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.

Predicting prognosis in amyotrophic lateral sclerosis: a simple algorithm

MONTUSCHI, ANNA;CHIO', Adriano;
2015-01-01

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.
2015
262
6
1447
1454
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4469087/
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
Elamin, Marwa; Bede, Peter; Montuschi, Anna; Pender, Niall; Chio, Adriano; Hardiman, Orla
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1563255
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