Development and Validation of a Prediction Model for Cardiovascular Events in Exercise Assessment of Coronary Heart Disease Patients After Percutaneous Coronary Intervention
Bibliographic record
Abstract
Objective: This study aimed to develop a model for predicting cardiovascular events in the exercise assessment of patients with coronary heart disease after percutaneous coronary intervention (PCI) based on multidimensional clinical information. Methods: A total of 2,455 post-PCI patients who underwent cardiopulmonary exercise testing (CPET) at the Peking University Third Hospital from January 2016 to September 2019 were retrospectively included in this study; 1,449 post-PCI patients from January 2018 to September 2019 were assigned as the development cohort; and 1,006 post-PCI patients from January 2016 to December 2017 were assigned as the validation cohort. Clinical data of patients before testing and various indicators in the exercise assessment were collected. CPET-related cardiovascular events were also collected, including new-onset angina pectoris, frequent premature ventricular contractions, ventricular tachycardia, atrial tachycardia, and bundle branch block during the examination. A nomogram model for predicting CPET-related cardiovascular events was further developed and validated. Results: slope > 30. The C-index of the nomogram model for predicting CPET-related cardiovascular events was 0.830, and the area under the ROC curve was 0.830 (95% CI: 0.764-0.896). For the validation cohort of 1,006 patients, the area under the ROC curve was 0.807 (95% CI: 0.737-0.877). Conclusion: Post-PCI patients with older age, unsatisfactory blood glucose control, impaired left ventricular systolic function, oxygen uptake parameter trajectory inflection, and poor ventilation efficiency have a higher risk of cardiovascular events in exercise assessment. The nomogram prediction model performs well in predicting cardiovascular events in the exercise assessment of post-PCI patients and can provide an individualized plan for exercise risk prediction.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".