scholarly journals POS0461 RESPONSE TO BIOLOGIC THERAPY IN RHEUMATOID ARTHRITIS: RETHINKING OUR CLASSIFICATION

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 462.1-462
Author(s):  
E. Vallejo-Yagüe ◽  
S. Kandhasamy ◽  
E. Keystone ◽  
A. Finckh ◽  
R. Micheroli ◽  
...  

Background:In rheumatoid arthritis (RA), primary failure with biologic treatment may be understood as lack of initial clinical response, while secondary failure would be loss of effectiveness after an initial response. Despite these clinical concepts, there is no unifying operational definition of primary and secondary non-response to RA treatment in observational studies using real-world data. On top of data-driven challenges, when conceptualizing secondary non-responders, it is unclear if the mechanism behind loss of effectiveness after a brief initial response is similar to loss of effectiveness after previous benefit sustained over time.Objectives:This viewpoint aims to motivate discussion on how to define primary and secondary non-response in observational studies. Ultimately, we aim to trigger expert committees to develop standard terminology for these concepts.Methods:We discuss different methodologies for defining primary and secondary non-response in observational studies. To do so, we shortly overview challenges characteristic of performing observational studies in real-world data, and subsequently, we conceptualize whether treatment response should be a dichotomous classification (Primary response/non-response; Secondary response/non-response), or whether one should consider three response categories (Primary response/non-response; Primary sustained/non-sustained response; Secondary response/non-response).Results:RA or biologic registries are a common data source for studying treatment response in real-world data. While registries include disease-specific variables to assess disease progression, missing data, loss of follow-up, and visits restricted to the year or mid-year visit may present a challenge. We believe there is a general agreement to assess primary response within the first 6 month of treatment. However, conceptualizing secondary non-response, one could wonder if a patient with brief initial response and immediate loss of it should belong to the same response category as a patient who relapses after a period of prior benefit that was sustained over time. Until this concern is clarified, we recommend considering a period of sustained response as a pre-requisite for secondary failure. This would result in the following three categories: a) Primary non-response: Lack of response within the first 6 months of treatment; b) Primary sustained response: Maintenance of a positive effectiveness outcome for at least the first 12 months since treatment start; c) Secondary non-response: Loss of effectiveness after achieved primary sustained response. Figure 1 illustrates this classification through a decision tree. Since the underlying mechanisms for treatment failure may differ among the above-mentioned categories, we recommend to use the three-category classification. However, since this may pose additional methodological challenges in real-world data, optionally, a dichotomous 12-month time-point may be used to assess secondary non-response (unfavourable outcome after 12-months) in comparison to primary non-response or non-sustained response (unfavourable outcome within the first 12-months). Similarly, to study primary response, the solely 6-month timepoint may be used.Conclusion:A unified operational definition of treatment response will minimize heterogeneity among observational studies and help improve the ability to draw cross-study comparisons, which we believe would be of particular interest when identifying predictors of treatment failure. Thus, we hope to open the room for discussion and encourage expert committees to work towards a common approach to assess treatment primary and secondary non-response in RA in observational studies.Disclosure of Interests:Enriqueta Vallejo-Yagüe: None declared, Sreemanjari Kandhasamy: None declared, Edward Keystone Speakers bureau: Amgen, AbbVie, F. Hoffmann-La Roche Inc., Janssen Inc., Merck, Novartis, Pfizer Pharmaceuticals, Sanofi Genzyme, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb Company, Celltrion, Myriad Autoimmune, F. Hoffmann-La Roche Inc, Gilead, Janssen Inc, Lilly Pharmaceuticals, Merck, Pfizer Pharmaceuticals, Sandoz, Sanofi-Genzyme, Samsung Bioepsis, Grant/research support from: Amgen, Merck, Pfizer Pharmaceuticals, PuraPharm, Axel Finckh Speakers bureau: Pfizer, Eli-Lilly, Paid instructor for: Pfizer, Eli-Lilly, Consultant of: AbbVie, AB2Bio, BMS, Gilead, Pfizer, Viatris, Grant/research support from: Pfizer, BMS, Novartis, Raphael Micheroli Consultant of: Gilead, Eli-Lilly, Pfizer and Abbvie, Andrea Michelle Burden: None declared

2017 ◽  
Vol 4 (suppl_1) ◽  
pp. S659-S659
Author(s):  
Jaspreet Banga ◽  
Sobia Nizami ◽  
Jihad Slim ◽  
Sandhya Nagarakanti ◽  
Mario Portilla ◽  
...  

2014 ◽  
Vol 17 (3) ◽  
pp. A189 ◽  
Author(s):  
K.E. Smoyer-Tomic ◽  
K.C. Young ◽  
C. Winchester

2018 ◽  
Vol 20 ◽  
pp. 47-58 ◽  
Author(s):  
Sudesna Chatterjee ◽  
Melanie J Davies ◽  
Kamlesh Khunti

2019 ◽  
Vol 26 (1) ◽  
pp. 23-37 ◽  
Author(s):  
Jeffrey A Cohen ◽  
Maria Trojano ◽  
Ellen M Mowry ◽  
Bernard MJ Uitdehaag ◽  
Stephen C Reingold ◽  
...  

Randomized controlled clinical trials and real-world observational studies provide complementary information but with different validity. Some clinical questions (disease behavior, prognosis, validation of outcome measures, comparative effectiveness, and long-term safety of therapies) are often better addressed using real-world data reflecting larger, more representative populations. Integration of disease history, clinician-reported outcomes, performance tests, and patient-reported outcome measures during patient encounters; imaging and biospecimen analyses; and data from wearable devices increase dataset utility. However, observational studies utilizing these data are susceptible to many potential sources of bias, creating barriers to acceptance by regulatory agencies and the medical community. Therefore, data standardization and validation within datasets, harmonization across datasets, and application of appropriate analysis methods are important considerations. We review approaches to improve the scope, quality, and analyses of real-world data to advance understanding of multiple sclerosis and its treatment, as an example of opportunities to better support patient care and research.


ANALES RANM ◽  
2021 ◽  
Vol 138 (138(01)) ◽  
pp. 16-23
Author(s):  
Luis Martí-Bonmatí

This work defines a research on data strategy focused on medical imaging and derived image biomarkers to critically assess the concept of causal inference and uncertainties. Computational observational studies will be valued to generate casual inference from real world data. Our main goal is to propose a scientific methodology that allows to estimate causalities from observational studies through quality control of large databases, definition of plausible hypotheses, using computational estimated models and artificial intelligence tools. The computational approach of radiology to precision medicine by using epidemiological strategies is based on causal inference studies relies on real-world data observational, longitudinal, case-control analysis designed (being case the presence, and control the absence of the event to be estimated). In this new research setting, we consider disease in classical epidemiology as phenotyping, response to treatment and final prognosis; and exposure equals to the presence of a radiomic, dynamic image biomarker or AI modeling solution. Research with data on which causality is to be inferred must control for recruitment of closed cases, in which the researcher does not intervene in the patient’s clinical history but works on databases, collecting data to be secondary used in generating consistent causalities.


2016 ◽  
Vol 22 ◽  
pp. 219
Author(s):  
Roberto Salvatori ◽  
Olga Gambetti ◽  
Whitney Woodmansee ◽  
David Cox ◽  
Beloo Mirakhur ◽  
...  

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