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280 A screening questionnaire for transient loss of consciousness
  1. Wardrope Alistair1,
  2. Jamnadas-Khoda Jenny2,
  3. Broadhurst Mark3,
  4. Grünewald Richard A1,
  5. Howell Stephen J1,
  6. Koepp Matthias4,
  7. Parry Steve5,
  8. Sisodiya Sanjay4,
  9. Walker Matthew4,
  10. Reuber Markus1,6
  1. 1Sheffield Teaching Hospitals NHS Foundation Trust
  2. 2Divsion of Psychiatry and Applied Psychology, University of Nottingham
  3. 3Mental Health Liaison Team, Derbyshire Healthcare NHS Foundation Trust
  4. 4Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology
  5. 5Institute of Cellular Medicine, Newcastle University
  6. 6Academic Neurology Unit, University of Sheffield


Background Transient loss of consciousness (TLOC) is a common primary care presentation. 90% are due to syncope (S), epilepsy (E), or psychogenic non-epileptic seizures (PNES). Misdiagnosis and delayed diagnosis is common. We explore symptoms and witness observations that can classify patients with likely diagnoses of E, S, or PNES.

Methods Patients with objectively-documented diagnoses of E, S, or PNES, and an attack witness, were invited to complete a questionnaire (capturing medical history, 86 peri-episodal experiences, and 31 witness observations). Iterative feature selection identified questions strongly predictive of diagnosis; a random forest trained on these classified patients into likely diagnoses of E, S, or PNES.

Results 249 patients (86 E, 79 s, 84 PNES) were randomly assigned to training or validation in a 2:1 ratio. Feature selection identified 36 highly-predictive questionnaire items. The classifier correctly diagnosed 86% of patients in validation. 100% of S were correctly diagnosed, 85.7% E and 75% PNES. A simpler 12-feature model correctly classified 76.7% of cases (E: 75%; S: 92.3%; PNES: 65.6%).

Conclusions TLOC-associated symptoms and manifestations can contribute to a decision rule for primary/emergency care, assisting triage and referral. Determining a diagnostic pre-test probability from TLOC features can aid interpretation of investigation abnormalities of uncertain significance.

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