期刊
MULTIPLE SCLEROSIS JOURNAL
卷 27, 期 3, 页码 430-438出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/1352458520974366
关键词
Multiple sclerosis; secondary progressive; disease registry; big data; prognosis; data-driven algorithm
A data-driven algorithm (DDA) definition of SPMS identifies older, more disabled patients with faster disease progression, while disease-modifying therapy (DMT) exposure reduces the risk of SPMS conversion but does not prevent disability accumulation after the transition to SP.
Background: No uniform criteria for a sensitive identification of the transition from relapsing-remitting multiple sclerosis (MS) to secondary-progressive multiple sclerosis (SPMS) are available. Objective: To compare risk factors of SPMS using two definitions: one based on the neurologist judgment (ND) and an objective data-driven algorithm (DDA). Methods: Relapsing-onset MS patients (n = 19,318) were extracted from the Italian MS Registry. Risk factors for SPMS and for reaching irreversible Expanded Disability Status Scale (EDSS) 6.0, after SP transition, were estimated using multivariable Cox regression models. Results: SPMS identified by the DDA (n = 2343, 12.1%) were older, more disabled and with a faster progression to severe disability (p < 0.0001), than those identified by the ND (n = 3868, 20.0%). In both groups, the most consistent risk factors (p < 0.05) for SPMS were a multifocal onset, an age at onset >40 years, higher baseline EDSS score and a higher number of relapses; the most consistent protective factor was the disease-modifying therapy (DMT) exposure. DMT exposure during SP did not impact the risk of reaching irreversible EDSS 6.0. Conclusion: A DDA definition of SPMS identifies more aggressive progressive patients. DMT exposure reduces the risk of SPMS conversion, but it does not prevent the disability accumulation after the SP transition.
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