Predictors and associated clinical outcomes of low cardiac output syndrome following cardiac surgery: insights from the LEVO-CTS trial
Bibliographic record
Abstract
AIMS: High-risk cardiac surgery is commonly complicated by low cardiac output syndrome (LCOS), which is associated with high mortality. There are limited data derived from multi-centre studies with adjudicated endpoints describing factors associated with LCOS and its downstream clinical outcomes. METHODS AND RESULTS: The Levosimendan in Patients with Left Ventricular Systolic Dysfunction Undergoing Cardiac Surgery Requiring Cardiopulmonary Bypass (LEVO-CTS) trial evaluated prophylactic levosimendan vs. placebo in patients with a reduced ejection fraction undergoing coronary artery bypass grafting (CABG) and/or valve surgery. We conducted a pre-specified analysis on LCOS, which was characterized by a four-part definition. We constructed a multivariable logistical regression model to evaluate risk factors associated with LCOS and performed Cox proportional hazards modelling to determine the association of LCOS with 90-day mortality. A total of 186 (22%) of 849 patients in the LEVO-CTS trial developed LCOS. The factors most associated with a higher adjusted risk of LCOS were pre-operative ejection fraction [odds ratio (OR) 1.26; 95% confidence interval (CI): 1.08-1.46 per 5% decrease] and age (OR 1.13; 95% CI: 1.04-1.24 per 5-year increase), whereas isolated CABG surgery (OR 0.44, 95% CI: 0.31-0.64) and levosimendan use (OR 0.65; 95% CI: 0.46-0.92) were associated with a lower risk of LCOS. Patients with LCOS had worse outcomes, including renal replacement therapy at 30-day (10 vs. 1%) and 90-day mortality (16 vs. 3%, adjusted hazard ratio of 5.04, 95% CI: 2.66-9.55). CONCLUSION: Low cardiac output syndrome is associated with a high risk of post-operative mortality in high-risk cardiac surgery.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".