Identifying disability level in multiple sclerosis patients in a U.S.-based health plan claims database

Berkovich, Regina; Fox, Edward J.; Okai, Annette; Ding, Yao; Gorritz, Magdaliz; Bartolome, Lauren; Wade, Rolin L.; Su, Wendy; Johnson, Kristen; Russo, Patrícia A.

doi:10.1080/13696998.2020.1857257

Cited by 6 publications

(6 citation statements)

References 19 publications

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“…Moreover, as CPRD is a primary care-driven EMR database, a broad capture of data as seen in the AOK PLUS claims a dataset covering all healthcare settings (GP, specialties, inpatient) is not available without linkage to further sources. In the US, prior attempts to link claims data for MS patients to EMRs were hindered by high levels of missingness on the clinical indicators, such as the EDSS score, an issue that is not confronted in our data thanks to the high-quality assessment of the EDSS in the MSDS 3D system [ 11 ]. As such, with limited data sources available to address most research questions in MS, the MSDS-AOK PLUS linked database is a step towards improving the data gap and expanding the capabilities of real-world evidence in the field.…”

Section: Discussionmentioning

confidence: 99%

“…Of 1000 registry participants, they achieved linking for 698 participants [ 10 ]. Furthermore, one recent US study linked claims data for MS patients to EMRs [ 11 ]. The specific EMR source used in this study lacked complete information on the EDSS score, which they supplemented using a claims-based proxy for assessing patient disability status.…”

Section: Introductionmentioning

confidence: 99%

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Data Resource Profile: The Multiple Sclerosis Documentation System 3D and AOK PLUS Linked Database (MSDS-AOK PLUS)

Ghiani

Zhuleku²,

Dillenseger

et al. 2023

JCM

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Real-world evidence in multiple sclerosis (MS) is limited by the availability of data elements in individual real-world datasets. We introduce a novel, growing database which links administrative claims and medical records from an MS patient management system, allowing for the complete capture of patient profiles. Using the AOK PLUS sickness fund and the Multiple Sclerosis Documentation System MSDS3D from the Center of Clinical Neuroscience (ZKN) in Germany, a linked MS-specific database was developed (MSDS-AOK PLUS). Patients treated at ZKN and insured by AOK PLUS were recruited and asked for informed consent. For linkage, insurance IDs were mapped to registry IDs. After the deletion of insurance IDs, an anonymized dataset was provided to a university-affiliate, IPAM e.V., for further research applications. The dataset combines a complete record of patient diagnoses, treatment, healthcare resource use, and costs (AOK PLUS), with detailed clinical parameters including functional performance and patient-reported outcomes (MSDS3D). The dataset currently captures 500 patients; however, is actively expanding. To demonstrate its potential, we present a use case describing characteristics, treatment, resource use, and costs of a patient subsample. By linking administrative claims to clinical information in medical charts, the novel MSDS-AOK PLUS database can increase the quality and scope of real-world studies in MS.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data Resource Profile: The Multiple Sclerosis Documentation System 3D and AOK PLUS Linked Database (MSDS-AOK PLUS)

Ghiani

Zhuleku²,

Dillenseger

et al. 2023

JCM

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“…The algorithm was then validated against clinician-derived EDSS data obtained from a large, specialized MS care center. While a number of groups have previously attempted to derive EDSS or disability in MS from real-world data, especially claims or electronic medical records ( 6 – 8 , 10 – 15 ), only two previous studies followed a similar validation strategy using a clinical reference standard (i.e., true EDSS) ( 12 , 13 ).…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, several studies have proposed approaches to estimate disability levels using claims or EHR data, consisting of algorithms that ranged from expert-led code mapping ( 6 – 8 , 10 – 12 ), regression and machine learning models ( 13 , 14 ), to more sophisticated deep learning-based natural language processing methods ( 15 ). However, only two studies used clinician-recorded EDSS scores as the reference standard for validation of the algorithms ( 12 , 13 ), with model features derived from unstructured clinical notes, or indicators based on use of particular health care services, specific diagnostic codes, and codes based on employment or social security allowances.…”

Section: Introductionmentioning

confidence: 99%

Claims-based algorithm to estimate the Expanded Disability Status Scale for multiple sclerosis in a German health insurance fund: a validation study using patient medical records

Muros-Le Rouzic,

Ghiani,

Zhuleku

et al. 2023

Front. Neurol.

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BackgroundThe Expanded Disability Status Scale (EDSS) quantifies disability and measures disease progression in multiple sclerosis (MS), however is not available in administrative claims databases.ObjectivesTo develop a claims-based algorithm for deriving EDSS and validate it against a clinical dataset capturing true EDSS values from medical records.MethodsWe built a unique linked dataset combining claims data from the German AOK PLUS sickness fund and medical records from the Multiple Sclerosis Management System 3D (MSDS3D). Data were deterministically linked based on insurance numbers. We used 69 MS-related diagnostic indicators recorded with ICD-10-GM codes within 3 months before and after recorded true EDSS measures to estimate a claims-based EDSS proxy (pEDSS). Predictive performance of the pEDSS was assessed as an eight-fold (EDSS 1.0–7.0, ≥8.0), three-fold (EDSS 1.0–3.0, 4.0–5.0, ≥6.0), and binary classifier (EDSS <6.0, ≥6.0). For each classifier, predictive performance measures were determined, and overall performance was summarized using a macro F1-score. Finally, we implemented the algorithm to determine pEDSS among an overall cohort of patients with MS in AOK PLUS, who were alive and insured 12 months prior to and after index diagnosis.ResultsWe recruited 100 people with MS insured by AOK PLUS who had ≥1 EDSS measure in MSDS3D between 01/10/2015 and 30/06/2019 (620 measurements overall). Patients had a mean rescaled EDSS of 3.2 and pEDSS of 3.0. The pEDSS deviated from the true EDSS by 1.2 points, resulting in a mean squared error of prediction of 2.6. For the eight-fold classifier, the macro F1-score of 0.25 indicated low overall predictive performance. Broader severity groupings were better performing, with the three-fold and binary classifiers for severe disability achieving a F1-score of 0.68 and 0.84, respectively. In the overall AOK PLUS cohort (3,756 patients, 71.9% female, mean 51.9 years), older patients, patients with progressive forms of MS and those with higher comorbidity burden showed higher pEDSS.ConclusionGenerally, EDSS was underestimated by the algorithm as mild-to-moderate symptoms were poorly captured in claims across all functional systems. While the proxy-based approach using claims data may not allow for granular description of MS disability, broader severity groupings show good predictive performance.

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“…A severity score was assigned to each term based on expert opinion, and ICD codes, procedural codes, and orders for durable medical equipment were mapped to Current Procedural Terminology codes with the aim of assessing pharmacy and medical costs based on severity. 19 Ultimately, formal validation of the true patient EDSS scores was unable to be achieved because few patients had an EDSS score in the chart, although stratification of associated medical cost based on estimated severity of disability was confirmed.…”

Section: Disability Levelmentioning

confidence: 99%

Clinical and Research Applications of the Electronic Medical Record in Multiple Sclerosis: A Narrative Review of Current Uses and Future Applications

Bove

McGinley

2022

International Journal of MS Care

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CE INFORMATION ACTIVITY AVAILABLE ONLINE: To access the article and evaluation online, go to https://www.highmarksce.com/mscare. TARGET AUDIENCE: The target audience for this activity is physicians, advanced practice clinicians, nursing professionals, pharmacists, mental health professionals, social workers, and other health care providers involved in the research and management of patients with multiple sclerosis (MS). LEARNING OBJECTIVES: Characterize existing EMR platforms designed specifically for care of people with MS. Describe relevant variables that are captured in the EMR that allow identification of EMR-based cohorts of people with MS. ACCREDITATION: In support of improving patient care, this activity has been planned and implemented by the Consortium of Multiple Sclerosis Centers (CMSC) and Intellisphere, LLC. The CMSC is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. This activity was planned by and for the healthcare team, and learners will receive .5 Interprofessional Continuing Education (IPCE) credit for learning and change. PHYSICIANS: Physicians: The CMSC designates this journal-based activity for a maximum of .5 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity. NURSES: The CMSC designates this enduring material for .5 contact hour of nursing continuing professional development (NCPD) (none in the area of pharmacology). PHARMACISTS: This knowledge-based activity (UAN JA4008165-9999-22-033-H01-P) qualifies for (.5) contact hour (.05 CEUs) of continuing pharmacy education credit. PSYCHOLOGISTS: This activity is awarded 0.5 CE credits. SOCIAL WORKERS: As a Jointly Accredited Organization, the CMSC is approved to offer social work continuing education by the Association of Social Work Boards (ASWB) Approved Continuing Education (ACE) program. Organizations, not individual courses, are approved under this program. State and provincial regulatory boards have the final authority to determine whether an individual course may be accepted for continuing education credit. The CMSC maintains responsibility for this course. Social workers completing this course receive .5 continuing education credits. DISCLOSURES: It is the policy of the Consortium of Multiple Sclerosis Centers to mitigate all relevant financial disclosures from planners, faculty, and other persons that can affect the content of this CE activity. For this activity, all relevant disclosures have been mitigated. Francois Bethoux, MD, editor in chief of the International Journal of MS Care (IJMSC), has served as physician planner for this activity. He has disclosed no relevant relationships. Alissa Mary Willis, MD, associate editor of IJMSC, has disclosed no relevant relationships. Authors Carol Swetlik, MD, Riley Bove, MD, and Marisa McGinley, DO, have disclosed no relevant financial relationships. The staff at IJMSC, CMSC, and Intellisphere, LLC who are in a position to influence content have disclosed no relevant financial relationships. Laurie Scudder, DNP, NP, continuing education director CMSC, has served as a planner and reviewer for this activity. She has disclosed no relevant financial relationships. METHOD OF PARTICIPATION: Release Date: November 1, 2022; Valid for Credit through: November 1, 2023. In order to receive CE credit, participants must: 1) Review the continuing education information, including learning objectives and author disclosures.2) Study the educational content.3) Complete the evaluation, which is available at https://www.highmarksce.com/mscare. Statements of Credit are awarded upon successful completion of the evaluation. There is no fee to participate in this activity. DISCLOSURE OF UNLABELED USE: This educational activity may contain discussion of published and/or investigational uses of agents that are not approved by the FDA. The CMSC and Intellisphere, LLC do not recommend the use of any agent outside of the labeled indications. The opinions expressed in the educational activity are those of the faculty and do not necessarily represent the views of the CMSC or Intellisphere, LLC. DISCLAIMER: Participants have an implied responsibility to use the newly acquired information to enhance patient outcomes and their own professional development. The information presented in this activity is not meant to serve as a guideline for patient management. Any medications, diagnostic procedures, or treatments discussed in this publication should not be used by clinicians or other health care professionals without first evaluating their patients’ conditions, considering possible contraindications or risks, reviewing any applicable manufacturer’s product information, and comparing any therapeutic approach with the recommendations of other authorities.

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Identifying disability level in multiple sclerosis patients in a U.S.-based health plan claims database

Cited by 6 publications

References 19 publications

Data Resource Profile: The Multiple Sclerosis Documentation System 3D and AOK PLUS Linked Database (MSDS-AOK PLUS)

Data Resource Profile: The Multiple Sclerosis Documentation System 3D and AOK PLUS Linked Database (MSDS-AOK PLUS)

Claims-based algorithm to estimate the Expanded Disability Status Scale for multiple sclerosis in a German health insurance fund: a validation study using patient medical records

Clinical and Research Applications of the Electronic Medical Record in Multiple Sclerosis: A Narrative Review of Current Uses and Future Applications

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