A Systematic Review and Meta-Analysis to Inform Cancer Screening Guidelines in Idiopathic Inflammatory Myopathies

Alexander G. S. Oldroyd; Andrew B. Allard; Jeffrey P. Callen; Hector Chinoy; Lorinda Chung; David Fiorentino; Michael D. George; Patrick Gordon; Kate Kolstad; Drew J. B. Kurtzman; Pedro M. Machado; Neil J. McHugh; Anna Postolova; Albert Selva-O'Callaghan; Jens Schmidt; Sarah Tansley; Ruth Ann Vleugels; Victoria P. Werth; Rohit Aggarwal

Disclosures

Rheumatology. 2021;60(6):2615-2628. 

In This Article

Methods

We performed a systematic review of factors associated with cancer in IIM populations and screening practices. Evidence pertaining to factors associated with cancer were assimilated via meta-analysis. Results of studies relating to cancer screening in IIM populations were assimilated into a narrative review. Study selection, data extraction, quality assessment, data synthesis and analysis were all carried out in adherence to PRISMA guidelines (for PRISMA checklist see Supplementary Material, available at Rheumatology online).[8]

Data Sources

A systematic literature search was carried out on Medline via PubMed, Embase via OVID and Scopus. The following were used to identify appropriate studies: 'myositis', 'neoplasm', 'screening'. Full-length peer reviewed articles published in English language before 8 January 2020 were included. Case reports, letters and conference abstracts were excluded. References of each identified study were also examined for further appropriate studies.

Study Selection

Studies were included in the risk factor meta-analysis if they provided data on at least one risk factor, included at least 10 IIM study subjects, and provided data on an IIM control group. It is important to note that risk factors were assessed in comparison to each study's wider IIM population, not the general healthy population. Eligible IIM subtypes included DM, PM, anti-synthetase syndrome (ASS), immune-mediated necrotizing myopathy (IMNM) and clinically amyopathic DM (CADM). Data relating to inclusion body myositis were excluded due to the relationship with cancer being distinct from that of other IIM subtypes.[4] Only the study with the largest cohort was included where repeated studies utilized the same cohort data, where identifiable.

For the review of screening practices, studies that assessed at least one cancer screening approach/modality in an IIM population were included.

Data extraction

Each eligible article was independently reviewed by two reviewers (A.O., M.D.G., D.K., S.T., A.A., A.P. and K.K.). The title and study abstracts were reviewed to assess eligibility/ineligibility. Preliminary full text reviews were carried out where eligibility/ineligibility could not be decided using the title and abstract alone. Full text review of each eligible article was carried out by a single reviewer. Extracted data included study type, population studied, sample size, risk factors evaluated, number of cases (i.e. those with risk factors), controls (i.e. those without risk factors), and number of cases and controls diagnosed with cancer (excluding non-melanotic skin cancers). Available data (e.g. mean, S.D., median, range) on continuous risk factors, such as age, in those with/without cancer were also collected. A second reviewer reviewed selected studies to ensure accuracy of data extraction. The quality of studies and bias assessment was carried out using the GRADE system developed by the Scottish Intercollegiate Guidelines Network, where each study was given a quality assessment of either very low, low, moderate or high.[9] Studies were excluded if they were deemed to be of low or very low quality or subject to a high risk of bias according to the GRADE system. Agreement of both reviewers was required to remove a study according to bias. The decision of study inclusion/exclusion was made by a third reviewer in the case of differing assessments.

Data Synthesis and Analysis

Meta-analysis was carried out for each risk factor where data from at least two eligible studies were available. Investigated factors included IIM subtypes, demographics, clinical features, laboratory parameters and autoantibodies. The denominator used in cancer risk estimation for each factor was the remaining IIM population of each study, not the general population. The cancer risk associated with individual ASS-related autoantibodies (anti-Jo1, anti-PL7, anti-PL12, anti-EJ, anti-OJ, anti-KS) was considered. Subsequently, the risk associated with the presence of any ASS-related autoantibody was calculated by combining studies that compared risk against non-ASS IIM controls. Risk ratios (RRs) were calculated for binary variables (e.g. presence of ILD). The weighted mean difference (WMD) for each continuous variable (e.g. age) was calculated by comparing means and S.D.s. The mean (S.D.) was calculated from studies that reported only median and range using methods described by Hozo et al..[10]

The small number of studies that reported the utility of cancer screening approaches in IIM populations precluded a meta-analysis, therefore a narrative review was carried out.

Heterogeneity and Study Sample Size Analysis

Heterogeneity was assessed using the standard chi-squared test and I2 statistic. Further analysis was carried out for factors with very high levels of heterogeneity (I2 >75%). Influence analysis ('leave-one-out') was carried out to identify outlier studies, that is those with extreme effect sizes, and thus substantially contributing to heterogeneity. A study was considered an outlier if it fulfilled the cut-off criteria proposed by Viechtbauer et al..[11]

Egger's test was used to assess the influence of study cohort size on calculated effect sizes.[12] 'Trim and fill' was used to calculate adjusted effect sizes for factors with significant (<0.05) Egger's test P-values.[13]

All analysis was carried out using the statistical programme R,[14] and the meta[15] and metaphor[16] packages.

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