Development of risk prediction model for cognitive impairment in patients with coronary heart disease: A study protocol for a prospective, cross-sectional analysis
Bibliographic record
Abstract
Background Ischemic heart disease and degenerative encephalopathy are two main sources of disease burden for the global elderly population. Coronary heart disease (CHD) and cognitive impairment, as representative diseases, are prevalent and serious illnesses in the elderly. According to recent research, patients with CHD are more likely to experience cognitive impairment and their cognitive ability declines more quickly. Vascular risk factors have been associated with differences in cognitive performance in epidemiological studies, but evidence in patients with CHD is more limited. Inextricably linked between the heart and the brain. Considering the unique characteristics of recurrent cognitive impairment in patients with CHD, we will further study the related risk factors. We tried to investigate the potential predictors of cognitive impairment in patients with CHD through a prospective, cross-sectional study. Methods The cross-sectional study design will recruit 378 patients with CHD (≥65 years) from Xiyuan Hospital of China Academy of Chinese Medical Sciences. The subjects' cognitive function is evaluated with MoCA scale, and they are divided into cognitive impairment group and normal cognitive function group according to the score results. Demographic data, disease characteristics (results of coronary CT/ angiography, number of stents implanted, status of diseased vessels), laboratory tests (biochemistry, coagulation, serum iron levels, pulse wave velocity), metabolites (blood samples and intestinal metabolites), and lifestyle (smoking, alcohol consumption, sleep, physical activity) will be assessed as outcome indicators. Compare the two groups and the correlation analysis will be performed on the development of mild cognitive impairment. Mann-Whitney U or X2 test was selected to describe and evaluate the variation, and logistics regression analysis was employed to fit the prediction model. After that, do the calibration curve and decision curve to evaluate the model. The prediction model will be validated by a validation set. Discussion To explore the risk factors related to mild cognitive impairment (MCI) in patients with CHD, a new predictive model is established, which can achieve advanced intervention in the occurrence of MCI after CHD. Owing to its cross-sectional study design, the study has some limitations, but it will be further studied by increasing the observation period, adding follow-up data collection or prospective cohort study. The study has been registered with the China Clinical Trials Registry (ChiCTR2200063255) to conduct clinical trials.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".