Diagnostic accuracy of an algorithm identifying US veterans with Inclusion Body Myositis from the Corporate Data Warehouse

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Liarski This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8387113/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives Inclusion body myositis (IBM) is the most common idiopathic inflammatory myopathy (IIM) in adults over 50 years of age and has only one International Classification of Diseases (ICD) code across ICD-9 and ICD-10. Despite this, little is known about the performance of administrative data in identifying individuals with IBM. An algorithm based on billing and administrative data to create a veteran IBM cohort was tested and its performance compared to clinical diagnosis and consensus diagnostic criteria. Methods Of 732 previously identified veterans with IBM, 107 were randomly selected for manual record review using a keyword search for “Inclusion Body Myositis” or “IBM” via the tvf_TIU_FullTextSearch function. Rheumatology and Neurology notes from January 1, 2011 to June 1, 2024 were extracted and data tokenized using R packages tidytext and tokenizers . Incomplete records were supplemented with Voogle data. Results 83 (77.5%) of 107 veterans had definite and 7 (6.5%) suspected clinical IBM. There were 4 cases with HIV-associated myopathy and 5 with inherited myopathies. Only 7 individuals were concurrently identified among a separate cohort of 1,136 veterans with a diagnosis of polymyositis based on administrative data. Detailed muscle biopsy pathology reports were available for 40/107 veterans of whom 30 (75%) noted findings consistent with IBM and 30 (75%) met ENMC 2024 diagnostic criteria for IBM. Based on ENMC 2011 criteria, 2 (5%), 12 (30%), and 25 (62.5%) veterans met clinicopathologic, clinical, and probable IBM definitions, respectively. Using Griggs criteria, 2 (5%) and 15 (37.5%) individuals met definite and probable IBM thresholds. The minimum positive predictive value (PPV) was 74.4% for an IBM clinical diagnoses and 88.0% for cases meeting diagnostic criteria, with specificity ≥ 90% for most groups. Conclusions The algorithm demonstrates robust performance and excellent specificity in identifying Veterans with IBM, comparable to approaches for systemic lupus and other IIMs. Rheumatology Neurology Epidemiology Idiopathic inflammatory myopathy inclusion body myositis veterans Figures Figure 1 Key Points • The proposed algorithm, utilizing a combination of billing data and specialty clinic visits, has robust performance and excellent specificity in identifying veterans with underlying IBM • These data support the utility of using available big data resources to facilitate future IBM research. Introduction Inclusion body myositis (IBM) is the most common idiopathic inflammatory myopathy (IIM) in adults over 50 years of age and has only one International Classification of Diseases (ICD) code across ICD-9 and ICD-10 1–7 . Despite this, little is known about the performance of administrative data in accurately identifying individuals with IBM. Similarly, as IBM can share features with other subsets of IIM – most commonly polymyositis (PM) – its diagnosis cannot be routinely confirmed based on muscle biopsy features alone and requires the integration of clinical and pathologic data 8 – 10 . European Neuromuscular Center (ENMC) 2024 IBM diagnostic criteria have been updated from the previously published 2011 ENMC criteria, incorporating atypical IBM features with predominantly proximal muscle weakness as well as anti-cN1a autoantibody (also termed NT5c1a or cytosolic 5’-nucleotidase 1A) testing 11 – 14 . Although the Griggs criteria for IBM are still utilized and were the first standardized guidelines focusing on muscle biopsy findings, they were found to have less sensitivity, especially in identifying early disease 15 , 16 . In this study, a previously published algorithm (Fig. 1 ) employing administrative and billing data from Veterans Affairs (VA) Corporate Data Warehouse (CDW) to identify veterans with IBM was compared to expert clinical IBM diagnoses and the aforementioned diagnostic criteria 17 . This work also assessed the performance of these formal diagnostic guidelines in routine clinical care. Patients and Methods Data Sources and Setting Data were previously extracted from the VA CDW (Federal Register: 79 FR 4377) using a published algorithm (Fig. 1 ) 17 . Briefly, all adult veterans 18 years and older with an outpatient ICD-9 or ICD-10 visit code for IBM or entry for same in their problem list between January 1, 2011 and December 31, 2021 were identified. A minimum of 2 visit codes for IBM from neurology or rheumatology clinics at least 30 days apart were required to confirm a diagnosis. Date of first IBM ICD code or problem list entry was inferred to be the date of IBM diagnosis. Data on survival status were censored as of 06/01/2024. A separate CDW cohort, similarly based on blling and administrative data to identify veterans with PM, was used to derive true and false negative cases. PM was chosen as the main disease comparison as it is the most commonly occurring and most difficult to differentiate IBM mimic 8 – 10 . Human and Animal Rights This study complies with the Declaration of Helsinki and was reviewed and approved by the Pittsburgh VA Research and Development committee on 1/23/2023 and granted informed consent exemption status based on the Revised Common Rule/2018 (Project #1707389). No animals were used in this study. Individual Random Case Review Using a random number generator, 107 veterans were selected from 732 individuals identified by the algorithm as having IBM. The Text Integration Utility (TIU) domain was used to obtain raw text data from all rheumatology and neurology clinical notes from January 1, 2011 to June 1, 2024 in the VA electronic health record system (CPRS) by means of a keyword search for ‘Inclusion Body Myositis’ or ‘IBM’ using the tvf_TIU_FullTextSearch function provided by VINCI in Microsoft SQL Server Management Studio (Ver. 20.1). Note text underwent natural language processing (NLP) and tokenized using “tidytext” and “tokenizers” R packages for efficiency. Extracted data related to muscle biopsy findings, laboratory testing, clinical motor strength examination as well as past medical and IBM disease histories were prioritized, as required to determine an IBM diagnosis by formal diagnostic criteria 11 , 12 , 15 . Prior use of immunosuppressive therapy, creatine phosphokinase (CPK) levels, and autoantibody testing were queried along with a prior history of PM (mis)diagnosis and presence of IBM diagnostic mimics. Incomplete records were manually supplemented with data from Voogle. Statistical Analysis Continuous variables are displayed as mean and standard deviation. Categorical variables are displayed as frequency with percentage. Differences among groups greater than 3 were compared with Kruskal-Wallis tests for continuous variables and Chi-square test for categorical variables. Pairwise comparisons were performed with two-tailed t tests for continuous and Chi-square tests for categorical variables with a significance level set at p < 0.05. Bonferroni correction was instituted as necessary for multiple comparisons. All interim and final data analyses were performed using R statistical computing software (version 4.1.2, R Foundation for Statistical Computing) provided by VA Informatics and Computing Infrastructure (VINCI). Patient and Public Involvement No patients were involved in this study. Results Veterans with clinical diagnosis of IBM Randomly selected cases compared favorably with no statistically significant different features compared to the previously published cohort (Table 1 ) 17 . Selected veterans were overwhelmingly male, White, non-Hispanic or Latino, with a mean (±SD) age of 54.1 (±9.4) and a mean (±SD) follow-up of 5.3 (±9.4) years with 38 (36.9%) persons deceased as of the censor date. Among 107 individuals, 83 (77.5%) had a definite IBM clinical diagnosis based on the opinion(s) of their treating specialist(s), while 7 (6.5%) had suspected IBM (Table 2 ). No patients with a CPK exceeding 12 times the upper normal were identified, and all cases exhibited weakness for at least one year or more. Four veterans had HIV-associated myopathy indistinguishable from IBM and were not included in further analyses. Other non-IBM diagnoses included hereditary motor and sensory neuropathy with proximal dominance (HMSN-P), phosphorylase kinase alpha 1 (PHKA1) deficiency, suspected muscular dystrophy, suspected Pompe’s disease, steroid myopathy, myofibrillar myopathy, and Kennedy disease (n = 1 each). Regarding concurrent autoimmune disorders, one veteran had positive anti-MDA5 (melanoma differentiation-associated gene 5) and SSA (Ro) autoantibodies and was also excluded from the cohort. Four other individuals had concurrent autoimmune processes including vitiligo (n = 1), undifferentiated connective tissue disease (n = 1), rheumatoid arthritis (RA) coexisting with celiac disease and ulcerative colitis (n = 1), and discoid lupus erythematosus (n = 1). These veterans were kept in the cohort due to the very low likelihood of their other disease(s) causing myopathy 18 – 20 . Table 1 Comparison of baseline patient characteristics between original IBM veteran cohort and randomly selected cases for review. P value represents t test for continuous variables and Pearson’s χ 2 for categorical variables, α = 0.05. Case Review Original Cohort p n 107 732 Age, yr - median (SD) 54.1 (9.4) 55.5 (8.4) 0.147 Follow-up Period, yr – mean (SD) 5.3 (9.4) 5.5 (3.4) 0.828 Status at End of Follow-up Period Alive – no. (%) 69 (67.0) 416 (56.8) 0.134 Deceased or Censored – no. (%) 38 (36.9) 316 (43.2) Sex Male - no. (%) 103 (96.3) 708 (96.7) 0.803 Female - no. (%) 4 (3.7) 24 (3.3) Race White - no. (%) 78 (72.9) 514 (70.2) 0.567 Black or Other - no. (%) 29 (27.1) 218 (29.8) Ethnicity Not Hispanic or Latino - no. (%) 98 (91.6) 653 (89.2) 0.453 Hispanic or Latino or Other - no. (%) 9 (8.4) 79 (10.8) Table 2 Summary of clinical information for IBM cases selected for review. * Values may exceed 100% due to use of more than one therapy. Case Review n 107 Clinical IBM Diagnosis Definite – no. (%) 76 (71.0) Suspected – no. (%) 7 (6.5) Alternate Diagnosis – no. (%) 24 (22.5) Eating Difficulties Dysphagia – no. (%) 34 (31.8) Choking – no. (%) 5 (5.6) Ambulation Difficulties Falls 28 (26.2) Assistive Device Use Cane 15 (14.0) Walker 23 (21.5) Power Wheelchair 21 (19.6) Tests Available for Review Electromyography – no. (%) 41 (38.3) Muscle biopsy – no. (%) 40 (37.4) Magnetic Resonance Imaging – no. (%) 6 (5.6) Anti-cN1a antibody – no. (%) 12 (11.2) Myositis-specific Antibody Testing – no. (%) 8 (7.5) Prior Diagnosis of Polymyositis – no. (%) 12 (11.2) Prior Immunosuppressive Therapy* – no. (%) 40 (37.4) Glucocorticoids – no. (%) 25 (62.5) Methotrexate – no. (%) 19 (47.5) Azathioprine – no. (%) 8 (20.0) Mycophenolate – no. (%) 2 (5.0) Hydroxychloroquine – no. (%) 2 (5.0) Calcineurin Inhibitor – no. (%) 1 (2.5) Intravenous Immunoglobulin – no. (%) 11 (27.5) Rituximab – no. (%) 2 (5.0) Other Biologic – no. (%) 2 (5.0) Randomly selected cases reflected a spectrum of morbidity from IBM: almost one-third (34/107, 31.8%) of veterans with IBM had dysphagia and 5 (5.6%) reported choking symptoms. Similarly, 28 (26.2%) individuals noted impaired ambulation and falls, necessitating the use of assistive devices including canes (15, 14.0%), walkers 23 (21.5%), and power wheelchairs (21, 19.6%). In terms of available diagnostic data, electromyography (EMG) and muscle biopsy were the most frequent studies available (41, 38.3% and 40, 37.4%, respectively). Relatively few veterans had muscle magnetic resonance imaging (MRI) (6, 5.6%) or testing for anti-cN1a (also termed NT5c1a or cytosolic 5’-nucleotidase 1A) antibodies, other myositis-specific antibodies (MSAs), or anti-SSA (Ro) and SSB (La) antibodies, all of which have previously been associated with IBM 13 , 14 , 21 – 24 . The latter finding echoed a similar observation in the parent cohort 17 . Although 12 (11.2%) individuals were treated as PM at some point prior to their IBM diagnosis, the algorithm identified only 7 IBM cases out of 1,136 veterans comprising a separate CDW PM cohort, based on blling and administrative data, confirming a very low false negative rate. A significant number of veterans had prior immunosuppressive therapy (40, 37.4%), with glucocorticoids (25, 62.5%), methotrexate (19, 47.5%), and intravenous immunoglobulin (IVIg) being the most common. This was not limited solely to those (mis)diagnosed with PM or those with other autoimmune diseases. Two veterans with concurrent RA received adalimumab and etanercept, respectively, but it was not clear if this was related to their muscle or joint manifestations. Among veterans with a clinical IBM diagnosis, the algorithm displayed excellent sensitivity (Se) and specificity (Sp) (Se 92.2% and 97.9%, Sp 92.8% and 97.3%) while maintaining a reasonable positive predictive value (PPV) (77.6% and 74.4% for definite and definite & suspected cases, respectively) and robust negative predictive value (NPV) (99.4% for both groups) (Table 3 ) . Table 3 Algorithm performance in identifying veterans with IBM compared to expert clinical diagnosis and published diagnostic criteria. Sens. – sensitivity, spec. – specificity, PPV – positive predictive value, NPV – negative predictive value. n Sens. Spec. PPV NPV Clinical IBM Diagnosis Definite 76 92.2% 97.9% 77.6% 99.4% Definite & Suspected 83 92.8% 97.3% 74.4% 99.4% ENMC 2024 Common & Uncommon 30 70.0% 90.9% 95.5% 52.6% ENMC 2011 Probable 25 73.3% 70.0% 88.0% 46.7% Clinically Defined 12 26.7% 80.0% 80.0% 26.7% Clinico-pathologically Defined 2 3.3% 90.0% 50.0% 23.7% Griggs Possible 15 43.3% 90.0% 92.9% 34.6% Definite 2 3.3% 90.0% 50.0% 23.7% Veterans meeting formal diagnostic criteria for IBM Of the 40 veterans with available detailed muscle biopsy pathology reports, 30 (75%) met ENMC 2024 diagnostic criteria for IBM encompassing both common (25, 83.3%) and uncommon (5, 16.7%) disease presentations 11 . This compared to 25 (62.5%), 12 (30%), and 2 (5%) individuals fulfilling ENMC 2011 criteria for probable, clinically defined, and clinico-pathologically defined disease, respectively 12 . Only 15 (37.5%) and 2 (5%) veterans fulfilled Griggs criteria for possible and definite IBM, respectively 15 . Thus, the algorithm performed the best with ENMC 2024-defined cases with Se of 70.0%, Sp of 90.9%, and PPV of 95.5% and worst with those meeting ENCM 2011 clinico-pathologically defined and Griggs definite IBM criteria (Se of 3.3%, Sp of 90.0%, and PPV of 50% for both). Despite poor sensitivity, even the latter categories exhibited excellent specificity, demonstrating the algorithm’s low rate of false positive cases at the expense of total case number. Discussion This work is a continuation of a previously published project using big data to identify veterans with IBM 17 . While a cohort of 732 individuals with IBM was established, a formal algorithm-based validation of either a clinical or criteria-based diagnosis was not reported. Using a randomly selected subset of 107 cases from the original cohort with a spectrum of disease severity, the work herein demonstrated excellent performance in identifying definite and suspected clinical diagnoses of IBM with Se and Sp measures above 90% and negative predictive values exceeding 99%. Furthermore, this algorithm demonstrated robust Sp and PPV (> 90%) among veterans using the most recent ENMC 2024 diagnostic criteria 11 . Among IBM mimics, only HIV infection occurred at a high enough frequency (4 cases, 3.7%) to include as a rule out condition in subsequent algorithm iterations. While other myopathies were identified as false positive cases in this cohort, these represented disparate entities that would be difficult to exclude - although one could consider adding the ICD-10 code G12.1 (‘other inherited spinal muscular atrophy’) to a list of exclusion diagnoses in the future. None of these cases ultimately met formal IBM diagnostic criteria. The performance of this algorithm compares favorably to other autoimmune diseases, including systemic lupus erythematosus and other IIM subsets 25 , 26 . These results are encouraging with such a difficult to diagnose entity such as IBM, which can be challenging to distinguish from PM in early cases and requires a comprehensive combination of clinical and pathological data beyond the interpretation of a muscle biopsy. Of note is the observation that many pathology features were absent from routine muscle biopsy reports: while most noted endomysial inflammation and rimmed vacuoles, many omitted details regarding MHC class I expression, protein accumulation staining, or electron microscopy studies confirming characteristic filaments as required by the more stringent subsets of IBM diagnostic criteria. Less difficulty was encountered with extraction of clinical information such as muscle strength testing, CK values, or disease duration, although some clinical notes omitted full documentation of muscle strength, missing either lower or upper extremities (e.g. missing leg strength in wheelchair-dependent veteran). These factors also contributed to the significant discrepancy in number of cases meeting more stringent criteria subsets - especially for ENMC 2011. Thus, the algorithm performed best among cases meeting ENMC 2024 criteria, as these were the least clinically restrictive and offered an important benefit in allowing for cases with atypical presentation, which were seen in 16.7% of the selected cohort. Conversely, the low observed level of anti-cN1a and SSA (Ro) and SSB (La) testing was surprising. This was likely attributable to a combination of factors including cohort censor date occurring before ENMC 2024 criteria publication and suspicion that serologic testing was preferentially ordered and resulted via non-electronic means (e.g. paper slip for outside testing and PDF lab report scanned into the medical record system) and, thus, not captured in the CDW. This observation is supported by an equally low SSA (Ro) and SSB (La) antibody testing rate in the parent cohort 17 . A notable challenge of this study was the definition of IBM cases. To address this barrier, a strategy of including multiple options such as expert clinical diagnosis and the use of updated diagnostic criteria, all of which require muscle biopsy data in addition to clinical information, was employed. The latter significantly decreased the number of eligible veterans, as many biopsies were performed outside the VA system at academic institutions resulting in detailed pathology reports being available only for a subset of the selected cohort. Consise reports and third-party references such as ‘biopsy-proven IBM’ or ‘inflammatory muscle biopsy’ were considered inconclusive. Additionally, in the fraction of IBM veterans with concurrent autoimmune disease where it was difficult if not impossible to determine which process was ultimately responsible for their muscle disease, patients were kept in the cohort due to the very low likelihood of their other disease(s) causing myopathy based on published literature 18 – 20 . Another possible drawback was the use of PM as the only disease comparator to IBM – this was chosen due to previous unpublished work identifiying veterans with this diagnosis and its’ common occurrence as a frequent IBM mimic. While the algorithm was not validated against other diagnoses such as muscular dystrophy or HIV myopathy, these diagnoses were encountered at a low frequency during manual case review, suggesting that they would not appreciably affect the results or conclusions of this work. Further supportive of this is that no alternate diagnoses in the selected cohort met formal IBM diagnostic criteria. Finally, it should be acknolowedged that the current approach does not identify all possible veterans with IBM nor was that the goal of the original cohort. The algorithm was designed to maximize true positive IBM cases at the expense of overall cohort size for the purpose of future research. Despire the low prevalence of IBM, this goal was succefully accomplished, illustrating the importance and role of big data resources like the CDW in rare disease research. Abbreviations IBM Inclusion body myositis IIM Idiopathic inflammatory myopathy ICD International Classification of Disease PM polymyositis ENMC – European neuromuscular conference VA Veterans Affairs CDW Corporate Data Warehouse CPK creatine phosphokinase RA rheumatoid arthritis EMG electromyography MRI magnetic resonance imaging Se sensitivity Sp specificity PPV positive predictive value NPV negative predictive value MSA Myositis specific antibody. Declarations Data sharing statement The data used in this study is maintained by the VA CDW and VINCI, is not property of the authors, and is not publicly available. Thus, we are unable to share it with others. Those wishing to access raw data may do so by following the policies laid out by their respective owners. Funding No specific funding was received from any bodies in the public, commercial or not-for-profit sectors to carry out the work described in this article. Disclosures VML is employed part-time as follows: Staff Rheumatologist, Department of Medicine, Department of Veterans Affairs Medical Center, Pittsburgh, PA. Funding: None Acknowledgements The author would like to thank Drs. Dana Ascherman, MD, Chester V. Oddis, MD, Rohit Aggarwal, MD, MS and Siamak Moghadam-Kia, MD, MPH as well as other members of the University of Pittsburgh Myositis Center for their input and feedback regarding this study and thorough review of the manuscript. In addition, I would also like to acknowledge Dr. Steuart Richards, MD, Chief of Pittsburgh VA Rheumatology, as well as Rachel Socrates, MA, Claire Raible, MPH, and members of the Pittsburgh VA IRB committee as well as CDW and VINCI staff who helped with this project. This material is the result of work supported with resources and the use of facilities at the VA Pittsburgh Healthcare System, Pittsburgh, PA. 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Rheumatol Adv Pract 6(3):rkac102. 10.1093/rap/rkac102 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8387113","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":561869688,"identity":"3577ab70-f4d4-47bc-84bd-9268283c3c3e","order_by":0,"name":"Vladimir M. Liarski","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYDACdsYGIJnAw88AZkBJvIAZqkWygbGx4QBxWsBkAoPBAaBqorQYHGZu/FzxJ03G+HZz++MPDDayGw4Q1MLYLHm2LYfH7M5BkMPSjAlqkWxmbJBsbKjgMbuRCNJyOJEYLc0/G/5U8BjPAGv5T1gLPzNjm2QDWw6PgQRYywHitFg2tqXxSAD9MuOMQbLxTEJa2NjbH99s+JNszz+7/cGHigo72T5CWhBAAkQYEK0crmUUjIJRMApGARYAAHSrR19U33wRAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-7533-0491","institution":"VA Pittsburgh Health Care System; University of Pittsburgh","correspondingAuthor":true,"prefix":"","firstName":"Vladimir","middleName":"M.","lastName":"Liarski","suffix":""}],"badges":[],"createdAt":"2025-12-17 15:07:49","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8387113/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8387113/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98768388,"identity":"c05e4bd2-e6f6-4e7d-9bd9-fda0b09ea854","added_by":"auto","created_at":"2025-12-22 10:25:10","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":256542,"visible":true,"origin":"","legend":"","description":"","filename":"VACDWIBMICDCriteriaPaperSubmitVerClinRheumFinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-8387113/v1/942256aee22dbfb5163dbebe.docx"},{"id":98768372,"identity":"2d2c42fb-59d2-4a45-9926-b1899a00dae0","added_by":"auto","created_at":"2025-12-22 10:25:07","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342,"visible":true,"origin":"","legend":"","description":"","filename":"rs8387113.json","url":"https://assets-eu.researchsquare.com/files/rs-8387113/v1/0b824472e0daeb5ef7130a30.json"},{"id":98768250,"identity":"17b829fa-4c1f-4ab4-bd47-b4e7898801e7","added_by":"auto","created_at":"2025-12-22 10:25:02","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84383,"visible":true,"origin":"","legend":"","description":"","filename":"rs83871130enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8387113/v1/0978d65eabacd46afae01d81.xml"},{"id":98768251,"identity":"80324388-e2cf-429c-aea1-faf3840c773f","added_by":"auto","created_at":"2025-12-22 10:25:03","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":64128,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8387113/v1/296213d2202a977697715ed7.png"},{"id":98768384,"identity":"59b1a34e-1f5a-47e9-bc55-1f0b642c30bd","added_by":"auto","created_at":"2025-12-22 10:25:09","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":82638,"visible":true,"origin":"","legend":"","description":"","filename":"rs83871130structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8387113/v1/f32ae837db126b1950cd716b.xml"},{"id":98768368,"identity":"df046b5f-c69e-4e2f-9452-b0ddc9131632","added_by":"auto","created_at":"2025-12-22 10:25:07","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":90862,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8387113/v1/1d865ea91494d9de2cf2f62f.html"},{"id":98768329,"identity":"90ef14da-7b09-4e98-96b3-88dedf4fb65e","added_by":"auto","created_at":"2025-12-22 10:25:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":144479,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagram of algotirhm used to identify veterans with IBM\u003c/strong\u003e. Excluded autoimmune diseases include other inflammatory myopathies (such as dermatomyositis, polymyositis, anti-synthetase syndrome), systemic lupus erythematosus, inflammatory body disease, vasculitis among others.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8387113/v1/5d505f534515f0d1adbe741e.png"},{"id":98768536,"identity":"d47c9e72-5b93-42ce-97a4-461daa758a9b","added_by":"auto","created_at":"2025-12-22 10:25:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":851485,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8387113/v1/de11dae1-f008-41a8-a0d3-389e430d58c1.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eDiagnostic accuracy of an algorithm identifying US veterans with Inclusion Body Myositis from the Corporate Data Warehouse\u003c/p\u003e","fulltext":[{"header":"Key Points","content":"\u003cp\u003e\u0026bull; The proposed algorithm, utilizing a combination of billing data and specialty clinic visits, has robust performance and excellent specificity in identifying veterans with underlying IBM\u003c/p\u003e\u003cp\u003e\u0026bull; These data support the utility of using available big data resources to facilitate future IBM research.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eInclusion body myositis (IBM) is the most common idiopathic inflammatory myopathy (IIM) in adults over 50 years of age and has only one International Classification of Diseases (ICD) code across ICD-9 and ICD-10\u003csup\u003e1\u0026ndash;7\u003c/sup\u003e. Despite this, little is known about the performance of administrative data in accurately identifying individuals with IBM. Similarly, as IBM can share features with other subsets of IIM \u0026ndash; most commonly polymyositis (PM) \u0026ndash; its diagnosis cannot be routinely confirmed based on muscle biopsy features alone and requires the integration of clinical and pathologic data\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. European Neuromuscular Center (ENMC) 2024 IBM diagnostic criteria have been updated from the previously published 2011 ENMC criteria, incorporating atypical IBM features with predominantly proximal muscle weakness as well as anti-cN1a autoantibody (also termed NT5c1a or cytosolic 5\u0026rsquo;-nucleotidase 1A) testing\u003csup\u003e\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Although the Griggs criteria for IBM are still utilized and were the first standardized guidelines focusing on muscle biopsy findings, they were found to have less sensitivity, especially in identifying early disease\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In this study, a previously published algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) employing administrative and billing data from Veterans Affairs (VA) Corporate Data Warehouse (CDW) to identify veterans with IBM was compared to expert clinical IBM diagnoses and the aforementioned diagnostic criteria\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. This work also assessed the performance of these formal diagnostic guidelines in routine clinical care.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Sources and Setting\u003c/h2\u003e \u003cp\u003eData were previously extracted from the VA CDW (Federal Register: 79 FR 4377) using a published algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Briefly, all adult veterans 18 years and older with an outpatient ICD-9 or ICD-10 visit code for IBM or entry for same in their problem list between January 1, 2011 and December 31, 2021 were identified. A minimum of 2 visit codes for IBM from neurology or rheumatology clinics at least 30 days apart were required to confirm a diagnosis. Date of first IBM ICD code or problem list entry was inferred to be the date of IBM diagnosis. Data on survival status were censored as of 06/01/2024. A separate CDW cohort, similarly based on blling and administrative data to identify veterans with PM, was used to derive true and false negative cases. PM was chosen as the main disease comparison as it is the most commonly occurring and most difficult to differentiate IBM mimic\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHuman and Animal Rights\u003c/h3\u003e\n\u003cp\u003eThis study complies with the Declaration of Helsinki and was reviewed and approved by the Pittsburgh VA Research and Development committee on 1/23/2023 and granted informed consent exemption status based on the Revised Common Rule/2018 (Project #1707389). No animals were used in this study.\u003c/p\u003e\n\u003ch3\u003eIndividual Random Case Review\u003c/h3\u003e\n\u003cp\u003eUsing a random number generator, 107 veterans were selected from 732 individuals identified by the algorithm as having IBM. The Text Integration Utility (TIU) domain was used to obtain raw text data from all rheumatology and neurology clinical notes from January 1, 2011 to June 1, 2024 in the VA electronic health record system (CPRS) by means of a keyword search for \u0026lsquo;Inclusion Body Myositis\u0026rsquo; or \u0026lsquo;IBM\u0026rsquo; using the tvf_TIU_FullTextSearch function provided by VINCI in Microsoft SQL Server Management Studio (Ver. 20.1). Note text underwent natural language processing (NLP) and tokenized using \u0026ldquo;tidytext\u0026rdquo; and \u0026ldquo;tokenizers\u0026rdquo; R packages for efficiency. Extracted data related to muscle biopsy findings, laboratory testing, clinical motor strength examination as well as past medical and IBM disease histories were prioritized, as required to determine an IBM diagnosis by formal diagnostic criteria\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Prior use of immunosuppressive therapy, creatine phosphokinase (CPK) levels, and autoantibody testing were queried along with a prior history of PM (mis)diagnosis and presence of IBM diagnostic mimics. Incomplete records were manually supplemented with data from Voogle.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are displayed as mean and standard deviation. Categorical variables are displayed as frequency with percentage. Differences among groups greater than 3 were compared with Kruskal-Wallis tests for continuous variables and Chi-square test for categorical variables. Pairwise comparisons were performed with two-tailed \u003cem\u003et\u003c/em\u003e tests for continuous and Chi-square tests for categorical variables with a significance level set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Bonferroni correction was instituted as necessary for multiple comparisons. All interim and final data analyses were performed using R statistical computing software (version 4.1.2, R Foundation for Statistical Computing) provided by VA Informatics and Computing Infrastructure (VINCI).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatient and Public Involvement\u003c/h3\u003e\n\u003cp\u003eNo patients were involved in this study.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eVeterans with clinical diagnosis of IBM\u003c/h2\u003e \u003cp\u003eRandomly selected cases compared favorably with no statistically significant different features compared to the previously published cohort (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Selected veterans were overwhelmingly male, White, non-Hispanic or Latino, with a mean (\u0026plusmn;SD) age of 54.1 (\u0026plusmn;9.4) and a mean (\u0026plusmn;SD) follow-up of 5.3 (\u0026plusmn;9.4) years with 38 (36.9%) persons deceased as of the censor date. Among 107 individuals, 83 (77.5%) had a definite IBM clinical diagnosis based on the opinion(s) of their treating specialist(s), while 7 (6.5%) had suspected IBM (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). No patients with a CPK exceeding 12 times the upper normal were identified, and all cases exhibited weakness for at least one year or more. Four veterans had HIV-associated myopathy indistinguishable from IBM and were not included in further analyses. Other non-IBM diagnoses included hereditary motor and sensory neuropathy with proximal dominance (HMSN-P), phosphorylase kinase alpha 1 (PHKA1) deficiency, suspected muscular dystrophy, suspected Pompe\u0026rsquo;s disease, steroid myopathy, myofibrillar myopathy, and Kennedy disease (n\u0026thinsp;=\u0026thinsp;1 each). Regarding concurrent autoimmune disorders, one veteran had positive anti-MDA5 (melanoma differentiation-associated gene 5) and SSA (Ro) autoantibodies and was also excluded from the cohort. Four other individuals had concurrent autoimmune processes including vitiligo (n\u0026thinsp;=\u0026thinsp;1), undifferentiated connective tissue disease (n\u0026thinsp;=\u0026thinsp;1), rheumatoid arthritis (RA) coexisting with celiac disease and ulcerative colitis (n\u0026thinsp;=\u0026thinsp;1), and discoid lupus erythematosus (n\u0026thinsp;=\u0026thinsp;1). These veterans were kept in the cohort due to the very low likelihood of their other disease(s) causing myopathy\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eComparison of baseline patient characteristics between original IBM veteran cohort and randomly selected cases for review.\u003c/b\u003e P value represents t test for continuous variables and Pearson\u0026rsquo;s χ\u003csup\u003e2\u003c/sup\u003e for categorical variables, α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase Review\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOriginal Cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, yr - median (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.1 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.5 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up Period, yr \u0026ndash; mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.3 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatus at End of Follow-up Period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlive \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (67.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e416 (56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeceased or Censored \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e316 (43.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale - no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (96.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e708 (96.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale - no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (3.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite - no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (72.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e514 (70.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack or Other - no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e218 (29.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Hispanic or Latino - no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (91.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e653 (89.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic or Latino or Other - no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (10.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSummary of clinical information for IBM cases selected for review. *\u003c/b\u003eValues may exceed 100% due to use of more than one therapy.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase Review\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical IBM Diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefinite \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (71.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuspected \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (6.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlternate Diagnosis \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (22.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEating Difficulties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDysphagia \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (31.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChoking \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (5.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmbulation Difficulties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFalls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (26.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssistive Device Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (14.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWalker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (21.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePower Wheelchair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (19.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTests Available for Review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectromyography \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (38.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle biopsy \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (37.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnetic Resonance Imaging \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (5.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-cN1a antibody \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (11.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyositis-specific Antibody Testing \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (7.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior Diagnosis of Polymyositis \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (11.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior Immunosuppressive Therapy* \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (37.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucocorticoids \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (62.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethotrexate \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (47.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAzathioprine \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (20.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMycophenolate \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (5.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydroxychloroquine \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (5.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcineurin Inhibitor \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntravenous Immunoglobulin \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (27.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRituximab \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (5.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Biologic \u0026ndash; no. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (5.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRandomly selected cases reflected a spectrum of morbidity from IBM: almost one-third (34/107, 31.8%) of veterans with IBM had dysphagia and 5 (5.6%) reported choking symptoms. Similarly, 28 (26.2%) individuals noted impaired ambulation and falls, necessitating the use of assistive devices including canes (15, 14.0%), walkers 23 (21.5%), and power wheelchairs (21, 19.6%).\u003c/p\u003e \u003cp\u003eIn terms of available diagnostic data, electromyography (EMG) and muscle biopsy were the most frequent studies available (41, 38.3% and 40, 37.4%, respectively). Relatively few veterans had muscle magnetic resonance imaging (MRI) (6, 5.6%) or testing for anti-cN1a (also termed NT5c1a or cytosolic 5\u0026rsquo;-nucleotidase 1A) antibodies, other myositis-specific antibodies (MSAs), or anti-SSA (Ro) and SSB (La) antibodies, all of which have previously been associated with IBM\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The latter finding echoed a similar observation in the parent cohort\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough 12 (11.2%) individuals were treated as PM at some point prior to their IBM diagnosis, the algorithm identified only 7 IBM cases out of 1,136 veterans comprising a separate CDW PM cohort, based on blling and administrative data, confirming a very low false negative rate. A significant number of veterans had prior immunosuppressive therapy (40, 37.4%), with glucocorticoids (25, 62.5%), methotrexate (19, 47.5%), and intravenous immunoglobulin (IVIg) being the most common. This was not limited solely to those (mis)diagnosed with PM or those with other autoimmune diseases. Two veterans with concurrent RA received adalimumab and etanercept, respectively, but it was not clear if this was related to their muscle or joint manifestations.\u003c/p\u003e \u003cp\u003eAmong veterans with a clinical IBM diagnosis, the algorithm displayed excellent sensitivity (Se) and specificity (Sp) (Se 92.2% and 97.9%, Sp 92.8% and 97.3%) while maintaining a reasonable positive predictive value (PPV) (77.6% and 74.4% for definite and definite \u0026amp; suspected cases, respectively) and robust negative predictive value (NPV) (99.4% for both groups) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eAlgorithm performance in identifying veterans with IBM compared to expert clinical diagnosis and published diagnostic criteria.\u003c/b\u003e Sens. \u0026ndash; sensitivity, spec. \u0026ndash; specificity, PPV \u0026ndash; positive predictive value, NPV \u0026ndash; negative predictive value.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSens.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpec.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical IBM Diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefinite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e99.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefinite \u0026amp; Suspected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e99.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENMC 2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon \u0026amp; Uncommon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENMC 2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProbable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinically Defined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinico-pathologically Defined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGriggs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePossible\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefinite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVeterans meeting formal diagnostic criteria for IBM\u003c/h3\u003e\n\u003cp\u003eOf the 40 veterans with available detailed muscle biopsy pathology reports, 30 (75%) met ENMC 2024 diagnostic criteria for IBM encompassing both common (25, 83.3%) and uncommon (5, 16.7%) disease presentations\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. This compared to 25 (62.5%), 12 (30%), and 2 (5%) individuals fulfilling ENMC 2011 criteria for probable, clinically defined, and clinico-pathologically defined disease, respectively\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Only 15 (37.5%) and 2 (5%) veterans fulfilled Griggs criteria for possible and definite IBM, respectively\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Thus, the algorithm performed the best with ENMC 2024-defined cases with Se of 70.0%, Sp of 90.9%, and PPV of 95.5% and worst with those meeting ENCM 2011 clinico-pathologically defined and Griggs definite IBM criteria (Se of 3.3%, Sp of 90.0%, and PPV of 50% for both). Despite poor sensitivity, even the latter categories exhibited excellent specificity, demonstrating the algorithm\u0026rsquo;s low rate of false positive cases at the expense of total case number.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis work is a continuation of a previously published project using big data to identify veterans with IBM\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. While a cohort of 732 individuals with IBM was established, a formal algorithm-based validation of either a clinical or criteria-based diagnosis was not reported. Using a randomly selected subset of 107 cases from the original cohort with a spectrum of disease severity, the work herein demonstrated excellent performance in identifying definite and suspected clinical diagnoses of IBM with Se and Sp measures above 90% and negative predictive values exceeding 99%. Furthermore, this algorithm demonstrated robust Sp and PPV (\u0026gt;\u0026thinsp;90%) among veterans using the most recent ENMC 2024 diagnostic criteria\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Among IBM mimics, only HIV infection occurred at a high enough frequency (4 cases, 3.7%) to include as a rule out condition in subsequent algorithm iterations. While other myopathies were identified as false positive cases in this cohort, these represented disparate entities that would be difficult to exclude - although one could consider adding the ICD-10 code G12.1 (\u0026lsquo;other inherited spinal muscular atrophy\u0026rsquo;) to a list of exclusion diagnoses in the future. None of these cases ultimately met formal IBM diagnostic criteria. The performance of this algorithm compares favorably to other autoimmune diseases, including systemic lupus erythematosus and other IIM subsets\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. These results are encouraging with such a difficult to diagnose entity such as IBM, which can be challenging to distinguish from PM in early cases and requires a comprehensive combination of clinical and pathological data beyond the interpretation of a muscle biopsy.\u003c/p\u003e \u003cp\u003eOf note is the observation that many pathology features were absent from routine muscle biopsy reports: while most noted endomysial inflammation and rimmed vacuoles, many omitted details regarding MHC class I expression, protein accumulation staining, or electron microscopy studies confirming characteristic filaments as required by the more stringent subsets of IBM diagnostic criteria. Less difficulty was encountered with extraction of clinical information such as muscle strength testing, CK values, or disease duration, although some clinical notes omitted full documentation of muscle strength, missing either lower or upper extremities (e.g. missing leg strength in wheelchair-dependent veteran). These factors also contributed to the significant discrepancy in number of cases meeting more stringent criteria subsets - especially for ENMC 2011. Thus, the algorithm performed best among cases meeting ENMC 2024 criteria, as these were the least clinically restrictive and offered an important benefit in allowing for cases with atypical presentation, which were seen in 16.7% of the selected cohort.\u003c/p\u003e \u003cp\u003eConversely, the low observed level of anti-cN1a and SSA (Ro) and SSB (La) testing was surprising. This was likely attributable to a combination of factors including cohort censor date occurring before ENMC 2024 criteria publication and suspicion that serologic testing was preferentially ordered and resulted via non-electronic means (e.g. paper slip for outside testing and PDF lab report scanned into the medical record system) and, thus, not captured in the CDW. This observation is supported by an equally low SSA (Ro) and SSB (La) antibody testing rate in the parent cohort\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA notable challenge of this study was the definition of IBM cases. To address this barrier, a strategy of including multiple options such as expert clinical diagnosis and the use of updated diagnostic criteria, all of which require muscle biopsy data in addition to clinical information, was employed. The latter significantly decreased the number of eligible veterans, as many biopsies were performed outside the VA system at academic institutions resulting in detailed pathology reports being available only for a subset of the selected cohort. Consise reports and third-party references such as \u0026lsquo;biopsy-proven IBM\u0026rsquo; or \u0026lsquo;inflammatory muscle biopsy\u0026rsquo; were considered inconclusive. Additionally, in the fraction of IBM veterans with concurrent autoimmune disease where it was difficult if not impossible to determine which process was ultimately responsible for their muscle disease, patients were kept in the cohort due to the very low likelihood of their other disease(s) causing myopathy based on published literature\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAnother possible drawback was the use of PM as the only disease comparator to IBM \u0026ndash; this was chosen due to previous unpublished work identifiying veterans with this diagnosis and its\u0026rsquo; common occurrence as a frequent IBM mimic. While the algorithm was not validated against other diagnoses such as muscular dystrophy or HIV myopathy, these diagnoses were encountered at a low frequency during manual case review, suggesting that they would not appreciably affect the results or conclusions of this work. Further supportive of this is that no alternate diagnoses in the selected cohort met formal IBM diagnostic criteria.\u003c/p\u003e \u003cp\u003eFinally, it should be acknolowedged that the current approach does not identify all possible veterans with IBM nor was that the goal of the original cohort. The algorithm was designed to maximize true positive IBM cases at the expense of overall cohort size for the purpose of future research. Despire the low prevalence of IBM, this goal was succefully accomplished, illustrating the importance and role of big data resources like the CDW in rare disease research.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIBM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInclusion body myositis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIIM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIdiopathic inflammatory myopathy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Classification of Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epolymyositis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eENMC \u0026ndash; European neuromuscular conference\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVeterans Affairs\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCDW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCorporate Data Warehouse\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCPK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecreatine phosphokinase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erheumatoid arthritis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEMG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eelectromyography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emagnetic resonance imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSe\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esensitivity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSp\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003especificity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epositive predictive value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enegative predictive value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMyositis specific antibody.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData sharing statement\u003c/h2\u003e \u003cp\u003eThe data used in this study is maintained by the VA CDW and VINCI, is not property of the authors, and is not publicly available. Thus, we are unable to share it with others. Those wishing to access raw data may do so by following the policies laid out by their respective owners.\u003c/p\u003e \u003c/div\u003e\u003cp\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo specific funding was received from any bodies in the public, commercial or not-for-profit sectors to carry out the work described in this article.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eDisclosures\u003c/h2\u003e \u003cp\u003eVML is employed part-time as follows: Staff Rheumatologist, Department of Medicine, Department of Veterans Affairs Medical Center, Pittsburgh, PA.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNone\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe author would like to thank Drs. Dana Ascherman, MD, Chester V. Oddis, MD, Rohit Aggarwal, MD, MS and Siamak Moghadam-Kia, MD, MPH as well as other members of the University of Pittsburgh Myositis Center for their input and feedback regarding this study and thorough review of the manuscript. In addition, I would also like to acknowledge Dr. Steuart Richards, MD, Chief of Pittsburgh VA Rheumatology, as well as Rachel Socrates, MA, Claire Raible, MPH, and members of the Pittsburgh VA IRB committee as well as CDW and VINCI staff who helped with this project. This material is the result of work supported with resources and the use of facilities at the VA Pittsburgh Healthcare System, Pittsburgh, PA.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBadrising UA, Maat-Schieman M, van Duinen SG et al (2000) Epidemiology of inclusion body myositis in the Netherlands: a nationwide study. 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Rheumatol Adv Pract 6(3):rkac102. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/rap/rkac102\u003c/span\u003e\u003cspan address=\"10.1093/rap/rkac102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"VA Pittsburgh Healthcare System","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Idiopathic inflammatory myopathy, inclusion body myositis, veterans","lastPublishedDoi":"10.21203/rs.3.rs-8387113/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8387113/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eInclusion body myositis (IBM) is the most common idiopathic inflammatory myopathy (IIM) in adults over 50 years of age and has only one International Classification of Diseases (ICD) code across ICD-9 and ICD-10. Despite this, little is known about the performance of administrative data in identifying individuals with IBM. An algorithm based on billing and administrative data to create a veteran IBM cohort was tested and its performance compared to clinical diagnosis and consensus diagnostic criteria.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eOf 732 previously identified veterans with IBM, 107 were randomly selected for manual record review using a keyword search for \u0026ldquo;Inclusion Body Myositis\u0026rdquo; or \u0026ldquo;IBM\u0026rdquo; via the \u003cem\u003etvf_TIU_FullTextSearch\u003c/em\u003e function. Rheumatology and Neurology notes from January 1, 2011 to June 1, 2024 were extracted and data tokenized using R packages \u003cem\u003etidytext\u003c/em\u003e and \u003cem\u003etokenizers\u003c/em\u003e. Incomplete records were supplemented with Voogle data.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e83 (77.5%) of 107 veterans had definite and 7 (6.5%) suspected clinical IBM. There were 4 cases with HIV-associated myopathy and 5 with inherited myopathies. Only 7 individuals were concurrently identified among a separate cohort of 1,136 veterans with a diagnosis of polymyositis based on administrative data. Detailed muscle biopsy pathology reports were available for 40/107 veterans of whom 30 (75%) noted findings consistent with IBM and 30 (75%) met ENMC 2024 diagnostic criteria for IBM. Based on ENMC 2011 criteria, 2 (5%), 12 (30%), and 25 (62.5%) veterans met clinicopathologic, clinical, and probable IBM definitions, respectively. Using Griggs criteria, 2 (5%) and 15 (37.5%) individuals met definite and probable IBM thresholds. The minimum positive predictive value (PPV) was 74.4% for an IBM clinical diagnoses and 88.0% for cases meeting diagnostic criteria, with specificity\u0026thinsp;\u0026ge;\u0026thinsp;90% for most groups.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe algorithm demonstrates robust performance and excellent specificity in identifying Veterans with IBM, comparable to approaches for systemic lupus and other IIMs.\u003c/p\u003e","manuscriptTitle":"Diagnostic accuracy of an algorithm identifying US veterans with Inclusion Body Myositis from the Corporate Data Warehouse","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 10:23:33","doi":"10.21203/rs.3.rs-8387113/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9754dd94-d2d6-4d50-9eb8-6eae26fdce25","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59831288,"name":"Rheumatology"},{"id":59831289,"name":"Neurology"},{"id":59831290,"name":"Epidemiology"}],"tags":[],"updatedAt":"2025-12-22T10:23:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 10:23:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8387113","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8387113","identity":"rs-8387113","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}