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Abstract 4340725: Hyperkalemia Sequelae in Patients With Chronic Kidney Disease, Heart Failure, Neither or Both: Findings From the TRACK Study

2025· article· en· W4415792326 on OpenAlexaff
Judith Hsia, H Chen, Nitin Shivappa, Wolfgang C. Winkelmayer­, Navdeep Tangri, Anna-Karin Sundin, Markus P. Schneider, Jordi Bover, Linda F. Fried, Pietro Manuel Ferraro, Javed Butler, Meredith Bishop, Ameet Bakhai, Marc P. Bonaca

Bibliographic record

VenueCirculation · 2025
Typearticle
Languageen
FieldMedicine
TopicPotassium and Related Disorders
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsHyperkalemiaKidney diseaseHeart failureFast trackPopulationMedical recordClinical trial

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.010
GPT teacher head0.247
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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