Myocardial injury after gynecologic oncology surgery in septuagenarians and octogenarians: Is there a role for routine postoperative cardiac biomarker monitoring?
Bibliographic record
Abstract
220 Background: Accumulating evidence correlates myocardial injury after noncardiac surgery (MINS), even when asymptomatic, with increased cardiac and non-cardiac morbidity and mortality. There is no literature on MINS specific to Gynecologic Oncology. We sought to evaluate the incidence and risk factors of MINS in patients aged ≥70. Methods: Elective laparotomies between 01/2016-09/2020 for patients aged≥70 at a tertiary hospital in ON, Canada, were reviewed using prospectively-collected National Surgical Quality Improvement Program (NSQIP) data. MINS was defined as peak serum high-sensitivity troponin-T concentration ≥0.04ng/mL within 30 days postoperatively. Logistic regression analysis was performed. Results: In this cohort of 258 patients, of 242 (93.8%) who underwent postoperative troponin screening, 40 (16.5%) experienced MINS without exhibiting ischemic symptoms or ECG changes. The diagnosis of MINS led to a prescription or optimization of cardiovascular medications for 35 patients (87.5%). On univariate analysis, Revised Cardiac Risk Index (RCRI) of 3-5(p = 0.002), history of coronary artery disease (p = 0.003) or insulin-dependent diabetes (p = 0.006), preoperative use of antiplatelets (p = 0.009), beta-blockers (p = 0.02), ACE-inhibitors (ACEI) or angiotensin-receptor blockers (ARB)(p = 0.002) and frailty as defined by the NSQIP modified frailty index-5 (p = 0.02), were associated with greater risk of MINS. Factors reflecting surgical complexity including surgical complexity score, operative duration, blood loss and advanced oncologic stage were not predictive. Multivariable analysis using backward selection procedure identified elevated RCRI and preoperative ACE/ARB as significant risk factors (OR 5.93, 95% CI 1.52-24.31, p = 0.01 and OR 2.4, 95% CI 1.18-5.06, p = 0.02). Conclusions: One in 6 patients in our cohort experienced asymptomatic MINS irrespective of surgical complexity. Our analysis highlights a possible opportunity to optimize cardiac risk factors and to potentially improve perioperative patient safety by reducing morbidity. Routine preoperative cardiac risk-stratification and postoperative cardiac biomarkers monitoring should be considered in elderly patients with gynecologic malignancies.[Table: see text]
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".