Bleeding in Patients Undergoing Percutaneous Coronary Intervention
Bibliographic record
Abstract
BACKGROUND: Bleeding in patients undergoing percutaneous coronary intervention (PCI) is associated with increased morbidity, mortality, length of hospitalization, and cost. We identified baseline clinical characteristics associated with bleeding complications after PCI and developed a simplified, clinically useful algorithm to predict patient risk. METHODS AND RESULTS: Data were analyzed from 302 152 PCI procedures performed at 440 US centers participating in the National Cardiovascular Data Registry. As defined by the National Cardiovascular Data Registry, bleeding required transfusion, prolonged hospital stay, and/or a drop in hemoglobin >3.0 g/dL from any location, including percutaneous entry site, retroperitoneal, gastrointestinal, genitourinary, and other/unknown location. Bleeding complications occurred in 2.4% of patients. From the best-fitting model consisting of 15 clinical elements associated with post-PCI bleeding in a random 80% training cohort, we developed a parsimonious risk algorithm. Predictors of bleeding included age, gender, previous heart failure, glomerular filtration rate, peripheral vascular disease, no previous PCI, New York Heart Association/Canadian Cardiovascular Society Functional Classification class IV heart failure, ST-elevation myocardial infarction, non-ST-elevation myocardial infarction, and cardiogenic shock. The parsimonious model was validated in the remaining 20% of the population (c-statistic, 0.72) and in clinically relevant subgroups of patients. This simplified model was used to derive a clinical risk algorithm, with larger numbers corresponding with greater risk. In 3 categories, bleeding rates were greater in patients with higher estimates ( or=18, 5.1%). CONCLUSIONS: This report identifies baseline clinical factors associated with bleeding and proposes a clinically useful algorithm to estimate bleeding risk. This model is potentially actionable in altering therapeutic decision making and improving outcomes in patients undergoing PCI.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".