Identifying Relapses in Multiple Sclerosis Patients through Administrative Data: A Validation Study in the Lazio Region, Italy

Paola Colais, Nera Agabiti, Marina Davoli, Fabio Buttari, Diego Centonze, Chiara De Fino, Marta Di Folco, Graziella Filippini, Ada Francia, Simonetta Galgani, Claudio Gasperini, Manuela Giuliani, Massimiliano Mirabella, Viviana Nociti, Carlo Pozzilli, Anna Maria Bargagli

Research output: Contribution to journalArticlepeer-review


Background: Relapse is frequently considered an outcome measure of disease activity in relapsing-remitting multiple sclerosis (MS). The objectives of this study were to identify relapse episodes in patients with MS in the Lazio region using health administrative databases and to evaluate the validity of the algorithm using patients enrolled at MS treatment centers. Methods: MS cases were identified in the period between January 1, 2006 and December 31, 2009 using data from regional Health Information Systems (HIS). An algorithm based on HIS was used to identify relapse episodes, and patients recruited at MS centers were used to validate the algorithm. Positive and negative predictive values (PPV, NPV) and the Cohen's kappa coefficient were calculated. Results: The overall MS population identified through HIS consisted of 6,094 patients, of whom 67.1% were female and the mean age was 41.5. Among the MS patients identified by the algorithm, 2,242 attended the centers and 3,852 did not. The PPV was 58.9%, the NPV was 76.3%, and the kappa was 0.36. Conclusions: The proposed algorithm based on health administrative databases does not seem to be able to reliably detect relapses; however, it may be a helpful tool to detect healthcare utilization, and therefore to identify the worsening condition of a patient's health.

Original languageEnglish
Pages (from-to)171-178
Number of pages8
Publication statusPublished - Aug 2017


  • Administrative data
  • Algorithm
  • Health information systems
  • Multiple sclerosis
  • Relapse

ASJC Scopus subject areas

  • Epidemiology
  • Clinical Neurology

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