Cross-sectional and longitudinal characterisation of cognitive function and outcomes in patients presenting to hospital with cardiovascular risk factors
Bibliographic record
Abstract
Abstract Background/Introduction Cardiovascular (CV) diseases including atrial fibrillation and arteriosclerosis are associated with impaired cognitive function. Cognitive dysfunction can impact the process of shared clinical decision making, reduce adherence to polypharmacy, and decrease quality of life. The prevalence of cognitive dysfunction in contemporary patients with CV diseases and its implication on future CV events is not well known. Purpose We 1) quantified cognitive function in patients presenting to hospital with CV diseases, 2) identified clinical variables and blood biomarkers associated with cognitive dysfunction, and 3) quantified the hazard of abnormal cognitive function for predicting MACCE (major adverse CV and cerebrovascular events). Methods and results Of 1625 consecutive patients presenting acutely to a large teaching hospital with CV diseases, 614 patients (median age [Q1, Q3] 68 [58, 76] years; 66% male) who completed the Montreal Cognitive Assessment (MoCA) were analysed. The median [Q1, Q3] MoCA score was 25 points [21, 27]. 360 patients (59%) had an abnormal score (<26). At baseline, patients with abnormal scores were more likely to be female (odds ratio, OR [95% confidence intervals], 1.874 [1.287, 2.728]), have BMI<30 (OR 0.584 [0.410, 0.831]), heart failure (OR 1.492 [1.043, 2.135]), diabetes (OR 2.212 [1.529, 3.199]), chronic kidney disease (CKD-EPI<60 ml/min, OR 1.553 [1.021, 2.361]), and have more CV co-morbidities (OR per additional co-morbidity 1.415 [1.246, 1.605]). Amongst 12 CV biomarkers tested, elevated Bone Morphogenetic Protein 10 (OR 1.325 [1.022, 1.719]) and Growth Differentiation Factor 15 (OR 1.419 [1.054, 1.912]) increased odds of abnormal scores. Cox proportional hazards model adjusted for competing risk of non-CV death assessed the relationship between abnormal cognitive function and MACCE (stroke, TIA, myocardial infarction, hospitalisation for heart failure, CV death). Follow-up time ranged from 2.7 to 6.1 years. Patients were censored at 2.5 years for this analysis. 130 out of 614 patients experienced a MACCE (21%) and 71 had a non-CV death (12%). Patients with abnormal MoCA scores were at higher risk for MACCE (subhazard ratio, sHR [95% CI] 1.827 [1.253, 2.664]). The hazard remained significant after adjustment for age, sex, obesity, atrial fibrillation, stroke, heart failure, hypertension, coronary artery disease, diabetes, peripheral artery disease and renal dysfunction (sHR 1.367 [1.056, 2.326]; Figure). All-cause mortality was 1.785 times higher for those with abnormal MoCA scores [1.061, 3.002]. Conclusion In this study, 3 out of 5 patients with CV diseases had abnormal MoCA scores at baseline. Abnormal cognitive scores significantly predicted patients who went on to experience a MACCE within 2.5 years of follow-up. These observations call for further research and action to provide additional diagnostics, support and early intervention to address cognitive dysfunction in CV patients. Funding Acknowledgement Type of funding sources: Public grant(s) – EU funding. Main funding source(s): EU H2020 CATCH ME Cumulative incidence function
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".