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Validation of healthcare administrative data for the diagnosis of epilepsy
  1. C Franchi1,
  2. G Giussani2,
  3. P Messina2,
  4. M Montesano2,
  5. S Romi2,
  6. A Nobili1,
  7. I Fortino3,
  8. A Bortolotti3,
  9. L Merlino3,
  10. E Beghi2,
  11. and the EPIRES Group
  1. 1Laboratory for Quality Assessment of Geriatric Therapies and Services, IRCCS—Istituto di Ricerche Farmacologiche ‘Mario Negri’, Milano, Italy
  2. 2Laboratory of Neurological Disorders, Department of Neuroscience, IRCSS-Istituto di Ricerche ‘Farmacologiche Mario Negri’, Milan, Italy
  3. 3Regional Health Ministry, Lombardy Region, Milan, Italy
  1. Correspondence to Dr Carlotta Franchi, Laboratory for Quality Assessment of Geriatric Therapies and Services, IRCCS—Istituto di Ricerche Farmacologiche ‘Mario Negri’, Via Giuseppe La Masa, 19, Milan 20156, Italy; carlotta.franchi{at}


Background Administrative databases have become an important tool to monitor diseases. Patients with epilepsy could be traced using disease-specific codes and prescriptions, but formal validation is required to obtain an accurate case definition. The aim of the study was to correlate administrative data on epilepsy with an independent source of patients with epilepsy in a district of Lombardy, Northern Italy, from 2000 to 2008.

Methods Data of nearly 320 600 inhabitants in the district of Lecco collected from the Drug Administrative Database of the Lombardy Region were analysed. Among them were included patients who fulfilled the International Classification of Diseases 9 (ICD-9) codes and/or the disease-specific exemption code for epilepsy and those who had at least one EEG record and took antiepileptic drugs (AEDs) as monotherapy or in variable combinations. To ascertain epilepsy cases, 11 general practitioners (GPs) with 15 728 affiliates were contacted. Multiple versions of the diagnostic algorithm were developed using different logistic regression models and all combinations of the four independent variables.

Results Among the GP affiliates, 71 (4.5/1000) had a gold standard diagnosis of epilepsy. The best and most conservative algorithm included EEG and selected treatment schedules and identified 61/71 patients with epilepsy (sensitivity 85.9%, CI 76.0% to 92.2%) and 15 623/15 657 patients without epilepsy (specificity 99.8%,CI 99.7% to 99.8%). The positive and negative predictive values were 64.2% and 99.9%. Sensitivity (86.7%) and the positive predictive value (68.4%) increased only slightly when patients with single seizures were included.

Conclusions A diagnostic algorithm including EEG and selected treatment schedules is only moderately sensitive for the detection of epilepsy and seizures. These findings apply only to the Northern Italian scenario.


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