Benefit of the Awaji diagnostic algorithm for amyotrophic lateral sclerosis: a prospective study

Ann Neurol. 2011 Jul;70(1):79-83. doi: 10.1002/ana.22380. Epub 2011 Mar 17.


Objective: Early and accurate diagnosis of amyotrophic lateral sclerosis (ALS) is important for patient care and for entry in clinical trials. Retrospective studies suggest that the use of the Awaji algorithm for the diagnosis of ALS is more sensitive for early diagnosis than the currently used revised El Escorial criteria.

Methods: We prospectively compared the revised El Escorial criteria with the Awaji algorithm in patients seen with suspected ALS at the University Hospitals Leuven between January 2008 and April 2010.

Results: Out of 200 patients referred for the diagnosis of ALS, 66% and 85% could be categorized to definite or probable ALS at first presentation according to the revised El Escorial and the Awaji algorithm, respectively (p < 5.6 × 10(-17) ). This corresponds to a >50% reduction of patients not eligible for clinical trial entry. Application of the Awaji algorithm made the diagnosis of ALS more likely by at least 1 diagnostic category in 25.7% of patients and identified at least 1 additional region with electrodiagnostic signs of ongoing lower motor neuron loss in 46.4% of electrodiagnostic investigations. Application of the Awaji algorithm did not result in a single false-positive diagnosis of ALS in this study.

Interpretation: Our data demonstrate that the Awaji algorithm is significantly more sensitive compared to the revised El Escorial criteria, without resulting in false-positive diagnoses of ALS. It should therefore be used in clinical trials.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Algorithms*
  • Amyotrophic Lateral Sclerosis / diagnosis*
  • Amyotrophic Lateral Sclerosis / physiopathology
  • Electromyography / standards
  • Female
  • Humans
  • Male
  • Middle Aged
  • Neurologic Examination / standards
  • Prospective Studies
  • Severity of Illness Index*