A clinical tool for predicting survival in ALS

Background: Amyotrophic lateral sclerosis (ALS) is a progressive and usually fatal neurodegenerative disease. Survival from diagnosis varies considerably. Several prognostic factors are known, including site of onset (bulbar or limb), age at symptom onset, delay from onset to diagnosis and the use of riluzole and non-invasive ventilation (NIV). Clinicians and patients would benefit from a practical way of using these factors to provide an individualised prognosis. Methods: 575 consecutive patients with incident ALS from a population-based registry in South-East England register for ALS (SEALS) were studied. Their survival was modelled as a two-step process: the time from diagnosis to respiratory muscle involvement, followed by the time from respiratory involvement to death. The effects of predictor variables were assessed separately for each time interval. Findings: Younger age at symptom onset, longer delay from onset to diagnosis and riluzole use were associated with slower progression to respiratory involvement, and NIV use was associated with lower mortality after respiratory involvement, each with a clinically significant effect size. Riluzole may have a greater effect in younger patients and those with longer delay to diagnosis. A patient's survival time has a roughly 50% chance of falling between half and twice the predicted median. Interpretation: A simple and clinically applicable graphical method of predicting an individual patient's survival from diagnosis is presented. The model should be validated in an independent cohort, and extended to include other important prognostic factors

A clinical tool for predicting survival in ALS

Background: Amyotrophic lateral sclerosis (ALS) is a progressive and usually fatal neurodegenerative disease. Survival from diagnosis varies considerably. Several prognostic factors are known, including site of onset (bulbar or limb), age at symptom onset, delay from onset to diagnosis and the use of riluzole and non-invasive ventilation (NIV). Clinicians and patients would benefit from a practical way of using these factors to provide an individualised prognosis. Methods: 575 consecutive patients with incident ALS from a population-based registry in South-East England register for ALS (SEALS) were studied. Their survival was modelled as a two-step process: the time from diagnosis to respiratory muscle involvement, followed by the time from respiratory involvement to death. The effects of predictor variables were assessed separately for each time interval. Findings: Younger age at symptom onset, longer delay from onset to diagnosis and riluzole use were associated with slower progression to respiratory involvement, and NIV use was associated with lower mortality after respiratory involvement, each with a clinically significant effect size. Riluzole may have a greater effect in younger patients and those with longer delay to diagnosis. A patient's survival time has a roughly 50% chance of falling between half and twice the predicted median. Interpretation: A simple and clinically applicable graphical method of predicting an individual patient's survival from diagnosis is presented. The model should be validated in an independent cohort, and extended to include other important prognostic factors

Evidence of an environmental effect on survival in ALS

Amyotrophic lateral sclerosis (ALS, motor neuron disease) is a neurodegenerative disorder of motor neurons leading to paralysis and eventual death by respiratory failure. Median survival is 2–3 years. Susceptibility genes, environmental triggers and disease related prognostic factors have been established, but environmental effects on survival are yet to be investigated. We analysed survival in the South-East England ALS register (SEALS register). Kaplan-Meier and Cox regression analyses were used to investigate survival in London, coastal and rural areas according to postcode at diagnosis. Results showed that there were 933 cases of ALS identified in the catchment area during the study period (1994–January 2012). Cox regression demonstrated a highly significant model for survival with significant protective variables: coastal residency, riluzole use and younger age at onset. Significantly worse survival was associated with London residency, older age as well as definite and probable El Escorial classifications. In conclusion, these findings suggest the possibility of an environmental effect on survival in ALS.</p