An international Delphi consensus regarding best practice recommendations for hyperkalaemia across the cardiorenal spectrum
Bibliographic record
Abstract
AIMS: Renin-angiotensin-aldosterone system inhibitors (RAASi) are guideline-recommended therapy for individuals with cardiorenal disease. They are associated with increased risk of hyperkalaemia, a common and life-threatening disorder for this population. RAASi-induced hyperkalaemia often leads to dose reduction or discontinuation, reducing cardiorenal protection. Guideline recommendations differ between specialties for the clinical management of hyperkalaemia. Using a modified Delphi method, we developed consensus recommendations for optimal management of hyperkalaemia in adults with cardiorenal disease. METHODS AND RESULTS: An international steering group of cardiologists and nephrologists developed 39 statements regarding hyperkalaemia care, including risk factors and risk stratification, prevention, correction, and cross-specialty coordination. Consensus was determined by agreement on an online questionnaire administered to cardiorenal specialists across Europe and North America. The threshold for consensus agreement was established a priori by the steering group at 67%. Across November 2021, 520 responses were received from Canada (n = 50), France (n = 50), Germany (n = 54), Italy (n = 58), Spain (n = 57), the UK (n = 49), and the US (n = 202); 268 from cardiologists and 252 from nephrologists. Twenty-nine statements attained very high agreement (≥90%) and 10 attained high agreement (≥67%-<90%), with strong alignment between cardiologists and nephrologists. CONCLUSION: A high degree of consensus regarding hyperkalaemia evaluation and management exists among healthcare professionals. Based on high levels of agreement, the steering group derived six key recommendations for hyperkalaemia prevention and management in people with cardiorenal disease. Future studies examining the quality of hyperkalaemia care delivery are required.
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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 | 0.279 | 0.274 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".