Preoperative Predictors of Mortality in Short-Term Continuous-Flow Ventricular Assist Devices
Bibliographic record
Abstract
Short-term continuous-flow ventricular assist devices (STCF-VADs) are increasingly being utilized to support critically ill patients, despite limited information regarding overall outcomes. All adult patients supported with an STCF-VAD between June 2009 and December 2015 were included in this retrospective single-center study. Associations between preoperative characteristics and unsuccessful bridge (death on device or within 30 days postdecannulation) were assessed using logistic regression. A total of 61 patients (77% male) were identified with a median age at implant of 54.6 years. Left VADs were implanted in 51%, right VADs in 21%, and both VADs in 28%, and patients were supported for a median of 11 days. Overall, 23% were weaned to recovery, 13% underwent heart transplantation, 16% converted to long-term VADs, and 48% had an unsuccessful bridge. In multivariable analysis, only renal insufficiency or dialysis (odds ratio = 7.53; p = 0.002) remained a significant independent predictor of an unsuccessful bridge. Short-term continuous-flow VADs can successfully bridge adult patients with mortality around 50%. Preimplant renal insufficiency or dialysis is correlated with an unsuccessful bridge in our patient population, likely reflecting the severity of illness preimplant. Further studies are required to determine whether this factor remains significant in a larger patient population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".