Predictors of graft survival at diagnosis of antibody‐mediated renal allograft rejection: a retrospective single‐center cohort study

Waiser, Johannes; Lachmann, Nils; Wu, Kaiyin; Rudolph, Birgit; Halleck, Fabian; Liefeldt, Lutz; Bachmann, Friederike; Budde, Klemens; Duerr, Michael

doi:10.1111/tri.13525

Cited by 7 publications

(5 citation statements)

References 20 publications

(29 reference statements)

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“…This predictor suggests that the occurrence of chronic rejection significantly influences renal graft survival. This finding agrees with previous studies [26,27] which state that patients with chronic rejection were subjected to shorter graft survival. Managing immunosuppressive therapy and closely monitoring patients with a history of chronic rejection may be crucial for improving graft survival outcomes.…”

Section: Discussionsupporting

confidence: 93%

Developing Clinical Prognostic Models to Predict Graft Survival after Renal Transplantation: Comparison of Statistical and Machine Learning Models

Mulugeta,

Zewotir,

Tegegne

et al. 2024

Preprint

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Introduction: Renal transplantation is a critical treatment that can save the lives of individuals who are suffering from end-stage renal disease (ESRD), but graft failure remains a significant concern. Accurate prediction of graft survival after renal transplantation is crucial as it enables clinicians to identify patients at higher risk of graft failure. This study aimed to develop clinical prognostic models for predicting graft survival after renal transplantation and compare the performance of various statistical and machine learning models. Methodology: The study utilized data from a retrospective cohort of renal transplant recipients at the Ethiopian National Kidney Transplantation Center from September 2015 to February 2022. Various statistical and machine learning models were evaluated based on their discrimination, calibration, and interpretability. The comparison of models included standard Cox, Lasso-Cox, Ridge-Cox, Elastic net-Cox, Random Survival Forest, and Stochastic Gradient Boosting. The prognostic predictors of graft survival were selected based on the significance and relative importance of variables in different models. Results The study analyzed a total of 278 completed cases and observed the event of graft failure in 21 patients. The median graft survival time was 33 months, and the mean hazard of graft failure was 0.0755. The results revealed that the 1-year, 3-year, and 5-year graft survival rates are 0.936, 0.924, and 0.914 respectively. The study found that the Random Survival Forest and Stochastic Gradient Boosting models demonstrated the best calibration and discrimination performance shown by an equal AUC of 0.97 and the overlapped calibration plots. On the other hand, the Cox proportional hazards model has the highest interpretability and established superior accuracy in estimating survival probabilities, as evidenced by its lowest Brier score of 0.000071. The current study indicates that an episode of chronic rejection, recipient residence, an episode of acute rejection, post-transplant urological complications, post-transplant nonadherence, blood urea nitrogen level, and number of post-transplant admissions were consistently identified as the top significant prognostic predictors of renal graft survival. Conclusions The Random Survival Forest and Stochastic Gradient Boosting models demonstrated superior calibration and discrimination performance, while the Cox proportional hazards model offered accurate estimation of survival probabilities and interpretability. Clinicians should consider the trade-off between performance and interpretability when choosing a model. Incorporating these findings into clinical practice can improve risk stratification, enable early interventions, and inform personalized management strategies for kidney transplant recipients.

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Section: Discussionsupporting

confidence: 93%

Developing Clinical Prognostic Models to Predict Graft Survival after Renal Transplantation: Comparison of Statistical and Machine Learning Models

Mulugeta,

Zewotir,

Tegegne

et al. 2024

Preprint

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“…In dnDSA with ≥ 50% MFI reduction. specific active treatment for ABMR was recorded (57), excluding changes in chronic baseline immunosuppression. The Flowchart of the patients in our study.…”

Section: Patient Populationmentioning

confidence: 99%

The natural history of de novo donor-specific HLA antibodies after kidney transplantation

Cuesta

Naik

et al. 2022

Front. Med.

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“…The development of antibodies against donor human leukocyte antigens (HLA-DSA) and antibody mediated rejection (ABMR) remain important causes of late graft loss in kidney transplantation. [1][2][3][4] ABMR diagnosis is made by histologic examination of biopsies, along with simultaneous presence of circulating DSA, and has been progressively updated by the Banff schema. 5,6 The presence of microvascular inflammation (MVI) in biopsies remains a mainstay of ABMR diagnosis in these updates.…”

Section: Introductionmentioning

confidence: 99%

Role of time from transplantation to biopsy in histologic ABMR: A single center report

Chancay

Liu

Chauhan

et al. 2022

Clinical Transplantation

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Background Allograft biopsies with lesions of Antibody‐Mediated Rejection (ABMR) with Microvascular Inflammation (MVI) have shown heterogeneous etiologies and outcomes. Methods To examine factors associated with outcomes in biopsies that meet histologic ABMR criteria, we retrospectively evaluated for‐cause biopsies at our center between 2011 and 2017. We included biopsies that met the diagnosis of ABMR by histology, along with simultaneous evaluation for anti‐Human Leukocyte Antigen (HLA) donor‐specific antibodies (DSA). We evaluated death‐censored graft loss (DCGL) and used a principal component analysis (PCA) approach to identify key predictors of outcomes. Results Out of the histologic ABMR cohort (n = 118), 70 were DSA‐positive ABMR, while 48 had no DSA. DSA(+)ABMR were younger and more often female recipients. DSA(+)ABMR occurred significantly later post‐transplant than DSA(‐)ABMR suggesting time‐dependence. DSA(+)ABMR had higher inflammatory scores (i,t), chronicity scores (ci, ct) and tended to have higher MVI scores. Immunodominance of DQ‐DSA in DSA(+)ABMR was associated with higher i+t scores. Clinical/histologic factors significantly associated with DCGL after biopsy were inputted into the PCA. Principal component‐1 (PC‐1), which contributed 34.8% of the variance, significantly correlated with time from transplantation to biopsy, ci/ct scores and DCGL. In the PCA analyses, i, t scores, DQ‐DSA, and creatinine at biopsy retained significant correlations with GL‐associated PCs. Conclusions Time from transplantation to biopsy plays a major role in the prognosis of biopsies with histologic ABMR and MVI, likely due to ongoing chronic allograft injury over time.

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Predictors of graft survival at diagnosis of antibody‐mediated renal allograft rejection: a retrospective single‐center cohort study

Cited by 7 publications

References 20 publications

Developing Clinical Prognostic Models to Predict Graft Survival after Renal Transplantation: Comparison of Statistical and Machine Learning Models

Developing Clinical Prognostic Models to Predict Graft Survival after Renal Transplantation: Comparison of Statistical and Machine Learning Models

The natural history of de novo donor-specific HLA antibodies after kidney transplantation

Role of time from transplantation to biopsy in histologic ABMR: A single center report

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