ECAS-Based Neuropsychological Phenotyping in Amyotrophic Lateral Sclerosis: A Retrospective Study Comparing Different...

Area of Science:

• Neuroscience
• Neurology
• Psychology

Background:

• Amyotrophic lateral sclerosis (ALS) diagnosis requires neuropsychological phenotyping.
• The Edinburgh Cognitive and Behavioural ALS Screen (ECAS) is a tool used for this assessment.
• Discrepancies in ECAS-based classification algorithms may impact diagnostic accuracy.

Purpose of the Study:

• To compare different algorithms based on the ECAS for classifying ALS patients' neuropsychological phenotypes.
• To identify potential discrepancies among various ECAS-based classification systems.
• To evaluate the impact of different algorithms on classifying ALS patients according to Strong et al.'s criteria.

Main Methods:

• Retrospective analysis of ECAS-Cognitive and -Carer Interview (ECAS-C/-CI) scores from 901 ALS patients without dementia.
• Classification of patients into cognitively and behaviourally normal (ALScbn), impaired (ALSci/bi/cbi), or Possible ALS-FTD using three ECAS-based algorithms (Abrahams', Poletti et al.'s, Subscale).
• All algorithms utilized single-item ECAS-CI scores for behavioral classifications.

Main Results:

• Classification agreement among the three algorithms was moderate to high (84-86%, Cohen's k=0.78-0.81).
• Significant re-classifications occurred between "ALScbn" and "ALSci" categories (11-24%).
• The most substantial disagreements (43%) were observed for the ALScbi category when comparing single-task (Poletti) to total-level (Abrahams) algorithms, with reclassifications into ALSbi or Possible ALS-FTD.

Conclusions:

• Different ECAS-based algorithms for deriving Strong's phenotypes can lead to slight discrepancies.
• These discrepancies may result in the under- or overestimation of a patient's neuropsychological classification.
• Careful consideration of the algorithm used is necessary for accurate ALS phenotyping.