Renal Dysfunction in Patients With Heart Failure With Preserved Versus Reduced Ejection Fraction
Bibliographic record
Abstract
BACKGROUND: Prior studies in heart failure (HF) have used the Modification of Diet in Renal Disease (MDRD) equation to calculate estimated glomerular filtration rate (eGFR). The Chronic Kidney Disease-Epidemiology Collaboration Group (CKD-EPI) equation provides a more-accurate eGFR than the MDRD when compared against the radionuclide gold standard. The prevalence and prognostic import of renal dysfunction in HF if the CKD-EPI equation is used rather than the MDRD is uncertain. METHODS AND RESULTS: We used individual patient data from 25 prospective studies to stratify patients with HF by eGFR using the CKD-EPI and the MDRD equations and examined survival across eGFR strata. In 20 754 patients (15 962 with HF with reduced ejection fraction [HF-REF] and 4792 with HF with preserved ejection fraction [HF-PEF]; mean age, 68 years; deaths per 1000 patient-years, 151; 95% CI, 146-155), 10 589 (51%) and 11 422 (55%) had an eGFR <60 mL/min using the MDRD and CKD-EPI equations, respectively. Use of the CKD-EPI equation resulted in 3760 (18%) patients being reclassified into different eGFR risk strata; 3089 (82%) were placed in a lower eGFR category and exhibited higher all-cause mortality rates (net reclassification improvement with CKD-EPI, 3.7%; 95% CI, 1.5%-5.9%). Reduced eGFR was a stronger predictor of all-cause mortality in HF-REF than in HF-PEF. CONCLUSIONS: Use of the CKD-EPI rather than the MDRD equation to calculate eGFR leads to higher estimates of renal dysfunction in HF and a more-accurate categorization of mortality risk. Renal function is more closely related to outcomes in HF-REF than in HF-PEF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 | 0.001 |
| 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".