A Relative Survival Model to Compare the Risk of Mortality in Patients Awaiting Kidney Transplantation Versus Already Transplanted Patients

M. Lorent,^1,2 K. Trébern-Launay,^2,1 C. Legendre,⁴ H. Kreis,⁴ G. Mourad,⁵ V. Garrigue,⁵ L. Rostaing,⁶ N. Kamar,⁶ M. Kessler,⁷ M. Ladrière,⁷ E. Morelon,⁸ F. Buron,⁸ M. Giral,^2,3 Y. Foucher.^1,2

¹EA 4275 - SPHERE bioStatistics, Pharmacoepidemiology and Human Sciences Research, Nantes University, Nantes, France
²Institut de Transplantation, Urologie, Néphrologie (ITUN), Inserm U1064, CHU de Nantes, Nantes, France
³CIC Biotherapy, CHU Nantes, Nantes, France
⁴Service de Transplantation Rénale et de Soins Intensifs, Hôpital Necker, APHP Paris, Paris, France
⁵Service de Néphrologie-Transplantation, Hôpital Lapeyronie, Montpellier, France
⁶Service de Néphrologie, HTA, Dialyse et Transplantation d'Organes, CHU Rangueil, Toulouse, France
⁷Service de Transplantation Rénale, CHU Brabois, Nancy, France
⁸Service de Néphrologie, Transplantation et Immunologie Clinique, Hôpital Edouard Herriot, Lyon, France.

Meeting: 2015 American Transplant Congress

Abstract number: A47

Keywords: Allocation, Multivariate analysis, Prediction models, Risk factors

Session Information

Session Name: Poster Session A: Delayed Function/Acute Injury/Outcomes/Glomerulonephritis

Session Type: Poster Session

Date: Saturday, May 2, 2015

Session Time: 5:30pm-7:30pm

Presentation Time: 5:30pm-7:30pm

Location: Exhibit Hall E

Introduction: It would be useful for physicians in some specific cases to identify whom patients could not benefit of kidney transplantations.

Patients and method: To answer to this question, we compared 3941 kidney transplant recipient between 1996 to 2014 from the French DIVAT cohort (www.divat.fr) with a referent population of 9852 patients on waiting list registered on the French register REIN (Réseau Epidémiologie et Information en Néphrologie) during the same period. We used a relative survival model with multiplicative hazards.

Results: We estimated a mean post-transplantation period with an excess risk of death equals to 64 days compared with similar dialysis patients on waiting list. In contrast, at 3 years post-transplantation, the relative risk of death was 3 times higher in dialysis. We also highlighted 7 variables associated with this relative risk of death: blood group, body mass index, recipient age, anti HLA class II immunization, cardiac history, duration in dialysis before transplantation, donor age, and cold ischemia time. One can notice that such modelling allow taking into account risk factors specifically related to the transplantation in opposition to the Cox regression always used in the literature to compare mortality during dialysis and after kidney transplantation.

Conclusion: The model we proposed allows the stratification of potential recipients regarding their expected gain of survival or their excess of mortality related to transplantation. We believe that these developments will be useful for graft decision/allocation.

To cite this abstract in AMA style:

Lorent M, Trébern-Launay K, Legendre C, Kreis H, Mourad G, Garrigue V, Rostaing L, Kamar N, Kessler M, Ladrière M, Morelon E, Buron F, Giral M, Foucher Y. A Relative Survival Model to Compare the Risk of Mortality in Patients Awaiting Kidney Transplantation Versus Already Transplanted Patients [abstract]. Am J Transplant. 2015; 15 (suppl 3). https://atcmeetingabstracts.com/abstract/a-relative-survival-model-to-compare-the-risk-of-mortality-in-patients-awaiting-kidney-transplantation-versus-already-transplanted-patients/. Accessed May 18, 2025.

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