Medical chart validation of an algorithm for identifying multiple sclerosis relapse in healthcare claims

J Med Econ. 2010;13(4):618-25. doi: 10.3111/13696998.2010.523670. Epub 2010 Oct 1.


Objective: Relapse is a common measure of disease activity in relapsing-remitting multiple sclerosis (MS). The objective of this study was to test the content validity of an operational algorithm for detecting relapse in claims data.

Methods: A claims-based relapse detection algorithm was tested by comparing its detection rate over a 1-year period with relapses identified based on medical chart review. According to the algorithm, MS patients in a US healthcare claims database who had either (1) a primary claim for MS during hospitalization or (2) a corticosteroid claim following a MS-related outpatient visit were designated as having a relapse. Patient charts were examined for explicit indication of relapse or care suggestive of relapse. Positive and negative predictive values were calculated.

Results: Medical charts were reviewed for 300 MS patients, half of whom had a relapse according to the algorithm. The claims-based criteria correctly classified 67.3% of patients with relapses (positive predictive value) and 70.0% of patients without relapses (negative predictive value; kappa 0.373: p < 0.001). Alternative algorithms did not improve on the predictive value of the operational algorithm. Limitations of the algorithm include lack of differentiation between relapsing-remitting MS and other types, and that it does not incorporate measures of function and disability.

Conclusions: The claims-based algorithm appeared to successfully detect moderate-to-severe MS relapse. This validated definition can be applied to future claims-based MS studies.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Algorithms*
  • Drug Utilization
  • Female
  • Humans
  • Insurance Claim Review / statistics & numerical data*
  • Male
  • Middle Aged
  • Multiple Sclerosis, Relapsing-Remitting / physiopathology*
  • Reproducibility of Results
  • United States