Outcomes' predictors in Post-Cardiac Surgery Extracorporeal Life Support. An observational prospective cohort study
Bibliographic record
Abstract
OBJECTIVES: Extracorporeal Life Support (ECLS) may provide pulmonary and circulatory support for patients with acute heart failure refractory to conventional medical therapy. However, indications and effectiveness of ECLS engagement post-cardiac surgery remains a concern. We sought to analyze indications, modality and outcomes of Post-Cardiac Surgery Extracorporeal Life Support (PS-ECLS), to identify predictors of early and midterm survival after PS-ECLS. METHODS: Prospective, multicenter analysis of 209 consecutive PS-ECLS patients between January 2004 and December 2018. Demographic and clinical data before, during and after PS-ECLS were collected and their influence on hospital mortality and outcomes (early and midterm) were analyzed. RESULTS: Mean PS-ECLS duration was 5.3 ± 9.6 days. Multivariate analysis of pre PS-ECLS implantation factors revealed age >70years, female, insulin-dependent diabetes, severe pulmonary hypertension, STS score >35, type/A aortic dissection, aortic cross-clamp time >150 min and pre-ECLS blood lactate >15 mmol/L as risk factors of in-hospital mortality. Instead coronary artery disease (CAD), intra-aortic balloon pump (IABP) implantation, ECLS start in the operating room, and transapical left ventricular venting, were associated with a better outcome. Weaning from ECLS was possible in 56.8% of cases and survival at discharge was 42.6%. Overall, survival was 37.3%, 32.1% and 25.2%, at 6-months, 1-year and 5-years, respectively. Midterm outcome was influenced positively by younger age and CAD, negatively by diabetes, left ventricular ejection fraction (LVEF) < 35% and neurological dysfunction. CONCLUSIONS: PS-ECLS is a valuable option when conventional medical therapies are insufficient. The outcome predictors identified in the study could be an operative support for PS-ECLS indication and management.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".