The extended Czech uniform data set 2.0 neuropsychological battery: Reliable change indices and logistic regression analysis

Background Early identification of cognitive decline is crucial for diagnosing neurocognitive disorders at a stage when interventions may be most effective. Longitudinal neuropsychological assessment plays a key role in this process. However, distinguishing genuine cognitive decline from normal age-related variability remains a significant clinical challenge. Objective This study aimed to provide Reliable Change Indices (RCIs) and regression-based models for evaluating cognitive trajectories using the Czech version of the Uniform Data Set 2.0 (UDS 2.0) neuropsychological battery, enhanced with additional tests. Methods Based on a longitudinal study of 197 cognitively normal older adults, we computed various RCIs, alongside logistic regression models predicting follow-up scores from baseline data and demographic variables. Results Significant practice effects with small effect sizes were found only for a subset of tests, while other measures remained stable over time. Regression-based models allow for a nuanced interpretation of individual change trajectories, with baseline score being the most consistent predictor. Conclusions The provided indices and statistical tools support clinicians in making data-driven decisions regarding cognitive changes in individual patients. These findings enhance the clinical utility of neuropsychological assessments and may contribute to the early detection of pathological cognitive decline, including Alzheimer's disease-related decline.