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A Quick Phenotypic Neurological Scoring System for Evaluating Disease Progression in the SOD1-G93A Mouse Model of ALS
Published on: October 6, 2015
Clinical and prognostic features of ALS/MND in different phenotypes-data from a hospital-based registry
Qian-Qian Wei1, Yongping Chen1, Xueping Chen1
1Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Objectives:
To explore the natural history, clinical features, and survival relevance of patients presenting with amyotrophic lateral sclerosis/motor neuron disease (ALS/MND) with different phenotypes.
Methods:
All patients were prospectively enrolled in a hospital-based register from 2006 to 2015. Cases were classified according to established phenotypes: classic, bulbar, flail arm, flail leg, upper motor neuron dominate, respiratory, pure lower motor neuron and pure upper motor neuron. Survival was analyzed using Kaplan-Meier curves and Cox regression analysis.
Results:
A total of 1157 patients with complete clinical information were registered in the current cohort study. The classic phenotype was the most frequent phenotype. The mean age of onset, diagnostic delay, smoking status, exposure to pesticides and mean survival time were significantly different among different phenotypes. The median survival time was 3.1 years for all patients. At the end of the analysis, the 3-year survival rate was 62.1%, the 5-year survival rate was 39.6%, and the 10-year survival rate was 22.0%. The Cox analysis revealed that the disease phenotype was an independent predictor of survival (hazard ratio = 0.825, P < 0.001), after adjusting for other factors.
Conclusions:
This study showed the natural history data of ALS/MND and supported the theory that varied disease phenotypes had different clinical, demographic and prognostic characteristics, which provided the basis for analysis of future management and treatment for ALS/MND. Furthermore, the phenotypic expression of ALS/MND with distinctive characteristics is important for providing complementary information for identifying the underlying mechanisms of the diseases.
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