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Record W2551825405 · doi:10.1001/jamacardio.2016.4733

Reduced Risk of Hyperkalemia During Treatment of Heart Failure With Mineralocorticoid Receptor Antagonists by Use of Sacubitril/Valsartan Compared With Enalapril

2016· article· en· W2551825405 on OpenAlexaff
Akshay S. Desai, Orly Vardeny, Brian Claggett, John J.V. McMurray, Milton Packer, Karl Swedberg, Jean L. Rouleau, Michael R. Zile, Martin Lefkowitz, Victor Shi, Scott D. Solomon

Bibliographic record

VenueJAMA Cardiology · 2016
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsMedicineHyperkalemiaSacubitrilEnalaprilHeart failureValsartanInternal medicineCardiologyEjection fractionSpironolactoneAldosteroneAngiotensin-converting enzymeBlood pressure

Abstract

fetched live from OpenAlex

Importance: Consensus guidelines recommend the use of mineralocorticoid receptor antagonists (MRAs) for selected patients with symptomatic heart failure and reduced ejection fraction (HFrEF) to reduce morbidity and mortality; however, the use of MRAs in combination with other inhibitors of the renin-angiotensin-aldosterone system increases the risk of hyperkalemia. Objective: To determine whether the risk of hyperkalemia associated with use of MRAs for patients with HFrEF is reduced by sacubitril/valsartan in comparison with enalapril. Design, Setting, and Participants: The PARADIGM-HF (Prospective Comparison of ARNI With an ACE-Inhibitor to Determine Impact on Global Mortality and Morbidity in Heart Failure) trial randomly assigned 8399 patients with chronic HF, New York Heart Association class II to IV symptoms, and a left ventricular EF of 40% or less to treatment with enalapril 10 mg twice daily or sacubitril/valsartan 97/103 mg twice daily (previously known as LCZ696 [200 mg twice daily]) in addition to guideline-directed medical therapy. Use of MRAs was encouraged but left to the discretion of study investigators. Serum potassium level was measured at every study visit. The incidence of hyperkalemia (potassium level >5.5 mEq/L) and severe hyperkalemia (potassium level >6.0 mEq/L) among patients treated or not treated with an MRA at baseline and the risk of subsequent hyperkalemia for those newly treated with an MRA during study follow-up were defined in time-updated Cox proportional hazards models. Analyses were conducted between August 1 and October 15, 2016. Main Outcomes and Measures: Incident hyperkalemia and severe hyperkalemia. Results: In comparison with the 3728 patients (44.4% of enrolled participants [21.6% female]) not taking an MRA at baseline, the 4671 patients (55.6% [22.0% female]) taking an MRA tended to be younger, with a lower EF, lower systolic blood pressure, and more advanced HF symptoms. Among those taking an MRA at baseline, the overall rates of hyperkalemia were similar between treatment groups, but severe hyperkalemia was more common in patients randomly assigned to enalapril than to sacubitril/valsartan (3.1 vs 2.2 per 100 patient-years; HR, 1.37 [95% CI, 1.06-1.76]; P = .02). In analyses including patients who newly started taking MRAs during the PARADIGM-HF trial, severe hyperkalemia remained more common in those randomly assigned to enalapril than to those randomly assigned to sacubitril/valsartan (3.3 vs 2.3 per 100 patient-years; HR, 1.43 [95% CI, 1.13-1.81]; P = .003). Conclusions and Relevance: Among MRA-treated patients with symptomatic HFrEF, severe hyperkalemia is more likely during treatment with enalapril than with sacubitril/valsartan. These data suggest that neprilysin inhibition attenuates the risk of hyperkalemia when MRAs are combined with other inhibitors of the renin-angiotensin-aldosterone system in patients with HF. Trial Registration: clinicaltrials.gov Identifier: NCT01035255.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.236
Teacher spread0.222 · 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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Citations215
Published2016
Admission routes1
Has abstractyes

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