title: Predicting prognosis in amyotrophic lateral sclerosis: a simple algorithm.
abstract: 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 Irish Test sub-cohort and externally confirmed in the Italian replication cohort. In the Training sub-cohort (n = 117), significant predictors of prognoses were site of disease onset (HR = 1.7, p = 0.012); ALSFRS-R slope prior to first evaluation (HR = 2.8, p < 0.0001), and executive dysfunction (HR = 2.11, p = 0.001). The risk group system generated using these results predicted median survival time in the Training set, the Test set (n = 87) and the Italian cohort (n = 122) with no overlap of the 95 % CI (p < 0.0001). In the validation cohorts, a high-risk classification was associated with a positive predictive value for poor prognosis of 73.3-85.7 % and a negative predictive value (NPV) for good prognosis of 93.3-100 %. Classification into the low-risk group was associated with an NPV for bad prognosis of 100 %. A simple algorithm using variables that can be gathered at first patient encounter, validated in an independent patient series, reliably predicts prognoses in ALS patients.
authors: Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland, marwaelamin08@gmail.com.
authors: Marwa
authors: M
authors: Elamin
authors: Peter
authors: P
authors: Bede
authors: Anna
authors: A
authors: Montuschi
authors: Niall
authors: N
authors: Pender
authors: Adriano
authors: A
authors: Chio
authors: Orla
authors: O
authors: Hardiman
mesh_terms: D000368:Aged; D000465:Algorithms; D000690:Amyotrophic Lateral Sclerosis / Q000175:diagnosis* / Q000235:genetics; D000073885:C9orf72 Protein; D015331:Cohort Studies; D018672:Community Health Planning; D016206:Databases, Bibliographic / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D007494:Ireland; D007558:Italy; D053208:Kaplan-Meier Estimate; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D011506:Proteins / Q000235:genetics; D012306:Risk
chemical_list: D000073885:C9orf72 Protein; C568651:C9orf72 protein, human; D011506:Proteins