Fabio Pellegrini1, Massimiliano Copetti2, Francesca Bovis3, David Cheng4, Robert Hyde1, Carl de Moor5, Bernd C Kieseier6, Maria Pia Sormani7. 1. Biogen International GmbH, Baar, Switzerland. 2. Unit of Biostatistics, Fondazione IRCCS Casa Sollievo della Sofferenza Hospital, San Giovanni Rotondo, Italy. 3. Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy. 4. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 5. Biogen Inc., Cambridge, MA, USA. 6. Biogen Inc., Cambridge, MA, USA; Department of Neurology, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany. 7. Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy; IRCCS Ospedale Policlinico San Martino, Genoa, Italy.
Abstract
BACKGROUND: Stratified medicine methodologies based on subgroup analyses are often insufficiently powered. More powerful personalized medicine approaches are based on continuous scores. OBJECTIVE: We deployed a patient-specific continuous score predicting treatment response in patients with relapsing-remitting multiple sclerosis (RRMS). METHODS: Data from two independent randomized controlled trials (RCTs) were used to build and validate an individual treatment response (ITR) score, regressing annualized relapse rates (ARRs) on a set of baseline predictors. RESULTS: The ITR score for the combined treatment groups versus placebo detected differential clinical response in both RCTs. High responders in one RCT had a cross-validated ARR ratio of 0.29 (95% confidence interval (CI) = 0.13-0.55) versus 0.62 (95% CI = 0.47-0.83) for all other responders (heterogeneity p = 0.038) and were validated in the other RCT, with the corresponding ARR ratios of 0.31 (95% CI = 0.18-0.56) and 0.61 (95% CI = 0.47-0.79; heterogeneity p = 0.036). The strongest treatment effect modifiers were the Short Form-36 Physical Component Summary, age, Visual Function Test 2.5%, prior MS treatment and Expanded Disability Status Scale. CONCLUSION: Our modelling strategy detects and validates an ITR score and opens up avenues for building treatment response calculators that are also applicable in routine clinical practice.
BACKGROUND: Stratified medicine methodologies based on subgroup analyses are often insufficiently powered. More powerful personalized medicine approaches are based on continuous scores. OBJECTIVE: We deployed a patient-specific continuous score predicting treatment response in patients with relapsing-remitting multiple sclerosis (RRMS). METHODS: Data from two independent randomized controlled trials (RCTs) were used to build and validate an individual treatment response (ITR) score, regressing annualized relapse rates (ARRs) on a set of baseline predictors. RESULTS: The ITR score for the combined treatment groups versus placebo detected differential clinical response in both RCTs. High responders in one RCT had a cross-validated ARR ratio of 0.29 (95% confidence interval (CI) = 0.13-0.55) versus 0.62 (95% CI = 0.47-0.83) for all other responders (heterogeneity p = 0.038) and were validated in the other RCT, with the corresponding ARR ratios of 0.31 (95% CI = 0.18-0.56) and 0.61 (95% CI = 0.47-0.79; heterogeneity p = 0.036). The strongest treatment effect modifiers were the Short Form-36 Physical Component Summary, age, Visual Function Test 2.5%, prior MS treatment and Expanded Disability Status Scale. CONCLUSION: Our modelling strategy detects and validates an ITR score and opens up avenues for building treatment response calculators that are also applicable in routine clinical practice.
Authors: Jeff Rodgers; Tim Friede; Frederick W Vonberg; Cris S Constantinescu; Alasdair Coles; Jeremy Chataway; Martin Duddy; Hedley Emsley; Helen Ford; Leonora Fisniku; Ian Galea; Timothy Harrower; Jeremy Hobart; Huseyin Huseyin; Christopher M Kipps; Monica Marta; Gavin V McDonnell; Brendan McLean; Owen R Pearson; David Rog; Klaus Schmierer; Basil Sharrack; Agne Straukiene; Heather C Wilson; David V Ford; Rod M Middleton; Richard Nicholas Journal: Brain Date: 2022-05-24 Impact factor: 15.255
Authors: Rosa C Lucchetta; Letícia P Leonart; Marcus V M Gonçalves; Jefferson Becker; Roberto Pontarolo; Fernando Fernandez-Llimós; Astrid Wiens Journal: PLoS One Date: 2020-06-16 Impact factor: 3.240
Authors: Francesca Bovis; Tomas Kalincik; Fred Lublin; Gary Cutter; Charles Malpas; Dana Horakova; Eva Kubala Havrdova; Maria Trojano; Alexandre Prat; Marc Girard; Pierre Duquette; Marco Onofrj; Alessandra Lugaresi; Guillermo Izquierdo; Sara Eichau; Francesco Patti; Murat Terzi; Pierre Grammond; Roberto Bergamaschi; Patrizia Sola; Diana Ferraro; Serkan Ozakbas; Gerardo Iuliano; Cavit Boz; Raymond Hupperts; Francois Grand'Maison; Celia Oreja-Guevara; Vincent van Pesch; Elisabetta Cartechini; Thor Petersen; Ayse Altintas; Aysun Soysal; Cristina Ramo-Tello; Pamela McCombe; Recai Turkoglu; Helmut Butzkueven; Jerry S Wolinsky; Claudio Solaro; Maria Pia Sormani Journal: Neurology Date: 2020-10-06 Impact factor: 9.910