[Register and cohort studies : Overview of the most important data sources at the German Rheumatism Research Center]

Outcome of adult patients with JIA treated with the biosimilar Benepali®: results of the biologic register JuMBO

BACKGROUND

To analyze therapy adherence, safety, and outcome in adult patients with juvenile idiopathic arthritis (JIA) treated with the etanercept biosimilar Benepali^® (Biogen Inc, Cambridge, USA).

METHODS

Data from the prospective registry, JuMBO (Juvenile arthritis MTX/Biologics long-term Observation), were used for the analysis. JuMBO is a long-term observational cohort study. It follows adult patients with JIA who were formerly included in the national JIA biologic register (BiKeR Registry). Both registries provide individual trajectories of clinical data and outcomes from childhood to adulthood in JIA patients treated with disease-modifying anti-rheumatic drugs (DMARDs).

RESULTS

Eighty-three patients from the German JuMBO registry were treated with Benepali^®. Of these, 74% had switched from Enbrel^® (Pfizer Inc., NYC, USA) the originator of etanercept to Benepali^® for cost reasons. Therapy survival of patients treated with Benepali^® in comparison to Enbrel^® in patients matched by significant parameters was comparable. Adverse events (AE) were reported in 25.3% and serious adverse events (SAE) in 9.6% of patients. Physicians rated no SAE causative related to Benepali^®. The majority of SAEs were surgical/medical procedures and there was only one infection. All efficacy parameters (cJADAS-10, Physician Global Assessment, number of joints with active arthritis, patients' overall well-being, pain, and HAQ) demonstrated improvement over 24 months (p-values were not significant). 9.6% of patients permanently discontinued Benepali® because of an AE.

CONCLUSIONS

Tolerability and effectiveness of the biosimilar Benepali^® were satisfactory and therapy survival was comparable to the originator. Further data on therapy with biologics and biosimilars such as Benepali^® must be collected by registries such as BiKeR and JuMBO in order to optimize therapy and patient outcomes and to reduce costs in the health system in the long term.

[Latest findings from the RABBIT register]

Seit 2001 rekrutieren Rheumatolog:innen deutschlandweit Patient:innen mit rheumatoider Arthritis in das Biologikaregister RABBIT, um die Langzeitsicherheit und -wirksamkeit moderner antirheumatischer Therapien zu untersuchen. In den vergangenen 20 Jahren wurden mehr als 20.000 Patient:innen in die prospektive Kohortenstudie eingeschlossen. In diesem Beitrag fassen wir aktuelle Forschungsergebnisse der Jahre 2020 und 2021 zusammen; dabei stehen Sicherheitsaspekte, Einflussfaktoren auf die Therapiewirksamkeit und patientenberichtete Outcomes im Fokus. Mit Herpes zoster, Fazialisparese und Psoriasis wurden verschiedene unerwünschte Ereignisse untersucht, die entweder als Sicherheitssignal aus klinischen Studien oder durch die EudraVigilance-Datenbank gemeldet wurden oder als paradoxe Reaktion unter medikamentöser Behandlung aufgetreten sind. Für diese Ereignisse wurde der Einfluss der biologischen DMARD(„disease-modifying antirheumatic drug“)-Therapie analysiert. In der Publikation zu Herpes zoster berücksichtigten wir auch die medikamentöse Behandlung mit Januskinaseinhibitoren. Starkes Übergewicht kann den Erfolg einer Therapie beeinflussen. Hier gibt es geschlechtsspezifische Unterschiede, und auch das Wirkprinzip einer Therapie entscheidet, ob eine Adipositas das Therapieansprechen reduziert. Die Mehrheit der in RABBIT beobachteten Patient:innen ist nach 1 Jahr Behandlung mit der erhaltenen Therapie zufrieden. Wir konnten zeigen, welche Faktoren die Zufriedenheit mit der Wirksamkeit und der Sicherheit der Behandlung begünstigen oder aber negativ beeinflussen. Diese Übersichtsarbeit zeigt, dass Langzeitbeobachtungsstudien wie das RABBIT-Register auch nach 2 Dekaden der Datenerhebung zum Verständnis von Therapierisiken beiträgt sowie Faktoren identifiziert, die die Wirkung der Therapien beeinflussen können.

Implementing an automated monitoring process in a digital, longitudinal observational cohort study

Background

Clinical data collection requires correct and complete data sets in order to perform correct statistical analysis and draw valid conclusions. While in randomized clinical trials much effort concentrates on data monitoring, this is rarely the case in observational studies- due to high numbers of cases and often-restricted resources. We have developed a valid and cost-effective monitoring tool, which can substantially contribute to an increased data quality in observational research.

Methods

An automated digital monitoring system for cohort studies developed by the German Rheumatism Research Centre (DRFZ) was tested within the disease register RABBIT-SpA, a longitudinal observational study including patients with axial spondyloarthritis and psoriatic arthritis. Physicians and patients complete electronic case report forms (eCRF) twice a year for up to 10 years. Automatic plausibility checks were implemented to verify all data after entry into the eCRF. To identify conflicts that cannot be found by this approach, all possible conflicts were compiled into a catalog. This “conflict catalog” was used to create queries, which are displayed as part of the eCRF. The proportion of queried eCRFs and responses were analyzed by descriptive methods. For the analysis of responses, the type of conflict was assigned to either a single conflict only (affecting individual items) or a conflict that required the entire eCRF to be queried.

Results

Data from 1883 patients was analyzed. A total of n = 3145 eCRFs submitted between baseline (T0) and T3 (12 months) had conflicts (40–64%). Fifty-six to 100% of the queries regarding eCRFs that were completely missing were answered. A mean of 1.4 to 2.4 single conflicts occurred per eCRF, of which 59–69% were answered. The most common missing values were CRP, ESR, Schober’s test, data on systemic glucocorticoid therapy, and presence of enthesitis.

Conclusion

Providing high data quality in large observational cohort studies is a major challenge, which requires careful monitoring. An automated monitoring process was successfully implemented and well accepted by the study centers. Two thirds of the queries were answered with new data. While conventional manual monitoring is resource-intensive and may itself create new sources of errors, automated processes are a convenient way to augment data quality.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-021-02563-2.