4.7 Article

Stratification of ALS patients' survival: a population-based study

期刊

JOURNAL OF NEUROLOGY
卷 263, 期 1, 页码 100-111

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s00415-015-7940-z

关键词

Amyotrophic lateral sclerosis; Epidemiology; Prognosis; Survival; Mortality

资金

  1. Association de Recherche sur la Sclerose Laterale Amyotrophique (ARSLA)
  2. Limoges teaching hospital

向作者/读者索取更多资源

The natural history of amyotrophic lateral sclerosis (ALS) and patient risk stratification are areas of considerable research interest. We aimed (1) to describe the survival of a representative cohort of French ALS patients, and (2) to identify covariates associated with various patterns of survival using a risk classification analysis. ALS patients recruited in the FRALim register (2000-2013) were included. Time-to-death analyses were performed using Kaplan-Meier method and Cox model. A recursive partitioning and amalgamation (RECPAM) algorithm analysis identified subgroups of patients with different patterns of survival. Among 322 patients, median survival times were 26.2 and 15.6 months from time of onset and of diagnosis, respectively. Four groups of patients were identified, depending on their baseline characteristics and survival (1) ALSFRS-R slope > 0.46/month and definite or probable ALS (median survival time (MST) 10.6 months); (2) ALSFRS-R slope > 0.46/month and possible or probable laboratory-supported ALS (MST: 18.1 months); (3) ALSFRS-R slope a parts per thousand currency sign0.46/month and definite or probable ALS (MST: 22.5 months), and (4) ALSFRS-R slope a parts per thousand currency sign0.46/month and possible or probable laboratory-supported ALS (MST: 37.6 months). Median survival time is among the shortest ever reported by a worldwide population-based study. This is probably related to the age structure of the patients (the oldest identified to date), driven by the underlying population (30 % of subjects older than 60 years). Further research in the field of risk stratification could help physicians better anticipate prognosis of ALS patients, and help improve the design of randomized controlled trials.

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