Predictive value of cardiopulmonary fitness parameters in the prognosis of patients with acute coronary syndrome after percutaneous coronary intervention
Bibliographic record
Abstract
OBJECTIVES: We aimed to determine the predictive value of cardiopulmonary exercise testing (CPX) in the prognosis of patients with acute coronary syndrome (ACS) treated with percutaneous coronary intervention (PCI). METHODS: We conducted a retrospective study including patients who underwent CPX within 1 year of PCI between September 2012 and October 2017. Patients were followed-up until the occurrence of a major adverse cardiac event (MACE) or administrative censoring (September 2019). A Cox regression model was used to identify significant predictors of a MACE. Model performance was evaluated in terms of discrimination (C-statistic) and calibration (calibration-in-the-large). RESULTS: In total, 184 patients were included and followed-up for a median 51 months (interquartile range: 36-67 months) and 32 events occurred. Multivariable analysis revealed that body mass index and Gensini score were significant predictors of a MACE. Four CPX-related variables were found to be predictive of a MACE: premature CPX termination, peak oxygen uptake, heart rate reserve, and ventilatory equivalent for carbon dioxide slope. The final prediction model had a C-statistic of 0.92 and calibration-in-the-large 0.58%. CONCLUSION: CPX-related parameters may have high predictive value for poor outcomes in patients with ACS who undergo PCI, indicating a need for appropriate treatment and timely management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".