RT Journal Article
SR Electronic
T1 PROGNOSTIC CATEGORIES FOR AMYOTROPHIC LATERAL SCLEROSIS
JF Journal of Neurology, Neurosurgery & Psychiatry
JO J Neurol Neurosurg Psychiatry
FD BMJ Publishing Group Ltd
SP A33
OP A33
DO 10.1136/jnnp-2012-304200a.120
VO 83
IS Suppl 2
A1 WJ Scotton
A1 KM Scott
A1 L Almedom
A1 LC Wijesekera
A1 A Janssen
A1 C Nigro
A1 M Sakel
A1 PN Leigh
A1 C Shaw
A1 A Al-Chalabi
YR 2012
UL http://jnnp.bmj.com/content/83/Suppl_2/A33.1.abstract
AB 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.