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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".