Relationship Between Preventability of Death After Coronary Artery Bypass Graft Surgery and All-Cause Risk-Adjusted Mortality Rates
Bibliographic record
Abstract
BACKGROUND: The goal of this study was to determine the relationship between all-cause, risk-adjusted, in-hospital mortality after coronary artery bypass graft surgery and the proportion of preventable in-hospital deaths as a measure of quality of care at an institution level. METHODS AND RESULTS: We conducted a retrospective analysis of 347 randomly selected in-hospital deaths after isolated coronary artery bypass graft surgery at 9 institutions in Ontario over the period of 1998 to 2003. Nurse-abstracted chart summaries were reviewed by 2 experienced cardiac surgeons who were blinded to patient, surgeon, and hospital and used a standardized implicit tool to identify preventable death. A third reviewer reassessed all cases in which the first 2 reviewers disagreed. Rates of preventable deaths were estimated for each hospital and compared with all-cause mortality rates. A structured adverse event audit completed by each surgeon-reviewer was used to identify quality improvement opportunities for the preventable deaths. A total of 111 of 347 deaths (32%) were judged preventable despite a low risk-adjusted mortality range (1.3% to 3.1%) across hospitals. No significant correlation was found between all-cause, risk-adjusted in-hospital mortality rates and the proportion of preventable deaths at the hospital level (Spearman coefficient, -0.42; P=0.26). A large proportion of preventable deaths were related to problems in the operating room (86%) and intensive care unit (61%). Many deaths were associated with deviations in perioperative care (32% based on concurrence of 2 reviewers, and another 42% in cases in which 1 reviewer reached that opinion). CONCLUSIONS: Approximately one third of in-hospital coronary artery bypass graft deaths were judged preventable by surgeon reviewers. All-cause risk-adjusted mortality rates are convenient measures of institutional quality of care but were not correlated with preventable mortality in our jurisdiction. Providers should conduct detailed adverse event audits to drive meaningful improvements in quality.
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 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) | 0.000 | 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 | 0.000 |
| 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".