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Published on: July 16, 2021
A predictive model for amyotrophic lateral sclerosis (ALS) diagnosis
P K Gupta1, S Prabhakar, S Sharma
1Department of Neurology, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh-160012, India.
Journal of the Neurological Sciences
|September 13, 2011
Summary
This study developed a statistical model to predict amyotrophic lateral sclerosis (ALS) risk early. The model uses serum markers, smoking, and alcohol consumption, achieving high accuracy for timely intervention.
Area of Science:
- Neurology
- Biostatistics
- Biomarker Discovery
Background:
- Amyotrophic lateral sclerosis (ALS) diagnosis is often delayed, hindering timely treatment.
- Early detection of ALS is crucial for effective patient management and therapeutic intervention.
Purpose of the Study:
- To develop a statistical model for early prediction of ALS risk.
- To identify key predictors for earlier ALS diagnosis and improved patient outcomes.
Main Methods:
- Recruited 44 sporadic ALS patients and 29 controls.
- Utilized forward stepwise logistic regression with 13 independent variables.
- Assessed predictors including serum markers, mRNA levels, smoking, and alcohol consumption.
Main Results:
- The logistic regression model demonstrated high accuracy (90.4% overall validity).
- Significant predictors identified: serum chemokine ligand-2 (CCL2), CCL2 mRNA, vascular endothelial growth factor-A (VEGFA) mRNA, smoking, and alcohol consumption.
- The model achieved 93.2% sensitivity and 86.2% specificity.
Conclusions:
- Forward stepwise logistic regression accurately predicts ALS risk using serum CCL2, CCL2 mRNA, VEGFA mRNA, smoking, and alcohol consumption.
- The model shows high sensitivity and specificity for ALS prediction.
- Further validation in larger cohorts is recommended for bedside diagnostic utility.
