BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a disease with a widely varying prognosis. The majority of patients survive about 3 years, but a significant number survive for 10 years or more, leading to problems in clinical trial design. OBJECTIVE: To demonstrate that simple clinical variables can be used to construct a robust predictive model for survival, and to assess the effect of a known treatment within this model. METHODS: We carried out a retrospective multivariate modelling of a database of 841 patients with ALS seen over a 10-year period in a specialist motor neuron disorders clinic. The use of riluzole was tested as a prognostic factor within the model. RESULTS: A prognostic score generated from one cohort of patients predicted survival for a second cohort of patients (r(2) = 0.78). Prognostic variables included site of onset, age of onset, time from symptom onset to diagnosis, and El Escorial category at presentation. Riluzole therapy was an independently significant prognostic factor (relative risk of death 0.48, P < 0.0001, model chi(2) 297, P < 0.0001). CONCLUSIONS: Clinical databases can be used to generate multivariate prognostic models in ALS. Such models could be used to predict survival, to improve criteria for matching of patients in future clinical trials, and to test the impact of interventions.
BACKGROUND:Amyotrophic lateral sclerosis (ALS) is a disease with a widely varying prognosis. The majority of patients survive about 3 years, but a significant number survive for 10 years or more, leading to problems in clinical trial design. OBJECTIVE: To demonstrate that simple clinical variables can be used to construct a robust predictive model for survival, and to assess the effect of a known treatment within this model. METHODS: We carried out a retrospective multivariate modelling of a database of 841 patients with ALS seen over a 10-year period in a specialist motor neuron disorders clinic. The use of riluzole was tested as a prognostic factor within the model. RESULTS: A prognostic score generated from one cohort of patients predicted survival for a second cohort of patients (r(2) = 0.78). Prognostic variables included site of onset, age of onset, time from symptom onset to diagnosis, and El Escorial category at presentation. Riluzole therapy was an independently significant prognostic factor (relative risk of death 0.48, P < 0.0001, model chi(2) 297, P < 0.0001). CONCLUSIONS: Clinical databases can be used to generate multivariate prognostic models in ALS. Such models could be used to predict survival, to improve criteria for matching of patients in future clinical trials, and to test the impact of interventions.
Authors: Benoît Marin; Philippe Couratier; Simona Arcuti; Massimiliano Copetti; Andrea Fontana; Marie Nicol; Marie Raymondeau; Giancarlo Logroscino; Pierre Marie Preux Journal: J Neurol Date: 2015-10-30 Impact factor: 4.849
Authors: Martin R Turner; Alice Brockington; Jakub Scaber; Hannah Hollinger; Rachael Marsden; Pamela J Shaw; Kevin Talbot Journal: Amyotroph Lateral Scler Date: 2010-08
Authors: Jeban Ganesalingam; Daniel Stahl; Lokesh Wijesekera; Clare Galtrey; Christopher E Shaw; P Nigel Leigh; Ammar Al-Chalabi Journal: PLoS One Date: 2009-09-22 Impact factor: 3.240