Kidney Function Alters the Relationship between Postoperative Troponin T Level and Death
Bibliographic record
Abstract
Cardiac troponin T (cTnT), even at low concentrations, is a risk factor for 30-day mortality in patients undergoing noncardiac surgery, but it is uncertain whether that risk is generalizable to patients with poor kidney function. We, therefore, evaluated the relationship between cTnT concentration and kidney function on the outcome of 30-day mortality in a post hoc analysis of a prospective cohort study of patients undergoing noncardiac surgery. cTnT was measured for 3 days after surgery and considered abnormal if the peak was ≥0.02 ng/ml. Of the included 14,037 patients, 267 (1.9%) patients died within 30 days of surgery. The adjusted hazard ratios for death with an abnormal cTnT concentration were 4.37 (95% confidence intervals [95% CI], 3.21 to 6.22), 6.15 (95% CI, 2.95 to 140.9), 6.30 (95% CI, 3.12 to 21.23), 1.33 (95% CI, 0.56 to 4.85), and 1.46 (95% CI, 0.46 to 9.21) for eGFR≥60, 45 to <60, 30 to <45, 15 to <30, and <15 ml/min per 1.73 m(2) or on dialysis, respectively. Compared with patients with eGFR≥60 ml/min per 1.73 m(2), the adjusted hazard ratio was significantly lower for patients with eGFR=15 to <30 ml/min per 1.73 m(2) (interaction P value=0.02). Redefining abnormal cTnT concentration as ≥0.03 ng/ml or a change of ≥0.02 ng/ml did not alter results. Because the risk associated with postoperative cTnT levels may be different for patients with eGFR<30 ml/min per 1.73 m(2), additional research is required to determine how to interpret perioperative cTnT values for patients with low kidney function.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".