The Impact of Admission Serum Creatinine on Major Adverse Clinical Events in ST-Segment Elevation Myocardial Infarction Patients Undergoing Primary Percutaneous Coronary Intervention
Bibliographic record
Abstract
Background: Impaired renal function has been shown in previous studies to be an independent predictor of cardiovascular adverse events amongst patients admitted for percutaneous coronary intervention (PCI) following ST-segment elevation myocardial infarction (STEMI). This study investigates the impact of admission serum creatinine (SCr) on major cardiovascular outcomes among STEMI patients undergoing PCI. Methods: A retrospective study of patients admitted for PCI following STEMI was conducted using the National Cardiovascular Database Action Registry (NCDR) at Cleveland Clinic Akron General (CCAG) Hospital. The primary outcome was a composite of major clinical events: cardiogenic shock, atrial fibrillation, ventricular tachycardia/fibrillation, heart failure, bleeding and mechanical ventilation. SCr was an independent and continuous variable. Results: A total of 656 patients included in the study with the diagnosis of STEMI who subsequently underwent primary PCI. Patients with eGFR < 60 mL/min/1.73 m 2 on admission had an increased incidence of cardiogenic shock (P = 0.001), bleeding (P < 0.001), heart failure (P < 0.0005) and higher mortality rates (P = 0.0005). Furthermore, in the setting of STEMI, elevated SCr was also associated with an increased risk of developing major adverse events like cardiogenic shock (P = 0.05), bleeding (P = 0.05), and heart failure (P = 0.005). Conclusions: In the setting of STEMI, elevated SCr and eGFR < 60 mL/min/1.73 m 2 was associated with an increased risk of developing major adverse events including cardiogenic shock, bleeding and heart failure. Cardiol Res. 2018;9(2):94-98 doi: https://doi.org/10.14740/cr689w Â
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".