Predictors of Low Cardiac Output Syndrome After Isolated Aortic Valve Surgery
Bibliographic record
Abstract
BACKGROUND: Low cardiac output syndrome (LCOS), defined as the need for postoperative intraaortic balloon pump or inotropic support for >30 minutes in the intensive care unit, remains a relatively common complication of aortic valve (AV) surgery. The aim of this study is to identify the preoperative predictors of LCOS in patients undergoing isolated AV surgery. METHODS AND RESULTS: We conducted a retrospective review of data prospectively entered into an institutional database. Between 1990 and 2003, 2255 patients underwent isolated AV surgery with no other concomitant cardiac surgery. The independent predictors of LCOS and operative mortality (OM) were determined by stepwise logistic regression analysis. The overall prevalence of LCOS was 3.9%. The independent predictors of LCOS were (odds ratio in parentheses) renal failure (5.0), earlier year of operation (4.4), left ventricular ejection fraction <40% (3.6), shock (3.2), female gender (2.8), and increasing age (1.02). Overall OM was 2.9%. The OM was higher in patients who experienced LCOS (38% versus 1.5%; P<0.001). The independent predictors of mortality were (odds ratio in parentheses) preoperative renal failure (8.3), urgency of surgery (3.4), previous stroke (2.9), congestive heart failure (2.6), previous cardiac surgery (2.3), hypertension (1.7), and small AV size (1.3). CONCLUSIONS: Low-output syndrome is associated with significantly increased morbidity and mortality. Novel strategies to preserve renal function, optimization of preexisting heart failure symptoms, and avoidance of prosthesis-patient mismatch may reduce the incidence of LCOS and lead to improved results after AV surgery.
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 | 0.001 | 0.004 |
| 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 | 0.001 |
| 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".