KDIGO 2024 clinical practice guideline on evaluation and management of chronic kidney disease: A primer on what pharmacists need to know
Bibliographic record
Abstract
PURPOSE: To review the key updates in the 2024 KDIGO clinical practice guideline for the evaluation and management of chronic kidney disease (CKD) and highlight the essential role of pharmacists in implementing these recommendations. SUMMARY: The updated guideline introduces significant changes in CKD management, including the use of validated equations for estimating glomerular filtration rate (GFR) for drug dosing, with incorporation of serum cystatin C into GFR estimates for specific patient populations, and an emphasis on a comprehensive approach to delay disease progression. The guideline recommends sodium-glucose cotransporter 2 inhibitor (SGLT2i) therapy for kidney disease with proteinuria, with or without diabetes, renin-angiotensin-aldosterone system inhibitors (RAASi) blood pressure control and proteinuria management, and statins to reduce the risk of atherosclerotic cardiovascular disease. New evidence supports the use of finerenone in patients with type 2 diabetes and CKD, and GLP-1 receptor agonists for their kidney-protective effects. The guidelines also emphasize the importance of nephrotoxin stewardship and prevention of acute kidney injury through patient education on sick day medication management. CONCLUSION: Pharmacists play a crucial role in implementing these updated guidelines through comprehensive medication management, nephrotoxin stewardship, drug dosing adjustments, and patient education. Their involvement in interprofessional care teams is essential for optimizing health outcomes in patients with CKD.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".