Abstract 4145783: Prevalence of Kidney Dysfunction and Hyperkalemia in a Specialized Heart Failure Clinic
Bibliographic record
Abstract
Introduction: Kidney dysfunction is highly comorbid in heart failure (HF) patients and contributes to suboptimal utilization of goal-directed medical therapy (GDMT). Worsening hyperkalemia is a common concern in GDMT implementation. We aimed to evaluate the prevalence of kidney dysfunction and rates of hyperkalemia in a specialized heart function clinic (HFC) cohort in Edmonton, Alberta, and characterize the impact of renal dysfunction on clinical outcomes. Methods: HF patients were enrolled in the HFC from Feb 2018 to Nov 2022. Outpatient serum creatinine measurements were used to estimate glomerular filtration rate (eGFR) using the 2021 CKD-EPI equation. Medication records (renin-angiotensin system inhibitors [RAASi], angiotensin receptor neprilysin inhibitors [ARNI], β-blockers, mineralocorticoid inhibitors [MRA], sodium-glucose cotransporter 2 inhibitors [SGLT2i]), laboratory results, and comorbidities using ICD-10 codes were obtained. Hyperkalemia events were defined as any serum potassium ≥5.5 mmol/L measured in a year. Adjusting for clinical covariates, we analyzed the association between GDMT use and hyperkalemia rates as well as between eGFR and all-cause mortality and hospitalization. Results: Our HFC cohort of 1401 patients (median age 68, 29% female) includes 54% with an eGFR ≥60, 37% with an eGFR from 30 to <60, and 9% with an eGFR <30. Prevalence of hyperkalemia increased with worsening renal function, from 9.1% in eGFR >60, 18.1% in eGFR 30-60, to 34.2% in eGFR <30 (p<0.001). After adjusting for baseline clinical characteristics, ARNI, RAASi, MRA, and SGLT2i uses were not associated with increased rates of hyperkalemia. Using eGFR ≥60 as a reference, all-cause mortality increased in eGFR 30-60 (aHR 1.58, 95% CI 1.23-2.02) and eGFR <30 (aHR 3.40, 95% CI 2.45-4.72). In addition, RAASi (aHR 0.57, 95% CI 0.43-0.74) and ARNI use (aHR 0.66, 95% CI 0.48-0.91) were associated with improved all-cause mortality. These results were similar across all-cause hospitalization. Conclusion: Although hyperkalemia is often viewed as a barrier to initiating and up-titrating GDMT, our results show no association between GDMT use and increased hyperkalemia rates. Given the high mortality and morbidity in patients with heart failure and renal dysfunction, further research on improving GDMT utilization and mitigating hyperkalemia in the context of worsening renal function is warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".