Managing Heart Failure With Reduced Ejection Fraction in Patients With Chronic Kidney Disease: A Case-Based Approach and Contemporary Review
Bibliographic record
Abstract
Patients with heart failure with reduced ejection fraction (HFrEF) often have concurrent chronic kidney disease (CKD), which can make initiating and titrating the 4 standard pharmacologic therapies a challenge. Drug dosing is often based on a calculation of the patient's creatine clearance or estimated glomerular filtration rate (eGFR), but it should also incorporate the trend in their renal function over time and the risk of toxicity of the drug. The presence of CKD in a patient should not preclude the use of a renin-angiotensin system inhibitor, although patients should be monitored frequently for worsening renal function and hyperkalemia. Sacubitril/valsartan is not recommended in patients with an eGFR < 30 mL/min per 1.73 m 2 . Of the 3 ß-blockers recommended in the management of HFrEF, only bisoprolol may accumulate in patients with renal impairment; however, patients should still be titrated to the target dose (10 mg daily) or the maximally tolerated dose, depending on their clinical response. The sodium-glucose cotransporter 2 inhibitors are effective at reducing adverse cardiovascular and renal outcomes in patients with HFrEF and CKD (eGFR ≥ 25 mL/min per 1.73 m 2 with dapagliflozin or ≥ 20 mL/min per 1.73 m 2 with empagliflozin), although declining kidney function is a risk, due to the osmotic diuretic effect. Finally, mineralocorticoid receptor antagonist therapy should be considered in all patients with HFrEF and an eGFR ≥ 30 mL/min per 1.73 m 2 . The starting dose should be low (eg, 6.25-12.5 mg daily or 12.5 mg every other day) and can be uptitrated based on the patient's renal function and serum potassium.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".