Abstract 4340725: Hyperkalemia Sequelae in Patients With Chronic Kidney Disease, Heart Failure, Neither or Both: Findings From the TRACK Study
Bibliographic record
Abstract
Background: Hyperkalemia (HK) prevalence in the general population is estimated at 2–3%; by contrast, prevalence is up to 73% in patients with CKD and 39% in those with heart failure (HF). TRACK is a prospective, real-world evidence study of HK management strategies, therapeutic objectives, and outcomes during 12 months follow-up of patients with HK. This analysis focuses on use of CKD and HF therapies, potassium (K + ) binder use, and HK complications in patients with CKD and/or HF. Methods: TRACK enrolled patients with serum K + >5.0 mmol/L in Germany, Italy, Spain, the UK, and the US. Data were gathered from participants’ medical records at 3-month intervals on therapeutic objectives, treatment regimens, K + normalization rates, continuation of RAASi and mineralocorticoid receptor antagonist (MRA) therapy, and clinical outcomes. All participants provided informed consent. We conducted descriptive statistical analyses to identify trends between participants with CKD, HF, neither, or both. Results: Of 1330 TRACK participants, 741 had CKD at baseline, 83 HF, 385 both, and 121 neither. Mean age was 68±14 years, 31% were female, 8% Latino, 66% White, 29% Black, and 1% Asian. At baseline, ACE/ARB/ARNI and MRA use, respectively, was 51% and 3% among patients with CKD; 84% and 51% for those with HF; 62% and 28% for those with both; 60% and 6% for those with neither ( P =0.0006 for ACE/ARB/ARNI use among the four groups and P <0.0001 for MRA). Dose adjustment was infrequent. K + binder initiation or dose increase was reported for 12%, 3%, 16%, and 0% of those with CKD, HF, both, or neither, respectively ( P =0.0008 for K + binder initiation/dose increase among the four groups). Metabolic acidosis and death were the most common sequelae of HK ( Table ); causes of death included renal, cardiac, and multisystem failure, infection, and cancer. Occurrence of any HK complication or death was similar in patients with CKD (event rate at 12 months: 13.6 [95% CI 11.1, 16.2], P =0.09) or HF alone (10.3 [95% CI 3.5, 17.0], P =0.08) versus those with CKD and HF (18.6 [95% CI 14.6, 22.6]). Complications/death were more frequent among patients with CKD and HF versus those with neither (7.5 [95% CI 2.3, 12.6], P =0.0123) ( Figure ). Conclusion: Patients with HK with CKD and HF are at particularly high risk for poor outcomes. More consistent guideline-directed HK management including K + binder use is needed to improve current suboptimal use of potentially life-saving CKD and HF therapies.
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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".