Implications of the 2021 CKD-EPI cystatin C/creatinine eGFR equation for eligibility for therapy in HFrEF: insights from PARADIGM-HF
Bibliographic record
Abstract
Abstract Background Estimated glomerular filtration rate (eGFR) is a key determinant of eligibility for many life-saving therapies in HFrEF. Recently, the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) provided new equations based on creatinine (CKD-EPIcr), cystatin C (CKD-EPIcys) or both (CKD-EPIcyscr) that do not include race. These new equations may reclassify individuals, irrespective of race, from one eGFR category to another, with implications for eligibility for HFrEF treatments. Purpose To assess the difference between eGFR estimation using the 2021 CKD-EPIcyscr equation and the 2009 CKD-EPIcr and Modification of Diet in Renal Disease Study (MDRD)-4 equations which are still standard in many European laboratories. Methods We included patients from PARADIGM-HF with cystatin C and creatinine values available at the time of randomization. For each patient, baseline eGFRs were calculated using the 3 equations described. Our focus was on patients with chronic kidney disease (CKD) stages III–V. Results Overall, 1910 patients were eligible. Mean age was 67.3 (10.1) year and 385 (18.7%) were female. Using 2009 CKD-EPIcr, 779 patients were in CKD stages 3–5, of which 233 (30%) were reclassified to a better CKD stage (higher eGFR) with the 2021 CKD-EPIcyscr equation (Table 1). Similar reclassification was seen when comparing MDRD-4 with the 2021 CKD-EPIcyscr equation: 277 (33%) of 831 patients in CKD stages 3–5 were reclassified to a better CKD stage (Figure 1). Conclusions The 2021 CKD-EPIcyscr equation favourably reclassified CKD stage in a large percentage of patients with HFrEF and a low eGFR, potentially increasing the proportion of these patients considered eligible for guideline-recommended therapies. Funding Acknowledgement Type of funding sources: None.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 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".