Mild cognitive impairment in middle-aged adults with coronary microvascular dysfunction
Bibliographic record
Abstract
Abstract Introduction Coronary microvascular dysfunction (CMD) may be a manifestation of systemic small vessel disease, including the brain. The prevalence of cognitive impairment in CMD patients is poorly understood. Purpose To assess the prevalence of cognitive impairment in patients with CMD. Methods Between April 2018-March 2020, we enrolled patients with chest discomfort who were admitted to a chest pain observation unit and underwent 3D cardiac positron emission tomography/computed tomography (PET/CT). Exclusions included myocardial infarction, hypertensive urgency, and heart failure. Patients were categorized as 1) Normal: coronary flow reserve (CFR) ≥2.5 without perfusion defect or calcification, 2) Possible CMD: CFR 2–2.5 without perfusion defects or calcification, 3) CMD: CFR <2 without perfusion defects or calcification and 4) coronary artery disease (CAD/CALC): any CFR with perfusion defects or calcifications. We assessed cognitive function with the Montreal Cognitive Assessment (MoCA) and used <23 as the cutoff for impaired cognition. We added 1 point to the total score for those with 12 years of education or less. Odds ratios of cognitive impairment in each group were calculated with the normal group as reference, adjusting for age, sex, and race. Results Of 111 patients consented, 109 patients had complete data for analysis. (Table 1) Mean age was 57 years (± 11), 68% were female, and 49% were non-White. All 11 patients with CMD were females, with a mean age of 59 years (±12). The majority (72%) of CMD patients had cognitive impairment on the MoCA compared to 25% of patients with normal flows (unadjusted OR: 8.00 [95% CI 1.70–37.67]), even after adjustment for age, sex, and race (OR: 37.23 (95% CI 2.01–677.05). MoCA scores did not differ significantly between the normal and the CAD/CALC group (unadjusted OR: 0.95 [95% CI 0.30–3.070]), or the possible CMD group (1.44 [95% CI 0.50–4.14]). Additionally, non-White patients were more likely to demonstrate cognitive impairment on MoCA than White patients (OR: 9.47 [95% CI 3.48–25.81]). There was no significant nonparametric correlation between CFR and the MoCA score (r=0.05, p=0.6). Conclusion Patients with CMD are more likely to have cognitive impairment, supporting the need to further investigate the heart-brain connection in systemic small vessel disease. FUNDunding Acknowledgement Type of funding sources: None.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".