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Nephrol Dial Transplant. 2019 Mar 11. pii: gfz027. doi: 10.1093/ndt/gfz027. [Epub ahead of print]

Dynamic predictions of long-term kidney graft failure: an information tool promoting patient-centred care.

Author information

1
INSERM UMR 1246 - SPHERE, Nantes University, Tours University, Nantes, France.
2
CRTI UMR 1064, Inserm, Université de Nantes, ITUN, CHU Nantes, RTRS Centaure, Nantes, France.
3
Centre Hospitalier Universitaire de Nantes, Nantes, France.
4
Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark.
5
Kidney Transplant Center, Necker University Hospital, APHP, RTRS « Centaure », Paris Descartes and Sorbonne Paris Cité Universities, Paris, France.
6
Renal Transplantation Department, Brabois University Hospital, Nancy, France.
7
Nephrology, Transplantation and Clinical Immunology Department, RTRS « Centaure », Edouard Herriot University Hospital, Hospices Civils, Lyon, France.
8
Nephrology, Dialysis, and Organ Transplantation Department, Rangueil University Hospital and University Paul Sabatier, Toulouse, France.
9
Nephrology, Dialysis and Transplantation Department, Lapeyronie University Hospital, University of Montpellier, Montpellier, France.
10
Department of Nephrology, Dialysis and Transplantation, Departmental Hospital of Vendée, La Roche-sur-Yon, France.
11
Centre d'Investigation Clinique en Biothérapie, Nantes, France.

Abstract

BACKGROUND:

Informing kidney transplant recipients of their prognosis and disease progression is of primary importance in a patient-centred vision of care. By participating in decisions from the outset, transplant recipients may be more adherent to complex medical regimens due to their enhanced understanding.

METHODS:

We proposed to include repeated measurements of serum creatinine (SCr), in addition to baseline characteristics, in order to obtain dynamic predictions of the graft failure risk that could be updated continuously during patient follow-up. Adult recipients from the French Données Informatisées et VAlidées en Transplantation (DIVAT) cohort transplanted for the first or second time from a heart-beating or living donor and alive with a functioning graft at 1 year post-transplantation were included.

RESULTS:

The model was composed of six baseline parameters, in addition to the SCr evolution. We validated the dynamic predictions by evaluating both discrimination and calibration accuracy. The area under the receiver operating characteristic curve varied from 0.72 to 0.76 for prediction times at 1 and 6 years post-transplantation, respectively, while calibration plots showed correct accuracy. We also provided an online application tool (https://shiny.idbc.fr/DynPG).

CONCLUSION:

We have created a tool that, for the first time in kidney transplantation, predicts graft failure risk both at an individual patient level and dynamically. We believe that this tool would encourage willing patients into participative medicine.

KEYWORDS:

dynamic prediction; graft failure; kidney transplantation; patient-centred care; shared decision-making

PMID:
30859193
DOI:
10.1093/ndt/gfz027

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