MSBase Registry Helmut Butzkueven Director, MS Service, Royal - PowerPoint PPT Presentation

“Real - world” disease outcomes : Experience with the use of the MSBase Registry Helmut Butzkueven Director, MS Service, Royal Melbourne and Box Hill Hospitals, Australia Managing Director, MS Base Foundation

Data collection • Data collection has always been a core activity of doctors • “Medical notes”

Clinical practice data collection was very separate from research

Computerisation

General research challenges • Using these data for research blurs traditional boundaries of research and service • When is consent required? • How specific does it have to be?

Electronic medical record (EMR) for clinical practice and research • Generic EMR’s are not very useful for disease - specific data collection, because they are built for generic needs • Disease-specific modification of EMRs can be time-consuming and very expensive • Most centres wishing to collect disease - specific data have a specific disease interest – Build their own database – Use an available disease-specific EMR

A registry, like a trial, uses an agreed minimum dataset

The basic language of Multiple Sclerosis (a typical chronic disease) • Demographics • Diagnostic test (LP, VEP, MRI Brain and spinal cord classifiers) • EDSS/ Kurtzke Functional System Scores • Relapse date, site, treatment • Disease modifying drug start and stop dates

Information • The complexity grows over time….

iMed 35 yo woman: 12 years of MS…

The history of MSBase • The iMed electronic medical record launched by Serono as a service to doctors in 2001 • Rapidly became very popular in Europe, Australia and Canada • Thought-leaders associated with Serono (Nazih Ammoury, JP Malkowski, Samir Mechati) believed that these iMed records could create codified extract files and that • These extracts could be combined into a global outcomes database: MSBase

MSBase Principles: Investigator Autonomy • All investigators (centres) agree to a prospective minimum dataset collection • Use either iMed or an online data collection tool • All can propose and conduct studies utilsiing the minimum dataset • All can propose and request access to the dataset for analyses • Investigators must follow all local rules (consent, ethics approvals) • Investigators remain as custodians of their own datasets

Ethics/Consent • For research on the minimum dataset • Not specific – Demographic trends, global comparisons – Treatment effects – Serious adverse event rates • Allows – Collaboration with the pharmaceutical industry – Investigator reimbursements

Governance • Independence (Not for profit company) provides great flexibility • Clear separation of roles – Administration/Operations – Research teams – Business and scientific leadership • Formality : Following the mutually agreed rules – Documentation of procedures, committee terms of reference, delegations of authority

• The MSBase Registry – 28 Countries – Over 150 participating Centres – Over 34,400 patient datasets – 165,000 patient years of follow up – 338,000 EDSS (neurological score) evaluations – Median visit density is at 5.5 months

Enrolment since 2004

Can create clinically meaningful feedback to clinicians • Benchmarking • Severity calculators

Severity calculator

Severity calculator

A few recent analyses from MSBase • Therapy persistence: DMD discontinuation in clinical practice • Head-to-Head treatment comparisons

Therapy persistence

Background • Interferon-beta and glatiramer acetate are the most common initial therapies in relapsing MS. Their route of administration and tolerability profiles can limit persistence • Persistence in clinical practice and major factors determining persistence remain incompletely characterised. • We prospectively characterised treatment persistence in International MS populations using MSBase.

Patient studied in seen from onset cohort with first treatment initiation

Discontinuation rates 2015 Warrender-Sparkes et al, under revision MSJ

Treatment identity predicting discontinuation: Multivariable Survival model (3) Predictor Annualised rate HR P-value IFN β -1a SC 0.20 1.0 (Ref) IFN β -1a IM 0.19 0.98 NS IFN β -1b 0.21 1.10 NS GA 0.23 1.13 NS NAT 0.21 0.93 NS FTY 0.11 0.44 < 0.001

Factors predicting discontinuation: Multivariable Survival model (1) Predictor Annualised rate HR P-value Female Sex 0.23 1 Male Sex 0.16 0.73 <0.001 Australia 0.33 1 Netherlands 0.28 0.86 NS Canada 0.21 0.86 NS Italy 0.18 0.67 <0.001 Spain 0.15 0.49 <0.001

Head-to-head efficacy studies in MSBase

Introduction to Propensity Score Matching

Propensity Score Covariates • Propensity score: probability of receiving a Sex Age treatment based on a series of covariates Treating centre / country • Propensity score estimated using Disease duration Any prior immunosuppresive treatment multivariate logistic regression Number of treatment starts Number of treatment starts / disease duration EDSS Total relapse onsets last 12 months Total steroid-treated relapses last 12 months Total relapse onsets last 24 months Total steroid-treated relapses last 24 months Multivariate logistic regression model 0.41 Treatment A Treatment B 0 1 Propensity score 0.41 Kalincik et al., PLoS One 8:e63480

Propensity Score Overlapping A - Randomized Trial (a posteriori) Propensity score matching: 0 1 0.5 • Method that mimics Propensity Score randomization in observational B – Observational Study studies • Compares individuals who had a similar probability (propensity score) of receiving the same 0 1 0.5 Propensity Score treatment but actually received C – Unusable Observational Study different treatments. 0 1 0.5 Propensity Score

Propensity Score Matching Propensity score determined for each patient; patients received Treatment A are matched with patients with a similar propensity score who received Treatment B 0.90 0.48 0.22 0.35 0.70 0.76 0.15 0.71 0.15 0.87 0.91 0.79 0.10 0.74 0.65 0.07 0.33 0.74 0.32 0.95 0.35 0.82 0.39 0.69 0.42 0.42 0.15 0.81 0.82

Fingolimod or Natalizumab Relapse Fingolimod Natalizumab injectable

Fingolimod versus Natalizumab • After relapse on Injectable – 560 natalizumab switchers – 232 fingolimod switchers – Could match 407 NAT to 171 FNG

Persistence: highly similar in the treatment failure population (in the first two years only) Kalincik et al, Ann Neurol. 2015;77:425-35.

Time to first relapse Kalincik et al, Ann Neurol. 2015;77:425-35.

Annualised relapse rate Kalincik et al, Ann Neurol. 2015;77:425-35.

Disability Progression events- no difference Kalincik et al, Ann Neurol. 2015;77:425-35.

Disability regression events Kalincik et al, Ann Neurol. 2015;77:425-35.

Conclusions • Natalizumab was equal to fingolimod in – Persistence (over two years- it is likely that natalizumab will persistence will drop after that) – Disability progression • Natalizumab was superior to fingolimod in – Relapse rate reduction – Disability regression (20 versus 10 %) Kalincik et al, Ann Neurol. 2015;77:425-35.

Summary

MSBase registry • Is user-friendly • Value-add to clinical practice – Graphical representations of patient course – Benchmarking – Helps to create better decision tools

MS registries: • Are a great way to capture large populations for – Comparative DMD effectiveness – Long-term disease trends ALSO…. – Pregnancy exposure outcomes data – Safety registries (Cancer, infection, mortality) – National and regional registries

• With special thanks to • 150 investigating centres, 34400 patients • Analysts – Discontinuation (Claire Meyniel, Vilija Jokubaitis, Tim Spelman, Matthew Warrender-Sparkes ) – Head to head (Anna He, Tomas Kalincik, Tim Spelman) • MSBase Administration (Jill Byron, Eloise Hinson, Lisa Morgan, James Milesi ) • MSBase Platform and IT (Samir Mechati, Eric Bianchi, Alex Bulla, Matthieu Corageoud )