Predicting Survival After VA-ECMO for Refractory Cardiogenic Shock: Validating the SAVE Score
Bibliographic record
Abstract
BACKGROUND: Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) is used increasingly to support patients who are in cardiogenic shock. Due to the risk of complications, prediction models may aid in identifying patients who would benefit most from VA-ECMO. One such model is the Survival After Veno-Arterial Extracorporeal Membrane Oxygenation (SAVE) score. Therefore, we wanted to validate the utility of the SAVE score in a contemporary cohort of adult patients. METHODS: Retrospective data were extracted from electronic health records of 120 patients with cardiogenic shock supported with VA-ECMO between 2011 and 2018. The SAVE score was calculated for each patient to predict survival to hospital discharge. We assessed the SAVE score calibration by comparing predicted vs observed survival at discharge. We assessed discrimination with the area under the receiver operating curve using logistic regression. RESULTS: < 0.001). SAVE score calibration was limited, as observed survival rates for risk classes II-V were higher in our cohort (II: 67% vs 58%; III: 78% vs 42%; IV: 61% vs 30%; and V: 29% vs 18%). CONCLUSIONS: The SAVE score underestimates survival in a contemporary North American cohort of adult patients with cardiogenic shock. Its inaccurate performance could lead to denying ECMO support to patients deemed to be too high risk. Further studies are needed to validate additional predictive models for patients requiring VA-ECMO.
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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 | 0.005 | 0.022 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".