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Abstract 4122650: 1-year comparison of quadruple therapy sequencing strategies for heart failure with reduced ejection fraction using an individual-based state-transition microsimulation model

2024· article· en· W4404324260 on OpenAlexaff
Ricky D. Turgeon, Huynh Van Minh, Peter Loewen, Nathaniel M. Hawkins, Mohsen Sadatsafavi, Wei Zhang, Karen Mackay

Bibliographic record

VenueCirculation · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsCentre for Advancing Health OutcomesUniversity of British Columbia
Fundersnot available
KeywordsMedicineEjection fractionHeart failureFraction (chemistry)MicrosimulationCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Guidelines recommend quadruple therapy (angiotensin receptor blocker-neprilysin inhibitor [ARNI]), beta-blocker, mineralocorticoid receptor antagonist (MRA), and sodium-glucose co-transporter 2 inhibitor [SGLT2I]) as the cornerstone of heart failure (HF) with reduced ejection fraction (HFrEF) management. Yet, guidelines do not propose a specific order of initiation and titration (or “sequencing strategy”) of these agents, due to the absence of trial evidence. Aims: To model the 1-year efficacy and harms of proposed HFrEF quadruple therapy sequencing strategies using a microsimulation model. Methods: We conducted an individual-based state-transition microsimulation modeling study to compare the 1-year cumulative incidence of death, total HF hospitalization, and adverse events with 6 different HFrEF medication sequencing strategies (emulating 2 different traditional strategies, 2 two-drug combinations, a "cluster" strategy, and four-drug "simultaneous" strategy), each with two versions (weekly or biweekly medication adjustments), applied to treatment-naïve outpatients with HFrEF. We modeled death as an incidence at 1 year, and total HF hospitalization (first and recurrent events) and adverse events (bradycardia, hyperkalemia, hypotension, and renal impairment) as incidence rates per 100 patient-years. Results: At 1 year, an estimated 15.5% died without treatment compared to 6.9% with the traditional sequence adjusted biweekly, and 5.2-6.3% with other sequencing strategies. Similarly, the HF hospitalization rate decreased from 32.8 per 100 patient-years with no treatment to 11.1 per 100 patient-years traditional sequencing adjusted biweekly, and 6.9-9 per 100 patient-years with other strategies. The incidence rates of medication-related adverse events per 100 patient-years were: hypotension (5.8-6.9), renal impairment (4.6-6.0), bradycardia (2.9-3.2), and hyperkalemia (approximately 0.5). Conclusions: For treatment-naïve outpatients with HFrEF, pharmacotherapy sequencing strategies that started 2 to 4 medications on the first visit reduced the risk of death and hospitalization at 1 year compared to a traditional sequencing strategy. Weekly medication adjustments did not outperform biweekly adjustments when >=2 medications were started on the initial visit. These findings can inform clinicians and policymakers in developing HFrEF medication optimization programs with sequencing strategy protocols that fit the local practical context.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.386
GPT teacher head0.444
Teacher spread0.058 · 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 designSimulation or modeling
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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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