Contributions
Abstract: P878
Type: Poster
Abstract Category: Clinical aspects of MS - 5 Epidemiology
Background: A relapse in multiple sclerosis (MS) may be indicated by unexpected, new, or worsening MS symptoms, without other discernible cause. Identifying relapses in databases is important for assessing patient disease status, implementing comparative effectiveness studies, measuring unmet need. Claims-based algorithms to identify relapses have been validated previously with varying performance.
Objectives: To develop and validate an operational healthcare claims-based algorithm to identify relapse among relapsing-remitting MS (RRMS) patients.
Methods: IDN data (2010-2014) were queried for study inclusion criteria: MS diagnosis, adults, ≥1 year baseline history, and no other demyelinating diseases. The claims-based algorithms were developed using 2 options. Option 1 considered type of hospital/neurology encounter with MS as primary diagnosis, MS symptoms/treatment, plasmapheresis, or high dose corticosteroid or adrenocorticotropic hormone (ACTH) use within 7 days after admission/visit. Option 2 included MS symptoms/treatment and combinations of type of hospital/neurology encounter, high dose corticosteroid or ACTH, and plasmapheresis identifying mild, moderate and severe relapses. For preliminary algorithm validation, identified relapses were confirmed by experienced clinician review of patient profiles. Patient profiles were developed from a random patient sample including all diagnoses, medications, procedures, brain/spinal MRI reports, and any clinical note during the study period.
Results: Of 2,271 RRMS patients identified using the claims-based algorithm, Options 1/2 identified 11,362/3,444 unique relapses among 2,029/1,351 patients, respectively. All patients with relapses identified by Option 2 were also identified by Option 1, and Option 2 identified 1 additional relapse not identified by Option 1. Option 2, which categorized relapses according to severity, identified 1,111 (82%) mild, 635 (47%) moderate, and 6 (0.4%) severe. Preliminary validation of 24 relapses yielded 16 (67%) confirmed relapses, 6 (25%) possible, and 2(8%) negative.
Conclusions: While validation is ongoing, preliminary results are encouraging. The broader Option 1 identified many more relapse episodes, while Option 2, designed to categorize severity among relapses, identified fewer. Given the broadly and narrowly defined components of these algorithms, this study demonstrates their effectiveness at providing upper and lower bounds for identifying relapses among RRMS patients.
Disclosure: This study was funded by Merck KGaA.
- Aaron W.C. Kamauu received a research grant from Merck KGaA.
- Chi Thi Le Truong is an employee of MedCodeWorld.
- Hoa Van Le is an employee of PAREXEL and a stockholder of GlaxoSmithKline, and was a Harry Guess-Merck merit scholarship recipient.
- Camelia M Graham is an employee of PAREXEL.
- John R. Holmen has nothing to disclose.
- Christopher L. Fillmore has nothing to disclose.
- Meritxell Sabidó-Espin is an employee of Merck KGaA.
- Schiffon L. Wong is an employee EMD Serono.
Abstract: P878
Type: Poster
Abstract Category: Clinical aspects of MS - 5 Epidemiology
Background: A relapse in multiple sclerosis (MS) may be indicated by unexpected, new, or worsening MS symptoms, without other discernible cause. Identifying relapses in databases is important for assessing patient disease status, implementing comparative effectiveness studies, measuring unmet need. Claims-based algorithms to identify relapses have been validated previously with varying performance.
Objectives: To develop and validate an operational healthcare claims-based algorithm to identify relapse among relapsing-remitting MS (RRMS) patients.
Methods: IDN data (2010-2014) were queried for study inclusion criteria: MS diagnosis, adults, ≥1 year baseline history, and no other demyelinating diseases. The claims-based algorithms were developed using 2 options. Option 1 considered type of hospital/neurology encounter with MS as primary diagnosis, MS symptoms/treatment, plasmapheresis, or high dose corticosteroid or adrenocorticotropic hormone (ACTH) use within 7 days after admission/visit. Option 2 included MS symptoms/treatment and combinations of type of hospital/neurology encounter, high dose corticosteroid or ACTH, and plasmapheresis identifying mild, moderate and severe relapses. For preliminary algorithm validation, identified relapses were confirmed by experienced clinician review of patient profiles. Patient profiles were developed from a random patient sample including all diagnoses, medications, procedures, brain/spinal MRI reports, and any clinical note during the study period.
Results: Of 2,271 RRMS patients identified using the claims-based algorithm, Options 1/2 identified 11,362/3,444 unique relapses among 2,029/1,351 patients, respectively. All patients with relapses identified by Option 2 were also identified by Option 1, and Option 2 identified 1 additional relapse not identified by Option 1. Option 2, which categorized relapses according to severity, identified 1,111 (82%) mild, 635 (47%) moderate, and 6 (0.4%) severe. Preliminary validation of 24 relapses yielded 16 (67%) confirmed relapses, 6 (25%) possible, and 2(8%) negative.
Conclusions: While validation is ongoing, preliminary results are encouraging. The broader Option 1 identified many more relapse episodes, while Option 2, designed to categorize severity among relapses, identified fewer. Given the broadly and narrowly defined components of these algorithms, this study demonstrates their effectiveness at providing upper and lower bounds for identifying relapses among RRMS patients.
Disclosure: This study was funded by Merck KGaA.
- Aaron W.C. Kamauu received a research grant from Merck KGaA.
- Chi Thi Le Truong is an employee of MedCodeWorld.
- Hoa Van Le is an employee of PAREXEL and a stockholder of GlaxoSmithKline, and was a Harry Guess-Merck merit scholarship recipient.
- Camelia M Graham is an employee of PAREXEL.
- John R. Holmen has nothing to disclose.
- Christopher L. Fillmore has nothing to disclose.
- Meritxell Sabidó-Espin is an employee of Merck KGaA.
- Schiffon L. Wong is an employee EMD Serono.