Facilitating Clinical Use of the Amsterdam Instrumental Activities of Daily Living Questionnaire: Normative Data and Diagnostic Cutoff Values

Abstract

AbstractBackgroundThe Amsterdam Instrumental Activities of Daily Living Questionnaire(A‐IADL‐Q) measures impairments in everyday functioning in the context of dementia. To facilitate interpretation and clinical implementation of the A‐IADL‐Q, we aimed to generate demographically‐adjusted norm scores and a clinical cutoff value.MethodCross‐sectional data were used from Dutch Brain Research Registry(N = 1,064; mean (M) age = 62±11year; 69.5% female) European Medial Information Framework‐Alzheimer’s Disease(EMIF‐AD) 90+ (N = 63; Mage = 92±2year; 52.4% female), and European Prevention of Alzheimer’s Dementia Longitudinal Cohort Study(EPAD‐LCS)(N = 300; Mage = 62±7year; 71.7% female)(Table 1). Norm scores were generated using a I) linear regression model approach; and II) Box‐Cox Power Exponential(BCPE) model approach (the latter falling within the generalized model for location shape and size(GAMLSS) framework). For both methods, different combinations of the variables age, sex and education were tested (including polynomial age and age*education interaction) and the best model was selected based on the lowest Aikake Information Criteria(AIC) value. Next, diagnostic contrast was generated using an independent memory clinic sample (the Amsterdam Dementia Cohort(ADC)(N = 2,123, Mage = 64±8year, 46.1% female)(Table 1), including people with dementia (both Alzheimer’s Disease(AD) and non‐AD). The optimal cut‐off value for cognitively healthy versus dementia was determined, and corresponding diagnostic accuracy was calculated using area under the receiver operating curves(AUC‐ROC). This optimal cutoff was then applied to different diagnostic contrasts, including mild cognitive impairment(MCI) and subjective cognitive decline(SCD).ResultThe best suitable normative model was the linear regression model, including cubic polynomial for age, education, and age*education interaction. This model was considered superior to the best BCPE model, which had infinitely large residuals due to bad model fit. The optimal cutoff to distinguish normal functioning from dementia, based on bootstrap average estimates of the Youden Index, was 1.82 standard deviation(SD) below the population mean, resulting in an AUC of 0.97[95%CI 0.98‐0.98]). Normative data are made publicly available in an online tool(Fig.1).ConclusionWe established demographically adjusted norm scores for the A‐IADL‐Q using a large representative sample, and provided an optimal cutoff value for cognitively healthy versus dementia, with outstanding accuracy. Our results, captured in the online tool, can help clinicians to interpret A‐IADL‐Q scores more easily by allowing comparison of functioning against a reference population.