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1.

001-es BibID:BIBFORM120944
035-os BibID:(Scopus)85122546501
Első szerző:Brouwer, Edward De
Cím:Corrigendum to Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression : [Computer Methods and Programs in Biomedicine, Volume 208, (September 2021) 106180] / Edward De Brouwer, Thijs Becker, Yves Moreau, Eva Kubala Havrdova, Maria Trojano, Sara Eichau, Serkan Ozakbas, Marco Onofrj, Pierre Grammond, Jens Kuhle, Ludwig Kappos, Patrizia Sola, Elisabetta Cartechini, Jeannette Lechner-Scott, Raed Alroughani, Oliver Gerlach, Tomas Kalincik, Franco Granella, Francois Grand'Maison, Roberto Bergamaschi, Maria José Sá, Bart Van Wijmeersch, Aysun Soysal, Jose Luis Sanchez-Menoyo, Claudio Solaro, Cavit Boz, Gerardo Iuliano, Katherine Buzzard, Eduardo Aguera-Morales, Murat Terzi, Tamara Castillo Trivio, Daniele Spitaleri, Vincent Van Pesch, Vahid Shaygannejad, Fraser Moore, Celia Oreja-Guevara, Davide Maimone, Riadh Gouider, Tunde Csepany, Cristina Ramo-Tello, Liesbet Peeters
Dátum:2022
ISSN:0169-2607
Megjegyzések:Background and Objectives Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and controlled data sets. In this work we aim to predict disability progression by optimally extracting information from longitudinal patient data in the real-world setting, with a special focus on the sporadic sampling problem. Methods We use machine learning methods suited for patient trajectories modeling, such as recurrent neural networks and tensor factorization. A subset of 6682 patients from the MSBase registry is used. Results We can predict disability progression of patients in a two-year horizon with an ROC-AUC of 0.85, which represents a 32% decrease in the ranking pair error (1-AUC) compared to reference methods using static clinical features. Conclusions Compared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction and represents a step forward towards AI-assisted precision medicine in MS.
Tárgyszavak:Orvostudományok Klinikai orvostudományok hozzászólás
folyóiratcikk
Multiple sclerosis
Machine learning
Longitudinal data
Recurrent neural networks
Electronic health records
Disability progression
Real-world data
Megjelenés:Computer Methods And Programs In Biomedicine. - 213 (2022), p. 1-3. -
További szerzők:Becker, Thijs Moreau, Yves Havrdova, Eva Trojano, Maria Eichau, Sara Ozakbas, Serkan Onofrj, Marco Grammond, Pierre Kuhle, Jens Kappos, Ludwig Sola, Patrizia Cartechini, Elisabetta Lechner-Scott, Jeannette Alroughani, Raed Gerlach, Oliver Kalincik, Tomas Granella, Franco Grand'Maison, Francois Bergamaschi, Roberto Sá, Maria José Wijmeersch, Bart Van Soysal, Aysun Sanchez-Menoyo, Jose Solaro, Claudio Boz, Cavit Iuliano, Gerardo Buzzard, Katherine Aguera-Morales, Eduardo Terzi, Murat Trivio, Tamara Castillo Spitaleri, Daniele Pesch, Vincent van Shaygannejad, Vahid Moore, Fraser Oreja-Guevara, Celia Maimone, Davide Gouider, Riadh Csépány Tünde (1956-) (neurológus, pszichiáter) Ramo-Tello, Cristina Peeters, Liesbet
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2.

001-es BibID:BIBFORM085749
Első szerző:Brown, Jeremy William L.
Cím:The risk of relapse following on-treatment clinically silent lesions in patients with relapsing-remitting multiple sclerosis / Brown, J. W. L., Lugaresi A., Horakova D., Havrdova E., Jokubaitis V., Lechner-Scott J., Trojano M., Min M., Shaw C., Shuey N., Slee M., Mccombe P., Van Pesch V., Van Wijmeersch B., Prevost J., Moore F., Prat A., Girard M., Duquette P., Ayrignac X., Sempere A. Perez, Sanchez-Menoyo J. L., Ramo-Tello C., Csépány Tünde, Hutchinson M., De Luca G., Bergamaschi R., Granella F., Curti E., Tsantes E., Sola P., Ferraro D., Alroughani R., Hupperts R., Al-Harbi T., Sidhom Y., Boz C., Terzi M., Ozakbas S., Soysal A., Pucci E., Izquierdo G., Iuliano G., Rio M. Edite, Spitaleri D., Grammond P., Grand'Maison F., Butzkueven H., Kalincik T.
Dátum:2017
ISSN:1352-4585
Tárgyszavak:Orvostudományok Klinikai orvostudományok idézhető absztrakt
folyóiratcikk
Megjelenés:Multiple Sclerosis. - 23 : Suppl. 3 (2017), p. 992-994. -
További szerzők:Lugaresi, Alessandra Horakova, Dana Havrdova, Eva Jokubaitis, Vilija Lechner-Scott, Jeannette Trojano, Maria Min, M. Shaw, C. A. Shuey, Neil Slee, Mark McCombe, Pamela Pesch, Vincent van Wijmeersch, Bart Van Prevost, Julie Moore, Fraser Prat, Alexandre Girard, Marc Duquette, Pierre Ayrignac, X. Sempere, Perez A. Sanchez-Menoyo, Jose Ramo-Tello, Cristina Csépány Tünde (1956-) (neurológus, pszichiáter) Hutchinson, Michael De Luca, Giacomo Bergamaschi, Roberto Granella, Franco Curti, E. Tsantes, E. Sola, Patrizia Ferraro, D. Alroughani, Raed Hupperts, Raymond Al-Harbi, Talal Sidhom, Youssef Boz, Cavit Terzi, Murat Ozakbas, Serkan Soysal, Aysun Pucci, Eugenio Izquierdo, Guillermo Iuliano, Gerardo Rio, Edite M. Spitaleri, Daniele Grammond, Pierre Grand'Maison, Francois Butzkueven, Helmut Kalincik, Tomas
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3.

001-es BibID:BIBFORM126277
035-os BibID:(scopus)85201493485
Első szerző:De Brouwer, Edward
Cím:Machine-learning-based prediction of disability progression in multiple sclerosis : an observational, international, multi-center study / De Brouwer E., Becker T., Werthen-Brabants L., Dewulf P., Iliadis D., Dekeyser C., Laureys G., Van Wijmeersch B., Popescu V., Dhaene T., Deschrijver D., Waegeman W., De Baets B., Stock M., Horakova D., Patti F., Izquierdo G., Eichau S., Girard M., Prat A., Lugaresi A., Grammond P., Kalincik T., Alroughani R., Grand'Maison F., Skibina O., Terzi M., Lechner-Scott J., Gerlach O., Khoury S. J., Cartechini E., Van Pesch V., Sa M. J., Weinstock-Guttman B., Blanco Y., Ampapa R., Spitaleri D., Solaro C., Maimone D., Soysal A., Iuliano G., Gouider R., Castillo-Trivino T., Sánchez-Menoyo J. L., Laureys G., van der Walt A., Oh J., Aguera-Morales E., Altintas A., Al-Asmi A., de Gans K., Fragoso Y., Csepany T., Hodgkinson S., Deri N., Al-Harbi T., Taylor B., Gray O., Lalive P., Rozsa C., McGuigan C., Kermode A., Sempere A. P., Mihaela S., Simo M., Hardy T., Decoo D., Hughes S., Grigoriadis N., Sas A., Vella N., Moreau Y., Peeters L.
Dátum:2024
ISSN:2767-3170
Megjegyzések:Background Disability progression is a key milestone in the disease evolution of people with multiple sclerosis (PwMS). Prediction models of the probability of disability progression have not yet reached the level of trust needed to be adopted in the clinic. A common benchmark to assess model development in multiple sclerosis is also currently lacking. Methods Data of adult PwMS with a follow-up of at least three years from 146 MS centers, spread over 40 countries and collected by the MSBase consortium was used. With basic inclusion criteria for quality requirements, it represents a total of 15, 240 PwMS. External validation was performed and repeated five times to assess the significance of the results. Transparent Reporting for Individual Prognosis Or Diagnosis (TRIPOD) guidelines were followed. Confirmed disability progression after two years was predicted, with a confirmation window of six months. Only routinely collected variables were used such as the expanded disability status scale, treatment, relapse information, and MS course. To learn the probability of disability progression, state-of-the-art machine learning models were investigated. The discrimination performance of the models is evaluated with the area under the receiver operator curve (ROC-AUC) and under the precision recall curve (AUC-PR), and their calibration via the Brier score and the expected calibration error. All our preprocessing and model code are available at https://gitlab.com/edebrouwer/ms_benchmark, making this task an ideal benchmark for predicting disability progression in MS. Findings Machine learning models achieved a ROC-AUC of 0?71 ? 0?01, an AUC-PR of 0?26 ? 0?02, a Brier score of 0?1 ? 0?01 and an expected calibration error of 0?07 ? 0?04. The history of disability progression was identified as being more predictive for future disability progression than the treatment or relapses history. Conclusions Good discrimination and calibration performance on an external validation set is achieved, using only routinely collected variables. This suggests machine-learning models can reliably inform clinicians about the future occurrence of progression and are mature for a clinical impact study.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Machine learning
disability progression
multiple sclerosis
Megjelenés:PLOS Digital Health. - 3 : 7 (2024), p. 1-25. -
További szerzők:Becker, Thijs Werthen-Brabants, Lorin Dewulf, Pieter Iliadis, Dimitrios Dekeyser, Cathérine Laureys, Guy Wijmeersch, Bart Van Popescu, Veronica Dhaene, Tom Deschrijver, Dirk Waegeman, Willem De Baets, Bernard Stock, Michael J. Horakova, Dana Patti, Francesco Izquierdo, Guillermo Eichau, Sara Girard, Marc Prat, Alexandre Lugaresi, Alessandra Grammond, Pierre Kalincik, Tomas Alroughani, Raed Grand'Maison, Francois Skibina, Olga Terzi, Murat Lechner-Scott, Jeannette Gerlach, Oliver Khoury, Samia J. Cartechini, Elisabetta Pesch, Vincent van Sá, Maria José Weinstock-Guttman, Bianca Blanco, Yolanda Ampapa, Radek Spitaleri, Daniele Solaro, Claudio Maimone, Davide Soysal, Aysun Iuliano, Gerardo Gouider, Riadh Castillo Triviño, Tamara Sanchez-Menoyo, Jose Laureys, Guy (Universitary Hospital Ghent) Walt, Anneke van der Oh, Jiwon Aguera-Morales, Eduardo Altintas, Ayse Al-Asmi, Abdullah de Gans, Koen Fragoso, Yara Csépány Tünde (1956-) (neurológus, pszichiáter) Hodgkinson, Suzanne Deri, Norma Al-Harbi, Talal Taylor, Bruce V. Gray, Orla Lalive, Patrice H. Rózsa Csilla McGuigan, Christopher Kermode, Allan G. Sempere, Perez A. Mihaela, Simu Simó Magdolna Hardy, Todd A. Decoo, Danny Hughes, Stella Grigoriadis, Nikolaos Sas Attila Vella Norbert Moreau, Yves Peeters, Liesbet
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4.

001-es BibID:BIBFORM103017
035-os BibID:(Wos)000685503300008 (Scopus)85107912293
Első szerző:De Brouwer, Edward
Cím:Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression / De Brouwer Edward, Becker Thijs, Moreau Yves, Havrdova Eva Kubala, Trojano Maria, Eichau Sara, Ozakbas Serkan, Onofrj Marco, Grammond Pierre, Kuhle Jens, Kappos Ludwig, Sola Patrizia, Cartechini Elisabetta, Lechner-Scott Jeannette, Alroughani Raed, Gerlach Oliver, Kalincik Tomas, Granella Franco, Grand'Maison Francois, Bergamaschi Roberto, José Sá Maria, Van Wijmeersch Bart, Soysal Aysun, Sanchez-Menoyo Jose Luis, Solaro Claudio, Boz Cavit, Iuliano Gerardo, Buzzard Katherine, Aguera-Morales Eduardo, Terzi Murat, Trivio Tamara Castillo, Spitaleri Daniele, Van Pesch Vincent, Shaygannejad Vahid, Moore Fraser, Oreja-Guevara Celia, Maimone Davide, Gouider Riadh, Csepany Tunde, Ramo-Tello Cristina, Peeters Liesbet
Dátum:2021
ISSN:0169-2607
Megjegyzések:Background and Objectives: Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and controlled data sets. In this work we aim to predict disability progression by optimally extracting information from longitudinal patient data in the real-world setting, with a special focus on the sporadic sampling problem. Methods: We use machine learning methods suited for patient trajectories modeling, such as recurrent neural networks and tensor factorization. A subset of 6682 patients from the MSBase registry is used. Results: We can predict disability progression of patients in a two-year horizon with an ROC-AUC of 0.85, which represents a 32% decrease in the ranking pair error (1-AUC) compared to reference methods using static clinical features. Conclusions: Compared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction and represents a step forward towards AI-assisted precision medicine in MS.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Computer Methods And Programs In Biomedicine. - 208 (2021), p. 1-14. -
További szerzők:Becker, Thijs Moreau, Yves Havrdova, Eva Trojano, Maria Eichau, Sara Ozakbas, Serkan Onofrj, Marco Grammond, Pierre Kuhle, Jens Kappos, Ludwig Sola, Patrizia Cartechini, Elisabetta Lechner-Scott, Jeannette Alroughani, Raed Gerlach, Oliver Kalincik, Tomas Granella, Franco Grand'Maison, Francois Bergamaschi, Roberto José Sá, Maria Wijmeersch, Bart Van Soysal, Aysun Sanchez-Menoyo, Jose Solaro, Claudio Boz, Cavit Iuliano, Gerardo Buzzard, Katherine Aguera-Morales, Eduardo Terzi, Murat Trivio, Tamara Castillo Spitaleri, Daniele Pesch, Vincent van Shaygannejad, Vahid Moore, Fraser Oreja-Guevara, Celia Maimone, Davide Gouider, Riadh Csépány Tünde (1956-) (neurológus, pszichiáter) Ramo-Tello, Cristina Peeters, Liesbet
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5.

001-es BibID:BIBFORM126270
035-os BibID:(scopus)85190160325 (wos)001201473600001
Első szerző:Dekeyser, Cathérine
Cím:Routine CSF parameters as predictors of disease course in multiple sclerosis : an MSBase cohort study / Dekeyser C., Hautekeete M., Cambron M., Van Pesch V., Patti F., Kuhle J., Khoury S., Lechner Scott J., Gerlach O., Lugaresi A., Maimone D., Surcinelli A., Grammond P., Kalincik T., Habek M., Willekens B., Macdonell R., Lalive P., Csepany T., Butzkueven H., Boz C., Tomassini V., Foschi M., Sánchez-Menoyo J. L., Altintas A., Mrabet S., Iuliano G., Sa M. J., Alroughani R., Karabudak R., Aguera-Morales E., Gray O., de Gans K., van der Walt A., McCombe P. A., Deri N., Garber J., Al-Asmi A., Skibina O., Duquette P., Cartechini E., Spitaleri D., Gouider R., Soysal A., Van Hijfte L., Slee M., Amato M. P., Buzzard K., Laureys G.
Dátum:2024
ISSN:0022-3050 1468-330X
Megjegyzések:Background: It remains unclear whether routine cerebrospinal fluid (CSF) parameters can serve as predictors of multiple sclerosis (MS) disease course. Methods: This large-scale cohort study included persons with MS with CSF data documented in the MSBase registry. CSF parameters to predict time to reach confirmed Expanded Disability Status Scale (EDSS) scores 4, 6 and 7 and annualised relapse rate in the first 2 years after diagnosis (ARR2) were assessed using (cox) regression analysis. Results: In total, 11 245 participants were included of which 93.7% (n=10 533) were persons with relapsing-remitting MS (RRMS). In RRMS, the presence of CSF oligoclonal bands (OCBs) was associated with shorter time to disability milestones EDSS 4 (adjusted HR=1.272 (95% CI, 1.089 to 1.485), p=0.002), EDSS 6 (HR=1.314 (95% CI, 1.062 to 1.626), p=0.012) and EDSS 7 (HR=1.686 (95% CI, 1.111 to 2.558), p=0.014). On the other hand, the presence of CSF pleocytosis (?5 cells/?L) increased time to moderate disability (EDSS 4) in RRMS (HR=0.774 (95% CI, 0.632 to 0.948), p=0.013). None of the CSF variables were associated with time to disability milestones in persons with primary progressive MS (PPMS). The presence of CSF pleocytosis increased ARR2 in RRMS (adjusted R2=0.036, p=0.015). Conclusions: In RRMS, the presence of CSF OCBs predicts shorter time to disability milestones, whereas CSF pleocytosis could be protective. This could however not be found in PPMS. CSF pleocytosis is associated with short-term inflammatory disease activity in RRMS. CSF analysis provides prognostic information which could aid in clinical and therapeutic decision-making.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
CLINICAL NEUROLOGY
CSF
MULTIPLE SCLEROSIS
NEUROIMMUNOLOGY
Megjelenés:Journal of Neurology, Neurosurgery, and Psychiatry . - 95 : 11 (2024), p. 1021-1031. -
További szerzők:Hautekeete, Matthias Cambron, Melissa Pesch, Vincent van Patti, Francesco Kuhle, Jens Khoury, Samia J. Lechner Scott, Jeanette Gerlach, Oliver Lugaresi, Alessandra Maimone, Davide Surcinelli, Andrea Grammond, Pierre Kalincik, Tomas Habek, Mario Willekens, Barbara Macdonell, Richard Lalive, Patrice H. Csépány Tünde (1956-) (neurológus, pszichiáter) Butzkueven, Helmut Boz, Cavit Tomassini, Valentina Foschi, Matteo Sanchez-Menoyo, Jose Altintas, Ayse Mrabet, Saloua Iuliano, Gerardo Sá, Maria José Alroughani, Raed Karabudak, Rana Aguera-Morales, Eduardo Gray, Orla de Gans, Koen Walt, Anneke van der McCombe, Pamela Deri, Norma Garber, Justin Al-Asmi, Abdullah Skibina, Olga Duquette, Pierre Cartechini, Elisabetta Spitaleri, Daniele Gouider, Riadh Soysal, Aysun Van Hijfte, Liesbeth Slee, Mark Amato, Maria Pia Buzzard, Katherine Laureys, Guy
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6.

001-es BibID:BIBFORM107545
035-os BibID:(Scopus)85148460657 (WoS)000952991100026
Első szerző:Diouf, Ibrahima
Cím:Variability of the response to immunotherapy among subgroups of patients with multiple sclerosis / Diouf Ibrahima, Malpas Charles B., Sharmin Sifat, Roos Izanne, Horakova Dana, Havrdova Eva Kubala, Patti Francesco, Shaygannejad Vahid, Ozakbas Serkan, Izquierdo Guillermo, Eichau Sara, Onofrj Marco, Lugaresi Alessandra, Alroughani Raed, Prat Alexandre, Girard Marc, Duquette Pierre, Terzi Murat, Boz Cavit, Grand'Maison Francois, Hamdy Sherif, Sola Patrizia, Ferraro Diana, Grammond Pierre, Turkoglu Recai, Buzzard Katherine, Skibina Olga, Yamout Bassem, Altintas Ayse, Gerlach Oliver, van Pesch Vincent, Blanco Yolanda, Maimone Davide, Lechner-Scott Jeannette, Bergamaschi Roberto, Karabudak Rana, Iuliano Gerardo, McGuigan Chris, Cartechini Elisabetta, Barnett Michael, Hughes Stella, Sa Maria José, Solaro Claudio, Kappos Ludwig, Ramo-Tello Cristina, Cristiano Edgardo, Hodgkinson Suzanne, Spitaleri Daniele, Soysal Aysun, Petersen Thor, Slee Mark, Butler Ernest, Granella Franco, de Gans Koen, McCombe Pamela, Ampapa Radek, Van Wijmeersch Bart, van der Walt Anneke, Butzkueven Helmut, Prevost Julie, Sinnige L. G. F., Sanchez-Menoyo Jose Luis, Vucic Steve, Laureys Guy, Van Hijfte Liesbeth, Khurana Dheeraj, Macdonell Richard, Gouider Riadh, Castillo-Trivino Tamara, Gray Orla, Aguera-Morales Eduardo, Al-Asmi Abdullah, Shaw Cameron, Deri Norma, Al-Harbi Talal, Fragoso Yara, Csepany Tunde, Perez Sempere Angel, Trevino-Frenk Irene, Schepel Jan, Moore Fraser, Kalincik Tomas
Dátum:2023
ISSN:1351-5101
Megjegyzések:Background This study assessed the effect of patient characteristics on the response to disease modifying therapy (DMT) in in multiple sclerosis (MS). Methods We extracted data from 61,810 patients from 135 centres across 35 countries from the MSBase registry. The selection criteria were: clinically isolated syndrome or definite MS; follow-up ?1 year; ?3 EDSS scores; and with ?1 score recorded per year. Marginal structural models with interaction terms were used to compare the hazards of 12-month confirmed worsening and improvement of disability, and the incidence of relapses between treated and untreated patients stratified by their characteristics. Results Among 24,344 patients with relapsing MS, those on DMTs experienced 48% reduction in relapse incidence (hazard ratio (HR)=0.52, 95%CI=0.45-0.60), 46% lower risk of disability worsening (HR=0.54, 95%CI=0.41-0.71) and 32% greater chance of disability improvement (HR=1.32, 95%CI=1.09-1.59). The effect of DMTs on EDSS worsening and improvement and the risk of relapses was attenuated with more severe disability. The magnitude of the effect of DMT on suppressing relapses declined with higher prior relapse rate and prior cerebral MRI activity. We did not find any evidence for the effect of age on the effectiveness of DMT. After inclusion of 1985 participants with progressive MS, the effect of DMT on disability mostly depended on MS phenotype, whereas its effect on relapses was driven mainly by prior relapse activity. Conclusions DMT is generally most effective among patients with lower disability and in relapsing MS phenotypes. There is no evidence attenuation of the effect of DMT with age.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:European Journal Of Neurology. - 30 : 4 (2023), p. 1014-1024. -
További szerzők:Malpas, Charles B. Sharmin, Sifat Roos, Izanne Horakova, Dana Havrdova, Eva Patti, Francesco Shaygannejad, Vahid Ozakbas, Serkan Izquierdo, Guillermo Eichau, Sara Onofrj, Marco Lugaresi, Alessandra Alroughani, Raed Prat, Alexandre Girard, Marc Duquette, Pierre Terzi, Murat Boz, Cavit Grand'Maison, Francois Hamdy, Sherif Sola, Patrizia Ferraro, Diana Grammond, Pierre Turkoglu, Recai Buzzard, Katherine Skibina, Olga Yamout, Bassem Altintas, Ayse Gerlach, Oliver Pesch, Vincent van Blanco, Yolanda Maimone, Davide Lechner-Scott, Jeannette Bergamaschi, Roberto Karabudak, Rana Iuliano, Gerardo McGuigan, Christopher Cartechini, Elisabetta Barnett, Michael Hughes, Stella Sá, Maria José Solaro, Claudio Kappos, Ludwig Ramo-Tello, Cristina Cristiano, Edgardo Hodgkinson, Suzanne Spitaleri, Daniele Soysal, Aysun Petersen, Thor Slee, Mark Butler, Ernest Granella, Franco de Gans, Koen McCombe, Pamela Ampapa, Radek Wijmeersch, Bart Van Walt, Anneke van der Butzkueven, Helmut Prevost, Julie Sinnige, L. G. F. Sanchez-Menoyo, Jose Vucic, Steve Laureys, Guy Van Hijfte, Liesbeth Khurana, Dheeraj Macdonell, Richard Gouider, Riadh Castillo Triviño, Tamara Gray, Orla Aguera-Morales, Eduardo Al-Asmi, Abdullah Shaw, Cameron Deri, Norma Al-Harbi, Talal Fragoso, Yara Csépány Tünde (1956-) (neurológus, pszichiáter) Perez Sempere, Angel Trevino-Frenk, Irene Schepel, Jan Moore, Fraser Kalincik, Tomas
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7.

001-es BibID:BIBFORM083269
Első szerző:Fambiatos, Adam
Cím:Risk of secondary progressive multiple sclerosis : a longitudinal study / Adam Fambiatos, Vilija Jokubaitis, Dana Horakova, Eva Kubala Havrdova, Maria Trojano, Alexandre Prat, Marc Girard, Pierre Duquette, Alessandra Lugaresi, Guillermo Izquierdo, Francois Grand'Maison, Pierre Grammond, Patrizia Sola, Diana Ferraro, Raed Alroughani, Murat Terzi, Raymond Hupperts, Cavit Boz, Jeannette Lechner-Scott, Eugenio Pucci, Roberto Bergamaschi, Vincent Van Pesch, Serkan Ozakbas, Franco Granella, Recai Turkoglu, Gerardo Iuliano, Daniele Spitaleri, Pamela McCombe, Claudio Solaro, Mark Slee, Radek Ampapa, Aysun Soysal, Thor Petersen, Jose Luis Sanchez-Menoyo, Freek Verheul, Julie Prevost, Youssef Sidhom, Bart Van Wijmeersch, Steve Vucic, Edgardo Cristiano, Maria Laura Saladino, Norma Deri, Michael Barnett, Javier Olascoaga, Fraser Moore, Olga Skibina, Orla Gray, Yara Fragoso, Bassem Yamout, Cameron Shaw, Bhim Singhal, Neil Shuey, Suzanne Hodgkinson, Ayse Altintas, Talal Al-Harbi, Tunde Csepany, Bruce Taylor, Jordana Hughes, Jae-Kwan Jun, Anneke van der Walt, Tim Spelman, Helmut Butzkueven, Tomas Kalincik, MSBase Study Group
Dátum:2020
ISSN:1352-4585
Megjegyzések:Background: The risk factors for conversion from relapsing-remitting to secondary progressive multiple sclerosis remain highly contested. Objective: The aim of this study was to determine the demographic, clinical and paraclinical features that influence the risk of conversion to secondary progressive multiple sclerosis. Methods: Patients with adult-onset relapsing?remitting multiple sclerosis and at least four recorded disability scores were selected from MSBase, a global observational cohort. The risk of conversion to objectively defined secondary progressive multiple sclerosis was evaluated at multiple time points per patient using multivariable marginal Cox regression models. Sensitivity analyses were performed. Results: A total of 15,717 patients were included in the primary analysis. Older age (hazard ratio (HR) = 1.02, p < 0.001), longer disease duration (HR = 1.01, p = 0.038), a higher Expanded Disability Status Scale score (HR = 1.30, p < 0.001), more rapid disability trajectory (HR = 2.82, p < 0.001) and greater number of relapses in the previous year (HR = 1.07, p = 0.010) were independently associated with an increased risk of secondary progressive multiple sclerosis. Improving disability (HR = 0.62, p = 0.039) and disease-modifying therapy exposure (HR = 0.71, p = 0.007) were associated with a lower risk. Recent cerebral magnetic resonance imaging activity, evidence of spinal cord lesions and oligoclonal bands in the cerebrospinal fluid were not associated with the risk of conversion. Conclusion: Risk of secondary progressive multiple sclerosis increases with age, duration of illness and worsening disability and decreases with improving disability. Therapy may delay the onset of secondary progression.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Multiple Sclerosis. - 26 : 1 (2020), p. 79-90. -
További szerzők:Jokubaitis, Vilija Horakova, Dana Kubala Havrdova, Eva Trojano, Maria Prat, Alexandre Girard, Marc Duquette, Pierre Lugaresi, Alessandra Izquierdo, Guillermo Grand'Maison, Francois Grammond, Pierre Sola, Patrizia Ferraro, Diana Alroughani, Raed Terzi, Murat Hupperts, Raymond Boz, Cavit Lechner-Scott, Jeannette Pucci, Eugenio Bergamaschi, Roberto Pesch, Vincent van Ozakbas, Serkan Granella, Franco Turkoglu, Recai Iuliano, Gerardo Spitaleri, Daniele McCombe, Pamela Solaro, Claudio Slee, Mark Ampapa, Radek Soysal, Aysun Petersen, Thor Sanchez-Menoyo, Jose Verheul, Freek Prevost, Julie Sidhom, Youssef Wijmeersch, Bart Van Vucic, Steve Cristiano, Edgardo Saladino, Maria Laura Deri, Norma Barnett, Michael Olascoaga, Javier Moore, Fraser Skibina, Olga Gray, Orla Fragoso, Yara Yamout, Bassem Shaw, Cameron Singhal, Bhim Shuey, Neil Hodgkinson, Suzanne Altintas, Ayse Al-Harbi, Talal Csépány Tünde (1956-) (neurológus, pszichiáter) Taylor, Bruce V. Hughes, Jordana Jun, Jae-Kwan Walt, Anneke van der Spelman, Tim Butzkueven, Helmut Kalincik, Tomas MSBase Study Group
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Intézményi repozitóriumban (DEA) tárolt változat
Borító:

8.

001-es BibID:BIBFORM083270
035-os BibID:(PMID)31877445
Első szerző:Kunchok, Amy
Cím:Clinical and therapeutic predictors of disease outcomes in AQP4-IgG+ neuromyelitis optica spectrum disorder / Amy Kunchok, Charles Malpas, Petra Nytrova, Eva Kubala Havrdova, Raed Alroughani, Murat Terzi, Bassem Yamout, Jyh Yung Hor, Rana Karabudak, Cavit Boz, Serkan Ozakbas, Javier Olascoaga, Magdolna Simo, Franco Granella, Francesco Patti, Pamela McCombe, Tunde Csepany, Bhim Singhal, Roberto Bergamaschi, Yara Fragoso, Talal Al-Harbi, Recai Turkoglu, Jeannette Lechner-Scott, Guy Laureys, Celia Oreja-Guevara, Eugenio Pucci, Patrizia Sola, Diana Ferraro, Ayse Altintas, Aysun Soysal, Steve Vucic, Francois Grand'Maison, Guillermo Izquierdo, Sara Eichau, Alessandra Lugaresi, Marco Onofrj, Maria Trojano, Mark Marriott, Helmut Butzkueven, Ilya Kister, Tomas Kalincik
Dátum:2020
ISSN:2211-0348
Megjegyzések:Aquaporin-4-IgG positive (AQP4-IgG+) Neuromyelitis Optica Spectrum Disorder (NMOSD) is an uncommon central nervous system autoimmune disorder. Disease outcomes in AQP4-IgG+NMOSD are typically measured by relapse rate and disability. Using the MSBase, a multi-centre international registry, we aimed to examine the impact immunosuppressive therapies and patient characteristics as predictors of disease outcome measures in AQP4-IgG+NMOSD. METHOD: This MSBase cohort study of AQP4-IgG+NMOSD patients examined modifiers of relapse in a multivariable proportional hazards model and expanded disability status score (EDSS) using a mixed effects model. RESULTS: 206 AQP4-IgG+ patients were included (median follow-up 3.7 years). Age (hazard ratio [HR] = 0.82 per decade, p = 0.001), brainstem onset (HR = 0.45, p = 0.009), azathioprine (HR = 0.46, p<0.001) and mycophenolate mofetil (HR = 0.09, p = 0.012) were associated with a reduced risk of relapse. A greater EDSS was associated with age (β = 0.45 (per decade), p<0.001) and disease duration (β = 0.07 per year, p<0.001). A slower increase in EDSS was associated with azathioprine (β = -0.48, p<0.001), mycophenolate mofetil (β = -0.69, p = 0.04) and rituximab (β = -0.35, p = 0.024). INTERPRETATION: This study has demonstrated that azathioprine and mycophenolate mofetil reduce the risk of relapses and disability progression is modified by azathioprine, mycophenolate mofetil and rituximab. Age and disease duration were the only patient characteristics that modified the risk of relapse and disability in our cohort.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Disability
Immunosuppression
Neuromyelitis optica spectrum disorder
Predictors
Relapses
Therapy
Megjelenés:Multiple Sclerosis and Related Disorders. - 38 (2020), p. 1-8. -
További szerzők:Malpas, Charles Nytrova, Petra Havrdova, Eva Alroughani, Raed Terzi, Murat Yamout, Bassem Hor, Jyh Yung Karabudak, Rana Boz, Cavit Ozakbas, Serkan Olascoaga, Javier Simó Magdolna Granella, Franco Patti, Francesco McCombe, Pamela Csépány Tünde (1956-) (neurológus, pszichiáter) Singhal, Bhim Bergamaschi, Roberto Fragoso, Yara Al-Harbi, Talal Turkoglu, Recai Lechner-Scott, Jeannette Laureys, Guy Oreja-Guevara, Celia Pucci, Eugenio Sola, Patrizia Ferraro, Diana Altintas, Ayse Soysal, Aysun Vucic, Steve Grand'Maison, Francois Izquierdo, Guillermo Eichau, Sara Lugaresi, Alessandra Onofrj, Marco Trojano, Maria Marriott, Mark Butzkueven, Helmut Kister, Ilya Kalincik, Tomas
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Intézményi repozitóriumban (DEA) tárolt változat
Borító:

9.

001-es BibID:BIBFORM135460
035-os BibID:(Scopus)105002792941 (WoS)001470351300001
Első szerző:Müller, Jannis
Cím:Standardized Definition of Progression Independent of Relapse Activity (PIRA) in Relapsing-Remitting Multiple Sclerosis / Jannis Müller, Sifat Sharmin, Johannes Lorscheider, Serkan Ozakbas, Rana Karabudak, Dana Horakova, Bianca Weinstock-Guttman, Vahid Shaygannejad, Masoud Etemadifar, Raed Alroughani, Francesco Patti, Sara Eichau, Alexandre Prat, Alessandra Lugaresi, Valentina Tomassini, Allan G. Kermode, Maria Pia Amato, Recai Turkoglu, Ayse Altintas, Katherine Buzzard, Aysun Soysal, Anneke van der Walt, Helmut Butzkueven, Yolanda Blanco, Oliver Gerlach, Samia J. Khoury, Michael Barnett, Nevin John, Jeannette Lechner-Scott, Matteo Foschi, Andrea Surcinelli, Vincent van Pesch, Julie Prevost, Maria Jose Sa, Davide Maimone, Marie D'hooghe, Stella Hughes, Suzanne Hodgkinson, Chris McGuigan, Elisabetta Cartechini, Bruce Taylor, Daniele Spitaleri, Mark Slee, Pamela McCombe, Bassem Yamout, Pascal Benkert, Jens Kuhle, Ludwig Kappos, Izanne Roos, Tomas Kalincik, PGCertBiostat, MSBase Study Group
Dátum:2025
Megjegyzések:Importance: Progression independent of relapse activity (PIRA) is a significant contributor to long-term disability accumulation in relapsing-remitting multiple sclerosis (MS). Prior studies have used varying PIRA definitions, hampering the comparability of study results. Objective: To compare various definitions of PIRA. Design, setting, and participants: This cohort study involved a retrospective analysis of prospectively collected data from the MSBase registry from July 2004 to July 2023. The participants were patients with MS from 186 centers across 43 countries who had clinically definite relapsing-remitting MS, a complete minimal dataset, and 3 or more documented Expanded Disability Status Scale (EDSS) assessments. Exposure: Three-hundred sixty definitions of PIRA as combinations of the following criteria: baseline disability (fixed baseline with re-baselining after PIRA, or plus re-baselining after relapses, or plus re-baselining after improvements), minimum confirmation period (6, 12, or 24 months), confirmation magnitude (EDSS score at/above worsening score or at/above threshold compared with baseline), freedom from relapse at EDSS score worsening (90 days prior, 90 days prior and 30 days after, 180 days prior and after, since previous EDSS assessment, or since baseline), and freedom from relapse at confirmation (30 days prior, 90 days prior, 30 days before and after, or between worsening and confirmation). Main outcome and measure: For each definition, we quantified PIRA incidence and persistence (ie, absence of a 3-month confirmed EDSS improvement over ?5 years). Results: Among 87 239 patients with MS, 33 303 patients fulfilled the inclusion criteria; 24 152 (72.5%) were female and 9151 (27.5%) were male. At the first visits, the mean (SD) age was 36.4 (10.9) years; 28 052 patients (84.2%) had relapsing-remitting MS, and the median (IQR) EDSS score was 2.0 (1.0-3.0). Participants had a mean (SD) 15.1 (11.9) visits over 8.9 (5.2) years. PIRA incidence ranged from 0.141 to 0.658 events per decade and persistence from 0.753 to 0.919, depending on the definition. In particular, the baseline and confirmation period influenced PIRA detection. The following definition yielded balanced incidence and persistence: a significant disability worsening compared with a baseline (reset after each PIRA event, relapse, and EDSS score improvement), in absence of relapses since the last EDSS assessment, confirmed with EDSS scores (not preceded by relapses within 30 days) that remained above the worsening threshold for at least 12 months. Conclusion and relevance: Incidence and persistence of PIRA are determined by the definition used. The proposed standardized definition aims to enhance comparability among studies.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:JAMA Neurology. - 82 : 6 (2025), p. 614-625. -
További szerzők:Sharmin, Sifat Lorscheider, Johannes Ozakbas, Serkan Karabudak, Rana Horakova, Dana Weinstock-Guttman, Bianca Shaygannejad, Vahid Etemadifar, Masoud Alroughani, Raed Patti, Francesco Eichau, Sara Prat, Alexandre Lugaresi, Alessandra Tomassini, Valentina Kermode, Allan G. Amato, Maria Pia Turkoglu, Recai Altintas, Ayse Buzzard, Katherine Soysal, Aysun Walt, Anneke van der Butzkueven, Helmut Blanco, Yolanda Gerlach, Oliver Khoury, Samia J. Barnett, Michael John, Nevin Lechner-Scott, Jeannette Foschi, Matteo Surcinelli, Andrea Pesch, Vincent van Prevost, Julie Sa, Maria Jose Maimone, Davide D'hooghe, Marie Hughes, Stella Hodgkinson, Suzanne McGuigan, Christopher Cartechini, Elisabetta Taylor, Bruce V. Spitaleri, Daniele Slee, Mark McCombe, Pamela Yamout, Bassem Benkert, Pascal Kuhle, Jens Kappos, Ludwig Roos, Izanne Kalincik, Tomas Csépány Tünde (1956-) (neurológus, pszichiáter) MSBase Study Group PGCertBiostat
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Intézményi repozitóriumban (DEA) tárolt változat
Borító:

10.

001-es BibID:BIBFORM132947
035-os BibID:(scopus)105011413182 (wos)001536298500003
Első szerző:Pirmani, Ashkan
Cím:Personalized federated learning for predicting disability progression in multiple sclerosis using real-world routine clinical data / Pirmani Ashkan, De Brouwer Edward, Arany Ádám, Oldenhof Martijn, Passemiers Antoine, Faes Axel, Kalincik Tomas, Ozakbas Serkan, Gouider Riadh, Willekens Barbara, Horakova Dana, Havrdova Eva Kubala, Patti Francesco, Prat Alexandre, Lugaresi Alessandra, Tomassini Valentina, Grammond Pierre, Cartechini Elisabetta, Roos Izanne, Boz Cavit, Alroughani Raed, Amato Maria Pia, Buzzard Katherine, Lechner-Scott Jeannette, Guimaraes Joana, Solaro Claudio, Gerlach Oliver, Soysal Aysun, Kuhle Jens, Sanchez-Menoyo Jose Luis, Spitaleri Daniele, Csepany Tunde, Van Wijmeersch Bart, Ampapa Radek, Prevost Julie, Khoury Samia J., Van Pesch Vincent, John Nevin, Maimone Davide, Weinstock-Guttman Bianca, Laureys Guy, McCombe Pamela, Blanco Yolanda, Altintas Ayse, Al-Asmi Abdullah, Garber Justin, Van der Walt Anneke, Butzkueven Helmut, de Gans Koen, Rozsa Csilla, Taylor Bruce, Al-Harbi Talal, Sas Attila, Rajda Cecilia, Gray Orla, Decoo Danny, Carroll William M., Kermode Allan G., Fabis-Pedrini Marzena, Mason Deborah, Perez-Sempere Angel, Simu Mihaela, Shuey Neil, Singhal Bhim, Cauchi Marija, Hardy Todd A., Ramanathan Sudarshini, Lalive Patrice, Sirbu Carmen-Adella, Hughes Stella, Castillo Trivino Tamara, Peeters Liesbet M., Moreau Yves
Dátum:2025
ISSN:2398-6352
Megjegyzések:Early prediction of disability progression in multiple sclerosis (MS) remains challenging despite its critical importance for therapeutic decision-making. We present the first systematic evaluation of personalized federated learning (PFL) for 2-year MS disability progression prediction, leveraging multi-center real-world data from over 26,000 patients. While conventional federated learning (FL) enables privacy-aware collaborative modeling, it remains vulnerable to institutional data heterogeneity. PFL overcomes this challenge by adapting shared models to local data distributions without compromising privacy. We evaluated two personalization strategies: a novel AdaptiveDualBranchNet architecture with selective parameter sharing, and personalized fine-tuning of global models, benchmarked against centralized and client-specific approaches. Baseline FL underperformed relative to personalized methods, whereas personalization significantly improved performance, with personalized FedProx and FedAVG achieving ROC-AUC scores of 0.8398 ? 0.0019 and 0.8384 ? 0.0014, respectively. These findings establish personalization as critical for scalable, privacy-aware clinical prediction models and highlight its potential to inform earlier intervention strategies in MS and beyond.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:npj Digital Medicine. - 8 : 1 (2025), p. 1-15. -
További szerzők:De Brouwer, Edward Arany Ádám Oldenhof, Martijn Passemiers, Antoine Faes, Axel Kalincik, Tomas Ozakbas, Serkan Gouider, Riadh Willekens, Barbara Horakova, Dana Havrdova, Eva Patti, Francesco Prat, Alexandre Lugaresi, Alessandra Tomassini, Valentina Grammond, Pierre Cartechini, Elisabetta Roos, Izanne Boz, Cavit Alroughani, Raed Amato, Maria Pia Buzzard, Katherine Lechner-Scott, Jeannette Guimaraes, Joana Solaro, Claudio Gerlach, Oliver Soysal, Aysun Kuhle, Jens Sanchez-Menoyo, Jose Spitaleri, Daniele Csépány Tünde (1956-) (neurológus, pszichiáter) Wijmeersch, Bart Van Ampapa, Radek Prevost, Julie Khoury, Samia J. Pesch, Vincent van John, Nevin Maimone, Davide Weinstock-Guttman, Bianca Laureys, Guy (Universitary Hospital Ghent) McCombe, Pamela Blanco, Yolanda Altintas, Ayse Al-Asmi, Abdullah Garber, Justin Walt, Anneke van der Butzkueven, Helmut de Gans, Koen Rózsa Csilla Taylor, Bruce V. Al-Harbi, Talal Sas Attila Rajda Cecília Gray, Orla Decoo, Danny Carroll, William M. Kermode, Allan G. Fabis-Pedrini, Marzena Mason, Deborah Perez-Sempere, Angel Simu, Mihaela Shuey, Neil Singhal, Bhim Cauchi, Marija Hardy, Todd A. Ramanathan, Sudarshini Lalive, Patrice H. Sirbu, Carmen-Adella Hughes, Stella Castillo Triviño, Tamara Peeters, Liesbet Moreau, Yves
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Intézményi repozitóriumban (DEA) tárolt változat
Borító:

11.

001-es BibID:BIBFORM114473
035-os BibID:(Scopus)85148502832 (WoS)000951172400001
Első szerző:Roos, Izanne
Cím:Comparative effectiveness in multiple sclerosis : A methodological comparison / Roos Izanne, Diouf Ibrahima, Sharmin Sifat, Horakova Dana, Havrdova Eva Kubala, Patti Francesco, Shaygannejad Vahid, Ozakbas Serkan, Izquierdo Guillermo, Eichau Sara, Onofrj Marco, Lugaresi Alessandra, Alroughani Raed, Prat Alexandre, Girard Marc, Duquette Pierre, Terzi Murat, Boz Cavit, Grand'Maison Francois, Sola Patrizia, Ferraro Diana, Grammond Pierre, Turkoglu Recai, Buzzard Katherine, Skibina Olga, Yamout Bassem, Altintas Ayse, Gerlach Oliver, van Pesch Vincent, Blanco Yolanda, Maimone Davide, Lechner-Scott Jeannette, Bergamaschi Roberto, Karabudak Rana, McGuigan Chris, Cartechini Elisabetta, Barnett Michael, Hughes Stella, Sa Maria José, Solaro Claudio, Ramo-Tello Cristina, Hodgkinson Suzanne, Spitaleri Daniele, Soysal Aysun, Petersen Thor, Granella Franco, de Gans Koen, McCombe Pamela, Ampapa Radek, Van Wijmeersch Bart, van der Walt Anneke, Butzkueven Helmut, Prevost Julie, Sanchez-Menoyo Jose Luis, Laureys Guy, Gouider Riadh, Castillo-Trivino Tamara, Gray Orla, Aguera-Morales Eduardo, Al-Asmi Abdullah, Shaw Cameron, Deri Norma, Al-Harbi Talal, Fragoso Yara, Csepany Tunde, Sempere Angel Perez, Trevino-Frenk Irene, Schepel Jan, Moore Fraser, Malpas Charles, Kalincik Tomas, MSBase study group
Dátum:2023
ISSN:1352-4585
Megjegyzések:Background: In the absence of evidence from randomised controlled trials, observational data can be used to emulate clinical trials and guide clinical decisions. Observational studies are, however, susceptible to confounding and bias. Among the used techniques to reduce indication bias are propensity score matching and marginal structural models. Objective: To use the comparative effectiveness of fingolimod vs natalizumab to compare the results obtained with propensity score matching and marginal structural models. Methods: Patients with clinically isolated syndrome or relapsing remitting MS who were treated with either fingolimod or natalizumab were identified in the MSBase registry. Patients were propensity score matched, and inverse probability of treatment weighted at six monthly intervals, using the following variables: age, sex, disability, MS duration, MS course, prior relapses, and prior therapies. Studied outcomes were cumulative hazard of relapse, disability accumulation, and disability improvement. Results: 4608 patients (1659 natalizumab, 2949 fingolimod) fulfilled inclusion criteria, and were propensity score matched or repeatedly reweighed with marginal structural models. Natalizumab treatment was associated with a lower probability of relapse (PS matching: HR 0.67 [95% CI 0.62-0.80]; marginal structural model: 0.71 [0.62-0.80]), and higher probability of disability improvement (PS matching: 1.21 [1.02 -1.43]; marginal structural model 1.43 1.19 -1.72]). There was no evidence of a difference in the magnitude of effect between the two methods. Conclusions: The relative effectiveness of two therapies can be efficiently compared by either marginal structural models or propensity score matching when applied in clearly defined clinical contexts and in sufficiently powered cohorts.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Observational
causal inference
multiple sclerosis
Megjelenés:Multiple Sclerosis. - 29 : 3 (2023), p. 326-332. -
További szerzők:Diouf, Ibrahima Sharmin, Sifat Horakova, Dana Havrdova, Eva Patti, Francesco Shaygannejad, Vahid Ozakbas, Serkan Izquierdo, Guillermo Eichau, Sara Onofrj, Marco Lugaresi, Alessandra Alroughani, Raed Prat, Alexandre Girard, Marc Duquette, Pierre Terzi, Murat Boz, Cavit Grand'Maison, Francois Sola, Patrizia Ferraro, Diana Grammond, Pierre Turkoglu, Recai Buzzard, Katherine Skibina, Olga Yamout, Bassem Altintas, Ayse Gerlach, Oliver Pesch, Vincent van Blanco, Yolanda Maimone, Davide Lechner-Scott, Jeannette Bergamaschi, Roberto Karabudak, Rana McGuigan, Christopher Cartechini, Elisabetta Barnett, Michael Hughes, Stella Sá, Maria José Solaro, Claudio Ramo-Tello, Cristina Hodgkinson, Suzanne Spitaleri, Daniele Soysal, Aysun Petersen, Thor Granella, Franco de Gans, Koen McCombe, Pamela Ampapa, Radek Wijmeersch, Bart Van Walt, Anneke van der Butzkueven, Helmut Prevost, Julie Sanchez-Menoyo, Jose Laureys, Guy Gouider, Riadh Castillo Triviño, Tamara Gray, Orla Aguera-Morales, Eduardo Al-Asmi, Abdullah Shaw, Cameron Deri, Norma Al-Harbi, Talal Fragoso, Yara Csépány Tünde (1956-) (neurológus, pszichiáter) Sempere, Perez A. Trevino-Frenk, Irene Schepel, Jan Moore, Fraser Malpas, Charles Kalincik, Tomas MSBase Study Group
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Intézményi repozitóriumban (DEA) tárolt változat
Borító:

12.

001-es BibID:BIBFORM103566
035-os BibID:(WoS)000874431500025 (Scopus)85141339945
Első szerző:Roos, Izanne
Cím:Disease Reactivation After Cessation of Disease-Modifying Therapy in Patients With Relapsing-Remitting Multiple Sclerosis / Roos Izanne, Malpas Charles, Leray Emmanuelle, Casey Romain, Horakova Dana, Havrdova Eva Kubala, Debouverie Marc, Patti Francesco, De Seze Jerome, Izquierdo Guillermo, Eichau Sara, Edan Gilles, Prat Alexandre, Girard Marc, Ozakbas Serkan, Grammond Pierre, Zephir Helene, Ciron Jonathan, Maillart Elisabeth, Moreau Thibault, Amato Maria Pia, Labauge Pierre, Alroughani Raed, Buzzard Katherine, Skibina Olga, Terzi Murat, Laplaud David Axel, Berger Eric, Grand'Maison Francois, Lebrun-Frenay Christine, Cartechini Elisabetta, Boz Cavit, Lechner-Scott Jeannette, Clavelou Pierre, Stankoff Bruno, Prevost Julie, Kappos Ludwig, Pelletier Jean, Shaygannejad Vahid, Yamout Bassem I., Khoury Samia J., Gerlach Oliver, Spitaleri Daniele L. A., Van Pesch Vincent, Gout Olivier, Turkoglu Recai, Heinzlef Olivier, Thouvenot Eric, McCombe Pamela Ann, Soysal Aysun, Bourre Bertrand, Slee Mark, Castillo-Trivino Tamara, Bakchine Serge, Ampapa Radek, Butler Ernest Gerard, Wahab Abir, Macdonell Richard A., Aguera-Morales Eduardo, Cabre Philippe, Ben Nasr Haifa, Van der Walt Anneke, Laureys Guy, Van Hijfte Liesbeth, Ramo-Tello Cristina M., Maubeuge Nicolas, Hodgkinson Suzanne, Sánchez-Menoyo José Luis, Barnett Michael H., Labeyrie Celine, Vucic Steve, Sidhom Youssef, Gouider Riadh, Csepany Tunde, Sotoca Javier, de Gans Koen, Al-Asmi Abdullah, Fragoso Yara Dadalti, Vukusic Sandra, Butzkueven Helmut, Kalincik Tomas, MSBase and OFSEP
Dátum:2022
ISSN:0028-3878 1526-632X
Megjegyzések:Objectives: To evaluate the rate of return of disease activity after cessation of multiple sclerosis (MS) disease-modifying therapy. Methods: This was a retrospective cohort study from two large observational MS registries: MSBase and OFSEP. Patients with relapsing-remitting MS who had ceased a disease-modifying therapy and were followed up for the subsequent 12-months were included in the analysis. The primary study outcome was annualised relapse rate in the 12 months after disease-modifying therapy discontinuation stratified by patients who did, and did not, commence a subsequent therapy. The secondary endpoint was the predictors of first relapse and disability accumulation after treatment discontinuation. Results: 14,213 patients, with 18,029 eligible treatment discontinuation epochs, were identified for seven therapies. Annualised rates of relapse (ARR) started to increase 2-months after natalizumab cessation (month 2-4 ARR, 95% confidence interval): 0.47, 0.43-0.51). Commencement of a subsequent therapy within 2-4 months reduced the magnitude of disease reactivation (mean ARR difference: 0.15, 0.08-0.22). After discontinuation of fingolimod, rates of relapse increased overall (month 1-2 ARR: 0.80, 0.70-0.89), and stabilised faster in patients who started a new therapy within 1-2 months (mean ARR difference: 0.14, -0.01-0.29). Magnitude of disease reactivation for other therapies was low, but reduced further by commencement of another treatment 1-10 months after treatment discontinuation. Predictors of relapse were higher relapse rate in the year before cessation, female sex, younger age and higher EDSS. Commencement of a subsequent therapy reduced both the risk of relapse (HR 0.76, 95%CI 0.72-0.81) and disability accumulation (0.73, 0.65-0.80). Conclusion: The rate of disease reactivation after treatment cessation differs among MS treatments, with the peaks of relapse activity ranging from 1 to 10 months in untreated cohorts that discontinued different therapies. These results suggest that untreated intervals should be minimised after stopping anti-trafficking therapies (natalizumab and fingolimod). Classification of evidence: This study provides class III that disease reactivation occurs within months of discontinuation of multiple sclerosis disease-modifying therapies. Risk of disease activity is reduced by commencement of a subsequent therapy.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Neurology. - 99 : 17 (2022), p. e1926-e1944. -
További szerzők:Malpas, Charles Leray, Emmanuelle Casey, Romain Horakova, Dana Havrdova, Eva Debouverie, Marc Patti, Francesco De Seze, Jérôme Izquierdo, Guillermo Eichau, Sara Edan, Gilles Prat, Alexandre Girard, Marc Ozakbas, Serkan Grammond, Pierre Zephir, Hélène Ciron, Jonathan Maillart, Elisabeth Moreau, Thibault Amato, Maria Pia Labauge, Pierre Alroughani, Raed Buzzard, Katherine Skibina, Olga Terzi, Murat Laplaud, David Berger, Eric Grand'Maison, Francois Lebrun-Frenay, Christine Cartechini, Elisabetta Boz, Cavit Lechner-Scott, Jeannette Clavelou, Pierre Stankoff, Bruno Prevost, Julie Kappos, Ludwig Pelletier, Jean Shaygannejad, Vahid Yamout, Bassem Khoury, Samia J. Gerlach, Oliver Spitaleri, Daniele L. A. Pesch, Vincent van Gout, Olivier Turkoglu, Recai Heinzlef, Olivier Thouvenot, Eric McCombe, Pamela Soysal, Aysun Bourre, Bertrand Slee, Mark Castillo Triviño, Tamara Bakchine, Serge Ampapa, Radek Butler, Ernest Wahab, Abir Macdonell, Richard Aguera-Morales, Eduardo Cabre, Philippe Ben Nasr, Haifa Walt, Anneke van der Laureys, Guy Van Hijfte, Liesbeth Ramo-Tello, Cristina Maubeuge, Nicolas Hodgkinson, Suzanne Sanchez-Menoyo, Jose Barnett, Michael Labeyrie, Céline Vucic, Steve Sidhom, Youssef Gouider, Riadh Csépány Tünde (1956-) (neurológus, pszichiáter) Sotoca, Javier de Gans, Koen Al-Asmi, Abdullah Fragoso, Yara Vukusic, Sandra Butzkueven, Helmut Kalincik, Tomas OFSEP and the MSBase investigators
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