Treatment Strategies in De Novo and Recurrent Hyperkalemia: TRACK Study Insights
Bibliographic record
Abstract
Background: Effective HK management is vital to reduce life-threatening HK-associated events. Real-world evidence is needed to elucidate the characteristics and treatment patterns of patients with HK, aiding guideline adherence and optimizing care. The longitudinal TRACK study examines HK management strategies, objectives, and outcomes over 12 months in five countries. Methods: TRACK included 1330 patients with HK (K+>5.0 mmol/L) from Germany, Italy, Spain, the UK, and the US. De novo† and recurrent† HK cases were assessed at 3-month intervals. Results: The de novo group (n=458) had lower CKD only prevalence vs the recurrent group (n=870; P<0.0001; Table). Across 12 months, conservative treatment was the most common HK management strategy but tended to decline over time. Similar trends were seen for K+ binder use. Overall, “ease of treatment” and “lower K+ levels to normal range” were the most common HK treatment objective and expectation, respectively. First occurrence of K+ normalization at follow-up was higher in the recurrent vs de novo group (P=0.0005; Table). Continuation of RAASi therapy increased from baseline to Month 12 in the de novo group and was significantly higher at Month 12 vs recurrent group (P<0.0001). The incidence of cumulative HK complications was higher in the recurrent vs de novo group (P=0.0008; Table). Conclusion: We observed similar trends in HK treatment strategies in de novo and recurrent cases, associated with suboptimal K+ normalization and RAASi use. HK management trended towards conservative approaches despite guideline recommendations, highlighting treatment gaps and the need for enhanced HK care standards. Funding: Commercial Support - AstraZeneca
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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".