the treatment of refractory status epilepticus. However, their benefits in terms of achieving seizure suppression must be balanced against the potential harm that could result from inducing an artificial deep coma. Identifying the optimal moment for withdrawal of anaesthetics is therefore key. But whereas the indications for anaesthesia initiation have been clearly outlined, recommendations regarding anaesthesia cessation are lacking. Efforts to establish criteria for weaning anaesthetics are hampered by the diverse aetiologies of patients with refractory status epilepticus, resulting in inhomogeneous cohorts and complicating the definition of precise inclusion criteria. Enrolment of patients is further complicated by ethical issues surrounding a highly vulnerable population unable to provide consent, and funding is unlikely to be obtained from pharmaceutical companies for investigations that could potentially lead to reduced use of their compounds. In this issue of Brain, Rubin and co-workers tackle these issues by showing that the EEG, paired with quantitative measures and machine learning tools, can be used to predict successful weaning from anaesthetics in refractory status epilepticus (Rubin et al., 2020).

ISSN/ISBN 1460-2156
Full Text on edoc
Digital Object Identifier DOI 10.1093/brain/awaa073
PubMed ID http://www.ncbi.nlm.nih.gov/pubmed/32318730
   

MCSS v5.8 PRO. 0.446 sec, queries - 0.000 sec ©Universität Basel  |  Impressum   |    
07/08/2020