Predictors of long survival in amyotrophic lateral sclerosis: a population-based study

J Neurol Sci. 2008 May 15;268(1-2):28-32. doi: 10.1016/j.jns.2007.10.023. Epub 2007 Nov 19.

Abstract

Background: Although amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurodegenerative disorder, some ALS cases can survive beyond 10 years. However, the predictors of long survival in ALS patients remain uncertain.

Objective: To define clinical predictors of long survival in a cohort of ALS incident cases.

Methods: One hundred-thirty incidents cases, diagnosed in 1998--1999 and classified according to the El Escorial criteria (EEC), were enrolled from a prospective population-based registry established in Puglia, Italy. All but two cases were followed-up until death or November 30, 2006.

Results: Thirteen patients (high 10% of the survivors) were classified as long survivors (LS), 13 as short survivors (SS) (low 10%), and 102 as average survivors (AS). LS presented a lower frequency of bulbar onset (8% versus 29% of AS and 39% of SS; p=0.1) and a significantly longer time between symptom onset to diagnosis [(ODI): 13 months versus 10 and 6; p=0.0005]. In multivariate analysis, predictors of long survival were younger age at diagnosis (>65 compared to < or =45 years: odds ratio (OR):18.9; 95%CI: 1.8-194.7; p=0.04), longer interval onset-diagnosis (< or =9 months compared to >9 months, OR: 7.9; 95%CI: 1.3-47; p=0.02) and clinical features with predominant upper motor neuron signs (OR: 8.5; 95%CI: 1.1-64.2; p=0.04).

Conclusions: In this population-based study, younger age, longer interval onset to diagnosis, and clinical features with predominance of upper motor signs predicted long survival, while EEC category at diagnosis did not.

MeSH terms

  • Adult
  • Aged
  • Amyotrophic Lateral Sclerosis / epidemiology*
  • Amyotrophic Lateral Sclerosis / mortality*
  • Community Health Planning
  • Female
  • Humans
  • Longitudinal Studies
  • Male
  • Middle Aged
  • Predictive Value of Tests
  • Retrospective Studies
  • Survival Analysis