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Predicting prognosis in amyotrophic lateral sclerosis: a simple algorithm.

Marwa Elamin1, Peter Bede, Anna Montuschi

  • 1Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland, marwaelamin08@gmail.com.

Journal of Neurology
|April 11, 2015
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Summary
This summary is machine-generated.

This study developed a practical prognostic index for amyotrophic lateral sclerosis (ALS) patients using early clinical data. The index accurately predicts survival, aiding in risk stratification for better patient management.

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Area of Science:

  • Neurology
  • Clinical Prognostics

Background:

  • Amyotrophic lateral sclerosis (ALS) prognosis is challenging to determine early.
  • Accurate prognostic tools are needed for patient management and clinical trial stratification.

Purpose of the Study:

  • To develop and validate a practical prognostic index for ALS patients.
  • To utilize information available at the first clinical consultation for prediction.
  • To stratify patients into distinct risk groups.

Main Methods:

  • Utilized datasets from population-based ALS projects in Ireland and Italy.
  • Employed Kaplan-Meier methods and Cox proportional hazards regression for predictor identification.
  • Derived a weighted grading system to create a prognostic index and risk groups.
  • Validated the index in independent Irish test and Italian replication cohorts.

Main Results:

  • Identified key predictors: site of onset, ALSFRS-R slope, and executive dysfunction.
  • The derived risk group system accurately predicted median survival across training, test, and validation cohorts.
  • High-risk classification showed a positive predictive value of 73.3-85.7% for poor prognosis.
  • Low-risk classification demonstrated a 100% negative predictive value for poor prognosis.

Conclusions:

  • A simple, validated prognostic index using early clinical data reliably predicts ALS patient outcomes.
  • This tool enables effective risk stratification at the initial patient encounter.
  • The index offers a valuable resource for clinical decision-making and research in ALS.