Association of Cognitive Impairment with Subsequent Cardiovascular Disease (CVD) Hospitalization: A Prospective ESKD Cohort Study
Bibliographic record
Abstract
Background: Cognitive impairment (CI) and stroke are common among patients with ESKD receiving hemodialysis. Whether worsened CI can be a clinical indicator of increased risk of stroke and other cardiovascular disease (CVD) events in patients with ESKD is unknown. Methods: Participants were incident hemodialysis patients from a prospective cohort study, the PACE Study. To assess CI, we used validated tests, the Multiple Mini-Mental State Exam (3MS) and Trail Making Test (TMT) Parts A and B. We used Cox proportional-hazards regression models to evaluate the association of CI at baseline with incident CVD events, adjusting for demographic characteristics, education, depression, and hypercholesterolemia. Results: Among 568 participants, the average age was 56.3 years (SD: 13.5 years; range: 20-90 years), and most participants are black (n=393; 69.2%) or white (n=161, 28.3%). Over a median follow-up of 2.9 years, 120 (21%) CVD hospitalizations occurred among the 568 participants. Worse TMT-A scores, indicating more severe CI, were associated with subsequent CVD hospitalization on unadjusted analysis (HR=1.08; 95% CI: 1.02-1.06). Similarly, those classified as cognitively “deficient” were at higher risk for subsequent CVD hospitalization, even after further adjustments for a history of diabetes, smoking, prevalent stroke, and atrial fibrillation (HR=1.53; 95% CI: 1.13-2.08). On interaction analysis, the TMT-A scores of patients without diabetes (HR=1.38; 95% CI: 1.02-1.87) were associated with future CVD events. The 3MS test and TMT-B results did not show an association between CI and future CVD hospitalization. Conclusions: In this study, patients with ESKD new to hemodialysis with worse TMT-A scores were more likely to have future CVD hospitalizations. Further studies should gauge whether this practical bedside test case helps predict a patient's cardiovascular risk. Funding: NIDDK Support, Private Foundation Support
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 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".