Evidence map›Paper›PMID 40495038›Full record

Observational studyThe European journal of health economics : HEPAC : health economics in prevention and care2026

Predicting EQ-5D-3L utility values from clinical data in a prospective cohort of kidney transplant recipients.

V Bonnemains, Y Foucher, P Tessier, C David, M Giral, E Dantan, DIVAT Consortium

Abstract readObservational StudyMulticenter Study
In one paragraph

Observational study in The European journal of health economics : HEPAC : health economics in prevention and care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

V BonnemainsNantes Université, Univ Tours, CHU Nantes, INSERM, MethodS in Patients-Centered Outcomes and HEalth Research SPHERE, 44000, Nantes, France.
Y FoucherINSERM, CIC-1402, Centre Hospitalier Universitaire de Poitiers, Université de Poitiers, Poitiers, France.
P TessierNantes Université, Univ Tours, CHU Nantes, INSERM, MethodS in Patients-Centered Outcomes and HEalth Research SPHERE, 44000, Nantes, France.
C DavidNantes Université, Univ Tours, CHU Nantes, INSERM, MethodS in Patients-Centered Outcomes and HEalth Research SPHERE, 44000, Nantes, France.
M GiralCRTI UMR 1064, Inserm, Université de Nantes, ITUN, CHU Nantes, RTRS Centaure, Nantes, France.
E DantanNantes Université, Univ Tours, CHU Nantes, INSERM, MethodS in Patients-Centered Outcomes and HEalth Research SPHERE, 44000, Nantes, France. Etienne.Dantan@univ-nantes.fr.ORCID http://orcid.org/0000-0001-7137-5051
DIVAT Consortium

Funding

Agence Nationale de la Recherche ANR-22-CE36-0009-01
6 · The paper itself

Abstract

objectivesModelling health-state utility values (HSUVs) from clinical data offers a means to conduct retrospective cost-effectiveness analyses using clinical studies that did not collect direct HSUV measures. Such studies can support the efficient allocation of resources in kidney transplantation (KT). We aim to model KT recipients' EQ-5D-3L HSUVs using routinely collected clinical data.

methodsFrom a French observational multicentric prospective cohort, we included 2,787 adult recipients of a first or second single renal graft transplanted between January 2014 and December 2021 who completed 5,679 EQ-5D-3L questionnaires post-KT, from which the HSUVs were calculated. Considering two time periods before and after 1-year post-KT, we estimated a linear mixed effect model (LME), a mixed adjusted limited dependent variable mixture model, and beta and two-part beta mixed models. We compared their predictive performances in terms of precision and calibration.

resultsIn each model, recipient age, female sex, higher body mass index, presence of comorbidities and time spent on dialysis prior to KT were associated with lower HSUVs. The predicted HSUVs increased during the first year post-KT before slowly decreasing afterwards. The two-part beta mixed model resulted in the most precise predictions but showed poor calibration. The LME was associated with better calibration than the other models.

conclusionsOur study illustrates the importance of estimating longitudinal predictive algorithms to consider possible time variations in HSUVs. We provide an online calculator for predicting the HSUVs of KT recipients over time. Future studies in international cohorts are important to support the external validity of our results.

Indexed as

Health StatusKidney TransplantationQuality of LifeTransplant RecipientsAdultAgedAge FactorsBody Mass IndexCost-Benefit AnalysisFemaleFranceHumansMaleMiddle AgedProspective StudiesSex FactorsEQ-5DHealth-state utilityKidney transplantationLongitudinal predictionMapping

Identifiers

PMID40495038
PMCPMC12929319

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.