Multivariable Predictors of Postoperative Cardiac Adverse Events after General and Vascular Surgery: Results from the Patient Safety in Surgery Study
Bibliographic record
Abstract
BACKGROUND: Cardiac adverse events (CAEs) are relatively infrequent, but highly lethal, after noncardiac operations. The value of available risk scoring systems is uncertain and these systems can be outdated. We used the Patient Safety in Surgery Study database to develop and test a model to predict patient risk for CAEs after general and vascular surgical operations. STUDY DESIGN: As part of the Patient Safety in Surgery Study, following the National Surgical Quality Improvement Program's protocol, multiple demographic, preoperative, perioperative, and outcomes variables were measured during a 3-year period. Data from 128 Veterans Affairs medical center hospitals and from 14 academic medical centers on 183,069 patients were used in a logistic regression analysis to model multivariable predictors of serious CAEs (cardiac arrest or acute myocardial infarction within 30 days of operation). RESULTS: CAEs occurred in 2,362 patients (1.29%) and of these, 59.44% expired. Multivariable stepwise logistic regression identified 20 independent predictors of CAEs, which excluded most cardiac-specific risk factors. The most important multivariable predictors of CAE were American Society of Anesthesiologists physical status classification, work relative value units of the most complex procedure, age, and type of operation. A risk prediction scoring system using the logistic regression odds ratios proved to be a useful prediction tool when tested using a random sample from the database. CONCLUSIONS: CAEs after noncardiac operations are relatively infrequent but highly lethal. Operation type and urgency and American Society of Anesthesiologists physical status assessment are important independent predictors of cardiac morbidity, but angina, recent MI, and earlier cardiac operation are not. A prediction scoring system based on the Patient Safety in Surgery Study multivariable odds ratios is likely to be predictive of future events in a similar population requiring noncardiac procedures. This risk model can also serve as a tool to measure quality and effectiveness of care by providers who perform noncardiac operations.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".