Introduction: Language impairment is an early marker in Alzheimer's disease (AD), manifesting in both semantic memory and syntactic comprehension. The comprehension of lexical (LAS) and syntactic (SAS) ambiguous sentences requires the integrity of both systems and is particularly challenging for patients with probable AD. The study aimed to investigate differences in performance on ambiguous sentence comprehension between healthy older adults and patients with probable AD, and to identify which type of ambiguity posed the greatest difficulty for each group. Methods: A cross-sectional observational study with 118 participants (60 patients with probable AD and 58 healthy controls) underwent an experimental task of comprehending 12 ambiguous sentences (6 LAS and 6 SAS). Multiple linear regression models were adjusted for age, sex, and education. Results: Patients with probable AD performed significantly worse in LAS (β = -4.664; 95% CI: -5.827 to -3.502; p < 0.001), SAS (β = -3.259; 95% CI: -4.294 to -2.225; p < 0.001), and TAS (β = -7.776; 95% CI: -9.624 to -5.927; p < 0.001). Both groups interpreted LAS significantly better than SAS. Education was associated with better performance in all three models (p < 0.05). Age and sex were not significantly associated. Conclusion: Patients with probable AD present significantly impaired comprehension of ambiguous sentences, with syntactic ambiguity posing the greatest challenge. Education proved to be a protective factor. These findings reinforce the multifactorial nature of linguistic deficits in probable AD and suggest that the ambiguous sentences task is a useful tool for assessing linguistic impairment.
Background: The aims of this study are to assess clinical and patient-centered outcomes following admission for HF as stratified by ADRD status. Methods: We identified patients 65 years or older in the OneFlorida+ data trust with first admission for HF between October 1, 2015 and December 31, 2022. ICD-10 diagnoses codes were used to identify HF, ADRD, and relevant comorbidities. Exclusion criteria were admission with ST-elevation myocardial infarction (STEMI) or shock. The primary clinical outcome was all-cause 1-year mortality, and the secondary clinical outcome was 90-day readmissions. Patient-centered outcomes were length of stay and discharge home. Results: 22,342 patients with first HF hospitalization during the study period were identified. Of these, 5,261 (23.5%) patients carried a diagnosis of ADRD. Patients with ADRD were more likely to be women (57.7% vs. 53.1%, p<0.05) and Black (21.1% vs. 14.6%, p<0.05). 1-year mortality was higher in patients with ADRD (HR 1.22, 95% CI 1.10-1.36, p < 0.005) as was 90-day readmissions (HR 1.23, 95% CI 1.18-1.28, p <0.005). Patients with ADRD demonstrated longer lengths of stays (5.7 vs. 5.4 days, p <0.005) and were less likely to be discharged home (OR .81, 95% CI 0.77-0.84, p<0.001). Conclusion: Patients with ADRD hospitalized for HF demonstrate higher 1-year mortality and 90-day readmissions. Patient-centered outcomes are also worse in patients with ADRD, with longer lengths of stay and are less discharge home. These findings should be used to identify research priorities and to facilitate advanced care planning in patients with ADRD and HF.
Background and aim: In memory clinic patients, assessing diagnostic relevance of white matter hyperintensities (WMHs), as a marker to explain cognitive deficits, can be challenging. Here, we aimed to quantify this relevance by measuring the variance in cognitive function they explain, and consider, beyond total WMH volume, WMH location in specific tracts and normative WMH values. Methods: We analyzed previously pooled and harmonized individual patient data (n=3,481, 71±9 years old, 50% females) from 11 international memory clinic cohorts from the Meta-VCI-Map consortium. Cognitive function was assessed in 4 domains: attention and executive function, information processing speed, verbal memory and language. We measured WMH volumes in 20 white matter tracts. These were also norm-adjusted for age and sex, using normative WMH values from 14,876 community-dwelling individuals. Explained variance in cognitive function was measured for demographics only (age, sex and education) and demographics combined with total WMH volume, tract-specific WMH volumes and norm-adjusted and tract-specific WMH z-scores. Results: Beyond demographics, total and tract-specific WMH volumes explained significantly more variance in attention and executive functioning (ΔR2total=3.7%, 95%CI=[3.3%;4.1%], ΔR2tracts=6.2%, 95%CI=[5.6%;6.7%]) and verbal memory (ΔR2total=3.6%, 95%CI=[3.2%;3.9%], ΔR2tracts=8.0%, 95%CI=[7.5%;8.6%]). In processing speed, tract-specific (ΔR2=2.5%, 95%CI=[2.1%;3.0%]) —but not total— WMH volumes explained more variance than demographics. Norm-adjusting tract-specific volumes explained no additional variance. Conclusion: Considering location of WMHs increased their diagnostic relevance, compared to total volume, explaining more variance in attention and executive function, processing speed and verbal memory. This advocates considering WMH location beyond total volume as a diagnostic marker.
Introduction: Detection of prevalent mild cognitive impairment (MCI) is difficult. We tested whether adding gait features to clinical variables improved prevalent MCI classification among older adults. Methods: This pooled cross-sectional analysis included 266 older adults. A Kinect sensor was used to capture skeletal trajectories, from which spatiotemporal, variability, asymmetry, center-of-gravity stability, and kinematic range-of-motion features were computed. MCI was assessed by Petersen’s criteria. Machine learning algorithms, trained using three predictor sets (clinical-only, gait-only, and a fused set combining both), were employed to predict prevalent MCI, with performance evaluated primarily using the area under the ROC curve (AUROC), supplemented by the area under the PR curve (AUPRC), etc. SHapley Additive exPlanations (SHAP) was used to identify key predictors. Results: The study included 76 MCI and 190 cognitively normal participants (mean age: 74.9 years; 73.2% women). The optimal model for clinical-only set was neural network, achieving an AUROC of 0.687 (SD: 0.081) and an AUPRC of 0.526 (SD: 0.111). For gait-only set, logistic regression performed best, with corresponding values of 0.813 (SD: 0.060) and 0.636 (SD: 0.122). Feature fusion yielded the best performance: LightGBM model attained an AUROC of 0.819 (SD: 0.070) and an AUPRC of 0.656 (SD: 0.118), with an accuracy of 0.776 (SD: 0.056), sensitivity of 0.596 (SD: 0.128), and specificity of 0.846 (SD: 0.057). SHAP analysis of the LightGBM identified step length asymmetry, step length variability, and velocity as key predictors. Conclusion: Markerless depth camera gait assessment showed potential in identifying prevalent MCI, which needs to be validated in external cohorts.
Introduction: The Uniform Data Set Neuropsychological Battery, version 3 (UDSNB 3), is a comprehensive, non-proprietary tool designed to assess cognitive domains relevant to neurodegenerative disorders, particularly Alzheimer’s disease. This study aimed to establish Czech norms for the UDSNB 3, including the Lewy body disease module, using continuous norming methods. Methods: A normative sample of 171 cognitively healthy adults aged 50 – 95 years was recruited. Participants completed the Czech adaptation of the UDSNB 3. Continuous norming was performed using a semi-parametric approach. Education and age effects were examined, and selected scores were compared with previous Czech normative studies. Results: Cognitive performance generally declined with age across measures. Compared to previous Czech normative studies, differences were small and inconsistent across tests. Discussion: The present study provides the first Czech normative data for UDSNB 3 using continuous norming approach, supporting its clinical applicability. Continuous norming appears to be a viable approach for developing norms in contexts of countries with limited resources or smaller populations, where collecting large stratified samples of older adults can be difficult.
