The Brief International Cognitive Assessment in Multiple Sclerosis (BICAMS): Regression‐based norms for German‐speaking countries
DOI:
10.1111/ene.16495
Publication Date:
2025-01-20T07:45:28Z
AUTHORS (8)
ABSTRACT
AbstractBackground and purposeCognitive impairment in multiple sclerosis (MS) is common and is associated with problems in employment, driving ability, and quality of life. Since cognitive impairment at time of diagnosis is predictive of disability progression, early assessment and annual monitoring is recommended. The Brief International Cognitive Assessment in Multiple Sclerosis (BICAMS) was introduced as a time‐efficient screening tool that can easily be applied in standard clinical care. However, besides application, tests need to be analysed and the raw values obtained must be interpreted accordingly. Since normative values have not been available for German‐speaking countries, BICAMS has not been implemented in clinical routine. The aim of this study was to calculate German normative BICAMS data to improve the current diagnostic process for people with MS.MethodsHealthy control subjects in different age categories were recruited to enable regression‐based norm analysis. Raw scores for each BICAMS test were converted into scaled scores before they were regressed for age, age squared, gender, and education. The obtained scores were then normalised to a z‐score by dividing the difference between the scaled score and the predicted score by the root mean squared error of the model.ResultsIn all, 237 HCs were recruited (68.8% female, 31.2% male) and examined with BICAMS. Datasets entered the regression‐based norms analysis and formed the basis for a final z‐score calculation. To simplify handling in everyday clinical practice, nomograms and look‐up tables were created which provide clinicians with age‐ and education‐corrected norms.ConclusionOur work fills the gaps between BICAMS application, evaluation and interpretation and will make a significant contribution to more regular cognitive assessment of MS patients.
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