Please use this identifier to cite or link to this item: https://repository.monashhealth.org/monashhealthjspui/handle/1/51678
Conference/Presentation Title: Should I Have a Transplant? Using Flexible Parametric Models To Predict Survival After Kidney Transplant Waitlisting.
Authors: Irish G.;Mulley W. ;Clayton P.A.
Monash Health Department(s): Centre for Inflammatory Diseases at Monash Health
Nephrology
Institution: (Irish, Clayton) ANZDATA, Adelaide, Australia
(Irish, Clayton) Department of Nephrology, Monash Medical Centre, Clayton, Australia
(Irish, Clayton) Central and Northern Adelaide Renal and Transplantation Service, Royal Adelaide Hospital, Adelaide, Australia
(Mulley) Health and Medical Sciences, University of Adelaide, Adelaide, Australia
(Mulley) Centre for Inflammatory Diseases, School of Clinical Sciences, Monash University, Clayton, Australia
Presentation/Conference Date: 24-Apr-2024
Copyright year: 2022
Publisher: Lippincott Williams and Wilkins
Publication information: Transplantation. Conference: 29th International Congress of The Transplantation Society, TTS 2022. Buenos Aires Argentina. 106(9 Supplement) (pp S265), 2022. Date of Publication: September 2022.
Journal: Transplantation
Abstract: Background: The Cox proportional hazards model is commonly used to compare survival between the waiting list (WL) and after deceased donor kidney transplantation (TP). However, the Cox model only allows estimation of relative but not absolute survival. Flexible parametric models (FPM) solve this by modelling the baseline hazard which enables the creation of an absolute survival prediction for individuals with different characteristics. Aim(s): To develop FPMs for predicting survival on the waiting list versus deceased donor kidney transplantation. Method(s): Using the Australia and New Zealand Dialysis and Transplant (ANZDATA) Registry, we included Australian adults waitlisted for first kidneyonly deceased donor transplants over 2007-2020. We developed FPMs for waitlist and post-transplant survival. Covariates were decided using backwards elimination and the baseline hazard function was modelled using cubic splines. Result(s): 7552 patients were included in this analysis: 5429 (72%) received a deceased donor kidney transplant. The models were adjusted for age, gender, primary kidney disease, dialysis duration, comorbidities, and smoking status. The FPM allowed calculations of individual mean life expectancy (Figure 1). Example 1, TP: 69 years (95% CI 63-75), WL: 41 years (95% CI 15-67), Difference: 28 years. Example 2, TP: 17 years (95% CI 13-22), WL: 9 years (95% CI 7-10), Difference: 8 years.Example 3, TP: 4 years (95% CI 3-6), WL:4 years (95% CI 3-5), Difference: 0.5 years. Conclusion(s): FPM can predict risk for patients on the kidney transplant waitlist. For the first time, this enables absolute survival prediction, to help patients and clinicians understand the likely outcomes of transplantation vs remaining on dialysis. The next step will be model validation and incorporation of quality-of-life utilities.
Conference Name: 29th International Congress of The Transplantation Society, TTS 2022
Conference Start Date: 2022-09-10
Conference End Date: 2022-09-14
Conference Location: Buenos Aires, Argentina
URI: https://repository.monashhealth.org/monashhealthjspui/handle/1/51678
Type: Conference Abstract
Subjects: kidney graft
kidney transplantation
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