Cognitive impairment among older persons with chronic illness attending primary care and its association with cardiovascular risk using the Framingham risk score
Bibliographic record
Abstract
BACKGROUND: Dementia is a major public health burden, particularly among the older persons with significant implications for individuals, caregivers, and society. Identifying mild cognitive impairment early can facilitate timely intervention and care.This cross-sectional study aims to investigate the association between Framingham risk score (FRS), a widely used tool for cardiovascular disease (CVD) risk prediction, and cognitive impairment among older persons with chronic illness in Malaysia. METHODS: A total of 289 participants aged 60 years and above with chronic illness were recruited from Klinik Primer Hospital Tuanku Chanselor Muhriz via simple random sampling via computer generator. The Montreal Cognitive Assessment Bahasa Malaysia version (MoCA-BM) was used to assess cognitive function and the FRS was calculated on the basis of CVD risk factors. RESULTS: The prevalence of cognitive impairment among the participants was found to be 19.7%. Multiple Logistic Regression revealed that age (AOR 1.101, 95% CI = 1.041,1.163, p < 0.001), systolic blood pressure (AOR 1.048, 95%CI = 1.024, 1.072, p < 0.001) diabetes (AOR 2.655, 95% CI = 1.194, 5.906, p = 0.017) increased the odds of having cognitive impairment among older persons with chronic illness whereas secondary education ( AOR 0.087, 95% CI = 0.008, 0.963, p = 0.047) and higher education ( AOR 0.037, 95% CI = 0.002, 0.833, p = 0.038) reduced the likelihood of having cognitive impairment. Individuals with higher FRS were more likely to have cognitive impairment (AOR 1.099, 95% CI = 1.049, 1.172, p < 0.001). The optimal cutoff point for the FRS to determine cognitive impairment is 30 for males with a sensitivity and specificity of 84.4% and 51.2% while the optimal cut off point for females is 18.5 with a sensitivity and specificity of 76% and 63.1% respectively. CONCLUSIONS: These findings suggest that the FRS which was originally designed for CVD risk assessment may also serve as a valuable predictive tool for cognitive impairment among older persons with chronic illness. Integrating FRS into routine primary care assessments could enhance the early identification of individuals at risk and enable appropriate cognitive screenings and interventions. Further research such as a longitudinal cohort study in a larger and more diverse population is warranted to validate the association of CVD risks with the development of dementia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".