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T-09: Use of Real-World Data to Optimize Identification of Systemic Lupus Erythematosus (SLE) Patients for Clinical Trial Enrolment

Poster Presenter

      Michael Gregory Cushion

      • Global Solutions Manager
      • IQVIA
        United Kingdom


Find physicians and sites treating SLE patients, including age and ethnicity as search parameters within the SLE population, through use of real world data (RWD), e.g. Medical/prescription claims and physician reference data, combined with proprietary advanced analytics.


Relevant standard diagnosis, drug, and procedure codes specific and/or relevant to SLE were applied to medical and prescription claims datasets, cross-linked with physician reference data, to identify SLE patient populations, and the most common treating specialists, e.g. rheumatologists, and sites.


SLE patient populations were identified in the US, UK, France and China, each with different approaches, reflecting local data availability. For the US, diagnosis data in combination with consumption of appropriate prescription drugs (anti-malarials, immunosuppressants and a biologic), was applied to identify the SLE population. The SLE population was further segmented by age; 18+ and <18 years. Since SLE prevalence, severity, and outcomes varies by ethnicity the SLE population was also segmented by ethnicity. For France, comprehensive medical claims and physician reference data were used to identify the desired patient populations, segmenting also by age; local laws and regulations prohibited the segmentation of patients by ethnicity. For the UK, the consumption of the aforementioned prescription drugs, in combination with secondary data, e.g. local overall SLE prevalence and prevalence by age, were used to identify the SLE populations of interest, linked to rheumatology (population aged 18+) and paediatrics departments (aged <18). Census data was combined with the analytics outputs in order to identify areas with high potential of SLE patients of the required ethnicity. A similar approach to that described for the UK was applied for China. Selected results are summarised below: • US - 150k adult patients and 2k adolescent patients identified, present at more than 150 high potential sites • France - 15k adult patients and 500 adolescent patients identified, present at more than 20 high potential sites • UK - 50k adult patients and 1k adolescent patients identified, present at more than 100 high potential sites • China - 600k patients identified at more than 100 high potential sites


Custom analytics were deployed within RWD assets to identify SLE patients with specific characteristics, such as age and ethnicity, with the aim of improving clinical trial operational efficiency. For example, the high screen failure rates observed in two recently conducted SLE Phase III clinical trials, 367/464 and 315/403 respectively, were attributed to ineligibility according to the study entry criteria. Current usage of corticosteroids (prednisone or equivalent) are listed amongst the inclusion criteria for these studies; the use of the aforementioned approaches with the inclusion of corticosteroids as ‘tracer’ drugs, where appropriate, would be expected to have reduced the screen failure rates for these studies. Together with identifying and segmenting patient populations, it was also possible to combine and link these outputs with other datasets to highlight the most promising physicians and sites to enrol, for example identifying those that are research active. Increasing clinical trial cost, long timelines and patient recruitment and retention are known to be barriers to clinical trial conduct and wider drug development. To facilitate faster patient access to effective medicines, innovative methods such as those outlined in this abstract are required to optimise clinical trial operations and increase efficiency. The methods used here could be applied across a broad range of therapeutic areas and geographies to improve clinical trial site identification, and indeed could be extended further to improve protocol development, i.e. to minimise the chances of a protocol amendment where low recruitment rates result from an entry criteria that may otherwise have not been included if the use of RWD had identified these as not being feasible. Furthermore, this approach would be invaluable in trials with small patient populations, for example rare diseases and precision medicine, where it would also be possible to set up patient referral networks.