Trajectories of glomerular filtration rate and progression to end stage kidney disease after kidney transplantation

Raynaud, Marc; Aubert, Olivier; Reese, Peter P.; Bouatou, Yassine; Naesens, Maarten; Kamar, Nassim; Bailly, Élodie; Giral, Magali; Ladrière, Marc; Quintrec, Moglie Le; Delahousse, Michel; Jurić, Ivana; Bašić‐Jukić, Nikolina; Gupta, Gaurav; Akalin, Enver; Yoo, Daniel; Chin, Chen-Shan; Proust‐Lima, Cécile; Böhmig, Georg A.; Oberbauer, Rainer; Stegall, Mark D.; Bentall, Andrew; Jordan, Stanley C.; Huang, Edmund; Glotz, Denis; Legendre, Christophe; Montgomery, Robert A.; Segev, Dorry L.; Empana, Jean‐Philippe; Grams, Morgan E.; Coresh, Josef; Jouven, Xavier; Lefaucheur, Carmen; Loupy, Alexandre

doi:10.1016/j.kint.2020.07.025

Cited by 48 publications

(44 citation statements)

References 36 publications

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“…We conducted this study to elucidate whether early eGFR slopes, from 12 up to 24 months after biopsy, could serve as a surrogate for future allograft loss in patients diagnosed with late ABMR. The search for a “minimally clinically meaningful difference” with respect to eGFR slopes as an accepted trial endpoint in kidney transplantation is ongoing and several recent studies have addressed this issue ( 8 , 9 , 21 – 23 ). In trials studying chronic kidney disease, eGFR slopes were recently accepted as a surrogate endpoint by the FDA, but their value in late ABMR remains elusive ( 12 ).…”

Section: Discussionmentioning

confidence: 99%

Early Estimated Glomerular Filtration Rate Trajectories After Kidney Transplant Biopsy as a Surrogate Endpoint for Graft Survival in Late Antibody-Mediated Rejection

et al. 2022

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BackgroundLate antibody-mediated rejection (ABMR) after kidney transplantation is a major cause of long-term allograft loss with currently no proven treatment strategy. Design for trials testing treatment for late ABMR poses a major challenge as hard clinical endpoints require large sample sizes. We performed a retrospective cohort study applying commonly used selection criteria to evaluate the slope of the estimated glomerular filtration rate (eGFR) within an early and short timeframe after biopsy as a surrogate of future allograft loss for clinical trials addressing late ABMR.MethodsStudy subjects were identified upon screening of the Vienna transplant biopsy database. Main inclusion criteria were (i) a solitary kidney transplant between 2000 and 2013, (ii) diagnosis of ABMR according to the Banff 2015 scheme at >12 months post-transplantation, (iii) age 15–75 years at ABMR diagnosis, (iv) an eGFR > 25 mL/min/1.73 m2 at ABMR diagnosis, and (v) a follow-up for at least 36 months after ABMR diagnosis. The primary outcome variable was death-censored graft survival. A mixed effects model with linear splines was used for eGFR slope modeling and association of graft failure and eGFR slope was assessed applying a multivariate competing risk analysis with landmarks set at 12 and 24 months after index biopsy.ResultsA total of 70 allografts from 68 patients were included. An eGFR loss of 1 ml/min/1.73 m2 per year significantly increased the risk for allograft failure, when eGFR slopes were modeled over 12 months [HR 1.1 (95% CI: 1.01–1.3), p = 0.020] or over 24 months [HR 1.3 (95% CI: 1.1–1.4), p = 0.001] after diagnosis of ABMR with landmarks set at both time points. Covariables influencing graft loss in all models were histologic evidence of glomerulonephritis concurring with ABMR as well as the administration of anti-thymocyte globulin (ATG) at the time of transplantation.ConclusionOur study supports the use of the eGFR slope modeled for at least 12 months after biopsy-proven diagnosis of late ABMR, as a surrogate parameter for future allograft loss. The simultaneous occurrence of glomerulonephritis together with ABMR at index biopsy and the use of ATG at the time of transplantation–likely representing a confounder in pre-sensitized recipients–were strongly associated with worse transplant outcomes.

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Section: Discussionmentioning

confidence: 99%

Early Estimated Glomerular Filtration Rate Trajectories After Kidney Transplant Biopsy as a Surrogate Endpoint for Graft Survival in Late Antibody-Mediated Rejection

et al. 2022

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“…Different studies have analyzed the evolution of renal function in patients with chronic diseases, giving greater importance to diabetes as a triggering factor or associated with its evolution, in some the evolution or progression of eGFR over time has simply been studied without considering some specific disease. 11 , 13 , 16 In our study, an analysis of the evolution of CKD was carried out in patients with hypertension as the main risk disease, based on its diagnosis made at the first level of care, where it was observed that In an average of 7 years, they lost 28.3 ml/min/1.73 m 2 in the eGFR and in an average 3.2 years, the loss was 14.6 ml/min/1.73 m 2 . A differential feature of the present study was the CKD staging at the time of diagnosis of arterial hypertension, where almost 60% of the patients were already in stage2 and with an average eGFR of 80.9 ml/min/1.73 m 2 and almost a 10% with some stage between stage 3a and 4, which leads to suppose that these patients had some previous condition that conditioned the presence of CKD (underdiagnosed arterial hypertension of several years of evolution, presence of previous kidney disease, mainly an over-aggregated glomerulopathy, which conditioned an important renal deterioration arriving with less than 60 ml/min of eGFR and with a potential secondary arterial hypertension, which was not ruled out in the present study).…”

Section: Discussionmentioning

confidence: 99%

Evolution of the stage of chronic kidney disease from the diagnosis of hypertension in primary care

et al. 2022

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“… 27 Raynaud et al recently demonstrated that the donor age, eGFR, proteinuria and pathological findings of the transplanted kidney predicted progression to end-stage kidney disease in kidney transplanted patients who were classified into eight groups. 37 Meanwhile, our model included features that we used as variables, including haemoglobin and CRP levels, which were not used in other models. Factors related to anaemia, such as haemoglobin ESA7 and the 90-day and 180-day averages, were associated with trajectories of eGFR in patients with an eGFR of approximately 90 mL/min/m 2 .…”

Section: Discussionmentioning

confidence: 99%

Development of a machine learning-based prediction model for extremely rapid decline in estimated glomerular filtration rate in patients with chronic kidney disease: a retrospective cohort study using a large data set from a hospital in Japan

et al. 2022

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ObjectivesTrajectories of estimated glomerular filtration rate (eGFR) decline vary highly among patients with chronic kidney disease (CKD). It is clinically important to identify patients who have high risk for eGFR decline. We aimed to identify clusters of patients with extremely rapid eGFR decline and develop a prediction model using a machine learning approach.DesignRetrospective single-centre cohort study.SettingsTertiary referral university hospital in Toyoake city, Japan.ParticipantsA total of 5657 patients with CKD with baseline eGFR of 30 mL/min/1.73 m2 and eGFR decline of ≥30% within 2 years.Primary outcomeOur main outcome was extremely rapid eGFR decline. To study-complicated eGFR behaviours, we first applied a variation of group-based trajectory model, which can find trajectory clusters according to the slope of eGFR decline. Our model identified high-level trajectory groups according to baseline eGFR values and simultaneous trajectory clusters. For each group, we developed prediction models that classified the steepest eGFR decline, defined as extremely rapid eGFR decline compared with others in the same group, where we used the random forest algorithm with clinical parameters.ResultsOur clustering model first identified three high-level groups according to the baseline eGFR (G1, high GFR, 99.7±19.0; G2, intermediate GFR, 62.9±10.3 and G3, low GFR, 43.7±7.8); our model simultaneously found three eGFR trajectory clusters for each group, resulting in nine clusters with different slopes of eGFR decline. The areas under the curve for classifying the extremely rapid eGFR declines in the G1, G2 and G3 groups were 0.69 (95% CI, 0.63 to 0.76), 0.71 (95% CI 0.69 to 0.74) and 0.79 (95% CI 0.75 to 0.83), respectively. The random forest model identified haemoglobin, albumin and C reactive protein as important characteristics.ConclusionsThe random forest model could be useful in identifying patients with extremely rapid eGFR decline.Trial registrationUMIN 000037476; This study was registered with the UMIN Clinical Trials Registry.

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Trajectories of glomerular filtration rate and progression to end stage kidney disease after kidney transplantation

Cited by 48 publications

References 36 publications

Early Estimated Glomerular Filtration Rate Trajectories After Kidney Transplant Biopsy as a Surrogate Endpoint for Graft Survival in Late Antibody-Mediated Rejection

Early Estimated Glomerular Filtration Rate Trajectories After Kidney Transplant Biopsy as a Surrogate Endpoint for Graft Survival in Late Antibody-Mediated Rejection

Evolution of the stage of chronic kidney disease from the diagnosis of hypertension in primary care

Development of a machine learning-based prediction model for extremely rapid decline in estimated glomerular filtration rate in patients with chronic kidney disease: a retrospective cohort study using a large data set from a hospital in Japan

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