Abstract
Objective
The clinical diagnosis of amyotrophic lateral sclerosis (ALS) usually takes several
months. The delay in diagnosis compromises the effective therapeutic interventions.
Therefore, the present study was aimed to develop a statistical model for predicting
the risk of ALS at earlier stages for better management of ALS patients.
Methods
The study recruited 44 sporadic ALS patients and 29 normal controls. Thirteen different
independent variables (predictors) which were believed to be associated with ALS were
included in the study. Forward stepwise (likelihood ratio) binary logistic regression
was used to find significant variables and probability of disease prediction.
Results
The Hosmer–Lemeshow goodness of fit statistic (χ2=4.379, df=8, p=0.821) indicate the appropriateness of forward stepwise (likelihood
ratio) binary logistic regression model. Serum chemokine ligand-2, chemokine ligand-2
mRNA, vascular endothelial growth factor-A mRNA, smoking and alcohol consumption are
the independent variables found significant to predict risk of ALS (p<0.05). The current
model yielded 93.2% sensitivity and 86.2% specificity with 90.4% overall validity
of correct ALS prediction.
Conclusion
Forward stepwise (likelihood ratio) binary logistic regression model is an accurate
method to predict ALS in the presence of serum CCL2, CCL2 mRNA, VEGFA mRNA, smoking
and alcohol consumption with high sensitivity and specificity. However, bed side diagnostic
utility of these variables needs to be validated further in larger ALS cohorts.
Keywords
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Article info
Publication history
Published online: September 12, 2011
Accepted:
August 11,
2011
Received in revised form:
August 9,
2011
Received:
April 18,
2011
Identification
Copyright
© 2011 Elsevier B.V. Published by Elsevier Inc. All rights reserved.
