Postmyocardial infarction ventricular septal rupture: optimizing surgical timing and repair
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review summarizes the most recent literature regarding the surgical management of postmyocardial infarction (MI) ventricular septal rupture and the optimal timing and performance of the operation. RECENT FINDINGS: There are conflicting data surrounding the optimal timing of surgical intervention for post-MI ventricular septal rupture. Patients often present in hemodynamic compromise which limits the ability to delay their intervention. A lack of randomized control trials mandates reliance on retrospective cohort studies that have a selection bias in favor of delayed surgery due to unstable patients requiring emergent intervention. Similarly, mechanical circulatory support may be associated with poorer outcomes in part due to selection bias. There is a trend towards better prognosis in patients with lower preoperative lactate and lower vasopressor requirements. SUMMARY: A diagnosis of a post-MI ventricular septal rupture carries a poor prognosis. Without surgical intervention, the likelihood of 1-year survival is very low. Percutaneous treatment options have limited success, and the gold standard remains surgical intervention. Surgical timing is often dictated by patients being hemodynamically unstable and requiring emergent surgery. When a patient can have delayed intervention, there is a trend towards better outcomes. Optimized hemodynamics, metabolic parameters, and initial medical management are associated with improved outcomes. Mechanical circulatory support is of benefit in sicker patients if it can assist with preoperative optimization.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".