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Diagnostic Accuracy of Transient Ischemic Attack from Physician Claims

  • Jodi D. Edwards (a1), Mieke Koehoorn (a1), Lara A. Boyd (a2), Boris Sobolev (a1) and Adrian R. Levy (a1) (a3)...

Background: Hospitalization data underestimate the occurrence of transient ischemic attack (TIA). As TIA is frequently diagnosed in primary care, methodologies for the accurate ascertainment of a TIA from physician claims data are required for surveillance and health systems planning in this population. The present study evaluated the diagnostic accuracy of multiple algorithms for TIA from a longitudinal population-based physician billing database. Methods: Population-based administrative data from the province of British Columbia were used to identify the base population (1992–2007; N=102,492). Using discharge records for hospital admissions for acute ischemic stroke with a recent (<90 days) TIA as the reference standard, we performed receiver-operating characteristic analyses to calculate sensitivity, specificity, positive and negative predictive values and overall accuracy, and to compare area under the curve for each physician billing algorithm. To evaluate the impact of different case definitions on population-based TIA burden, we also estimated the annual TIA occurrence associated with each algorithm. Results: Physician billing algorithms showed low to moderate sensitivity, with the algorithm for two consecutive physician visits within 90 days showing the highest sensitivity at 37.7% (CI 95%=37.4–38.1). All algorithms demonstrated high specificity and moderate to high overall accuracy, resulting in low positive predictive values (≤5%), low discriminability (0.53–0.57) and high false positive rates (1 – specificity). Population-based estimates of TIA occurrence were comparable to prior studies and declined over time. Conclusions: Physician billing data have insufficient sensitivity to identify TIAs but may be used in combination with hospital discharge data to improve the accuracy of estimating the population-based occurrence of TIAs.


Exactitude du diagnostic de l’ischémie cérébrale transitoire selon les déclarations des médecins. Contexte: Les données d’hospitalisation sous-estiment l’incidence de l’ischémie cérébrale transitoire (ICT). L’ICT est souvent diagnostiquée dans un contexte de soins de première ligne. Pour la surveillance et la planification des soins de santé dans cette population, il est donc nécessaire d’utiliser une méthodologie, qui soit basée sur les données des déclarations des médecins pour la détermination exacte du nombre de cas d’ICT. Cette étude a évalué l’exactitude du diagnostic de l’ICT au moyen d’algorithmes dans une base de données longitudinale de population pour la facturation des médecins. Méthodologie: Les données démographiques administratives de la Colombie-Britannique ont été utilisées pour définir la population à l’étude (1992-2007 ; n=102 492). Nous avons utilisé les données au moment du congé hospitalier des patients admis pour un accident vasculaire ischémique aigu en utilisant une ICT récente comme standard et référence. Nous avons procédé à des analyses de la fonction d’efficacité du récepteur pour calculer la sensibilité, la spécificité, les valeurs prédictives positives et négatives et l’exactitude globale ainsi que pour comparer la surface sous la courbe pour l’algorithme de facturation de chaque médecin. Résultats: Les algorithmes de facturation des médecins ont montré une sensibilité de faible à modérée, l’algorithme pour deux visites consécutives chez le médecin en dedans de 90 jours ayant la sensibilité la plus élevée, soit 37,7% (IC à 95% de 37,4 à 38,1). Tous les algorithmes avaient une spécificité élevée et une exactitude globale de modérée à élevée, avec des valeurs prédictives positives faibles (≤ 5%), un faible pouvoir discriminant (0,53 à 0,57) et des taux élevés de faux positifs (1 – la spécificité). Les estimés populationnels de l’incidence de l’ICT étaient comparables à ceux des études antérieures et diminuaient avec le temps. Conclusions: les données de facturation des médecins ont une sensibilité insuffisante pour identifier les ICT mais peuvent être utilisées conjointement avec les données du congé hospitalier pour améliorer l’exactitude de l’estimation de l’incidence populationnelle de l’ICT.

Corresponding author
Correspondence to: Jodi Edwards, Heart and Stroke Foundation Postdoctoral Fellow, Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, M6, 2075 Bayview Avenue, Toronto, Ontario, Canada M4N 3M5. Email:
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Canadian Journal of Neurological Sciences
  • ISSN: 0317-1671
  • EISSN: 2057-0155
  • URL: /core/journals/canadian-journal-of-neurological-sciences
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