Relative value of cystatin C and creatinine-based estimates of glomerular filtration rate in predicting long-term mortality after cardiac surgery: a cohort study
Bibliographic record
Abstract
OBJECTIVE: Renal dysfunction predicts an increased risk of both early and long-term mortality after cardiac surgery. Cystatin C enables glomerular filtration rate (GFR) to be estimated accurately and may be superior in this regard to creatinine-based estimates. We hypothesised, therefore, that cystatin C and derived estimates of GFR would independently predict long-term survival after cardiac surgery and would be superior in this respect to traditional estimates of GFR. The current study tests this hypothesis in a large and well-characterised cohort of patients. DESIGN: A prospective cohort study. SETTING: Regional cardiothoracic centre in Northeast Scotland. PARTICIPANTS: 1010 patients undergoing non-emergent cardiac surgery between 2004 and 2007. Serum creatinine and cystatin C levels were measured preoperatively and demographic and clinical variables were recorded. PRIMARY OUTCOME MEASURE: All-cause mortality, established from the National Records of Scotland. RESULTS: The median duration of follow-up after surgery was 9.7 years (IQR 8.9-10.6 years), during which 297 participants died. Preoperative creatinine and cystatin C levels and estimates of GFR derived from these were all strong predictors of death using Cox regression and remained independently predictive after adjustment for the logistic European System for Cardiac Operative Risk Evaluation, a well-validated clinical risk score and a range of other clinical predictors. Cystatin C-based measures were superior to creatinine-based estimates of GFR. CONCLUSIONS: Cystatin C and creatinine derived eGFR are powerful and independent predictors of long-term mortality following cardiac surgery. Estimates of GFR derived from cystatin C convey superior prognostic information to conventional creatinine-based estimates, but the observed differences are modest.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".