HYPERCOG COGNITIVE SCREENING IN OLDER ADULTS WITH HYPERTENSION: A PILOT STUDY
Bibliographic record
Abstract
Objective: Hypertension is a risk factor for cognitive impairment. According to the 2018 ESH/ESC guidelines for hypertension management, cognitive screening tests should be included in the assessment of older hypertensive adults. Numerous screening tests are available, but their diagnostic accuracy in hypertensive people has been scarcely investigated. The present study aimed at analyzing and comparing the diagnostic accuracy of the MiniCog, the Montreal Cognitive Assessment (MoCA), the Mini Mental State Examination (MMSE) and the Clock Drawing Test (CDT) in older hypertensive patients. Design and method: The study was carried out in the Referral Centre for Hypertension in Elderly of Careggi Hospital, Florence, Italy, between February 2017 and May 2019. Subjects aged 65 or older without a prior diagnosis of cognitive impairment were enrolled. Participants underwent a cognitive screening using the MMSE, the MoCa, the Mini-Cog and the CDT, followed by a complete neuropsychological evaluation. Depressive symptoms and functional status were assessed with the Geriatric Depression Scale and the Basic and Instrumental Activities of Daily Life, respectively. Sensitivity and specificity were assessed for each tests and their combinations, using the ROC curves for the MMSE and the MoCA. Results: Among 94 participants undergoing a complete cognitive evaluation, 35 (37.2%) had mild cognitive impairment or dementia. Seven patients (7.44%) had a multi-domain cognitive impairment. According to the ROC curves, the best detection of cognitive impairment could be achieved with a cut-off score of 24 for the MoCA (AUC 0.746) and 27.5 for the MMSE (AUC 0.689). The MoCA had the highest diagnostic accuracy, providing a sensitivity of 80% and a specificity of 59%. A cognitive screening including both the Mini-Cog and the MMSE provided a higher sensitivity (74%) and specificity (51%) than the MMSE alone (sensitivity and specificity of 69% and 52%, respectively). Conclusions: In conclusion, we observed a high prevalence of cognitive impairment (37.2%) among older hypertensive outpatients with no prior diagnosis of dementia. The MoCA with a cut-off of 24 seems to have a good diagnostic accuracy in this population and could be included in the assessment of hypertension-related organ damage, to screen for cognitive impairment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".