Quantifying the risk of disease reactivation after interferon and glatiramer acetate discontinuation in multiple sclerosis: The VIAADISC score

Abstract

Background and purpose There is a lack of evidence guiding discontinuation of disease‐modifying therapy (DMT) in relapsing multiple sclerosis (RMS). Thus, the objective of this study was to generate and validate a risk score for disease reactivation after DMT discontinuation in RMS. Methods We drew a generation and validation dataset from two separate prospectively collected observational databases including RMS patients who received interferon‐β or glatiramer acetate for ≥12 months, then discontinued DMT for ≥6 months and had ≥2 years of follow‐up available. In the generation sample (n = 168), regression analysis was performed to identify clinical or magnetic resonance imaging (MRI) variables independently predicting disease reactivation after DMT discontinuation. A predictive score was calculated using the variables included in the multivariable model and applied to the validation sample (n = 98). Results The variables included in the final model as independent predictors of disease reactivation were age at discontinuation, MRI activity at discontinuation, and duration of clinical stability (all p < 0.001). The resulting score was able to robustly identify patients at high (83%–85%), moderate (36%–38%), and low risk (7%) of disease reactivation within 5 years after DMT discontinuation in both cohorts. Conclusions The composite VIAADISC score is a valuable tool to inform and support patients and neurologists in the process of decision making to discontinue injectable DMTs.

Evolution of Disease Modifying Therapy Benefits and Risks: An Argument for De-escalation as a Treatment Paradigm for Patients With Multiple Sclerosis

“…Therefore, age and disease activity are drivers of outcomes of DMT discontinuation. The Vienna-Innsbruck DMT discontinuation score predicts risk of disease reactivation after discontinuing glatiramer acetate or beta-interferons using a weighted calculation based on age, disease activity on MRI (≥3 new/enlarged T2 lesions or ≥1 gadolinium-enhancing lesion), and duration of stability (years since relapse or EDSS change) ( 24 ). In that study, patients age <45 years had a HR 4.3 (2.5, 7.1), p < 0.001 and patients age ≥45 and <55 years HR 2.1 (1.4, 3.8), p < 0.001, as compared to patients ≥55 years.…”

Estimating Risk of Multiple Sclerosis Disease Reactivation in Pregnancy and Postpartum: The VIPRiMS Score

“…The characteristics of the study cohorts with respect to age at conception, disease duration, and disease activity are in line with other cohorts presented in the literature suggesting generalizability of our results ( 3 , 4 , 7 , 10 , 23 ). Another strength is the robust and standardized statistical approach to generation and validation of the risk score ( 16 , 19 , 20 ).…”

“…The scoring system was developed and validated through the following steps (16,19,20) survival times are independent). A two-sided p-value < 0.05 was considered as statistically significant.…”

“…For this study, we included women aged ≥18 years with relapsing MS (RMS) from the Vienna Innsbruck MS database (VIMSD), who had documented pregnancy with conception ≥12 months after MS diagnosis and who had follow-up during whole pregnancy and ≥6 months postpartum ( 11 , 16 ). Pregnancies resulting in abortion, termination, or preterm delivery before the 24th gestation week were excluded to minimize confounding influence of pregnancy duration.…”

Clinical risk factors and prognostic model for idiopathic inflammatory demyelinating diseases after haploidentical hematopoietic stem cell transplantation in patients with hematological malignancies

“…As a kind of prediction model, prognostic models focus on the probability of disease recurrence, death, disability, and complications in the future. Several prediction models have been established for predicting the outcome of CNS demyelinating diseases, including Susan's prediction model, 135 the VIAADISC score model, 136 and Seery's prognostic model 137 . Susan's prediction model focused on the prediction of quality of life in adults with MS based on disease‐related risk factors and protective factors.…”