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A Clinical Decision Aid for Diagnosing Transient Loss of Consciousness

Sheffield Teaching Hospitals NHS Foundation Trust · 2022
ClinicalTrials.gov · Datasets · License: Public Domain · 2022
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epilepsyseizuressyncope
BACKGROUND: Transient loss of consciousness (TLOC) - defined as spontaneous disruption of consciousness not due to head trauma and with complete recovery - has a lifetime prevalence of 50%. It is one of the commonest neurological complaints in primary and emergency care. Over 90% of TLOC is due to either syncope, epilepsy or dissociative seizures (DS, also known as 'Psychogenic Nonepileptic Seizures'). The rapid and accurate distinction of these diagnoses is vital to allow appropriate further management but at least 20-30% of patients are not managed optimally or misdiagnosed. We have previously demonstrated that, in patients with established diagnoses of epilepsy, syncope, or DS, an automated classifier using only information from 36 questions based on patient experience and lay witness reports (the initial Paroxysmal Event Profile, iPEP) could accurately diagnose 86.0% of patients (with 100% sensitivity and 91.7% specificity for syncope) AIMS: To calibrate the iPEP for discrimination between syncope, epilepsy, and DS in patients newly presenting with TLOC, validate its performance in an independent sample, and to explore acceptability of the use of such a tool to people with TLOC and witnesses. METHODS: Nested qualitative-quantitative prospective single-centre development and validation of the iPEP in patients presenting to Emergency Departments, syncope or epilepsy clinics with first presentations of TLOC, with semi-structured interviews conducted with a purposive sample of participants from the quantitative study. The iPEP will be calibrated using a previously-described procedure for variable selection and training of Random Forest (RF) classifiers, and validated with assessment of overall classification accuracy, alongside sensitivity, specificity, positive and negative predictive values, and area under receiver-operating curve for each of the three target diagnoses. Performance will be evaluated against a benchmark set by results from previous research in pa
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