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Research paper
A clinical tool for predicting survival in ALS
  1. Jonathan A Knibb1,
  2. Noa Keren2,
  3. Anna Kulka2,
  4. P Nigel Leigh3,
  5. Sarah Martin2,
  6. Christopher E Shaw2,
  7. Miho Tsuda4,
  8. Ammar Al-Chalabi2
  1. 1Brighton and Sussex University Hospitals NHS Trust, Royal Sussex County Hospital, Brighton, UK
  2. 2Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
  3. 3Trafford Centre for Biomedical Research, Brighton and Sussex Medical School, Falmer, UK
  4. 4Macclesfield District General Hospital, Macclesfield, Cheshire, UK
  1. Correspondence to Professor Ammar Al-Chalabi, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK;{at}


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.

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