Prophylactic Right Ventricular Assist Device for High-Risk Patients Undergoing Valve Corrective Surgery
Bibliographic record
Abstract
BACKGROUND: Right ventricular failure (RVF) after cardiac surgery is associated with poor outcomes. Treatment commonly consists of afterload reduction, contractility optimization, and systemic vasopressors. The aim of this study was to propose a novel strategy of prophylactic right ventricular assist device (RVAD) insertion during valve corrective surgery for patients at high risk for RVF. METHODS: Between 2014 and 2017, 10 consecutive patients at high risk for RVF (severe baseline right ventricular dysfunction or systemic pulmonary artery pressures) underwent valve reconstructive surgery with prophylactic RVAD insertion. We reviewed patient characteristics and outcomes. RESULTS: All 10 patients had successful RVAD insertion, support and wean, and survival to hospital discharge. Generally, the right ventricle showed echocardiographic evidence of worsening function perioperatively but recovery of function at the time of follow-up. Patients required minimal inotropic support, and no patients required extracorporeal membrane oxygenation. Major complications included prolonged mechanical ventilation (n = 4), metabolic encephalopathy (n = 1), and sternal wound infection (n = 2). At a mean follow-up of 445.1 ± 230.9 days, 7 of 8 patients had clinically New York Heart Association functional class 1 (n = 7), and 1 patient had New York Heart Association functional class 2 (n = 1). There were 2 late mortalities. CONCLUSION: Prophylactic RVAD insertion may be useful in supporting patients at high risk for RVF perioperatively when undergoing high-risk valve corrective surgery. Further investigation is warranted.
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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 | 0.001 |
| 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) | 0.001 | 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".