Physicians Make Progress Forecasting ALS Prognosis

A new tool may help physicians predict the outcome of people with ALS. The personalized approach, developed by a research team led by UMC Utretcht’s Leonard van den Berg in Amsterdam, uses 8 factors to estimate survival time including age and site of onset, breathing ability (FVC), the presence (or absence) of a C9orf72 repeat expansion and progression rate.

Forecasting ALS? A new tool may help physicians predict the prognosis of people with ALS. [Courtesy of Westeneng et al., 2018, Lancet Neurology.]

The strategy is based on the analysis of medical records of nearly 11,500 people with ALS obtained from 14 clinics in 9 countries in Europe.

The approach estimated the survival time of people with ALS upon the day of diagnosis at a probability of more than 98% of good performance (c-statistic = 0.78). The inclusion in future of surgical interventions and treatments including riluzole use is anticipated to further improve predictions (Westeneng et al., 2018; see also Fang et al., 2018).

The computational approach is one of a growing number of strategies that aims to help forecast the prognosis of people with ALS by predicting the rate of progression and/or outcome of the disease (see April 2017November 2017 news).

The study appeared on March 26 in Lancet Neurology.

The online tool is available to neurologists at no cost. The approach may also facilitate testing of ALS drug candidates in the clinic in part, by helping to stratify participants and identify “responders” that may benefit from these potential therapies (see April 2017 news).

Featured Paper

Westeneng HJ, Debray TPA, Visser AE, van Eijk RPA, Rooney JPK, Calvo A, Martin S, McDermott CJ, Thompson AG, Pinto S, Kobeleva X, Rosenbohm A, Stubendorff B, Sommer H, Middelkoop BM, Dekker AM, van Vugt JJFA, van Rheenen W, Vajda A, Heverin M, Kazoka M, Hollinger H, Gromicho M, Körner S, Ringer TM, Rödiger A, Gunkel A, Shaw CE, Bredenoord AL, van Es MA, Corcia P, Couratier P, Weber M, Grosskreutz J, Ludolph AC, Petri S, de Carvalho M, Van Damme P, Talbot K, Turner MR, Shaw PJ, Al-Chalabi A, Chiò A, Hardiman O, Moons KGM, Veldink JH, van den Berg LH. Prognosis for patients with amyotrophic lateral sclerosis: development and validation of a personalised prediction model. Lancet Neurol. 2018 Mar 26. pii: S1474-4422(18)30089-9. [PubMed].


Fang T, Al Khleifat A, Meurgey JH, Jones A, Leigh PN, Bensimon G, Al-Chalabi A. Stage at which riluzole treatment prolongs survival in patients with amyotrophic lateral sclerosis: a retrospective analysis of data from a dose-ranging study. Lancet Neurol. 2018 Mar 7. pii: S1474-4422(18)30054-1. [PubMed].

Knibb JA, Keren N, Kulka A, Leigh PN, Martin S, Shaw CE, Tsuda M, Al-Chalabi A. A clinical tool for predicting survival in ALS. J Neurol Neurosurg Psychiatry. 2016 Dec;87(12):1361-1367. [PubMed].

Further Reading

Mitsumoto H. What if you knew the prognosis of your patients with ALS? Lancet Neurol. 2018 Mar 26. pii: S1474-4422(18)30111-X. [PubMed].

Talbot K. Clinical tool for predicting survival in ALS: do we need one? J Neurol Neurosurg Psychiatry. 2016 Dec;87(12):1275. doi: 10.1136/jnnp-2016-313683. Epub 2016 Jul 4. [PubMed].


disease-als patient stratification prognosis responder analysis survival topic-clinical topic-randd
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