Usefulness of cognitive assessment in the prediction of documented atrial fibrillation and ischaemic stroke: a cross-sectional study
Bibliographic record
Abstract
Abstract Background/Introduction Atrial fibrillation (AF) is sometimes detected when thromboembolic events occur, commonly ischaemic stroke (IS). In patients with AF, the prevalence of cognitive dysfunction (CD) is higher, independently from IS. Purpose Evaluating the usefulness of cognitive evaluation and the CHA2DS2-VASc score for the prediction of documented AF and IS. Methods In this cross-sectional retrospective study, we included 469 patients with cardiovascular diseases. Between December 2016 and November 2019, all patients completed two cognitive tests during hospitalization. The Montreal Cognitive Assessment (MoCA) and the Mini Mental State Examination (MMSE) were used. The associations between cognitive test scores (standard and optimal cut-off values) and AF/IS were analyzed by logistic regression. We determined the area under the curve (AUC) of CD tests and optimal cut-off values using the receiver operating characteristic curves and the maximum Youden index. Results We found a significant association between the standard MoCA cut-off value for CD (<26 points) and the presence of documented AF (OR: 1.83, 95% CI: 1.11-3.01, p= 0.0174) and IS (OR: 2.09, 95% CI: 1.04-4.22, p= 0.0379). The standard MMSE score of <24 points was a risk factor for the presence of a previous IS (OR: 2.43, 95% CI: 1.3-4.53, p= 0.0051). For the prediction of documented AF, the optimal cut-off score was <26 for MoCA and <28 points for MMSE. For MoCA, the optimal cut-off was <23 points in the prediction of IS and <28 points for MMSE. The determined CHA2DS2-VASc cut-off score for AF was >3 points and for IS >5 points. The receiver operating characteristic curve analyses showed non-inferiority between CD tests and CHA2DS2-VASc score in anticipating documented episodes of AF and were superior to CHA2DS2-VASc score in the prediction of a previous IS (MoCA: AUC dif: 0.128; p= 0.0110, MMSE: AUC dif: 0.130, p= 0.0084). Conclusions In this study, we demonstrated that the MoCA and MMSE tests can detect the presence of documented AF and IS. Risk factors for AF were MoCA <26 points and MMSE <28 points while for IS MoCA <23 points and MMSE <28 points. In patients who present cognitive impairment AF screening may be appropriate.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".