Внешняя проверка результатов принятия решения по множественным методам склероза с использованием данных когортного исследования ProVal-MS.
Обработка статьи · этапы, ошибки и управление
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Buchka Stefan, Hapfelmeier Alexander, Kirschke Jan S, Steuerwald Verena, Naumann Markus, Soto-Rey Iñaki, Rohr Sven Olaf, Kramer Frank, Behrens Lars, Oswald Eva, Kümpfel Tania, Zimmermann Hanna, Hoffmann Verena S, Hagedorn Marlien, Albashiti Fady, Krumbholz Markus, Ziemann Ulf, Kohlbacher Oliver, Sailer Benjamin, Braunmüller Viola, Biergans Stephanie, de Arruda Botelho Herr Marius, Ernemann Ulrike, Bürkle Eva, Bender Benjamin, Daul Andreas, Ruff Christer, Römhild Jörg, Wiestler Benedikt, Sepp Dominik, Spengler Helmut, Pallaoro Peter, Boeker Martin, Kohlmayer Florian, Dehmelt Vera, Berthele Achim, Mühlau Mark, Uibel Paula, Wauschkuhn Josephine, Kuhn Klaus, Senel Makbule, Vardakas Ioannis, Taranu Daniela, Kestler Hans Armin, Sollmann Nico, Irmak Ön Begüm, Bilger Sandra, Mansmann Ulrich, Bayas Antonios, Havla Joachim, Kowarik Markus C, Tumani Hayrettin, Hemmer Bernhard, ProVal-MS Study Group
Аннотация
Оригинал публикации
External validation of a multiple sclerosis treatment decision score using data from the ProVal-MS cohort study.
BACKGROUND: The course of relapsing-remitting multiple sclerosis (RRMS), frequently preceded by the clinically isolated syndrome (CIS), is variable and challenging to predict. Given many treatment options available, prognostic algorithms are gaining importance in informing initial treatment decisions. However, to date, only a few externally validated exists. External validation, which involves the application of a model to independent data, is essential. Privacy-preserving federated analyses of individual-level data facilitate external validation using clinical datasets that are typically difficult to access.
OBJECTIVES: Using data from the ProVal-MS study to externally validate the multiple sclerosis treatment decision score (MS-TDS), a predictive algorithm for early RRMS and CIS. The MS-TDS predicts the probability of the occurrence of at least one new or enlarging T2 lesion within 6-24 months following the onset of the disease and supports choosing between initiating platform treatment or a 'wait-and-see' approach. A secondary objective is to demonstrate the feasibility of privacy-preserving federated concepts within the Data Integration for Future Medicine (DIFUTURE) consortium.
DESIGN: Prospective, multicentric, non-interventional cohort study (ProVal-MS) within DIFUTURE.
METHODS: The calibrated MS-TDS was evaluated using the area under the receiver operating characteristic curve (AUROC) and the Brier score in both pooled and distributed settings. A decision curve analysis (DCA) was used to evaluate the net benefit of treatment decisions made by the MS-TDS in comparison to those made by treating neurologists.
RESULTS: Of the 271 individuals diagnosed with CIS or early RRMS, 202 (78.2%) received platform treatment, while 59 (21.8%) did not receive treatment. The AUROC was 0.561 (95% CI: 0.492-0.630) in the pooled analysis and 0.567 (95% CI: 0.496-0.634) in the distributed analysis. DCA demonstrated a net benefit that was commensurate with that achieved by decisions made by experienced neurologists.
CONCLUSION: The external validation of the MS-TDS demonstrated low, non-significant predictive performance; however, it may serve as a useful complement, particularly for less-experienced neurologists. The distributed validation was found to be both feasible and compliant with data protection regulations.