Journal of neurology · 2023
Establishing normative data for the evaluation of cognitive performance in Huntington's disease considering the impact of gender, age, language, and education.
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Mühlbäck Alžbeta, Mana Josef, Wallner Michael, Frank Wiebke, Lindenberg Katrin S, Hoffmann Rainer, Klempířová Olga, Klempíř Jiří, Landwehrmeyer G Bernhard, Bezdicek Ondrej, REGISTRY investigators of the European Huntington’s Disease Network, the Enroll-HD investigators
Аннотация
BACKGROUND: A declining cognitive performance is a hallmark of Huntington's disease (HD). The neuropsychological battery of the Unified HD Rating Scale (UHDRS'99) is commonly used for assessing cognition. However, there is a need to identify and minimize the impact of confounding factors, such as language, gender, age, and education level on cognitive decline.
OBJECTIVES: Aim is to provide appropriate, normative data to allow clinicians to identify disease-associated cognitive decline in diverse HD populations by compensating for the impact of confounding factors METHODS: Sample data, N = 3267 (60.5% females; mean age of 46.9 years (SD = 14.61, range 18-86) of healthy controls were used to create a normative dataset. For each neuropsychological test, a Bayesian generalized additive model with age, education, gender, and language as predictors was constructed to appropriately stratify the normative dataset.
RESULTS: With advancing age, there was a non-linear decline in cognitive performance. In addition, performance was dependent on educational levels and language in all tests. Gender had a more limited impact. Standardized scores have been calculated to ease the interpretation of an individual's test outcome. A web-based online tool has been created to provide free access to normative data.
CONCLUSION: For defined neuropsychological tests, the impact of gender, age, education, and language as factors confounding disease-associated cognitive decline can be minimized at the level of a single patient examination.
OBJECTIVES: Aim is to provide appropriate, normative data to allow clinicians to identify disease-associated cognitive decline in diverse HD populations by compensating for the impact of confounding factors METHODS: Sample data, N = 3267 (60.5% females; mean age of 46.9 years (SD = 14.61, range 18-86) of healthy controls were used to create a normative dataset. For each neuropsychological test, a Bayesian generalized additive model with age, education, gender, and language as predictors was constructed to appropriately stratify the normative dataset.
RESULTS: With advancing age, there was a non-linear decline in cognitive performance. In addition, performance was dependent on educational levels and language in all tests. Gender had a more limited impact. Standardized scores have been calculated to ease the interpretation of an individual's test outcome. A web-based online tool has been created to provide free access to normative data.
CONCLUSION: For defined neuropsychological tests, the impact of gender, age, education, and language as factors confounding disease-associated cognitive decline can be minimized at the level of a single patient examination.