Background: Anti-glycan antibodies can be found in autoimmune diseases. IgM against glycan P63 was identified in clinically isolated syndromes (CIS) and included in gMS-Classifier2, an algorithm designed with the aim of identifying patients at risk of a second demyelinating attack.
Objective: To determine the value of gMS-Classifier2 as an early and independent predictor of conversion to clinically definite multiple sclerosis (CDMS).
Methods: Data were prospectively acquired from a CIS cohort. gMS-Classifier2 was determined in patients first seen between 1995 and 2007 with ≥ two 200 µL serum aliquots (N = 249). The primary endpoint was time to conversion to CDMS at two years, the factor tested was gMS-Classifier2 status (positive/negative) or units; other exploratory time points were 5 years and total time of follow-up.
Results: Seventy-five patients (30.1%) were gMS-Classifier2 positive. Conversion to CDMS occurred in 31/75 (41.3%) of positive and 45/174 (25.9%) of negative patients (p = 0.017) at two years. Median time to CDMS was 37.8 months (95% CI 10.4-65.3) for positive and 83.9 months (95% CI 57.5-110.5) for negative patients. gMS-Classifier2 status predicted conversion to CDMS within two years of follow-up (HR = 1.8, 95% CI 1.1-2.8; p = 0.014). gMS-Classifier2 units were also independent predictors when tested with either Barkhof criteria and OCB (HR = 1.2, CI 1.0-1.5, p = 0.020) or with T2 lesions and OCB (HR = 1.3, CI 1.1-1.5, p = 0.008). Similar results were obtained at 5 years of follow-up. Discrimination measures showed a significant change in the area under the curve (ΔAUC) when adding gMS-Classifier2 to a model with either Barkhof criteria (ΔAUC 0.0415, p = 0.012) or number of T2 lesions (ΔAUC 0.0467, p = 0.009), but not when OCB were added to these models.
Conclusions: gMS-Classifier2 is an independent predictor of early conversion to CDMS and could be of clinical relevance, particularly in cases in which OCB are not available.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3610690 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0059953 | PLOS |
BMC Med Inform Decis Mak
January 2025
Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Background: Environmental exposures such as airborne pollutant exposures and socio-economic indicators are increasingly recognized as important to consider when conducting clinical research using electronic health record (EHR) data or other sources of clinical data such as survey data. While numerous public sources of geospatial and spatiotemporal data are available to support such research, the data are challenging to work with due to inconsistencies in file formats and spatiotemporal resolutions, computational challenges with large file sizes, and a lack of tools for patient- or subject-level data integration.
Results: We developed FHIR PIT (HL7® Fast Healthcare Interoperability Resources Patient data Integration Tool) as an open-source, modular, data-integration software pipeline that consumes EHR data in FHIR® format and integrates the data at the level of the patient or subject with environmental exposures data of varying spatiotemporal resolutions and file formats.
Mult Scler
January 2025
EMD Serono Research & Development Institute, Inc., an affiliate of Merck KGaA, Billerica, MA, USA.
Background: CLASSIC-MS explored long-term outcomes of patients treated with cladribine tablets.
Objective: Assess long-term efficacy in patients previously enrolled in ORACLE-MS, a Phase III parent trial.
Methods: ORACLE-MS included patients with a first clinical demyelinating event (FCDE or clinically isolated syndrome) who received ⩾1 course of cladribine tablets or placebo.
Stud Health Technol Inform
August 2024
Clinical Informatics Service, Hospital Clínic de Barcelona. 08036 - Barcelona, Spain.
Common Data Models (CDMs) enhance data exchange and integration across diverse sources, preserving semantics and context. Transforming local data into CDMs is typically cumbersome and resource-intensive, with limited reusability. This article compares OntoBridge, an ontology-based tool designed to streamline the conversion of local datasets into CDMs, with traditional ETL methods in adopting the OMOP CDM.
View Article and Find Full Text PDFClin Neurol Neurosurg
October 2024
School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Department of Neurosurgery, Isfahan University of Medical Sciences, Isfahan, Iran.
Background: Since data is limited on radiologically isolated syndrome (RIS) subjects in certain regions like the Middle East, we aimed to further explore the replicability and generalizability of previously suggested predictors among a cohort of Iranian RIS subjects and report the long-term clinically definite MS (CDMS) conversion rate in this cohort.
Methods: We conducted a prospective 10-year cohort on our RIS participants, during which we collected the MRI, paraclinical, and demographic data of the subjects, and identified those who converted to CDMS.
Results: Out of 35 participants, 10 (28.
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