Validation of an algorithm for identifying MS cases in administrative health claims datasets

Abstract

ObjectiveTo develop a valid algorithm for identifying multiple sclerosis (MS) cases in administrative health claims (AHC) datasets.MethodsWe used 4 AHC datasets from the Veterans Administration (VA), Kaiser Permanente Southern California (KPSC), Manitoba (Canada), and Saskatchewan (Canada). In the VA, KPSC, and Manitoba, we tested the performance of candidate algorithms based on inpatient, outpatient, and disease-modifying therapy (DMT) claims compared to medical records review using sensitivity, specificity, positive and negative predictive values, and interrater reliability (Youden J statistic) both overall and stratified by sex and age. In Saskatchewan, we tested the algorithms in a cohort randomly selected from the general population.ResultsThe preferred algorithm required ≥3 MS-related claims from any combination of inpatient, outpatient, or DMT claims within a 1-year time period; a 2-year time period provided little gain in performance. Algorithms including DMT claims performed better than those that did not. Sensitivity (86.6%–96.0%), specificity (66.7%–99.0%), positive predictive value (95.4%–99.0%), and interrater reliability (Youden J = 0.60–0.92) were generally stable across datasets and across strata. Some variation in performance in the stratified analyses was observed but largely reflected changes in the composition of the strata. In Saskatchewan, the preferred algorithm had a sensitivity of 96%, specificity of 99%, positive predictive value of 99%, and negative predictive value of 96%.ConclusionsThe performance of each algorithm was remarkably consistent across datasets. The preferred algorithm required ≥3 MS-related claims from any combination of inpatient, outpatient, or DMT use within 1 year. We recommend this algorithm as the standard AHC case definition for MS.

Multiple Sclerosis in the Campania Region (South Italy): Algorithm Validation and 2015–2017 Prevalence

“…Considering that previously published MS case-finding algorithms to identify people with MS using administrative healthcare databases varied in relation to the number of MS records considered for each patient for case identification [26][27][28][29], we aimed to validate two versions of the algorithm (aim 1), which considered the presence of: 1) at least one MS record during the study period; and 2) at least two MS records during the study period. To validate the algorithm, we merged our dataset with a clinical registry and identified individuals who accessed the MS Clinical Care and Research Centre at the "Federico II" University of Naples using the information provided by the algorithm.…”

Incidence and prevalence of multiple sclerosis in Hungary based on record linkage of nationwide multiple healthcare administrative data

“…It was found that the Marrie-definition has a sensitivity of 99.5%, a specificity of 98.5%, a positive predictive value of 99.5% and a negative predictive value of 97.5%, altogether presenting a better performance. Recently, Culpepper and Marrie both have participated in the United States Multiple Sclerosis Prevalence Workgroup who published [51] on testing the performance of different administrative algorithms for identifying MS cases. They recommended the definition of �3 MS-related claims in any combination of inpatient, outpatient, or DMT use within 1 calendar year to become standard.…”

Pre-clinical and Clinical Implications of “Inside-Out” vs. “Outside-In” Paradigms in Multiple Sclerosis Etiopathogenesis

“…Multiple Sclerosis presents most often in young adulthood and is chronic as most patients live with the disease for decades. Recent studies on prevalence uncovered that nearly a million people live with MS within the United States (Culpepper et al, 2019;Nelson et al, 2019;Wallin et al, 2019). Approximately 85% of patients present with the relapsing-remitting form of MS (RRMS) that involves episodes of neurological deficits followed by phases of recovery (Steinman, 2009).…”