Prognostic categories for amyotrophic lateral sclerosis

Abstract

Our objective was to generate a prognostic classification method for amyotrophic lateral sclerosis (ALS) from a prognostic model built using clinical variables from a population register. 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. The score was used as a predictor variable to split the test cohort in four prognostic categories (good, moderate, average, poor). The accuracy of the score in predicting survival was tested by checking whether the predicted survival fell within the actual survival tertile which that patient was in. 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), and the prognostic score generated for the test cohort accurately predicted survival in 64% of the patients. In conclusion, it is possible to correctly classify patients into prognostic categories using clinical data easily available at time of diagnosis.

Key words::

• Amyotrophic lateral sclerosis
• motor neuron disease
• prognostic modelling
• population register
• clinical classification

Acknowledgements

The authors thank the Motor Neurone Disease Association of Great Britain and Northern Ireland, the ALS Association, the ALS Therapy Alliance and the NIHR Specialist Biomedical Research Centre for Mental Health and Specialist Biomedical Research Unit for Dementia at the South London and Maudsley NHS Foundation Trust (SLaM) and the Institute of Psychiatry, King's College London. This project was supported by the European Community's Health Seventh Framework Programme.

Declaration of interest: The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.