Canadian administrative health data can identify patients with myasthenia gravis

Neuroepidemiology
Ari BreinerKaren Tu

Abstract

Incidence and prevalence estimates for myasthenia gravis (MG) have varied widely, and the ability of administrative health data (AHD) records to accurately identify cases of MG is yet to be ascertained. The goal of the current study was to validate an algorithm to identify patients with MG in Ontario, Canada using AHD - thereby enabling future disease surveillance. A reference standard population was established using automated key word searching within EMRALD (Electronic Medical Record Administrative data Linked Database) and chart review of potential cases. AHD algorithms were generated and tested against the reference standard. The data was used to calculate MG prevalence rates. There were 123,997 eligible adult patients, and 49 patients had definite MG (forming the reference standard). An algorithm requiring: (1 hospital discharge abstract with MG listed as a reason for hospitalization or a comorbid condition), or (5 outpatient MG visits and 1 relevant diagnostic test, within 1 year), or (3 pyridostigmine prescriptions, within 1 year) identified MG with sensitivity = 81.6%, specificity = 100%, positive predictive value = 80.0% and negative predictive value = 100%. The population prevalence within our cohort was 0.04%. This ...Continue Reading

Citations

Nov 18, 2015·Neuromuscular Disorders : NMD·Ari BreinerKaren Tu
Jun 4, 2016·Dimensions of Critical Care Nursing : DCCN·Darlene DetersSamantha Thomas
Nov 17, 2020·Journal of Neuro-ophthalmology : the Official Journal of the North American Neuro-Ophthalmology Society·Ali G HamedaniHeather E Moss
Nov 16, 2021·The Journal of Spinal Cord Medicine·John ShepherdSusan Jaglal

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