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Serum GFAP for stroke diagnosis in regions with limited access to brain imaging (BE FAST India)
(2021)
Introduction: Despite a high burden of stroke, access to rapid brain imaging is limited in many middle- and low-income countries. Previous studies have described the astroglial protein GFAP (glial fibrillary acidic protein) as a biomarker of intracerebral hemorrhage. The aim of this study was to test the diagnostic accuracy of GFAP for ruling out intracranial hemorrhage in a prospective cohort of Indian stroke patients. Patients and methods: This study was conducted in an Indian tertiary hospital (Christian Medical College, Ludhiana). Patients with symptoms suggestive of acute stroke admitted within 12 h of symptom onset were enrolled. Blood samples were collected at hospital admission. Single Molecule Array technology was used for determining serum GFAP concentrations. Results: A total number of 155 patients were included (70 intracranial hemorrhage, 75 ischemic stroke, 10 stroke mimics). GFAP serum concentrations were elevated in intracranial hemorrhage patients compared to ischemic stroke patients [median (interquartile range) 2.36 µg/L (0.61–7.16) vs. 0.18 µg/L (0.11–0.38), p < 0.001]. Stroke mimics patients had a median GFAP serum level of 0.14 µg/L (0.09–0.26). GFAP values below the cut-off of 0.33 µg/L (area under the curve 0.871) ruled out intracranial hemorrhage with a negative predictive value of 89.7%, (at a sensitivity for detecting intracranial hemorrhage of 90.0%). Discussion: The high negative predictive value of a GFAP test system allows ruling out patients with intracranial hemorrhage. Conclusion: In settings where immediate brain imaging is not available, this would enable to implement secondary prevention (e.g., aspirin) in suspected ischemic stroke patients as soon as possible.
Background and purpose: The transition from relapsing–remitting to secondary progressive multiple sclerosis (SPMS) is not well defined. Different definitions and tools to identify SPMS have been proposed. Meanwhile, early diagnosis of “active” SPMS is getting progressively more important as pharmaceutical treatment options are developed. In this study, we compared different classification methods regarding their accuracy to reliably identify “active SPMS.”
Methods: Independent from previous diagnostic classification, we descriptively analyzed the disease course (regarding relapses, progression, and magnetic resonance imaging activity) in 208 consecutive multiple sclerosis (MS) patients treated in our MS outpatient clinic in 2018. Patients were reclassified according to different SPMS criteria and tools. Diagnostic accuracy in identifying patients with “active SPMS” was determined.
Results: Comparing the tools to each other, significant variability in the number of patients identified as having SPMS as well as in the proportion of these patients having “active SPMS” was noted. Applying both diagnostic criteria “SPMS” and “active disease” reduced the sensitivity in identifying patients with active progressive disease in all approaches.
Conclusions: We propose lessening the emphasis on the label “SPMS” in favor of the more open term “active progressive disease” to simplify the process of identifying patients who may benefit from immune therapy.