Facilitating clinical use of the Amsterdam instrumental activities of daily living questionnaire: Dutch normative data and clinical cutoff values

Abstract

AbstractBackgroundThe Amsterdam Instrumental Activities of Daily Living Questionnaire (A‐IADL‐Q) is a proxy‐based instrument aimed at measuring difficulties in daily functioning due to cognitive decline in the context of dementia. To facilitate its interpretation and clinical implementation, normative data from the general population are necessary. Here, we aimed to compose normative scores for the A‐IADL‐Q and provide clinical cutoff values for mild cognitive impairment (MCI) and dementia.MethodCross‐sectional data from three Dutch cohorts (i.e., Dutch Brain Research Registry, European Medial Information Framework‐Alzheimer’s Disease (EMIF‐AD) 90+, and Amsterdam Dementia Cohort (ADC)) were used, including 1127 cognitively healthy individuals and 380 individuals with MCI and/or dementia. Regression‐based norms were constructed in the healthy sample, using linear models including age, sex, education(high/low) and their interactions, and the optimal model was selected using backward elimination. Individual differences between observed and expected IADL t‐scores were divided by the residual standard error of the model. Optimal normative score cutoffs to distinguish healthy from MCI and/or dementia were based on bootstrap average estimates(500 repeats) of the Youden Index. Diagnostic accuracy was evaluated using receiver operating curves (ROC), area under the curve (AUC), sensitivity and specificity.ResultNormative data (mean age = 64±13year, 68.6%female, 67.8% highly educated) and memory clinic data (mean age = 66±8year, 43.9%female, 38.9% highly educated) were used (Table1). The best normative model (i.e., with the lowest Akaike information criterion) included age‐squared, sex, education and the interaction between age and education. The optimal normative score cutoffs to distinguish healthy from dementia and MCI/dementia were, respectively, 1.78SD and 1.79SD below the mean, with high AUCs of 0.96 [95%CI 0.95‐0.98] and 0.94 [95%CI 0.92,0.95]. The optimal cutoff to distinguish healthy from MCI was higher; 1.01SD below the mean, also with a high AUC‐score of 0.86 [95%C 0.81‐0.9])(Table2, Figure1).ConclusionWe composed normative scores with clinical cutoff values that can aid in the distinction between cognitively healthy from MCI and/or dementia with high diagnostic accuracy. These normative scores are accessible via an online tool to further facilitate and implement clinical use of the A‐IADL‐Q. Future studies should include participants from different countries, cultures and different socio‐economic background to facilitate world‐wide use.