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  1. WJ Scotton1,2,*,
  2. KM Scott1,2,
  3. L Almedom1,2,
  4. LC Wijesekera1,2,
  5. A Janssen1,2,
  6. C Nigro1,2,
  7. M Sakel1,2,
  8. PN Leigh1,2,
  9. C Shaw1,2,
  10. A Al-Chalabi1,2
  1. 1East Kent University Foundation Hospital Trust
  2. 2King's College London, MRC Centre for Neurodegeneration Research


    Objectives To generate a prognostic classification method for Amyotrophic Lateral Sclerosis (ALS) from a prognostic model built using clinical variables from a population register.

    Materials and Methods We carried out a retrospective multivariate analysis of 713 patients with ALS over a 20 year period from the South-East England Amyotrophic Lateral Sclerosis (SEALS) population register. Patients were randomly allocated to ‘discovery’ or ‘test’ cohorts. A prognostic score was calculated using the discovery cohort and then used to predict survival in the test cohort. This score was used as a predictor variable in subsequent survival analyses, either as a raw value for a Cox regression or split into four prognostic categories (good, moderate, average, poor).

    Results A prognostic score generated from one cohort of patients predicted survival for a second cohort of patients (r2=0.72). Six variables were included in the survival model: age at onset, diagnostic delay, El Escorial category, use of riluzole, gender and site of onset. Cox regression demonstrated a strong relationship between these variables and survival (χ2 80.8, df 1, p<0.0001, n=343) in the test cohort. Kaplan-Meier analysis demonstrated a significant difference in survival between clinical categories (log rank 161.932, df 3, p<0.001).

    Conclusion It is possible to correctly classify patients into prognostic categories using clinical data easily available at time of diagnosis.

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