publication . Article . 2015

Predicting prognosis in amyotrophic lateral sclerosis: a simple algorithm

Elamin, Marwa; Bede, Peter; Montuschi, Anna; Pender, Niall; Chio', Adriano; Hardiman, Orla;
Open Access English
  • Published: 01 Apr 2015 Journal: Journal of Neurology, volume 262, issue 6, pages 1,447-1,454 (issn: 0340-5354, eissn: 1432-1459, Copyright policy)
  • Publisher: Springer Berlin Heidelberg
  • Country: Italy
The objective of the study was to develop and validate a practical prognostic index for patients with amyotrophic lateral scleroses (ALS) using information available at the first clinical consultation. We interrogated datasets generated from two population-based projects (based in the Republic of Ireland and Italy). The Irish patient cohort was divided into Training and Test sub-cohorts. Kaplan–Meier methods and Cox proportional hazards regression were used to identify significant predictors of prognoses in the Training set. Using a weighted grading system, a prognostic index was derived that separated three risk groups. The validity of index was tested in the I...
free text keywords: Original Communication, Amyotrophic lateral scleroses, Motor neuron disease, Population-based, Prognoses, Amyotrophic lateral scleroses; Motor neuron disease; Population-based; Prognoses; Aged; Amyotrophic Lateral Sclerosis; Cohort Studies; Community Health Planning; Databases, Bibliographic; Female; Humans; Ireland; Italy; Kaplan-Meier Estimate; Male; Middle Aged; Prognosis; Proportional Hazards Models; Proteins; Risk; Algorithms; Predictive Value of Tests; Neurology (clinical); Neurology, Neurology, Clinical Neurology, Cohort, Amyotrophic lateral sclerosis, medicine.disease, medicine, Neuroradiology, Proportional hazards model, Cohort study, Predictive value of tests, Physical therapy, medicine.medical_specialty, business.industry, business, Executive dysfunction, Population, education.field_of_study, education
Funded by
European multidisciplinary ALS network identification to cure motor neuron degeneration
  • Funder: European Commission (EC)
  • Project Code: 259867
  • Funding stream: FP7 | SP1 | HEALTH
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