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Record W4412489986 · doi:10.1016/j.eclinm.2025.103338

Effects of empagliflozin on quality of life and healthcare use and costs in chronic kidney disease: a health economic analysis of the EMPA-KIDNEY trial

2025· article· en· W4412489986 on OpenAlexaff
Junwen Zhou, Claire Williams, Natalie Staplin, Parminder K. Judge, Kaitlin J. Mayne, Nikita Agrawal, Ryoki Arimoto, Jennifer B. Green, David Z.I. Cherney, Katherine R. Tuttle, José Leal, Philip A. Clarke, Jonathan Emberson, David Preiss, Christoph Wanner, Martin Landray, Colin Baigent, Richard Haynes, William G. Herrington, Borislava Mihaylova

Bibliographic record

VenueEClinicalMedicine · 2025
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of Toronto
FundersHealth Data Research UKNIHR Oxford Biomedical Research CentreMedical Research CouncilBritish Heart FoundationBoehringer Ingelheim
KeywordsEmpagliflozinMedicineEMPAKidney diseaseHealth careDiseaseQuality of life (healthcare)Alternative medicineIntensive care medicineInternal medicineDiabetes mellitusNursingType 2 diabetesEconomic growthEndocrinologyPathology

Abstract

fetched live from OpenAlex

Background: Sodium-glucose co-transporter 2 inhibitors (SGLT2i) slow progression of chronic kidney disease (CKD) but there is no randomised evidence of their effects on health-related quality of life (QoL) and healthcare use. We explored the effects of empagliflozin on health-related QoL, healthcare use and UK healthcare costs in the EMPA-KIDNEY trial. Methods: and a urinary albumin-to-creatinine ratio (uACR) of ≥200 mg/g at screening. They were randomly assigned (1:1) to receive empagliflozin 10 mg once daily or matching placebo. We estimated the effect of empagliflozin (UK£1.31/day) on exploratory outcomes (unless otherwise specified) of quality-adjusted life years (QALYs), UK costs (2023 UK£) of hospital admissions (a prespecified secondary outcome), concomitant medications and end-stage kidney disease (ESKD; a prespecified tertiary outcome) management over 2 years on study treatment (median active-trial follow-up) and on ESKD costs over 2 further years off study treatment (median post-trial follow-up) using shared parameter models analysing outcomes together with time to death or negative binomial models. The trial is registered with ClinicalTrials.gov, NCT03594110. Findings: Between May 15, 2019 and April 16, 2021, 6609 participants were randomly assigned to empagliflozin (3304 participants) or matching placebo (3305 participants) in the active-trial which lasted for a median of 2.0 years. Among them, 4891 (74%) were enrolled in the post-trial follow-up. Per participant allocated to empagliflozin over 2 years, total empagliflozin cost was £826 (95% confidence interval: 818 to 835), QALYs were 0.012 higher (0.001 to 0.022), with less cost for hospital admission (-£239, -449 to -29), concomitant medications (-£130, -214 to -47), and management of ESKD (-£208, -414 to -2) compared to placebo. Over a further 2 years of post-trial follow-up off study treatment, there were additional per participant ESKD cost savings (-£842, -1441 to -242), resulting in net total healthcare cost of -£593 (-1384 to 198) over 4 years. The probability of 2 years of empagliflozin treatment being cost-effective at £20 K threshold in the UK was 43% over 2 years of follow-up and 91% over 4 years. The relative effects of empagliflozin on each cost component were similar across categories by baseline levels of eGFR, uACR and diabetes status, with larger reductions in healthcare costs estimated in categories at higher risk of CKD progression. Interpretation: In EMPA-KIDNEY, 2 years treatment with empagliflozin improved QALYs, and reduced use and cost of other healthcare, resulting in high likelihood of cost-effectiveness across a broad range of patients with CKD. The study's key limitation is its relatively short active treatment period and follow-up duration, which may lead to underestimation of the cost-effectiveness of long-term SGLT2i treatment in CKD. Funding: Boehringer Ingelheim, Germany; Eli Lilly, USA; Medical Research Council, UK; British Heart Foundation, UK; Health Data Research, UK; National Institute for Health and Care Research, UK.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.034
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.041
GPT teacher head0.395
Teacher spread0.354 · 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 teacher head, 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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Citations5
Published2025
Admission routes1
Has abstractyes

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