Clinical prediction model for prognosis in kidney transplant recipients (KIDMO): study protocol.
Details
Serval ID
serval:BIB_69CC7CCAA980
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Clinical prediction model for prognosis in kidney transplant recipients (KIDMO): study protocol.
Journal
Diagnostic and prognostic research
Working group(s)
Swisstransplant Kidney Working Group (STAN), Swiss Transplant Cohort Study
Contributor(s)
Amico P., Folie P., Gannagé M., Matter M., Nilsson J., Peloso A., de Rougemont O., Schnyder A., Spartà G., Storni F., Villard J., Wirth-Müller U., Wolff T., Aubert J.D., Banz V., Beckmann S., Beldi G., Berger C., Berishvili E., Berzigotti A., Bochud P.Y., Branca S., Bucher H., Catana E., Cairoli A., Chalandon Y., De Geest S., De Seigneux S., Dickenmann M., Dreifuss J.L., Duchosal M., Ferrari-Lacraz S., Garzoni C., Goossens N., Halter J., Heim D., Hess C., Hillinger S., Hirsch H.H., Hirt P., Hoessly L., Hofbauer G., Huynh-Do U., Laesser B., Lamoth F., Lehmann R., Leichtle A., Manuel O., Marti H.P., Martinelli M., McLin V., Merçay A., Mettler K., Mueller N.J., Müller-Arndt U., Müllhaupt B., Nägeli M., Oldani G., Pascual M., Passweg J., Pazeller R., Posfay-Barbe K., Rick J., Rosselet A., Rossi S., Rothlin S., Ruschitzka F., Schachtner T., Scherrer A., Schuurmans M., Sengstag T., Simonetta F., Stampf S., Steiger J., Stirnimann G., Van Delden C., Venetz J.P., Vionnet J., Wick M., Wilhelm M., Yerly P.
ISSN
2397-7523 (Electronic)
ISSN-L
2397-7523
Publication state
Published
Issued date
07/03/2023
Peer-reviewed
Oui
Volume
7
Number
1
Pages
6
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Publication Status: epublish
Abstract
Many potential prognostic factors for predicting kidney transplantation outcomes have been identified. However, in Switzerland, no widely accepted prognostic model or risk score for transplantation outcomes is being routinely used in clinical practice yet. We aim to develop three prediction models for the prognosis of graft survival, quality of life, and graft function following transplantation in Switzerland.
The clinical kidney prediction models (KIDMO) are developed with data from a national multi-center cohort study (Swiss Transplant Cohort Study; STCS) and the Swiss Organ Allocation System (SOAS). The primary outcome is the kidney graft survival (with death of recipient as competing risk); the secondary outcomes are the quality of life (patient-reported health status) at 12 months and estimated glomerular filtration rate (eGFR) slope. Organ donor, transplantation, and recipient-related clinical information will be used as predictors at the time of organ allocation. We will use a Fine & Gray subdistribution model and linear mixed-effects models for the primary and the two secondary outcomes, respectively. Model optimism, calibration, discrimination, and heterogeneity between transplant centres will be assessed using bootstrapping, internal-external cross-validation, and methods from meta-analysis.
Thorough evaluation of the existing risk scores for the kidney graft survival or patient-reported outcomes has been lacking in the Swiss transplant setting. In order to be useful in clinical practice, a prognostic score needs to be valid, reliable, clinically relevant, and preferably integrated into the decision-making process to improve long-term patient outcomes and support informed decisions for clinicians and their patients. The state-of-the-art methodology by taking into account competing risks and variable selection using expert knowledge is applied to data from a nationwide prospective multi-center cohort study. Ideally, healthcare providers together with patients can predetermine the risk they are willing to accept from a deceased-donor kidney, with graft survival, quality of life, and graft function estimates available for their consideration.
Open Science Framework ID: z6mvj.
The clinical kidney prediction models (KIDMO) are developed with data from a national multi-center cohort study (Swiss Transplant Cohort Study; STCS) and the Swiss Organ Allocation System (SOAS). The primary outcome is the kidney graft survival (with death of recipient as competing risk); the secondary outcomes are the quality of life (patient-reported health status) at 12 months and estimated glomerular filtration rate (eGFR) slope. Organ donor, transplantation, and recipient-related clinical information will be used as predictors at the time of organ allocation. We will use a Fine & Gray subdistribution model and linear mixed-effects models for the primary and the two secondary outcomes, respectively. Model optimism, calibration, discrimination, and heterogeneity between transplant centres will be assessed using bootstrapping, internal-external cross-validation, and methods from meta-analysis.
Thorough evaluation of the existing risk scores for the kidney graft survival or patient-reported outcomes has been lacking in the Swiss transplant setting. In order to be useful in clinical practice, a prognostic score needs to be valid, reliable, clinically relevant, and preferably integrated into the decision-making process to improve long-term patient outcomes and support informed decisions for clinicians and their patients. The state-of-the-art methodology by taking into account competing risks and variable selection using expert knowledge is applied to data from a nationwide prospective multi-center cohort study. Ideally, healthcare providers together with patients can predetermine the risk they are willing to accept from a deceased-donor kidney, with graft survival, quality of life, and graft function estimates available for their consideration.
Open Science Framework ID: z6mvj.
Keywords
Estimated glomerular filtration rate, Graft survival, Kidney transplantation, Patient-reported health status, Prediction model, Prognosis, Prognostic model, Quality of life, Risk calculator, Risk score, eGFR
Pubmed
Open Access
Yes
Create date
13/03/2023 10:22
Last modification date
01/03/2024 8:06
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