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Reeve K, On BI, Havla J, Burns J, Gosteli-Peter MA, Alabsawi A, Alayash Z, Götschi A, Seibold H, Mansmann U, Held U. Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis. Cochrane Database Syst Rev 2023; 9:CD013606. [PMID: 37681561 PMCID: PMC10486189 DOI: 10.1002/14651858.cd013606.pub2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
BACKGROUND Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system that affects millions of people worldwide. The disease course varies greatly across individuals and many disease-modifying treatments with different safety and efficacy profiles have been developed recently. Prognostic models evaluated and shown to be valid in different settings have the potential to support people with MS and their physicians during the decision-making process for treatment or disease/life management, allow stratified and more precise interpretation of interventional trials, and provide insights into disease mechanisms. Many researchers have turned to prognostic models to help predict clinical outcomes in people with MS; however, to our knowledge, no widely accepted prognostic model for MS is being used in clinical practice yet. OBJECTIVES To identify and summarise multivariable prognostic models, and their validation studies for quantifying the risk of clinical disease progression, worsening, and activity in adults with MS. SEARCH METHODS We searched MEDLINE, Embase, and the Cochrane Database of Systematic Reviews from January 1996 until July 2021. We also screened the reference lists of included studies and relevant reviews, and references citing the included studies. SELECTION CRITERIA We included all statistically developed multivariable prognostic models aiming to predict clinical disease progression, worsening, and activity, as measured by disability, relapse, conversion to definite MS, conversion to progressive MS, or a composite of these in adult individuals with MS. We also included any studies evaluating the performance of (i.e. validating) these models. There were no restrictions based on language, data source, timing of prognostication, or timing of outcome. DATA COLLECTION AND ANALYSIS Pairs of review authors independently screened titles/abstracts and full texts, extracted data using a piloted form based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), assessed risk of bias using the Prediction Model Risk Of Bias Assessment Tool (PROBAST), and assessed reporting deficiencies based on the checklist items in Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD). The characteristics of the included models and their validations are described narratively. We planned to meta-analyse the discrimination and calibration of models with at least three external validations outside the model development study but no model met this criterion. We summarised between-study heterogeneity narratively but again could not perform the planned meta-regression. MAIN RESULTS We included 57 studies, from which we identified 75 model developments, 15 external validations corresponding to only 12 (16%) of the models, and six author-reported validations. Only two models were externally validated multiple times. None of the identified external validations were performed by researchers independent of those that developed the model. The outcome was related to disease progression in 39 (41%), relapses in 8 (8%), conversion to definite MS in 17 (18%), and conversion to progressive MS in 27 (28%) of the 96 models or validations. The disease and treatment-related characteristics of included participants, and definitions of considered predictors and outcome, were highly heterogeneous amongst the studies. Based on the publication year, we observed an increase in the percent of participants on treatment, diversification of the diagnostic criteria used, an increase in consideration of biomarkers or treatment as predictors, and increased use of machine learning methods over time. Usability and reproducibility All identified models contained at least one predictor requiring the skills of a medical specialist for measurement or assessment. Most of the models (44; 59%) contained predictors that require specialist equipment likely to be absent from primary care or standard hospital settings. Over half (52%) of the developed models were not accompanied by model coefficients, tools, or instructions, which hinders their application, independent validation or reproduction. The data used in model developments were made publicly available or reported to be available on request only in a few studies (two and six, respectively). Risk of bias We rated all but one of the model developments or validations as having high overall risk of bias. The main reason for this was the statistical methods used for the development or evaluation of prognostic models; we rated all but two of the included model developments or validations as having high risk of bias in the analysis domain. None of the model developments that were externally validated or these models' external validations had low risk of bias. There were concerns related to applicability of the models to our research question in over one-third (38%) of the models or their validations. Reporting deficiencies Reporting was poor overall and there was no observable increase in the quality of reporting over time. The items that were unclearly reported or not reported at all for most of the included models or validations were related to sample size justification, blinding of outcome assessors, details of the full model or how to obtain predictions from it, amount of missing data, and treatments received by the participants. Reporting of preferred model performance measures of discrimination and calibration was suboptimal. AUTHORS' CONCLUSIONS The current evidence is not sufficient for recommending the use of any of the published prognostic prediction models for people with MS in clinical routine today due to lack of independent external validations. The MS prognostic research community should adhere to the current reporting and methodological guidelines and conduct many more state-of-the-art external validation studies for the existing or newly developed models.
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Affiliation(s)
- Kelly Reeve
- Epidemiology, Biostatistics and Prevention Institute, University of Zürich, Zurich, Switzerland
| | - Begum Irmak On
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Joachim Havla
- lnstitute of Clinical Neuroimmunology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Jacob Burns
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | | | - Albraa Alabsawi
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Zoheir Alayash
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
- Institute of Health Services Research in Dentistry, University of Münster, Muenster, Germany
| | - Andrea Götschi
- Epidemiology, Biostatistics and Prevention Institute, University of Zürich, Zurich, Switzerland
| | | | - Ulrich Mansmann
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
- Pettenkofer School of Public Health, Munich, Germany
| | - Ulrike Held
- Epidemiology, Biostatistics and Prevention Institute, University of Zürich, Zurich, Switzerland
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Rispoli MG, D'Apolito M, Pozzilli V, Tomassini V. Lessons from immunotherapies in multiple sclerosis. HANDBOOK OF CLINICAL NEUROLOGY 2023; 193:293-311. [PMID: 36803817 DOI: 10.1016/b978-0-323-85555-6.00013-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
The improved understanding of multiple sclerosis (MS) neurobiology alongside the development of novel markers of disease will allow precision medicine to be applied to MS patients, bringing the promise of improved care. Combinations of clinical and paraclinical data are currently used for diagnosis and prognosis. The addition of advanced magnetic resonance imaging and biofluid markers has been strongly encouraged, since classifying patients according to the underlying biology will improve monitoring and treatment strategies. For example, silent progression seems to contribute significantly more than relapses to overall disability accumulation, but currently approved treatments for MS act mainly on neuroinflammation and offer only a partial protection against neurodegeneration. Further research, involving traditional and adaptive trial designs, should strive to halt, repair or protect against central nervous system damage. To personalize new treatments, their selectivity, tolerability, ease of administration, and safety must be considered, while to personalize treatment approaches, patient preferences, risk-aversion, and lifestyle must be factored in, and patient feedback used to indicate real-world treatment efficacy. The use of biosensors and machine-learning approaches to integrate biological, anatomical, and physiological parameters will take personalized medicine a step closer toward the patient's virtual twin, in which treatments can be tried before they are applied.
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Affiliation(s)
- Marianna G Rispoli
- Institute for Advanced Biomedical Technologies (ITAB) and Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; MS Centre, SS. Annunziata University Hospital, Chieti, Italy
| | - Maria D'Apolito
- Institute for Advanced Biomedical Technologies (ITAB) and Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; MS Centre, SS. Annunziata University Hospital, Chieti, Italy
| | - Valeria Pozzilli
- Institute for Advanced Biomedical Technologies (ITAB) and Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; MS Centre, SS. Annunziata University Hospital, Chieti, Italy
| | - Valentina Tomassini
- Institute for Advanced Biomedical Technologies (ITAB) and Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; MS Centre, SS. Annunziata University Hospital, Chieti, Italy.
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3
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Freund M, Schiffmann I, Rahn AC, Chard D, Lukas C, Scheiderbauer J, Sippel A, Heesen C. Understanding Magnetic Resonance Imaging in Multiple Sclerosis (UMIMS): Development and Piloting of an Online Education Program About Magnetic Resonance Imaging for People With Multiple Sclerosis. Front Neurol 2022; 13:856240. [PMID: 35418941 PMCID: PMC8996193 DOI: 10.3389/fneur.2022.856240] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 02/22/2022] [Indexed: 11/17/2022] Open
Abstract
Background People with multiple sclerosis (pwMS) lack sufficient magnetic resonance imaging (MRI) knowledge to truly participate in frequently occurring MRI-related therapy decisions. An evidence-based patient information (EBPI) about MRI is currently lacking. Objective The aim of this study was to develop an evidence-based online education program about limitations and benefits of MRI for pwMS. Ultimately, our goal was to improve MRI risk-knowledge, empower pwMS, and promote shared decision-making. Methods The program's contents were based on literature research and a previous pilot study. It was revised following 2 evaluation rounds with pwMS, MRI experts and expert patients. In a pilot study, n = 92 pwMS received access to the program for 4 weeks. User experiences and acceptance, MRI knowledge (MRI-RIKNO 2.0 questionnaire) and emotions and attitudes toward MRI (MRI-EMA questionnaire) were assessed. Results were compared to a previous survey population of n = 508 pwMS without access to the program. Results Participants rated the program as easy to understand, interesting, relevant, recommendable, and encouraging. In comparison to pwMS without access to the program, MRI risk-knowledge and perceived MRI competence were higher. Conclusion Satisfaction with the program and good MRI-risk knowledge after usage demonstrates the need and applicability of EBPI about MRI in MS.
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Affiliation(s)
- Magalie Freund
- Department of Neurology, Institute of Neuroimmunology and Multiple Sclerosis, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Insa Schiffmann
- Department of Neurology, Institute of Neuroimmunology and Multiple Sclerosis, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany.,Department of Neurology, University Medical Centre Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Anne Christin Rahn
- Department of Neurology, Institute of Neuroimmunology and Multiple Sclerosis, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany.,Institute for Social Medicine and Epidemiology, Nursing Research Unit, University of Lübeck, Lübeck, Germany
| | - Declan Chard
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom.,National Institute for Health Research (NIHR), University College London Hospitals (UCLH), Biomedical Research Centre, London, United Kingdom
| | - Carsten Lukas
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, Institute of Neuroradiology, St. Josef Hospital, Ruhr-University Bochum, Bochum, Germany.,Department of Diagnostic and Interventional Radiology and Nuclear Medicine, St. Josef Hospital, Ruhr-University Bochum, Bochum, Germany
| | - Jutta Scheiderbauer
- Foundation for Self-Determination and Self-Representation for People With MS, Trier, Germany
| | - Anna Sippel
- Department of Neurology, Institute of Neuroimmunology and Multiple Sclerosis, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Christoph Heesen
- Department of Neurology, Institute of Neuroimmunology and Multiple Sclerosis, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany.,Department of Neurology, University Medical Centre Hamburg-Eppendorf (UKE), Hamburg, Germany
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Zacharzewska-Gondek A, Pokryszko-Dragan A, Budrewicz S, Sąsiadek M, Trybek G, Bladowska J. The role of ADC values within the normal-appearing brain in the prognosis of multiple sclerosis activity during interferon-β therapy in the 3-year follow-up: a preliminary report. Sci Rep 2020; 10:12828. [PMID: 32732968 PMCID: PMC7393067 DOI: 10.1038/s41598-020-69383-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Accepted: 07/03/2020] [Indexed: 11/17/2022] Open
Abstract
Predictors of multiple sclerosis (MS) activity during disease-modifying treatment are being extensively investigated. The aim of this study was to assess the prognosis of NEDA (no evidence of disease activity) status during IFN-β (interferon-β) treatment, using apparent diffusion coefficient (ADC) measurements obtained at initial MRI (magnetic resonance imaging). In 87 MS patients treated with IFN-β, ADC values were calculated for 13 regions of normal-appearing white and grey matter (NAWM, NAGM) based on MRI performed with a 1.5 T magnet before (MS0, n = 45) or after one year of therapy (MS1, n = 42). Associations were evaluated between ADC, conventional MRI findings, demographic and clinical factors and NEDA status within the following 3 years using logistic, Cox and multinomial logistic regression models. NEDA rates in the MS0 group were 64.4%, 46.5% and 33.3% after the 1st, 2nd and 3rd year of treatment, respectively and in MS1 patients 71.4% and 48.7% for the periods 1st–2nd and 1st–3rd years of treatment, respectively. ADC values in the NAWM regions contributed to loss of NEDA and its clinical and radiological components, with a 1–3% increase in the risk of NEDA loss (p = 0.0001–0.0489) in both groups. ADC measurements may have an additional prognostic value with regard to NEDA status.
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Affiliation(s)
- Anna Zacharzewska-Gondek
- Department of General and Interventional Radiology and Neuroradiology, Wroclaw Medical University, 213 Borowska Street, 50-556, Wroclaw, Poland.
| | - Anna Pokryszko-Dragan
- Department and Clinic of Neurology, Wroclaw Medical University, 213 Borowska Street, 50-556, Wroclaw, Poland
| | - Sławomir Budrewicz
- Department and Clinic of Neurology, Wroclaw Medical University, 213 Borowska Street, 50-556, Wroclaw, Poland
| | - Marek Sąsiadek
- Department of General and Interventional Radiology and Neuroradiology, Wroclaw Medical University, 213 Borowska Street, 50-556, Wroclaw, Poland
| | - Grzegorz Trybek
- Department of Oral Surgery, Pomeranian Medical University, 72 Powstańców Wielkopolskich Street, 70-111, Szczecin, Poland
| | - Joanna Bladowska
- Department of General and Interventional Radiology and Neuroradiology, Wroclaw Medical University, 213 Borowska Street, 50-556, Wroclaw, Poland
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Colombo C, Confalonieri P, Rovaris M, La Mantia L, Galeazzi P, Silena Trevisan, Pariani A, Gerevini S, De Stefano N, Guglielmino R, Caserta C, Mosconi P, Filippini G. The IN-DEEP project "INtegrating and Deriving Evidence, Experiences, Preferences": a web information model on magnetic resonance imaging for people with multiple sclerosis. J Neurol 2020; 267:2421-2431. [PMID: 32361839 DOI: 10.1007/s00415-020-09864-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 04/22/2020] [Accepted: 04/23/2020] [Indexed: 12/31/2022]
Abstract
INTRODUCTION The IN-DEEP project aims to provide people with multiple sclerosis (PwMS) with evidence-based information on magnetic resonance imaging (MRI) in diagnosis and monitoring the disease through a website, and to collect their opinions on the clarity of the website's contents and its usefulness. METHODS AND ANALYSIS A multidisciplinary advisory board committee was set up. We investigated the experience, attitude and information needs on MRI through three meetings with 24 PwMS, facilitated by an expert researcher and an observer. We developed the website on the basis of input from PwMS and systematic reviews and guidelines, assessed with AMSTAR and AGREE II. We sought feedback from nine PwMS who pilot-tested the beta-version of the website, during a meeting and through phone interviews and judged whether the contents were clear, understandable and useful, and the website was easily navigable. The website is in Italian. RESULTS The website ( https://www.istituto-besta.it/in-deep-risonanza-magnetica2 ) provides two levels of information, different layouts and visualization of data covering MRI diagnostic accuracy, sensitivity and specificity, contents on how MRI can monitor PwMS over time to determine changes in the condition and evaluate treatment effects, practical information on how to prepare for the exam, educational tools and a glossary. The website was judged clear and useful by a sample of PwMS. CONCLUSIONS The website is a tool to address PwMS information needs on the role of MRI. It could be used by neurologists to facilitate communication with PwMS.
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Affiliation(s)
- Cinzia Colombo
- Laboratory of Research and Consumer Involvement, Department of Public Health, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy.
| | - Paolo Confalonieri
- Multiple Sclerosis Centre, Unit of Neuroimmunology and Neuromuscular Diseases, Fondazione IRCCS, Istituto Neurologico Carlo Besta, via Celoria 11, 20133, Milan, Italy
| | - Marco Rovaris
- IRCCS Don C. Gnocchi Foundation ONLUS, Via Capecelatro 66, 20148, Milan, Italy
| | - Loredana La Mantia
- IRCCS Don C. Gnocchi Foundation ONLUS, Via Capecelatro 66, 20148, Milan, Italy
| | | | | | | | - Simonetta Gerevini
- Unit of Neuroradiology, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery, and Neurosciences, University of Siena, Siena, Italy
| | - Roberta Guglielmino
- Scientific Research Area, Italian Multiple Sclerosis Foundation (FISM), Via Operai 40, 16149, Genoa, Italy
| | - Cinzia Caserta
- Multiple Sclerosis Center, Policlinico G. Rodolico, University of Catania, Catania, Italy
| | - Paola Mosconi
- Laboratory of Research and Consumer Involvement, Department of Public Health, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy
| | - Graziella Filippini
- Scientific Direction, Carlo Besta Foundation and Neurological Institute, via Celoria 11, 20133, Milan, Italy
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Lorefice L, Murgia F, Fenu G, Frau J, Coghe G, Murru MR, Tranquilli S, Visconti A, Marrosu MG, Atzori L, Cocco E. Assessing the Metabolomic Profile of Multiple Sclerosis Patients Treated with Interferon Beta 1a by 1H-NMR Spectroscopy. Neurotherapeutics 2019; 16:797-807. [PMID: 30820880 PMCID: PMC6694336 DOI: 10.1007/s13311-019-00721-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Metabolomic research has emerged as a promising approach to identify potential biomarkers in multiple sclerosis (MS). The aim of the present study was to determine the effect of interferon beta (IFN ß) on the metabolome of MS patients to explore possible biomarkers of disease activity and therapeutic response. Twenty-one MS patients starting IFN ß therapy (Rebif® 44 μg; s.c. 3 times per week) were enrolled. Blood samples were obtained at baseline and after 6, 12, and 24 months of IFN ß treatment and were analyzed by high-resolution nuclear magnetic resonance spectroscopy. Changes in metabolites were analyzed. After IFN ß exposure, patients were divided into responders and nonresponders according to the "no evidence of disease activity" (NEDA-3) definition (absence of relapses, disability progression, and magnetic resonance imaging activity), and samples obtained at baseline were analyzed to evaluate the presence of metabolic differences predictive of IFN ß response. The results of the investigation demonstrated differential distribution of baseline samples compared to those obtained during IFN ß exposure, particularly after 24 months of treatment (R2X = 0.812, R2Y = 0.797, Q2 = 0.613, p = 0.003). In addition, differences in the baseline metabolome between responder and nonresponder patients with respect to lactate, acetone, 3-OH-butyrate, tryptophan, citrate, lysine, and glucose levels were found (R2X = 0.442, R2Y = 0.768, Q2 = 0.532, p = 0.01). In conclusion, a metabolomic approach appears to be a promising, noninvasive tool that could potentially contribute to predicting the efficacy of MS therapies.
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Affiliation(s)
- Lorena Lorefice
- Multiple Sclerosis Centre, Department of Medical Sciences and Public Health, Binaghi Hospital, University of Cagliari, via Is Guadazzonis 2, 09126, Cagliari, Italy.
| | - Federica Murgia
- Multiple Sclerosis Centre, Department of Medical Sciences and Public Health, Binaghi Hospital, University of Cagliari, via Is Guadazzonis 2, 09126, Cagliari, Italy
| | - Giuseppe Fenu
- Multiple Sclerosis Centre, Department of Medical Sciences and Public Health, Binaghi Hospital, University of Cagliari, via Is Guadazzonis 2, 09126, Cagliari, Italy
| | - Jessica Frau
- Multiple Sclerosis Centre, Department of Medical Sciences and Public Health, Binaghi Hospital, University of Cagliari, via Is Guadazzonis 2, 09126, Cagliari, Italy
| | - Giancarlo Coghe
- Multiple Sclerosis Centre, Department of Medical Sciences and Public Health, Binaghi Hospital, University of Cagliari, via Is Guadazzonis 2, 09126, Cagliari, Italy
| | - Maria Rita Murru
- Multiple Sclerosis Centre, Department of Medical Sciences and Public Health, Binaghi Hospital, University of Cagliari, via Is Guadazzonis 2, 09126, Cagliari, Italy
| | - Stefania Tranquilli
- Multiple Sclerosis Centre, Department of Medical Sciences and Public Health, Binaghi Hospital, University of Cagliari, via Is Guadazzonis 2, 09126, Cagliari, Italy
| | | | - Maria Giovanna Marrosu
- Multiple Sclerosis Centre, Department of Medical Sciences and Public Health, Binaghi Hospital, University of Cagliari, via Is Guadazzonis 2, 09126, Cagliari, Italy
| | - Luigi Atzori
- Department of Biomedical Sciences, University of Cagliari, 09126, Cagliari, Italy
| | - Eleonora Cocco
- Multiple Sclerosis Centre, Department of Medical Sciences and Public Health, Binaghi Hospital, University of Cagliari, via Is Guadazzonis 2, 09126, Cagliari, Italy
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Boziki M, Lagoudaki R, Melo P, Kanidou F, Bakirtzis C, Nikolaidis I, Grigoriadou E, Afrantou T, Tatsi T, Matsi S, Grigoriadis N. Induction of apoptosis in CD4(+) T-cells is linked with optimal treatment response in patients with relapsing-remitting multiple sclerosis treated with Glatiramer acetate. J Neurol Sci 2019; 401:43-50. [DOI: 10.1016/j.jns.2019.03.030] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 02/11/2019] [Accepted: 03/28/2019] [Indexed: 11/29/2022]
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8
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Kalincik T, Manouchehrinia A, Sobisek L, Jokubaitis V, Spelman T, Horakova D, Havrdova E, Trojano M, Izquierdo G, Lugaresi A, Girard M, Prat A, Duquette P, Grammond P, Sola P, Hupperts R, Grand'Maison F, Pucci E, Boz C, Alroughani R, Van Pesch V, Lechner-Scott J, Terzi M, Bergamaschi R, Iuliano G, Granella F, Spitaleri D, Shaygannejad V, Oreja-Guevara C, Slee M, Ampapa R, Verheul F, McCombe P, Olascoaga J, Amato MP, Vucic S, Hodgkinson S, Ramo-Tello C, Flechter S, Cristiano E, Rozsa C, Moore F, Luis Sanchez-Menoyo J, Laura Saladino M, Barnett M, Hillert J, Butzkueven H. Towards personalized therapy for multiple sclerosis: prediction of individual treatment response. Brain 2017; 140:2426-2443. [PMID: 29050389 DOI: 10.1093/brain/awx185] [Citation(s) in RCA: 87] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Accepted: 06/20/2017] [Indexed: 11/14/2022] Open
Abstract
Timely initiation of effective therapy is crucial for preventing disability in multiple sclerosis; however, treatment response varies greatly among patients. Comprehensive predictive models of individual treatment response are lacking. Our aims were: (i) to develop predictive algorithms for individual treatment response using demographic, clinical and paraclinical predictors in patients with multiple sclerosis; and (ii) to evaluate accuracy, and internal and external validity of these algorithms. This study evaluated 27 demographic, clinical and paraclinical predictors of individual response to seven disease-modifying therapies in MSBase, a large global cohort study. Treatment response was analysed separately for disability progression, disability regression, relapse frequency, conversion to secondary progressive disease, change in the cumulative disease burden, and the probability of treatment discontinuation. Multivariable survival and generalized linear models were used, together with the principal component analysis to reduce model dimensionality and prevent overparameterization. Accuracy of the individual prediction was tested and its internal validity was evaluated in a separate, non-overlapping cohort. External validity was evaluated in a geographically distinct cohort, the Swedish Multiple Sclerosis Registry. In the training cohort (n = 8513), the most prominent modifiers of treatment response comprised age, disease duration, disease course, previous relapse activity, disability, predominant relapse phenotype and previous therapy. Importantly, the magnitude and direction of the associations varied among therapies and disease outcomes. Higher probability of disability progression during treatment with injectable therapies was predominantly associated with a greater disability at treatment start and the previous therapy. For fingolimod, natalizumab or mitoxantrone, it was mainly associated with lower pretreatment relapse activity. The probability of disability regression was predominantly associated with pre-baseline disability, therapy and relapse activity. Relapse incidence was associated with pretreatment relapse activity, age and relapsing disease course, with the strength of these associations varying among therapies. Accuracy and internal validity (n = 1196) of the resulting predictive models was high (>80%) for relapse incidence during the first year and for disability outcomes, moderate for relapse incidence in Years 2-4 and for the change in the cumulative disease burden, and low for conversion to secondary progressive disease and treatment discontinuation. External validation showed similar results, demonstrating high external validity for disability and relapse outcomes, moderate external validity for cumulative disease burden and low external validity for conversion to secondary progressive disease and treatment discontinuation. We conclude that demographic, clinical and paraclinical information helps predict individual response to disease-modifying therapies at the time of their commencement.
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Affiliation(s)
- Tomas Kalincik
- CORe, Department of Medicine, University of Melbourne, 300 Grattan St, Melbourne, 3050, Australia.,Department of Neurology, Royal Melbourne Hospital, 300 Grattan St, Melbourne, 3050, Australia
| | - Ali Manouchehrinia
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, SE-17177, Sweden
| | - Lukas Sobisek
- Department of Neurology and Center of Clinical Neuroscience, General University Hospital and Charles University in Prague, Katerinska 30, Prague, 12808, Czech Republic.,Department of Statistics and Probability, University of Economics in Prague, Winston Churchill Sq 1938/4, Prague, 13067, Czech Republic
| | - Vilija Jokubaitis
- Department of Neurology, Royal Melbourne Hospital, 300 Grattan St, Melbourne, 3050, Australia.,Department of Medicine, University of Melbourne, 300 Grattan St, Melbourne, 3050, Australia
| | - Tim Spelman
- Department of Neurology, Royal Melbourne Hospital, 300 Grattan St, Melbourne, 3050, Australia.,Department of Medicine, University of Melbourne, 300 Grattan St, Melbourne, 3050, Australia
| | - Dana Horakova
- Department of Neurology and Center of Clinical Neuroscience, General University Hospital and Charles University in Prague, Katerinska 30, Prague, 12808, Czech Republic
| | - Eva Havrdova
- Department of Neurology and Center of Clinical Neuroscience, General University Hospital and Charles University in Prague, Katerinska 30, Prague, 12808, Czech Republic
| | - Maria Trojano
- University of Bari, Via Calefati 53, Bari, 70122, Italy
| | - Guillermo Izquierdo
- Hospital Universitario Virgen Macarena, Amador de los Rios 48-50. 4a, Sevilla, 41003, Spain
| | - Alessandra Lugaresi
- Department of Neuroscience, Imaging and Clinical Sciences, University 'G. d'Annunzio', Via dei Vestini, Chieti, 66100, Italy.,Department of Biomedical and Neuromotor Sciences, University of Bologna, Via dei Vestini, Bologna, 66100, Italy
| | - Marc Girard
- Hopital Notre Dame, 1560 Sherbrooke East, Montreal, H2L 4M1, Canada; CHUM and Universite de Montreal, Montreal, Canada
| | - Alexandre Prat
- Hopital Notre Dame, 1560 Sherbrooke East, Montreal, H2L 4M1, Canada; CHUM and Universite de Montreal, Montreal, Canada
| | - Pierre Duquette
- Hopital Notre Dame, 1560 Sherbrooke East, Montreal, H2L 4M1, Canada; CHUM and Universite de Montreal, Montreal, Canada
| | - Pierre Grammond
- Centre de réadaptation déficience physique Chaudière-Appalache, 9500 blvd Centre-Hospitalier, Levis, G6X 0A1, Canada
| | - Patrizia Sola
- Nuovo Ospedale Civile Sant'Agostino/Estense, via giardini 1355, Modena, 41100, Italy
| | - Raymond Hupperts
- Zuyderland Ziekenhuis, Walramstraat 23, Sittard, 6131 BK, The Netherlands
| | | | - Eugenio Pucci
- Azienda Sanitaria Unica Regionale Marche - AV3, Via Santa Lucia 2, Macerata, 62100, Italy
| | - Cavit Boz
- KTU Medical Faculty Farabi Hospital, Karadeniz Technical University, Trabzon, 61080, Turkey
| | - Raed Alroughani
- Amiri Hospital, P.O. Box 1661. Qurtoba, Kuwait, 73767, Kuwait
| | - Vincent Van Pesch
- Cliniques Universitaires Saint-Luc, avenue Hippocrate, 10 UCL10/80, Brussels, 1200 BXL, Belgium
| | | | - Murat Terzi
- Ondokuz Mayis University, Medical Faculty, Kurupelit, Samsun, 55160, Turkey
| | - Roberto Bergamaschi
- C. Mondino National Neurological Institute, via Mondino 2, Pavia, 27100, Italy
| | - Gerardo Iuliano
- Ospedali Riuniti di Salerno, Via s. Leonardo, Salerno, 84100, Italy
| | | | - Daniele Spitaleri
- Azienda Ospedaliera di Rilievo Nazionale San Giuseppe Moscati Avellino, Contrada Amoretta, Avellino, 83100, Italy
| | | | - Celia Oreja-Guevara
- Hospital Universitario La Paz, Paseo de la Castellana 261, Madrid, 28050, Spain
| | - Mark Slee
- Flinders Medical Centre, Flinders Drive, Adelaide, 5042, Australia
| | - Radek Ampapa
- Nemocnice Jihlava, Vrchlickeho 59, Jihlava, 58633, Czech Republic
| | - Freek Verheul
- Groene Hart ziekenhuis, bleulandweg 10, Gouda, 2800 BB, The Netherlands
| | - Pamela McCombe
- Royal Brisbane and Women's Hospital, 33 North Street, Spring Hill, QLD 4000, Australia
| | - Javier Olascoaga
- Hospital Donostia, Paseo de Begiristain, San Sebastián, 20014, Spain
| | - Maria Pia Amato
- University of Florence, Viale Morgagni 85, Florence, 50134, Italy
| | - Steve Vucic
- Westmead Hospital, Hawkesbury Rd, Sydney, 2145, Australia
| | | | | | - Shlomo Flechter
- Assaf Harofeh Medical Center, Zerifin, Beer-Yaakov, 70100, Israel
| | | | - Csilla Rozsa
- Jahn Ferenc Teaching Hospital, Köves u. 1., Budapest, 1101, Hungary
| | - Fraser Moore
- Jewish General Hospital, 3755 Cote-Sainte-Catherine, Montreal, J7A 4T8, Canada
| | | | - Maria Laura Saladino
- INEBA - Institute of Neuroscience Buenos Aires, Guardia Vieja 4435, Buenos Aires, C1192AAW, Argentina
| | - Michael Barnett
- Brain and Mind Centre, University of Sydney, 100 Mallett, Camperdown, 2050, Australia
| | - Jan Hillert
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, SE-17177, Sweden
| | - Helmut Butzkueven
- Department of Neurology, Royal Melbourne Hospital, 300 Grattan St, Melbourne, 3050, Australia.,Department of Medicine, University of Melbourne, 300 Grattan St, Melbourne, 3050, Australia.,Department of Neurology, Box Hill Hospital, Monash University, Melbourne, Australia
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