LAUSR

Background Atrial fibrillation (AF) is associated with the worsening of cognitive function. Strategies that are both convenient and reliable for cognitive screening of AF patients remain underdeveloped. We aimed to analyze the sensitivity and specificity of computerized cognitive screening strategies using subtests from Cambridge Neuropsychological Test Automated Battery (CANTAB) in AF patients. Methods The Multitasking Test (MTT), Rapid Visual Information Processing (RVP), and Paired Associates Learning (PAL) subtests from CANTAB were performed in 105 AF patients. Traditional standard neuropsychological tests were used as a reference standard. Cognitive screening models using different CANTAB subtests were established using multivariable logistic regression. Further stepwise regression using the Akaike Information Criterion (AIC) was applied to optimize the models. Receiver operating characteristic curve analyses were used to study the sensitivity and specificity of these models. Results Fifty-eight (55%) patients were diagnosed with mild cognitive impairment (MCI). MTT alone had reasonable sensitivity (82.8%) and specificity (74.5%) for MCI screening, while RVP (sensitivity 72.4%, specificity 70.2%) and PAL (sensitivity 70.7%, specificity 57.4%) were less effective. Stepwise regression of all available variables revealed that a combination of MTT and RVP brought about higher specificity (sensitivity 82.8%, specificity 85.8%), while PAL was not included in the optimal model. Moreover, adding education to the models did not result in improved validity for MCI screening. Conclusion The CANTAB subtests are feasible and effective strategies for MCI screening among AF patients independent of patients' education levels. Hence, they are practical for cardiologists or general practitioners.