Use of a claims-based algorithm to estimate disease severity in the multiple sclerosis Medicare population

Toliver, Joshua C.; Barner, Jamie C.; Lawson, Kenneth A.; Rascati, Karen L.

doi:10.1016/j.msard.2021.102741

Cited by 9 publications

(10 citation statements)

References 17 publications

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“…The severity measure and health care costs were strongly associated with each other ( 27 ). The utility of this claims-based definition of disease severity was subsequently assessed in a Medicare-eligible population; the disability-eligible Medicare population had a higher severity score than the age-eligible Medicare population and an increased risk of hospitalization ( 28 ).…”

Section: Discussionmentioning

confidence: 99%

Development and Internal Validation of a Disability Algorithm for Multiple Sclerosis in Administrative Data

et al. 2021

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Objective: We developed and internally validated an algorithm for disability status in multiple sclerosis (MS) using administrative data.Methods: We linked administrative data from Manitoba, Canada to a clinical dataset with Expanded Disability Status Scale (EDSS) scores for people with MS. Clinical EDSS scores constituted the reference standard. We created candidate indicators using the administrative data. These included indicators based on use of particular health care services (home care, long-term care, rehabilitation admission), use of specific diagnostic codes (such as spasticity, quadriplegia), and codes based on use of Employment and Income Insurance. We developed algorithms to predict severe disability (EDSS ≥6.0), and to predict disability as a continuous measure. We manually developed algorithms, and also employed regression approaches. After we selected our preferred algorithms for disability, we tested their association with health care use due to any cause and infection after potential confounders.Results: We linked clinical and administrative data for 1,767 persons with MS, most of whom were women living in urban areas. All individual indicators tested had specificities >90% for severe disability, and all but a diagnosis of visual disturbance had positive predictive values (PPV) >70%. The combination of home care or long-term care use or rehabilitation admission had a sensitivity of 61.9%, specificity of 90.76%, PPV of 70.06% and negative predictive of 87.21%. Based on regression modeling, the best-performing algorithm for predicting the EDSS as a continuous variable included age, home care use, long-term care admission, admission for rehabilitation, visual disturbance, other paralytic syndromes and spasticity. The mean difference between observed and predicted values of the EDSS was −0.0644 (95%CI −0.1632, 0.0304). Greater disability, whether measured using the clinical EDSS or either of the administrative data algorithms was similarly associated with increased hospitalization rates due to any cause and infection.Conclusion: We developed and internally validated an algorithm for disability in MS using administrative data that may support population-based studies that wish to account for disability status but do not have access to clinical data sources with this information. We also found that more severe disability is associated with increased health care use, including due to infection.

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Section: Discussionmentioning

confidence: 99%

Development and Internal Validation of a Disability Algorithm for Multiple Sclerosis in Administrative Data

et al. 2021

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“…( Huang et al, 2021 ) Because EDSS assessments are not observable in claims data as no ICD-10 code proxies exist, wheelchair dependence was used as an EDSS surrogate to classify MS-related disability level (EDSS score ≥7) as defined by ICD-10 code Z99.3 and/or CPT codes for wheelchair requirement (97542). ( Ghiani et al, 2020 , Toliver et al, 2021 )…”

Section: Methodsmentioning

confidence: 98%

COVID-19 severity and outcome in multiple sclerosis: Results of a national, registry-based, matched cohort study

Pérez

Zhang

et al. 2021

Multiple Sclerosis and Related Disorders

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Background: Risk factors associated with coronavirus disease 2019 (COVID-19) severity in patients with multiple sclerosis (MS) have been described. Recent improvements in supportive care measures and increased testing capacity may modify the risk of severe COVID-19 outcome in MS patients. This retrospective study evaluates the severity and outcome of COVID-19 in MS and characterizes temporal trends over the course of the pandemic in the United States. Methods: We conducted a comparative cohort study using de-identified electronic health record (EHR) claims-based data. MS patients diagnosed with COVID-19 between February 2, 2020 and October 13, 2020 were matched (1:2) to a control group using propensity score analysis. The primary outcome was a composite of intensive care unit (ICU) admission, mechanical ventilation, and/or death. Results: A total of 2,529 patients (843 MS and 1,686 matched controls) were included. Non-ambulatory and pre-existing comorbidities were independent risk factors for COVID-19 severity. The risk for the severe composite outcome was lower in the late cohorts compared with the early cohorts. Conclusions: The majority of MS patients actively treated with a disease-modifying therapy (DMT) had mild disease. The observed trend toward a reduction in severity risk in recent months suggests an improvement in COVID-19 outcome.

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“…Although the algorithm has not been validated using the Expanded Disability Status Scale (EDSS), it has been shown to be strongly correlated with total healthcare cost and effective in estimating risk of relapse and MS-related hospitalizations. [25][26][27] Thus, there is a reasonable expectation of accuracy.…”

Section: Data Source and Patient Populationmentioning

confidence: 99%

“…MS disease severity in this study was derived from an algorithm that utilized summed weighted predictors in the study period. [25][26][27] The algorithm used the binary presences of MS symptoms, disease modifying therapy (DMT) utilization, durable medical equipment (DME) utilization, MS-related hospitalizations, and MS relapses as MS disease severity predictors. DME included canes, walkers, wheelchairs, and hospital beds.…”

Section: Multiple Sclerosis Disease Severitymentioning

confidence: 99%

Association between multiple sclerosis disease severity and adherence to disease-modifying therapies

Burkhard

Toliver

Rascati

2021

JMCP

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Use of a claims-based algorithm to estimate disease severity in the multiple sclerosis Medicare population

Cited by 9 publications

References 17 publications

Development and Internal Validation of a Disability Algorithm for Multiple Sclerosis in Administrative Data

Development and Internal Validation of a Disability Algorithm for Multiple Sclerosis in Administrative Data

COVID-19 severity and outcome in multiple sclerosis: Results of a national, registry-based, matched cohort study

Association between multiple sclerosis disease severity and adherence to disease-modifying therapies

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