Changing Risk of Perioperative Myocardial Infarction
Bibliographic record
Abstract
INTRODUCTION: Years ago, patients with recent myocardial infarction (MI) were reported to be at high risk of reinfarction (27%) and death after surgery. Therapy has changed in the 3 decades since those reports, so we reexamined that risk as well as other cardiac comorbidities and surgical work values in predicting adverse outcome. METHODS: We used the National Surgical Quality Improvement Program Participant Use Data File for 2005 to 2009. We included all patients of all included specialties, for outpatient and inpatient surgery. Cardiac comorbidities included history of congestive heart failure (30 days) or MI (6 months), percutaneous coronary intervention, previous cardiac surgery, and history of angina (30 days). Other predictors included a frailty index and American Society of Anesthesiologists (ASA) class. Adverse cardiac events included cardiac arrest requiring cardiopulmonary resuscitation, MI, and death. Cases were stratified according to surgical work units. Univariate χ(2) analysis and multivariate logistic regression established simple relationships and interactions, with p < 0.05 significant. RESULTS: Of patients who had recent MI, 2.1% had reinfarction perioperatively and 26% of those died. The odds ratio for infarction with vs without recent MI in inpatients age 40 years and older was 4.6. Frailty and ASA class were stronger predictors of perioperative MI and cardiac arrest than was history of MI, and risk increased as surgical work increased. DISCUSSION: The risk caused by preoperative MI has improved by an order of magnitude in the last 30 years. The ASA class and especially frailty are better predictors of adverse cardiac events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.004 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".