Impact of glomerular filtration rate estimation formulas on MECKI score performance and prognostic accuracy in heart failure: the MECKI-RENAL study
Bibliographic record
Abstract
AIMS: Appropriate interpretation of kidney function is essential for clinical and therapeutic management of heart failure (HF). We evaluated the prognostic accuracy of 6 glomerular filtration rate estimation (eGFR) formulas in HF patients with reduced ejection fraction (HFrEF) and their impact on the Metabolic Exercise test data combined with Cardiac and Kidney Indexes (MECKI) score prognostic accuracy. METHODS AND RESULTS: We retrospectively analyzed 6933 patients enrolled in the MECKI score database. GFR was estimated using Modification of Diet in Renal Disease (MDRD); MRDR modified (MDRDm); Cockcroft-Gault (CG), CG modified (CGm); Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), and the European Kidney Function Consortium (EKFC). Survival was assessed as the composite of cardiovascular death, left ventricular assist device implantation and urgent heart transplantation at 2 years. Each GFR estimation demonstrated similar but moderate prognostic capacity, with the area under the curve (AUC) for predicting survival ranging from 0.6271 (EKFC) to 0.635 (MDRD). For cardiovascular death, the AUC values ranged from 0.668 to 0.677. The prevalence of severe CKD, defined as eGFR <30 mL/min/1.73 m², ranged from 3.2% (MDRD) to 4.5% (EKFC). When included in MECKI score, the 6 formulas showed a MECKI AUC for prognosis ranging from 0.7841 to 0.7883, with the EKFC and CKD-EPI showing the best performance. CONCLUSION: GFR estimations play a role in HFrEF prognosis without difference among the 6 most frequently used formulas. Furthermore, using eGFR calculated from the 6 different formulas in MECKI score did not significantly alter its strong prognostic power, highlighting MECKI reliability in risk stratification.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".