Long-term outcomes of elderly kidney transplant recipients

Orlandi, Paula; Cristelli, Marina Pontello; Aldworth, Carolina; Freitas, Tainá Veras de Sandes; Felipe, Cláudia Rosso; Silva, Helio Tedesco; Pestana, José Osmar Medina

doi:10.5935/0101-2800.20150034

Cited by 17 publications

(19 citation statements)

References 21 publications

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“…The Dempster et al study showed a higher mortality rate in the first year after transplantation for older patients [16]. While older recipients have been shown to be at higher risk for complications after transplantation [16,19,20], the only alternative to transplantation would be dialysis, which has been shown to have even worse results concerning survival, quality of life, and economic factors [16,19,21,22].…”

Section: Discussionmentioning

confidence: 99%

“…The study by Orlandi et al did not find recipient age as an independent risk factor for negative outcomes but found recipient diabetic state was a relevant and independent risk factor for earlier death [21]. This is particularly important, as the increasing diabetes prevalence in the population leads to an increase in the frequency of diagnosed end-stage renal disease cases [22].…”

Section: Discussionmentioning

confidence: 99%

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Prediction of Three-Year Mortality After Deceased Donor Kidney Transplantation in Adults with Pre-Transplant Donor and Recipient Variables

et al. 2019

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Background Prognostic models for 3-year mortality after kidney transplantation based on pre-transplant donor and recipient variables may avoid futility and thus improve donor organ allocation. Material/Methods There were 1546 consecutive deceased-donor kidney transplants in adults (January 1, 2000 to December 31, 2012) used to identify pre-transplant donor and recipient variables with significant independent influence on long-term survival (Cox regression modelling). Detected factors were used to develop a prognostic model for 3-year mortality in 1289 patients with follow-up of >3 years (multivariable logistic regression). The sensitivity and specificity of this model’s prognostic ability was assessed with the area under the receiver operating characteristic curve (AUROC). Results Highly immunized recipients [hazard ratio (HR: 2.579, 95% CI: 1.272–4.631], high urgency recipients (HR: 3.062, 95% CI: 1.294–6.082), recipients with diabetic nephropathy (HR: 3.471, 95% CI: 2.476–4.751), as well as 0, 1, or 2 HLA DR mismatches (HR: 1.349, 95% CI: 1.160–1.569) were independent and significant risk factors for patient survival. Younger recipient age ≤42.1 years (HR: 0.137, 95% CI: 0.090–0.203), recipient age 42.2–52.8 years (HR: 0.374, 95% CI: 0.278–0.498), recipient age 52.9–62.8 years (HR: 0.553, 95% CI: 0.421–0.723), short cold ischemic times ≤11.8 hours (HR: 0.602, 95% CI: 0.438–0.814) and cold ischemic times 11.9–15.3 hours (HR: 0.736, 95% CI: 0.557–0.962) reduced this risk independently and significantly. The AUROC of the derived model for 3-year post-transplant mortality with these variables was 0.748 (95% CI: 0.689–0.788). Conclusions Older, highly immunized or high urgency transplant candidates with anticipated longer cold ischemic times, who were transplanted with the indication of diabetic nephropathy should receive donor organs with no HLA DR mismatches to improve their mortality risk.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Prediction of Three-Year Mortality After Deceased Donor Kidney Transplantation in Adults with Pre-Transplant Donor and Recipient Variables

et al. 2019

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“…Donor type was classified as such: living donor (LV), standard deceased donor (SDD) and expanded criteria donor (ECD). The other variables were: a) Demographic: sex, skin color and age, elderly (CKD-T ≥ 60 years old (Orlandi et al, 2015) b) Immunological: antibody reactivity panel (PRA), mismatch in the human leucocyte antigen (HLA) system, and aTCMR or aABMR post-KT. c) Pre-KT: obesity, systemic arterial hypertension (SAH), diabetes mellitus (DM), chronic kidney disease (CKD) etiology and KRT time until KT (∆T-KRT&KT).…”

Section: Analysis Of Covariablesmentioning

confidence: 99%

Cox regression analysis of compositional covariates related to death of the kidney transplant-recipient in northeastern Brazil: modeling of covariates associated with renal allograft failure

Júnior

Pereira

França

et al. 2020

RSD

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Introduction: Kidney transplant (KT) has the highest survival rate amongst kidney replacement therapies (KRT). Objective: Analyze the incidence density of all-cause mortality in chronic kidney disease transplant-recipients and to identify covariables associated with higher risk of death. Methodology: Cohort study using medical records of 605 KT patients with seven years follow-up (2011-2018). Records with insufficient data or from patients with incomplete treatment were excluded. The variables analyzed were demographic, clinical and laboratory data, duration of KRT, type of donor, immunological compatibility, panel-reactive HLA-antibody, infections, and use of hypothermic perfusion machine (HPPM). Hazard ratio (HR) and incidence density of all-cause deaths were estimated. Results: 15 of 553 KT-recipients died during the follow-up. The survival in the first year post-KT was 98.0% and in the fifth year was 93.2%. The incidence density of deaths is 10/1,000 person-years. Variables pre- and post-KT related with higher death risk were allograft pyelonephritis ≥6-months and delayed graft function >4 weeks. Survival among KT-recipients with loss >5 mL/min/1.73m2/year in the estimated glomerular filtration rate (eGFR) were lower than the others (88% vs. 97%). Covariates associated with mortality post-transplant included pre-KT obesity, HPPM, allograft pyelonephritis, and new-onset diabetes after transplantation. Conclusion: The mortality post-KT is low in these population. Cox's modelling demonstrated that the decline in eGFR >5 mL/min/1.73m2/year, allograft pyelonephritis ≥6-months, pre-KT obesity, fasting blood glucose ≥126 mg/dL presented worst probability of survival. Rapid decline in eGFR reduces substantially the survival probability in these population.

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“…Studies have demonstrated that KT in elderly people is safe and successful, thereby improving the QoL and even doubling the survival time compared to elderly people on the transplantation waiting list [ 19 ]. Despite the demonstrated benefits of KT, when expertly referred, there are preestablished concepts that elderly people have a shorter life expectancy, present a higher surgical risk, and experience potential serious complications [ 20 ]. Results from the Scientific Registry of Transplant Recipients (SRTR) in the United States demonstrate that elderly patients on the waiting list experience significant survival benefit with KT [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Logistic Regression Model in a Machine Learning Application to Predict Elderly Kidney Transplant Recipients with Worse Renal Function One Year after Kidney Transplant: Elderly KTbot

Júnior

Couto²,

Pereira

et al. 2020

Journal of Aging Research

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Background. Renal replacement therapy (RRT) is a public health problem worldwide. Kidney transplantation (KT) is the best treatment for elderly patients’ longevity and quality of life. Objectives. The primary endpoint was to compare elderly versus younger KT recipients by analyzing the risk covariables involved in worsening renal function, proteinuria, graft loss, and death one year after KT. The secondary endpoint was to create a robot based on logistic regression capable of predicting the likelihood that elderly recipients will develop worse renal function one year after KT. Method. Unicentric retrospective analysis of a cohort was performed with individuals aged ≥60 and <60 years old. We analysed medical records of KT recipients from January to December 2017, with a follow-up time of one year after KT. We used multivariable logistic regression to estimate odds ratios for elderly vs younger recipients, controlled for demographic, clinical, laboratory, data pre- and post-KT, and death. Results. 18 elderly and 100 younger KT recipients were included. Pretransplant immune variables were similar between two groups. No significant differences (P>0.05) between groups were observed after KT on laboratory data means and for the prevalences of diabetes mellitus, hypertension, acute rejection, cytomegalovirus, polyomavirus, and urinary infections. One year after KT, the creatinine clearance was higher (P = 0.006) in youngers (70.9 ± 25.2 mL/min/1.73 m2) versus elderlies (53.3 ± 21.1 mL/min/1.73 m2). There was no difference in death outcome comparison. Multivariable analysis among covariables predisposing chronic kidney disease epidemiology collaboration (CKD-EPI) equation <60 mL/min/1.73 m2 presented a statistical significance for age ≥60 years (P = 0.01) and reduction in serum haemoglobin (P = 0.03). The model presented goodness-fit in the evaluation of artificial intelligence metrics (precision: 90%; sensitivity: 71%; and F1 score: 0.79). Conclusion. Renal function in elderly KT recipients was lower than in younger KT recipients. However, patients aged ≥60 years maintained enough renal function to remain off dialysis. Moreover, a learning machine application built a robot (Elderly KTbot) to predict in the elderly populations the likelihood of worse renal function one year after KT.

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Long-term outcomes of elderly kidney transplant recipients

Abstract: Advanced age was not related to negative outcomes after kidney transplantation, after excluding recipient death as a cause of allograft loss. Higher mortality rate in this group was associated to a higher frequency of comorbidities, especially diabetes mellitus.

Cited by 17 publications

References 21 publications

Prediction of Three-Year Mortality After Deceased Donor Kidney Transplantation in Adults with Pre-Transplant Donor and Recipient Variables

Prediction of Three-Year Mortality After Deceased Donor Kidney Transplantation in Adults with Pre-Transplant Donor and Recipient Variables

Cox regression analysis of compositional covariates related to death of the kidney transplant-recipient in northeastern Brazil: modeling of covariates associated with renal allograft failure

Logistic Regression Model in a Machine Learning Application to Predict Elderly Kidney Transplant Recipients with Worse Renal Function One Year after Kidney Transplant: Elderly KTbot

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