Hyperkalemia Treatment Strategies by Specialty in the TRACK Study: Interim Analysis
Bibliographic record
Abstract
Background: We compared baseline treatment strategies by healthcare provider (HCP) specialty from TRACK, a prospective, observational study designed to address the evidence gap regarding HCP decision making in patients with hyperkalemia (HK). Methods: TRACK enrolled adults with serum potassium (sK+) >5.0 mmol/L and recorded HCP management decisions for 12 months. HCPs were asked, but not required, to record their specialty. An interim analysis was conducted when 600 enrolled participants had completed 6 months. Initial treatment strategy by specialty was compared using Fisher’s exact or Pearson’s Chi-squared tests. Treatment objectives and planned treatment duration were analyzed descriptively. Results: Participants (N=1330) were enrolled (July 2022–December 2023) in the USA and Europe (mean age, 68±14 years; female, 31%; mean sK+, 5.6±0.5 mmol/L; estimated glomerular filtration rate, 28±21 mL/min/1.73 m2). In total, 55% had chronic kidney disease (CKD) without heart failure (HF), 29% had CKD and HF, and 6% had HF without CKD. Overall, nephrologists managed 597 (45%) participants, 327 (25%) had another specified specialty (237 cardiologists), and the HCP specialty was not specified for 30%. Nephrologists were more likely to plan for indefinite treatment, cite CKD guideline compliance as an objective, and prescribe a low K+ diet (Table; P<0.0001); less likely to manage renin-angiotensin-aldosterone system inhibitor therapy as an initial strategy and more likely to manage K+ binder therapy (both P<0.0001). Conclusion: HCP specialty affected HK management approaches, possibly reflecting differences in specialty guidelines. Funding: Commercial Support - AstraZeneca
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".