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001-es BibID:	BIBFORM103017
035-os BibID:	(Wos)000685503300008 (Scopus)85107912293 (cikkazonosító)106180
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 Van Wijmeersch, Bart 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
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM119143
035-os BibID:	(scopus)85181176590 (wos)001130397900001
Első szerző:	Spelman, Tim
Cím:	Comparative effectiveness and cost-effectiveness of natalizumab and fingolimod in rapidly evolving severe relapsing-remitting multiple sclerosis in the United Kingdom / Spelman T., Herring W. L., Acosta C., Hyde R., Jokubaitis V. G., Pucci E., Lugaresi A., Laureys G., Havrdova E. K., Horakova D., Izquierdo G., Eichau S., Ozakbas S., Alroughani R., Kalincik T., Duquette P., Girard M., Petersen T., Patti F., Csepany T., Granella F., Grand'Maison F., Ferraro D., Karabudak R., Jose Sa M., Trojano M., van Pesch V., Van Wijmeersch B., Cartechini E., McCombe P., Gerlach O., Spitaleri D., Rozsa C., Hodgkinson S., Bergamaschi R., Gouider R., Soysal A., Prevost J., Garber J., de Gans K., Ampapa R., Simo M., Sanchez-Menoyo J. L., Iuliano G., Sas A., van der Walt A., John N., Gray O., Hughes S., De Luca G., Onofrj M., Buzzard K., Skibina O., Terzi M., Slee M., Solaro C., Ramo-Tello C., Fragoso Y., Shaygannejad V., Moore F., Rajda C., Aguera-Morales E., Butzkueven H.
Dátum:	2024
ISSN:	1369-6998 1941-837X
Megjegyzések:	Aim To evaluate the real-world comparative effectiveness and the cost-effectiveness, from a UK National Health Service perspective, of natalizumab versus fingolimod in patients with rapidly evolving severe relapsing-remitting multiple sclerosis (RES-RRMS). Methods Real-world data from the MSBase Registry were obtained for patients with RES-RRMS who were previously either naive to disease-modifying therapies or had been treated with interferon-based therapies, glatiramer acetate, dimethyl fumarate, or teriflunomide (collectively known as BRACETD). Matched cohorts were selected by 3-way multinomial propensity score matching, and the annualized relapse rate (ARR) and 6-month?confirmed disability worsening (CDW6M) and improvement (CDI6M) were compared between treatment groups. Comparative effectiveness results were used in a cost-effectiveness model comparing natalizumab and fingolimod, using an established Markov structure over a lifetime horizon with health states based on the Expanded Disability Status Scale. Additional model data sources included the UK MS Survey 2015, published literature, and publicly available sources. Results In the comparative effectiveness analysis, we found a significantly lower ARR for patients starting natalizumab compared with fingolimod (rate ratio [RR]?=?0.65; 95% confidence interval [CI], 0.57?0.73) or BRACETD (RR = 0.46; 95% CI, 0.42?0.53). Similarly, CDI6M was higher for patients starting natalizumab compared with fingolimod (hazard ratio [HR]?=?1.25; 95% CI, 1.01?1.55) and BRACETD (HR = 1.46; 95% CI, 1.16?1.85). In patients starting fingolimod, we found a lower ARR (RR = 0.72; 95% CI, 0.65?0.80) compared with starting BRACETD, but no difference in CDI6M (HR = 1.17; 95% CI, 0.91?1.50). Differences in CDW6M were not found between the treatment groups. In the base-case cost-effectiveness analysis, natalizumab dominated fingolimod (0.302 higher quality-adjusted life-years [QALYs] and ?17,141 lower predicted lifetime costs). Similar cost-effectiveness results were observed across sensitivity analyses. Conclusions This MSBase Registry analysis suggests that natalizumab improves clinical outcomes when compared with fingolimod, which translates to higher QALYs and lower costs in UK patients with RES-RRMS.
Tárgyszavak:	Orvostudományok Elméleti orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Multiple sclerosis natalizumab fingolimod real-world data comparative effectiveness cost-effectiveness
Megjelenés:	Journal of Medical Economics. - 27 : 1 (2024), p. 109-125. -
További szerzők:	Herring, W. L. Acosta, C. Hyde, R. Jokubaitis, Vilija Pucci, Eugenio Lugaresi, Alessandra Laureys, Guy Havrdova, Eva Horakova, Dana Izquierdo, Guillermo Eichau, Sara Ozakbas, Serkan Alroughani, Raed Kalincik, Tomas Duquette, Pierre Girard, Marc Petersen, Thor Patti, Francesco Csépány Tünde (1956-) (neurológus, pszichiáter) Granella, Franco Grand'Maison, Francois Ferraro, D. Karabudak, Rana José Sá, Maria Trojano, Maria Pesch, Vincent van Van Wijmeersch, Bart Cartechini, Elisabetta McCombe, Pamela Gerlach, Oliver Spitaleri, Daniele Rózsa Csilla Hodgkinson, Suzanne Bergamaschi, Roberto Gouider, Riadh Soysal, Aysun Prevost, Julie Garber, Justin de Gans, Koen Ampapa, Radek Simó Magdolna Sanchez-Menoyo, Jose Iuliano, Gerardo Sas Attila Walt, Anneke van der John, N. Gray, Orla Hughes, Stella De Luca, Giacomo Onofrj, Marco Buzzard, Katherine Skibina, Olga Terzi, Murat Slee, Mark Solaro, Claudio Ramo-Tello, Cristina Fragoso, Yara Shaygannejad, Vahid Moore, Fraser Rajda Cecília Aguera-Morales, Eduardo Butzkueven, Helmut
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