Validation of an algorithm to detect severe MS relapses in administrative health databases.

BACKGROUND

Severe relapses that required treatment were important outcomes in the sentinel trials of disease-modifying therapy (DMT). Identifying such relapses in administrative data would allow comparative-effectiveness studies of DMTs to be conducted in real-world clinical settings.

METHODS

All relapsing-remitting (RRMS) and secondary-progressive (SPMS) patients living in Manitoba between 1999 and 2015 were identified using a validated case definition and linkage to the Manitoba MS Clinic database. All healthcare interactions potentially due to relapses were extracted from population-based administrative (hospital, physician claims and prescription) databases. These "relapse markers" included varying thresholds of outpatient prednisone scripts, day hospital or emergency room (ER) codes for intravenous (IV) methylprednisolone therapy, family physician, neurologist or ER physician billing codes and hospital admissions due to MS. Algorithms using combinations of these markers were compared with a reference standard of neurologist-defined relapses. The most useful algorithms were also examined on a biannual basis over the study period to assess for trends in the sensitivity of relapse detection.

RESULTS

1131 participants with RRMS or SPMS were linked to administrative databases. Analysis of potential relapse markers over the whole 1999-2015 time period was limited by inconsistent coding of same day or ER admissions for IV methylprednisolone administration. Widespread adoption of high-dose oral outpatient prednisone for relapses since 2009 resulted in a progressive improvement in relapse marker sensitivity. The best algorithm consisted of oral prednisone prescriptions >50mg/day for 3-60 days and same day hospital or ER assessment codes with MS as the most responsible diagnosis (sensitivity 70%, specificity 100%, PPV 100%, NPV 96%, kappa 0.8 in 2013-2015).

CONCLUSIONS

Severe relapses can be identified from administrative datasets with reasonable accuracy. The trend since 2009 toward outpatient relapse treatment will improve the sensitivity of relapse detection with longitudinal follow-up of this cohort and will allow comparison of severe relapse rates between different DMTs, supporting future comparative effectiveness studies.