Browse by author
Lookup NU author(s): Dr Steve Parry
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
© American Academy of Neurology.BackgroundTransient loss of consciousness (TLOC) is a common reason for presentation to primary/emergency care; over 90% are because of epilepsy, syncope, or psychogenic non-epileptic seizures (PNES). Misdiagnoses are common, and there are currently no validated decision rules to aid diagnosis and management. We seek to explore the utility of machine-learning techniques to develop a short diagnostic instrument by extracting features with optimal discriminatory values from responses to detailed questionnaires about TLOC manifestations and comorbidities (86 questions to patients, 31 to TLOC witnesses).MethodsMulti-center retrospective self- and witness-report questionnaire study in secondary care settings. Feature selection was performed by an iterative algorithm based on random forest analysis. Data were randomly divided in a 2:1 ratio into training and validation sets (163:86 for all data; 208:92 for analysis excluding witness reports).ResultsThree hundred patients with proven diagnoses (100 each: epilepsy, syncope and PNES) were recruited from epilepsy and syncope services. Two hundred forty-nine completed patient and witness questionnaires: 86 epilepsy (64 female), 84 PNES (61 female), and 79 syncope (59 female). Responses to 36 questions optimally predicted diagnoses. A classifier trained on these features classified 74/86 (86.0% [95% confidence interval 76.9%-92.6%]) of patients correctly in validation (100 [86.7%-100%] syncope, 85.7 [67.3%-96.0%] epilepsy, 75.0 [56.6%-88.5%] PNES). Excluding witness reports, 34 features provided optimal prediction (classifier accuracy of 72/92 [78.3 (68.4%-86.2%)] in validation, 83.8 [68.0%-93.8%] syncope, 81.5 [61.9%-93.7%] epilepsy, 67.9 [47.7%-84.1%] PNES).ConclusionsA tool based on patient symptoms/comorbidities and witness reports separates well between syncope and other common causes of TLOC. It can help to differentiate epilepsy and PNES. Validated decision rules may improve diagnostic processes and reduce misdiagnosis rates.Classification of evidenceThis study provides Class III evidence that for patients with TLOC, patient and witness questionnaires discriminate between syncope, epilepsy and PNES.
Author(s): Wardrope A, Jamnadas-Khoda J, Broadhurst M, Grunewald RA, Heaton TJ, Howell SJ, Koepp M, Parry SW, Sisodiya S, Walker MC, Reuber M
Publication type: Article
Publication status: Published
Journal: Neurology: Clinical Practice
Year: 2020
Volume: 10
Issue: 2
Pages: 96-105
Print publication date: 01/04/2020
Online publication date: 06/09/2019
Acceptance date: 25/07/2019
ISSN (print): 2163-0402
ISSN (electronic): 2163-0933
Publisher: Lippincott Williams and Wilkins
URL: https://doi.org/10.1212/CPJ.0000000000000726
DOI: 10.1212/CPJ.0000000000000726
Altmetrics provided by Altmetric
