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Record W6945498725 · doi:10.25384/sage.c.4267547

Kidney Function, ACE-Inhibitor/Angiotensin Receptor Blocker Use, and Survival Following Hospitalization for Heart Failure: A Cohort Study

2018· other· en· W6945498725 on OpenAlexaboutno aff

Bibliographic record

VenueSage Journals Data · 2018
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsRenal functionMedical prescriptionHeart failureCohortRetrospective cohort studyProportional hazards modelCohort studyKidney disease

Abstract

fetched live from OpenAlex

Background:Angiotensin-converting enzyme inhibitors/angiotensin receptor blocker (ACE-I/ARB) improve outcomes in patients with heart failure and reduced left-ventricular (LV) systolic function. However, these medications can cause a rise in serum creatinine and their benefits in patients with HF accompanied by kidney disease are less certain.Objective:To characterize associations between estimated glomerular filtration rate (eGFR), patterns of ACE-Is and ARBs use, and 1-year survival following hospitalization for heart failure (HF).Design:We formed a retrospective cohort study of patients admitted with HF and followed HF medication prescriptions using the pharmaceutical information network, stratified by discharge eGFR.Setting:Cardiology services in 3 centers in Southern Alberta, Canada.Patients:The study cohort included patients admitted to hospital with a clinical diagnosis of HF.Measurements:eGFR was determined from inpatient laboratory data prior to discharge. Outpatient prescription data prior to and following the index hospitalization was obtained using the Pharmaceutical Information Network of Alberta and survival was determined from provincial vital statistics.Methods:Characteristics of the HF cohort were obtained from the Admissions Module of the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) database. Multivariable Cox proportional hazards models were used to evaluate the association between time-varying ACE-I/ARB use, and mortality, and to test whether eGFR modified this association.Results:Totally, 1404 patients were included. Within the first 3 months following discharge, ACE-I/ARBs were used in 71%, 67%, 62%, and 52% for those with eGFR > 90, 45-89, 30-44, and < 30 mL/min/1.73 m2, respectively, with differences in use persisting after 1 year of follow-up. Patients with eGFR < 45 mL/min/1.73 m2 had significantly lower rates of ACE-I/ARB use following hospitalization. In adjusted models, ACE-I/ARB use following discharge was associated with 25% lower risk of mortality (Hazard Ratio [HR]: 0.75, 95% confidence interval [CI]: 0.61-0.92; P < 0.01), without evidence that this association differed by eGFR (P = 0.75).Limitations:LV function measurements were not available for the cohort. Due to the observation design of the study, treatment-selection bias may be present.Conclusion:Patients with HF and reduced eGFR at time of hospital discharge were less likely to receive ACE-I/ARB despite these medications being associated with lower mortality independent of eGFR. These findings demonstrate the need for further research on strategies for safe use of ACE-I and ARB in patients with HF and kidney disease.

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)0.001
metaresearch head score (Gemma)0.002
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: Dataset · Consensus signal: none
Teacher disagreement score0.215
Threshold uncertainty score0.428

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.303
Teacher spread0.267 · 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
GenreDataset

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
Published2018
Admission routes1
Has abstractyes

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