Preoperative Biomarkers and Mortality Risk After Cardiac Surgery
Bibliographic record
Abstract
Background: Cardiac surgery patients are at an increased risk for developing adverse outcomes. Preoperative blood and urine biomarkers may help stratify cardiac surgery patients at high risk for mortality. Methods: The TRIBE-AKI study enrolled 1526 patients undergoing cardiac surgery in the USA and Canada from 2007-2010 and was randomly split into a training and test dataset (70:30). A total of 32 plasma and 17 urine biomarkers were measured preoperatively. The primary outcome was 3-year mortality. Random forest (RF) and LASSO logistic regression models were used to identify top biomarkers. Logistic regression models with the highest performing biomarkers and the Society of Thoracic Surgeons (STS) risk calculator were evaluated and the discriminatory ability was assessed in the test dataset. Results: Death by 3 years occurred in 163 of the 1526 (10.7%) patients. LASSO logistic regression models retained the STS score and 6 plasma biomarkers (Troponin, IL-6, KIM1, NT-proBNP, TNFR1, YKL-40). The top 6 biomarkers identified by random forest were plasma KIM-1, TNFR1, eGFR, TNF-R2, hsTNT, and urine IL-8. In logistic regression models, the AUC in the test dataset for the STS clinical model was 0.68 (0.61, 0.76) and increased to 0.72 (0.65, 0.79) with the addition of 8 plasma and 2 urine biomarkers (plasma Troponin, IL-6, KIM-1, NT-proBNP, TNFR1, YKL-40, hFABP, TNFR2, and urine IL-8 and albumin; p=0.24). Conclusions: The addition of biomarkers improved discrimination for 3-year mortality prediction minimally beyond clinical characteristics alone. The clinical utility of measurement of biomarkers pre-operatively prior to cardiac surgery is suspect. Funding: NIDDK Support
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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 | 0.008 |
| 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 | 0.000 |
| 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".