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Objectives <p>Modelling 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.</p> Methods <p>From 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.</p> Results <p>In 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.</p> Conclusions <p>Our 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月11日

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  • V. Bonnemains,
  • Y. Foucher,
  • P. Tessier,
  • C. David,
  • M. Giral,
  • E. Dantan,
  • Lionel Badet,
  • Maria Brunet,
  • Fanny Buron,
  • Rémi Cahen,
  • Ricardo Codas,
  • Sameh Daoud,
  • Valérie Dubois,
  • Coralie Fournie,
  • François Gaillard,
  • Arnaud Grégoire,
  • Alice Koenig,
  • Charlène Lévi,
  • Emmanuel Morelon,
  • Claire Pouteil-Noble,
  • Maud Rabeyrin,
  • Thomas Rimmelé,
  • Olivier Thaunat,
  • Nicolas Abdo,
  • Sylvie Delmas,
  • Moglie Le Quintrec,
  • Vincent Pernin,
  • Hélène Perrochia,
  • Jean-Emmanuel Serre,
  • Ilan Szwarc,
  • Alice Aarnink,
  • Asma Alla,
  • Pascal Eschwege,
  • Luc Frimat,
  • Sophie Girerd,
  • Jacques Hubert,
  • Raphaël Kormann,
  • Marc Ladriere,
  • François Lagrange,
  • Emmanuelle Laurain,
  • Pierre Lecoanet,
  • Jean-Louis Lemelle,
  • Anthony Mannuguerra,
  • Charles Mazeaud,
  • Michael Peres,
  • Gilles Blancho,
  • Julien Branchereau,
  • Diego Cantarovich,
  • Agnès Chapelet,
  • Jacques Dantal,
  • Clément Deltombe,
  • Lucile Figueres,
  • Raphael Gaisne,
  • Claire Garandeau,
  • Magali Giral,
  • Caroline Gourraud-Vercel,
  • Maryvonne Hourmant,
  • Georges Karam,
  • Clarisse Kerleau,
  • Delphine Kervella,
  • Christophe Masset,
  • Aurélie Meurette,
  • Simon Ville,
  • Christine Kandell,
  • Anne Moreau,
  • Karine Renaudin,
  • Florent Delbos,
  • Alexandre Walencik,
  • Anne Devis,
  • Laetitia Albano,
  • Damien Ambrosetti,
  • Nadia Ben Hassen,
  • Mathilde Blois,
  • Marion Cremoni,
  • Matthieu Durand,
  • Patricia Goldis,
  • Clément Gosset,
  • Fatimaezzahra Karimi,
  • Antoine Sicard,
  • Giorgo Toni,
  • Lucile Amrouche,
  • Dany Anglicheau,
  • Olivier Aubert,
  • Lynda Bererhi,
  • Christophe Legendre,
  • Alexandre Loupy,
  • Frank Martinez,
  • Arnaud Méjean,
  • Rébecca Sberro-Soussan,
  • Anne Scemla,
  • Marc-Olivier Timsit,
  • Julien Zuber,
  • Gillian Divard,
  • Carmen Lefaucheur,
  • Christophe Mariat,
  • Guillaume Claisse

摘要

Objectives

Modelling 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.

Methods

From 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.

Results

In 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.

Conclusions

Our 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.