Publications by authors named "Elisabetta Cartechini"

4 Publications

  • Page 1 of 1

Determinants of therapeutic lag in multiple sclerosis.

Mult Scler 2021 Jan 11:1352458520981300. Epub 2021 Jan 11.

CORe, Department of Medicine, University of Melbourne, Melbourne, VIC, Australia/Melbourne MS Centre, Department of Neurology, Royal Melbourne Hospital, Melbourne, VIC, Australia.

Background: A delayed onset of treatment effect, termed therapeutic lag, may influence the assessment of treatment response in some patient subgroups.

Objectives: The objective of this study is to explore the associations of patient and disease characteristics with therapeutic lag on relapses and disability accumulation.

Methods: Data from MSBase, a multinational multiple sclerosis (MS) registry, and OFSEP, the French MS registry, were used. Patients diagnosed with MS, minimum 1 year of exposure to MS treatment and 3 years of pre-treatment follow-up, were included in the analysis. Studied outcomes were incidence of relapses and disability accumulation. Therapeutic lag was calculated using an objective, validated method in subgroups stratified by patient and disease characteristics. Therapeutic lag under specific circumstances was then estimated in subgroups defined by combinations of clinical and demographic determinants.

Results: High baseline disability scores, annualised relapse rate (ARR) ⩾ 1 and male sex were associated with longer therapeutic lag on disability progression in sufficiently populated groups: females with expanded disability status scale (EDSS) < 6 and ARR < 1 had mean lag of 26.6 weeks (95% CI = 18.2-34.9), males with EDSS < 6 and ARR < 1 31.0 weeks (95% CI = 25.3-36.8), females with EDSS < 6 and ARR ⩾ 1 44.8 weeks (95% CI = 24.5-65.1), and females with EDSS ⩾ 6 and ARR < 1 54.3 weeks (95% CI = 47.2-61.5).

Conclusions: Pre-treatment EDSS and ARR are the most important determinants of therapeutic lag.
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January 2021

Treatment Response Score to Glatiramer Acetate or Interferon Beta-1a.

Neurology 2021 01 6;96(2):e214-e227. Epub 2020 Oct 6.

From the Department of Health Sciences (DISSAL) (F.B., M.P.S.), University of Genoa, Italy; CORe (T.K., C.M.), Department of Medicine, University of Melbourne, Australia; Department of Neurology (F.L.), Icahn School of Medicine at Mount Sinai, New York, NY; Department of Biostatistics (G.C.), University of Alabama at Birmingham; Department of Neurology and Center for Clinical Neuroscience (D.H., E.K.H.), First Medical Faculty, Charles University, Prague, Czech Republic; Department of Basic Medical Sciences, Neuroscience and Sense Organs (M. Trojano), University of Bari, Italy; Department of Neuroscience (A.P., M.G., P.D.), Faculty of Medicine, Université de Montréal, Quebec, Canada; Department of Neuroscience, Imaging, and Clinical Sciences (M.O.), University G. d'Annunzio, Chieti; IRCCS Istituto delle Scienze Neurologiche di Bologna (A.L.); Dipartimento di Scienze Biomediche e Neuromotorie (A.L.), Università di Bologna, Italy; Hospital Universitario Virgen Macarena (G. Izquierdo. S.E.), Sevilla, Spain; Department of Medical, Surgical Science and Advanced Technology "GF Ingrassia" (F.P.), University of Catania, Italy; Ondokuz Mayis University (M. Terzi), Department of Neurology, Samsun, Turkey; CISSS Chaudi're-Appalache (P.G.), Centre-Hospitalier, Levis, Quebec, Canada; IRCCS Mondino Foundation (R.B.), Pavia; Department of Neuroscience (P.S., D.F.), Azienda Ospedaliera Universitaria, Modena, Italy; Department of Neurology (S.O.), Dokuz Eylul University, Izmir, Turkey; Ospedali Riuniti di Salerno (G. Iuliano), Salerno, Italy; Department of Neurology (C.B.), Karadeniz Technical University, Trabzon, Turkey; Department of Neurology (R.H.), Zuyderland Medical Center, Sittard, the Netherlands; Neuro Rive-Sud (F.G.), Hôpital Charles LeMoyne, Greenfield Park, Quebec, Canada; Clinico San Carlos (C.O.-G), Madrid, Spain; Cliniques Universitaires Saint-Luc (V.v.P.); Université Catholique de Louvain (V.v.P.), Brussels, Belgium; UOC Neurologia (E.C.), Azienda Sanitaria Unica Regionale Marche-AV3, Macerata, Italy; Kommunehospitalet (T.P.), Arhus C, Denmark; Koc University (A.A.), School of Medicine; Bakirkoy Education and Research Hospital for Psychiatric and Neurological Diseases (A.S.), Istanbul, Turkey; Hospital Germans Trias i Pujol (C.R.-T.), Badalona, Spain; University of Queensland (P.M.), Brisbane, Australia; Haydarpasa Numune Training and Research Hospital (R.T.), Istanbul, Turkey; Central Clinical School (H.B.), Monash University, Melbourne, Australia; The University of Texas Health Science Center at Houston (J.S.W.); Rehabilitation Unit (C.S.), "Mons. L. Novarese" Hospital, Moncrivello; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genoa, Italy.

Objective: To compare the effectiveness of glatiramer acetate (GA) vs intramuscular interferon beta-1a (IFN-β-1a), we applied a previously published statistical method aimed at identifying patients' profiles associated with efficacy of treatments.

Methods: Data from 2 independent multiple sclerosis datasets, a randomized study (the Combination Therapy in Patients With Relapsing-Remitting Multiple Sclerosis [CombiRx] trial, evaluating GA vs IFN-β-1a) and an observational cohort extracted from MSBase, were used to build and validate a treatment response score, regressing annualized relapse rates (ARRs) on a set of baseline predictors.

Results: The overall ARR ratio of GA to IFN-β-1a in the CombiRx trial was 0.72. The response score (made up of a linear combination of age, sex, relapses in the previous year, disease duration, and Expanded Disability Status Scale score) detected differential response of GA vs IFN-β-1a: in the trial, patients with the largest benefit from GA vs IFN-β-1a (lower score quartile) had an ARR ratio of 0.40 (95% confidence interval [CI] 0.25-0.63), those in the 2 middle quartiles of 0.90 (95% CI 0.61-1.34), and those in the upper quartile of 1.14 (95% CI 0.59-2.18) (heterogeneity = 0.012); this result was validated on MSBase, with the corresponding ARR ratios of 0.58 (95% CI 0.46-0.72), 0.92 (95% CI 0.77-1.09,) and 1.29 (95% CI 0.97-1.71); heterogeneity < 0.0001).

Conclusions: We demonstrate the possibility of a criterion, based on patients' characteristics, to choose whether to treat with GA or IFN-β-1a. This result, replicated on an independent real-life cohort, may have implications for clinical decisions in everyday clinical practice.
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January 2021

Delay from treatment start to full effect of immunotherapies for multiple sclerosis.

Brain 2020 09;143(9):2742-2756

CORe, Department of Medicine, University of Melbourne, Melbourne, 3050, Australia.

In multiple sclerosis, treatment start or switch is prompted by evidence of disease activity. Whilst immunomodulatory therapies reduce disease activity, the time required to attain maximal effect is unclear. In this study we aimed to develop a method that allows identification of the time to manifest fully and clinically the effect of multiple sclerosis treatments ('therapeutic lag') on clinical disease activity represented by relapses and progression-of-disability events. Data from two multiple sclerosis registries, MSBase (multinational) and OFSEP (French), were used. Patients diagnosed with multiple sclerosis, minimum 1-year exposure to treatment, minimum 3-year pretreatment follow-up and yearly review were included in the analysis. For analysis of disability progression, all events in the subsequent 5-year period were included. Density curves, representing incidence of relapses and 6-month confirmed progression events, were separately constructed for each sufficiently represented therapy. Monte Carlo simulations were performed to identify the first local minimum of the first derivative after treatment start; this point represented the point of stabilization of treatment effect, after the maximum treatment effect was observed. The method was developed in a discovery cohort (MSBase), and externally validated in a separate, non-overlapping cohort (OFSEP). A merged MSBase-OFSEP cohort was used for all subsequent analyses. Annualized relapse rates were compared in the time before treatment start and after the stabilization of treatment effect following commencement of each therapy. We identified 11 180 eligible treatment epochs for analysis of relapses and 4088 treatment epochs for disability progression. External validation was performed in four therapies, with no significant difference in the bootstrapped mean differences in therapeutic lag duration between registries. The duration of therapeutic lag for relapses was calculated for 10 therapies and ranged between 12 and 30 weeks. The duration of therapeutic lag for disability progression was calculated for seven therapies and ranged between 30 and 70 weeks. Significant differences in the pre- versus post-treatment annualized relapse rate were present for all therapies apart from intramuscular interferon beta-1a. In conclusion we have developed, and externally validated, a method to objectively quantify the duration of therapeutic lag on relapses and disability progression in different therapies in patients more than 3 years from multiple sclerosis onset. Objectively defined periods of expected therapeutic lag allows insights into the evaluation of treatment response in randomized clinical trials and may guide clinical decision-making in patients who experience early on-treatment disease activity. This method will subsequently be applied in studies that evaluate the effect of patient and disease characteristics on therapeutic lag.
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September 2020

Role of electron microscopy in the diagnosis of cadasil syndrome: a study of 32 patients.

PLoS One 2013 17;8(6):e65482. Epub 2013 Jun 17.

Department of Experimental and Clinical Medicine, Section of Anatomy, School of Medicine, Università Politecnica delle Marche, Ancona, Italy.

Background And Purpose: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is caused by NOTCH3 gene mutations that result in vascular smooth muscle cell (VSMC) degeneration. Its distinctive feature by electron microscopy (EM) is granular osmiophilic material (GOM) detected in VSMC indentations and/or the extracellular space close to VSMCs. Reports of the sensitivity of EM in detecting GOM in biopsies from CADASIL patients are contradictory. We present data from 32 patients clinically suspected to have CADASIL and discuss the role of EM in its diagnosis in this retrospective study.

Methods: Skin, skeletal muscle, kidney and pericardial biopsies were examined by EM; the NOTCH3 gene was screened for mutations. Skin and muscle biopsies from 12 patients without neurological symptoms served as controls.

Results And Discussion: All GOM-positive patients exhibited NOTCH3 mutations and vice versa. This study i) confirms that EM is highly specific and sensitive for CADASIL diagnosis; ii) extends our knowledge of GOM distribution in tissues where it has never been described, e.g. pericardium; iii) documents a novel NOTCH3 mutation in exon 3; and iv) shows that EM analysis is critical to highlight the need for comprehensive NOTCH3 analysis. Our findings also confirm the genetic heterogeneity of CADASIL in a small Italian subpopulation and emphasize the difficulties in designing algorithms for molecular diagnosis.
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March 2014