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BRIEF REPORT
Ahead of print publication  

Combined case record forms for collaborative datasets of patients and controls of idiopathic inflammatory myopathies


 Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

Date of Submission25-Mar-2020
Date of Acceptance07-May-2020

Correspondence Address:
Latika Gupta,
Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow - 226 014, Uttar Pradesh
India
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/injr.injr_56_20

  Abstract 


Idiopathic inflammatory myopathies are heterogeneous, and the clinico-serologic phenotypes differ significantly in various populations. Collaborative work with a structured case record form (CRF) is vital to successful data collection and collation. We describe herein various CRFs to the same effort.

Keywords: Case record form, collaboration, idiopathic inflammatory myopathies



How to cite this URL:
Mehta P, Gupta L. Combined case record forms for collaborative datasets of patients and controls of idiopathic inflammatory myopathies. Indian J Rheumatol [Epub ahead of print] [cited 2020 Sep 20]. Available from: http://www.indianjrheumatol.com/preprintarticle.asp?id=291590




  Introduction Top


Idiopathic inflammatory myopathies (IIMs) are heterogeneous, with varied organ system involvement, and cause significant morbidity and at times even mortality. Considerable phenotypic variations have been described in different ethnic groups, suggesting that the current understanding of the disease is limited in certain populations. The rare occurrence of the disease makes obtaining data on the disease a challenge. Thus, collaborative efforts are the key[1],[2] as have been successful in various other rare rheumatic diseases.[3],[4] A structured case record form (CRF) is the first step in gathering data in a collaborative effort.[1]

Previously, a CRF was devised with the intent to gather data on the various types of IIM.[1] While gathering structured data on a rare disease is essential, it is prudent to pay due attention to controls of the disease. Gathering appropriate controls can enhance the ability to identify the various features of the illness and draw meaningful comparisons. Hence, CRFs were designed for healthy controls and patients presenting with noninflammatory myopathy and multisystem connective tissue disease (CTD), namely lupus.

The organ system involvement is known to evolve with time in various types of myositis, more so with overlap myositis and anti-synthetase syndromes.[5] Thus, a meticulous follow-up CRF is essential to understand the natural history of these illnesses. We describe herein a follow-up CRF to the same effort.


  Methods Top


The first CRF was developed for an investigator-initiated, single-center study funded by an extramural agency.[6] The cohort of patients being followed up as part of the study was called the MyoCite cohort. These CRFs were designed to collect data on the control population (healthy/diseased) and study the follow-up outcomes of the MyoCite cohort.[7]

The CRFs were first devised in March 2019. Data on disease controls requires to be objective, yet precise to avoid excessive time investment in data collection. Since the control forms may have to be filled by allied specialties such as neurology or internal medicine, the variables to be entered were kept to the bare minimum. The forms for patients presenting with noninflammatory myopathy, healthy controls, and patients with multisystem CTD such as lupus were labeled as versions 4.0, 5.0, and 6.0, respectively [Supplementary File 1][Additional file 1].

For the follow-up CRF (called version 3.0), the intention was to gather information on the current clinical features, treatment received, and disease as well as drug-related damage. For any disease, our goals are to achieve remission, prevent relapses, and aim for a better quality of life and survival. In addition, the criteria for relapses have not been validated, hence a section on relapses and its details was added. Based on these ideas, we devised the first draft, followed by repeated testing of it in the clinic, and further revisions were carried out for the ease of filling and capturing most of the data on follow-up visits. The clinical status at the visit in question and the activity and damage were quantified using the core set measures previously described.[8] These include the Myositis Damage Index (MDI), Myositis Disease Activity Assessment Tool (MDAAT), and Health Assessment Questionnaire (HAQ), alongside the physician and patient global assessments (PhGA and PtGA, respectively) and assessment of the Manual Muscle Testing score. The response criteria can be calculated from the baseline core set by direct calculations, as previously described.[8],[9] The clinical response was also graded as per the physician global assessment. These are in line with the IMACS response criteria and HAQ, MDAAT, and MDI indices. Hence, we included all variables included in these indices.

Besides, the criteria for relapses have not been validated, hence a section on relapses and its details was added. Based on these ideas, we devised the first draft, followed by testing it in the clinic, and further changes were introduced for ease in filling in and capturing most of the possibilities in an objective manner. Columns were added to enable capturing clinical details at more than one follow-up visit, thus enabling a longitudinal snapshot of the clinical course of an individual patient. A separate form called version 3.1 was devised for patients with juvenile myositis with a focus on growth and development.

Data on the baseline features such as the physician clinical diagnosis, demographics, baseline clinical characteristics and investigations such as autoantibody results, and muscle biopsy findings were excluded as they could be obtained from the versions 1.0 and 1.1 of the CRF filled at baseline for adults and juvenile myositis, respectively.

Anti-synthetase syndrome is being increasingly recognized among CTD-interstitial lung disease (ILD) cases referred by pulmonologists with a broader availability of myositis antibody testing. A certain proportion of patients can present with predominant lung manifestations in the absence of significant muscle disease.[5] Thus, variables such as pulmonary function tests (PFTs), diffusion lung capacity for carbon monoxide, and computed tomography (CT) involvement for ILD were added to the CRF. As patients with advanced lung disease are unable to perform a PFT, a 6-min walk test was also added as a surrogate for the extent of ILD as well as PAH. Meanwhile, in the absence of a formal quantitative lung CT scoring for ILD, a semi-quantitative assessment was added to the later revisions of the CRF.

Standard guidelines for CRF designing were followed.[10] The patient codes were as previously described. For the follow-up data set, the following were to be carried over from the initial study. The forms underwent six rounds of modifications over 3 weeks, done by two rheumatologists who reviewed this after testing on three patients in every round. The forms were further scrutinized for language and face validity. Ambiguity was removed and duplicates were eliminated. Wherever feasible, checkboxes were preferred over circling the most appropriate answer. For investigations, the option “Not done” was moved above the other options, wherever considered sensible, and more likely redundant bits were removed. Patient identifiers were placed at the top of the each page of the CRF. The average CRF filling time was 3 min for versions 4.0, 5.0, and 6.0 and 15 min for version 6.0.

A consensus on the final forms was reached on April 30, 2019 [Supplementary Files 2 and 3][Additional file 2][Additional file 3]. Highlights of the CRF include an intensive data collection, which makes it easy to enter data, and hence time efficient in a busy outpatient setting.

A glossary explaining the various items in the CRF was also devised for the users. It is vital for the physician to go through the glossary in detail before beginning data collection. The glossary is delineated as Supplementary File 4[Additional file 4].


  Discussion Top


CRF designing is a crucial step that requires enormous planning and attention to the minute details to ensure that data collection is in line with the objectives of the study. A carefully devised CRF reduces unclear clinical realities to definite categorical answers which are more amenable to statistical analysis.

The abovementioned CRF was structured for ease of filling in, both as hard copies and electronic data capture (EDC). The CRF has built-in checkboxes linked to each data element, thus almost eliminating any wordy entries and reducing the time and effort required by the data collection personnel. Such CRFs can utilize a scanning system to integrate entries into predesigned software for digital archiving and eliminating the need for manual entries and thus the human error. This has the dual advantage of avoiding EDC in the clinic and obtaining benefits of time efficiency in data recording as was done for the DC VAS study for systemic vasculitis.[11],[12]

The number of researchers with a dedicated interest and an appropriate placement to carry out structured research in rare disease is limited.[13] Providing them with a structured CRF is the first step in a time-efficient research model for sustainable research. The recent increase in multicentric research, such as the IMACS committee, demonstrates the commitment of the community to a collaborative group-based approach.[13] Recent review of the European Network of Pregnancy Registers in Rheumatology found that variables collected differ among the various collaborating registers, making data collation challenging.[14] The EULAR too recently recommended a structured data set for uniformity in collative research.[15]

Inclusion of outcome measures, especially patient-reported ones, has previously been found to provide more responsive and better treatment results. The core set has for the first time been used to describe treatment responses in the RIM trial,[16] though benefits with conventional immunosuppressants are still unknown. Designing such a structured CRF might be the first step in gathering such real-world quality evidence for the efficacy of routine oral and parenteral immunosuppressants in myositis. The current versions were labeled as 4.0, 5.0, and 6.0 so that revised versions (developed as and when the need be, in line with developments in the field in future) can be identified by the number after the decimals (e.g., 7.1 or 8.1).

Myositis can first present to different specialists depending upon the first organ system involved. Such structured data collection can provide the opportunity of cross-discipline collaboration to expand our perspective of disease beyond what is clinically visible, which of even greater relevance in times of big data and artificial intelligence.[17]

Lastly, this is the first step forward toward analytical approaches to multisource and harmonize data and perform collaborative studies on treatment effectiveness, safety, and health-economic outcomes.[18]



Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Gupta L, Appani SK, Janardana R, Muhammed H, Lawrence A, Amin S, et al. Meeting report: MyoIN–Pan-India collaborative network for myositis research. Indian Journal of Rheumatology 2019;14:136.  Back to cited text no. 1
    
2.
Lundberg IE, Vencovsky J. International collaboration including patients is essential to develop new therapies for patients with myositis. Curr Opinion Rheumatol 2017;29:234-40.  Back to cited text no. 2
    
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Clancy CM, Margolis PA, Miller M. Collaborative networks for both improvement and research. Pediatrics 2013;31:S210-4.  Back to cited text no. 3
    
4.
Jayne D, Rasmussen N. Twenty-five years of European Union collaboration in ANCA-associated vasculitis research. Nephrol Dialysis Transplant 2015;30:i1-7.  Back to cited text no. 4
    
5.
Cavagna L, Nuño L, Scirè CA, Govoni M, Longo FJ, Franceschini F, et al. Clinical spectrum time course in anti Jo-1 positive antisynthetase syndrome: Results from an international retrospective multicenter study. Medicine (Baltimore) 2015;94:e1144.  Back to cited text no. 5
    
6.
Available from: https://www.aplar.org/. [Last accessed on 2020 Apr 23].  Back to cited text no. 6
    
7.
Gupta L, Kumar D, Kumar U, Guleria A, Zanwar A, Raj R, et al. NMR-based serum, urine and muscle metabolomics in inflammatory myositis for diagnosis and activity assessment: Serum metabolomics can differentiate active from inactive myositis. Inarthritis Rheumatol 2019;71:111.  Back to cited text no. 7
    
8.
Rider LG, Werth VP, Huber AM, Alexanderson H, Rao AP, Ruperto N. Measures of adult and juvenile dermatomyositis, polymyositis, and inclusion body myositis: Physician and Patient/Parent Global Activity, Manual Muscle Testing (MMT), Health Assessment Questionnaire (HAQ)/Childhood Health Assessment Questionnaire (C-HAQ), Childhood Myositis Assessment Scale (CMAS), Myositis Disease Activity Assessment Tool (MDAAT), Disease Activity Score (DAS), Short Form 36 (SF-36), Child Health Questionnaire (CHQ), physician global damage, Myositis Damage Index (MDI), Quantitative. Arthritis Care Res (Hoboken) 2011;63 Suppl 11:S118-57.  Back to cited text no. 8
    
9.
Rider LG, Giannini EH, Brunner HI, Ruperto N, James-Newton L, Reed AM, et al. International consensus on preliminary definitions of improvement in adult and juvenile myositis. Arthritis Rheum 2004;50:2281-90.  Back to cited text no. 9
    
10.
Bellary S, Krishnankutty B, Latha MS. Basics of case report form designing in clinical research. Perspect Clin Res 2014;5:159-66.  Back to cited text no. 10
[PUBMED]  [Full text]  
11.
Walther B, Hossin S, Townend J, Abernethy N, Parker D, Jeffries D. Comparison of electronic data capture (EDC) with the standard data capture method for clinical trial data. PLoS One 2011;6:e25348.  Back to cited text no. 11
    
12.
Craven A, Robson J, Ponte C, Grayson PC, Suppiah R, Judge A, et al. ACR/EULAR-endorsed study to develop diagnostic and classification criteria for vasculitis (DCVAS). Clin Exp Nephrol 2013;17:619-21.  Back to cited text no. 12
    
13.
Mellins ED, Rider LG. Clinical research networks: A step towards evidence-based practice in pediatric rheumatology. Nat Clin Pract Rheumatol 2007;3:59.  Back to cited text no. 13
    
14.
Meissner Y, Strangfeld A, Costedoat-Chalumeau N, Förger F, Goll D, Molto A, et al. European network of pregnancy registers in rheumatology (EuNeP)-an overview of procedures and data collection. Arthritis Res Ther 2019;21:241.  Back to cited text no. 14
    
15.
Radner H, Chatzidionysiou K, Nikiphorou E, Gossec L, Hyrich KL, Zabalan C, et al. 2017 EULAR recommendations for a core data set to support observational research and clinical care in rheumatoid arthritis. Ann Rheum Dis 2018;77:476-9.  Back to cited text no. 15
    
16.
Oddis CV, Reed AM, Aggarwal R, Rider LG, Ascherman DP, Levesque MC, et al. Rituximab in the treatment of refractory adult and juvenile dermatomyositis and adult polymyositis: A randomized, placebo-phase trial. Arthritis Rheumatism 2013;65:314-24.  Back to cited text no. 16
    
17.
Alarcón-Riquelme ME. Big data: The opportunity to think outside the discipline. Nat Rev Rheumatol 2019;15:639-40.  Back to cited text no. 17
    
18.
Chatzidionysiou K, Hetland ML, Frisell T, Di Giuseppe D, Hellgren K, Glintborg B, et al. Opportunities and challenges for real-world studies on chronic inflammatory joint diseases through data enrichment and collaboration between national registers: The Nordic example. RMD Open 2018;4:e000655.  Back to cited text no. 18
    




 

 
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