Patient Survival After Kidney Transplantation: Important Role of Graft-sustaining Factors as Determined by Predictive Modeling Using Random Survival Forest Analysis

Developing clinical prognostic models to predict graft survival after renal transplantation: comparison of statistical and machine learning models

INTRODUCTION

Renal transplantation is a critical treatment for end-stage renal disease, but graft failure remains a significant concern. Accurate prediction of graft survival is crucial to identify high-risk patients. This study aimed to develop prognostic models for predicting renal graft survival and compare the performance of statistical and machine learning models.

METHODOLOGY

The study utilized data from 278 renal transplant recipients at the Ethiopian National Kidney Transplantation Center between September 2015 and February 2022. To address the class imbalance of the data, SMOTE resampling was applied. Various models were evaluated, including Standard and penalized Cox models, Random Survival Forest, and Stochastic Gradient Boosting. Prognostic predictors were selected based on statistical significance and variable importance.

RESULTS

The median graft survival time was 33 months, and the mean hazard of graft failure was 0.0755. The 3-month, 1-year, and 3-year graft survival rates were found to be 0.979, 0.953, and 0.911, respectively. The Stochastic Gradient Boosting (SGB) model demonstrated the best discrimination and calibration performance, with a C-index of 0.943 and a Brier score of 0.000351. The Ridge-based Cox model closely followed the SGB model's prediction performance with better interpretability. The key prognostic predictors of graft survival included an episode of acute and chronic rejections, post-transplant urological complications, post-transplant nonadherence, blood urea nitrogen level, post-transplant regular exercise, and marital status.

CONCLUSIONS

The Stochastic Gradient Boosting model demonstrated the highest predictive performance, while the Ridge-Cox model offered better interpretability with a comparable performance. Clinicians should consider the trade-off between prediction accuracy and interpretability when selecting a model. Incorporating these findings into the clinical practice can improve risk stratification and personalized management strategies for kidney transplant recipients.

Diabetes Mellitus as a Risk Factor for Complicated Urinary Tract Infections in Kidney Transplant Recipients

Background: Urinary tract infections (UTIs) are a common complication after kidney transplantation. The aim of this study was to evaluate the impact of pre-existing diabetes mellitus and post-transplant diabetes mellitus (PTDM) on the occurrence of pyelonephritis in kidney transplant recipients. Methods: We performed a retrospective analysis which included 299 adult patients transplanted with a kidney between 2018 and 2022. Patients were categorized into pre-transplantation diabetics, PTDM, and non-diabetics. Asymptomatic bacteriuria and lower urinary infections were not included. Results: During a median follow-up time of 31 [17-45] months, 100 UTIs were reported in the total cohort, with a mean time from transplantation to the first UTI episode of 10 ± 11 months. At 48 months, the cumulative incidence of UTIs was 34.9%, 56%, and 47.3% for patients without prior diabetes, pre-transplant diabetes, and PTDM, respectively. Pre-transplant diabetes was independently associated with 79% increased risk of UTIs (adjusted HR = 1.79, 95% CI = 1.14-2.81, p = 0.011). The risk associated with female gender increased to 85%. Patient survival was not significantly affected by the interaction between diabetes and UTI occurrence. Conclusions: Pre-transplant diabetes arises as a significant risk factor for UTIs after kidney transplantation.

Modeling BK Virus Infection in Renal Transplant Recipients

Kidney transplant recipients require a lifelong protocol of immunosuppressive therapy to prevent graft rejection. However, these same medications leave them susceptible to opportunistic infections. One pathogen of particular concern is human polyomavirus 1, also known as BK virus (BKPyV). This virus attacks kidney tubule epithelial cells and is a direct threat to the health of the graft. Current standard of care in BK virus-infected transplant recipients is reduction in immunosuppressant therapy, to allow the patient's immune system to control the virus. This requires a delicate balance; immune suppression must be strong enough to prevent rejection, yet weak enough to allow viral clearance. We seek to model viral and immune dynamics with the ultimate goal of applying optimal control methods to this problem. In this paper, we begin with a previously published model and make simplifying assumptions that reduce the number of parameters from 20 to 14. We calibrate our model using newly available patient data and a detailed sensitivity analysis. Numerical results for multiple patients are given to show that the newer model reflects observed dynamics well.

Consequences of low estimated glomerular filtration rate either before or early after kidney donation

In the general population, decreases in glomerular filtration rate (GFR) are associated with subsequent development of chronic kidney disease (CKD), cardiovascular disease (CVD), and death. It is unknown if low estimated GFR (eGFR) before or early after kidney donation was also associated with these risks. One thousand six hundred ninety-nine living donors who had both predonation and early (4-10 weeks) postdonation eGFR were included. We studied the relationships between eGFR, age at donation, and the time to sustained eGFR<45 (CKD stage 3b) and <30 mL/min/1.73m2 (CKD stage 4), hypertension, diabetes mellitus (DM), CVD, and death. Median follow-up was 12 (interquartile range, 6-21) years. Twenty-year event rates were 5.8% eGFR<45 mL/min/1.73m2; 1.2% eGFR<30 mL/min/1.73m2; 29.0% hypertension; 7.8% DM; 8.0% CVD; and 5.2% death. The median time to eGFR<45 mL/min/1.73m2 (N = 79) was 17 years, and eGFR<30 mL/min/1.73m2 (N = 22) was 25 years. Both low predonation and early postdonation eGFR were associated with eGFR<45 mL/min/1.73m2 (P < .0001) and eGFR<30 mL/min/1.73m2 (P < .006); however, the primary driver of risk for all ages was low postdonation (rather than predonation) eGFR. Predonation and postdonation eGFR were not associated with hypertension, DM, CVD, or death. Low predonation and early postdonation eGFR are risk factors for developing eGFR<45 mL/min/1.73m2 (CKD stage 3b) and <30 mL/min/1.73m2 (CKD stage 4), but not CVD, hypertension, DM, or death.

Malignancies After Lung Transplantation

Lung transplantation (LTx) is a well-known treatment for end-stage lung disease. This study aimed to report the incidence of cancer after LTx and long-term outcome among lung transplant recipients with a pretransplant diagnosis of cancer. Patients who underwent LTx between 1990-2016 were included in the study. Detection of cancer was obtained by cross-checking the study population with the Swedish Cancer Registry and the Cause-of-Death registry. A total of 614 patients were followed for a median of 5.1 years. In all, 159 malignancies were diagnosed. The excess risk of cancer or standardized incidence ratio (SIR) following LTx was 5.6-fold compared to the general Swedish population. The most common malignancies were non-melanoma skin cancer (NMSC) (SIR 76.5 (95%CI 61.7-94.8); non-Hodgkin lymphoma (SIR 23.5, 95%CI 14.8-37.2); and lung cancer (SIR 8.89, 95%CI 5.67-13.9). There was no significant difference in overall survival between those with and without a history of cancer before LTx (p = 0.56). In total, 159 malignancies were identified after LTx, which was a 5.6-fold higher relative to the general population. A history of previous cancer yields similar survival in selected recipients, compared to those without cancer prior to LTx.

Ensemble of machine learning techniques to predict survival in kidney transplant recipients

Kidney transplant recipients face a high cardiovascular risk, which is a leading cause of death in this patient group. This article proposes the application of clustering techniques and feature selection to predict the survival outcomes of kidney transplant recipients based on machine learning techniques and mainstream statistical methods. First, feature selection techniques (Boruta, Random Survival Forest and Elastic Net) are used to detect the most relevant variables. Subsequently, each set of variables obtained by each feature selection technique is used as input for the clustering algorithms used (Consensus Clustering, Self-Organizing Map and Agglomerative Clustering) to determine which combination of feature selection, clustering algorithm and number of clusters maximizes intercluster variability. Next, the mechanism called False Clustering Discovery Reduction is applied to obtain the minimum number of statistically differentiable populations after applying a control metric. This metric is based on a variance test to confirm that reducing the number of clusters does not generate significant losses in the heterogeneity obtained. This approach was applied to the Organ Procurement and Transplantation Network medical dataset (n = 11,332). The combination of Random Survival Forest and consensus clustering yielded the optimal result of 4 clusters starting from 8 initial ones. Finally, for each population, Kaplan-Meier survival curves are generated to predict the survival of new patients based on the predictions of the XGBoost classifier, with an overall multi-class AUC of 98.11%.

Predicting Kidney Transplantation Outcomes from Donor and Recipient Characteristics at Time Zero: Development of a Mobile Application for Nephrologists

(1) Background: We report on the development of a predictive tool that can estimate kidney transplant survival at time zero. (2) Methods: This was an observational, retrospective study including 5078 transplants. Death-censored graft and patient survivals were calculated. (3) Results: Graft loss was associated with donor age (hazard ratio [HR], 1.021, 95% confidence interval [CI] 1.018-1.024, p < 0.001), uncontrolled donation after circulatory death (DCD) (HR 1.576, 95% CI 1.241-2.047, p < 0.001) and controlled DCD (HR 1.567, 95% CI 1.372-1.812, p < 0.001), panel reactive antibody percentage (HR 1.009, 95% CI 1.007-1.011, p < 0.001), and previous transplants (HR 1.494, 95% CI 1.367-1.634, p < 0.001). Patient survival was associated with recipient age (> 60 years, HR 5.507, 95% CI 4.524-6.704, p < 0.001 vs. < 40 years), donor age (HR 1.019, 95% CI 1.016-1.023, p < 0.001), dialysis vintage (HR 1.0000263, 95% CI 1.000225-1.000301, p < 0.01), and male sex (HR 1.229, 95% CI 1.135-1.332, p < 0.001). The C-statistics for graft and patient survival were 0.666 (95% CI: 0.646, 0.686) and 0.726 (95% CI: 0.710-0.742), respectively. (4) Conclusions: We developed a mobile app to estimate survival at time zero, which can guide decisions for organ allocation.

Long-term follow-up of IgA nephropathy: clinicopathological features and predictors of outcomes

Background

The establishment of the Oxford classification and newly developed prediction models have improved the prognostic information for immunoglobulin A nephropathy (IgAN). Considering new treatment options, optimizing prognostic information and improving existing prediction models are favorable.

Methods

We used random forest survival analysis to select possible predictors of end-stage kidney disease among 37 candidate variables in a cohort of 232 patients with biopsy-proven IgAN retrieved from the Norwegian Kidney Biopsy Registry. The predictive value of variables with relative importance >5% was assessed using concordance statistics and the Akaike information criterion. Pearson's correlation coefficient was used to identify correlations between the selected variables.

Results

The median follow-up period was 13.7 years. An isolated analysis of histological variables identified six variables with relative importance >5%: T %, segmental glomerular sclerosis without characteristics associated with other subtypes (not otherwise specified, NOS), normal glomeruli, global sclerotic glomeruli, segmental adherence and perihilar glomerular sclerosis. When histopathological and clinical variables were combined, estimated glomerular filtration rate (eGFR), proteinuria and serum albumin were added to the list. T % showed a better prognostic value than tubular atrophy/interstitial fibrosis (T) lesions with C-indices at 0.74 and 0.67 and was highly correlated with eGFR. Analysis of the subtypes of segmental glomerulosclerosis (S) lesions revealed that NOS and perihilar glomerular sclerosis were associated with adverse outcomes.

Conclusions

Reporting T lesions as a continuous variable, normal glomeruli and subtypes of S lesions could provide clinicians with additional prognostic information and contribute to the improved performance of the Oxford classification and prognostic tools.

Early Versus Late Acute AMR in Kidney Transplant Recipients-A Comparison of Treatment Approaches and Outcomes From the ANZDATA Registry

BACKGROUND

Antibody-mediated rejection (AMR) is a major cause of kidney allograft failure and demonstrates different properties depending on whether it occurs early (<6 mo) or late (>6 mo) posttransplantation. We aimed to compare graft survival and treatment approaches for early and late AMR in Australia and New Zealand.

METHODS

Transplant characteristics were obtained for patients with an AMR episode reported to the Australia and New Zealand Dialysis and Transplant Registry from January 2003 to December 2019. The primary outcome of time to graft loss from AMR diagnosis, with death considered a competing risk, was compared between early and late AMR using flexible parametric survival models. Secondary outcomes included treatments used, response to treatment, and time from AMR diagnosis to death.

RESULTS

After adjustment for other explanatory factors, late AMR was associated with twice the risk of graft loss relative to early AMR. The risk was nonproportional over time, with early AMR having an increased early risk. Late AMR was also associated with an increased risk of death. Early AMR was treated more aggressively than late with more frequent use of plasma exchange and monoclonal/polyclonal antibodies. There was substantial variation in treatments used by transplant centers. Early AMR was reported to be more responsive to treatment than late.

CONCLUSIONS

Late AMR is associated with an increased risk of graft loss and death compared with early AMR. The marked heterogeneity in the treatment of AMR highlights the need for effective, new therapeutic options for these conditions.

Process mining and data mining applications in the domain of chronic diseases: A systematic review

The widespread use of information technology in healthcare leads to extensive data collection, which can be utilised to enhance patient care and manage chronic illnesses. Our objective is to summarise previous studies that have used data mining or process mining methods in the context of chronic diseases in order to identify research trends and future opportunities. The review covers articles that pertain to the application of data mining or process mining methods on chronic diseases that were published between 2000 and 2022. Articles were sourced from PubMed, Web of Science, EMBASE, and Google Scholar based on predetermined inclusion and exclusion criteria. A total of 71 articles met the inclusion criteria and were included in the review. Based on the literature review results, we detected a growing trend in the application of data mining methods in diabetes research. Additionally, a distinct increase in the use of process mining methods to model clinical pathways in cancer research was observed. Frequently, this takes the form of a collaborative integration of process mining, data mining, and traditional statistical methods. In light of this collaborative approach, the meticulous selection of statistical methods based on their underlying assumptions is essential when integrating these traditional methods with process mining and data mining methods. Another notable challenge is the lack of standardised guidelines for reporting process mining studies in the medical field. Furthermore, there is a pressing need to enhance the clinical interpretation of data mining and process mining results.

Deep Learning Approaches for Glioblastoma Prognosis in Resource-Limited Settings: A Study Using Basic Patient Demographic, Clinical, and Surgical Inputs

BACKGROUND

Glioblastoma (GBM) is the most common brain tumor in the United States, with an annual incidence rate of 3.21 per 100,000. It is the most aggressive type of diffuse glioma and has a median survival of months after treatment. This study aims to assess the accuracy of different novel deep learning models trained on a set of simple clinical, demographic, and surgical variables to assist in clinical practice, even in areas with constrained health care infrastructure.

METHODS

Our study included 37,095 patients with GBM from the SEER (Surveillance Epidemiology and End Results) database. All predictors were based on demographic, clinicopathologic, and treatment information of the cases. Our outcomes of interest were months of survival and vital status. Concordance index (C-index) and integrated Brier scores (IBS) were used to evaluate the performance of the models.

RESULTS

The patient characteristics and the statistical analyses were consistent with the epidemiologic literature. The models C-index and IBS ranged from 0.6743 to 0.6918 and from 0.0934 to 0.1034, respectively. Probabilistic matrix factorization (0.6918), multitask logistic regression (0.6916), and logistic hazard (0.6916) had the highest C-index scores. The models with the lowest IBS were the probabilistic matrix factorization (0.0934), multitask logistic regression (0.0935), and logistic hazard (0.0936). These models had an accuracy (1-IBS) of 90.66%; 90.65%, and 90.64%, respectively. The deep learning algorithms were deployed on an interactive Web-based tool for practical use available via https://glioblastoma-survanalysis.herokuapp.com/.

CONCLUSIONS

Novel deep learning algorithms can better predict GBM prognosis than do baseline methods and can lead to more personalized patient care regardless of extensive electronic health record availability.

Predicting Clinical Remission of Chronic Urticaria Using Random Survival Forests: Machine Learning Applied to Real-World Data

INTRODUCTION

The time required to reach clinical remission varies in patients with chronic urticaria (CU). The objective of this study is to develop a predictive model using a machine learning methodology to predict time to clinical remission for patients with CU.

METHODS

Adults with ≥ 2 ICD-9/10 relevant CU diagnosis codes/CU-related treatment > 6 weeks apart were identified in the Optum deidentified electronic health record dataset (January 2007 to June 2019). Clinical remission was defined as ≥ 12 months without CU diagnosis/CU-related treatment. A random survival forest was used to predict time from diagnosis to clinical remission for each patient based on clinical and demographic features available at diagnosis. Model performance was assessed using concordance, which indicates the degree of agreement between observed and predicted time to remission. To characterize clinically relevant groups, features were summarized among cohorts that were defined based on quartiles of predicted time to remission.

RESULTS

Among 112,443 patients, 73.5% reached clinical remission, with a median of 336 days from diagnosis. From 1876 initial features, 176 were retained in the final model, which predicted a median of 318 days to remission. The model showed good performance with a concordance of 0.62. Patients with predicted longer time to remission tended to be older with delayed CU diagnosis, and have more comorbidities, more laboratory tests, higher body mass index, and polypharmacy during the 12-month period before the first CU diagnosis.

CONCLUSIONS

Applying machine learning to real-world data enabled accurate prediction of time to clinical remission and identified multiple relevant demographic and clinical variables with predictive value. Ongoing work aims to further validate and integrate these findings into clinical applications for CU management.

Cytokines in Lung Transplantation

Lung transplantation has developed significantly in recent years, but post-transplant care and patients' survival still need to be improved. Moreover, organ shortage urges novel modalities to improve the quality of unsuitable lungs. Cytokines, the chemical mediators of the immune system, might be used for diagnostic and therapeutic purposes in lung transplantation. Cytokine monitoring pre- and post-transplant could be applied to the prevention and early diagnosis of injurious inflammatory events including primary graft dysfunction, acute cellular rejection, bronchiolitis obliterans syndrome, restrictive allograft syndrome, and infections. In addition, preoperative cytokine removal, specific inhibition of proinflammatory cytokines, and enhancement of anti-inflammatory cytokines gene expression could be considered therapeutic options to improve lung allograft survival. Therefore, it is essential to describe the cytokines alteration during inflammatory events to gain a better insight into their role in developing the abovementioned complications. Herein, cytokine fluctuations in lung tissue, bronchoalveolar fluid, peripheral blood, and exhaled breath condensate in different phases of lung transplantation have been reviewed; besides, cytokine gene polymorphisms with clinical significance have been summarized.

Risk factors for graft loss and death among kidney transplant recipients: A competing risk analysis

INTRODUCTION

Kidney transplantation is the best therapeutical option for CKD patients. Graft loss risk factors are usually estimated with the cox method. Competing risk analysis could be useful to determine the impact of different events affecting graft survival, the occurrence of an outcome of interest can be precluded by another. We aimed to determine the risk factors for graft loss in the presence of mortality as a competing event.

METHODS

A retrospective cohort of 1454 kidney transplant recipients who were transplanted between July 1, 2008, to May 31, 2019, in Colombiana de Trasplantes, were analyzed to determine risk factors of graft loss and mortality at 5 years post-transplantation. Kidney and patient survival probabilities were estimated by the competing risk analysis. The Fine and Gray method was used to fit a multivariable model for each outcome. Three variable selection methods were compared, and the bootstrapping technique was used for internal validation as split method for resample. The performance of the final model was assessed calculating the prediction error, brier score, c-index and calibration plot.

RESULTS

Graft loss occurred in 169 patients (11.6%) and death in 137 (9.4%). Cumulative incidence for graft loss and death was 15.8% and 13.8% respectively. In a multivariable analysis, we found that BKV nephropathy, serum creatinine and increased number of renal biopsies were significant risk factors for graft loss. On the other hand, recipient age, acute cellular rejection, CMV disease were risk factors for death, and recipients with living donor had better survival compared to deceased-donor transplant and coronary stent. The c-index were 0.6 and 0.72 for graft loss and death model respectively.

CONCLUSION

We developed two prediction models for graft loss and death 5 years post-transplantation by a unique transplant program in Colombia. Using a competing risk multivariable analysis, we were able to identify 3 significant risk factors for graft loss and 5 significant risk factors for death. This contributes to have a better understanding of risk factors for graft loss in a Latin-American population. The predictive performance of the models was mild.

Kidney injury after lung transplantation: Long-term mortality predicted by post-operative day-7 serum creatinine and few clinical factors

BACKGROUND

Acute kidney injury (AKI) after lung transplantation (LuTx) is associated with increased long-term mortality. In this prospective observational study, commonly used AKI-definitions were examined regarding prediction of long-term mortality and compared to simple use of the serum creatinine value at day 7 for patients who did not receive hemodialysis, and serum creatinine value immediately before initiation of hemodialysis (d7/preHD-sCr).

METHODS

185 patients with LuTx were prospectively enrolled from 2013-2014 at our center. Kidney injury was assessed within 7 days by: (1) the Kidney Disease Improving Global Outcomes criteria (KDIGO-AKI), (2) the Acute Disease Quality Initiative 16 Workgroup classification (ADQI-AKI) and (3) d7/preHD-sCr. Prediction of all-cause mortality was examined by Cox regression analysis, and clinical as well as laboratory factors for impaired kidney function post-LuTx were analyzed.

RESULTS

AKI according to KDIGO and ADQI-AKI occurred in 115 patients (62.2%) within 7 days after LuTx. Persistent ADQI-AKI, KDIGO-AKI stage 3 and higher d7/preHD-sCr were associated with higher mortality in the univariable analysis. In the multivariable analysis, d7/preHD-sCr in combination with body weight and intra- and postoperative platelet transfusions predicted mortality after LuTx with similar performance as models using KDIGO-AKI and ADQI-AKI (concordance index of 0.75 for d7/preHD-sCr vs., 0.74 and 0.73, respectively). Pre-transplant reduced renal function, diabetes, higher BMI, and intraoperative ECMO predicted higher d7/preHD-sCr (r2 = 0.354, p < 0.001).

CONCLUSION

Our results confirm the importance of AKI in lung transplant patients; however, a simple and pragmatic indicator of renal function, d7/preHD-sCr, predicts long-term mortality equally reliable as more complex AKI-definitions like KDIGO and ADQI.

Impact of diabetes mellitus developing after kidney transplantation on patient mortality and graft survival: a meta-analysis of adjusted data

BACKGROUND

Post-transplant diabetes mellitus (PTDM) occurs in 10-30% of kidney transplant recipients. However, its impact on mortality and graft survival is still ambiguous. Therefore, the current study aimed to analyze if PTDM increases mortality and graft failure by pooling multivariable-adjusted data from individual studies.

METHODS

PubMed, Embase, and CENTRAL, and Google Scholar were searched for studies comparing mortality and graft failure between PTDM and non-diabetic patients. Multivariable-adjusted hazard ratios (HR) were pooled in a random-effects model.

RESULTS

Fourteen retrospective studies comparing 9872 PTDM patients with 65,327 non-diabetics were included. On pooled analysis, we noted a statistically significant increase in the risk of all-cause mortality in patients with PTDM as compared to non-diabetics (HR: 1.67 95% CI 1.43, 1.94 I² = 57% p < 0.00001). The meta-analysis also indicated a statistically significant increase in the risk of graft failure in patients with PTDM as compared to non-diabetics (HR: 1.35 95% CI 1.15, 1.58 I² = 78% p = 0.0002). Results were stable on sensitivity analysis. There was no evidence of publication bias on funnel plots.

CONCLUSION

Kidney transplant patients developing PTDM have a 67% increased risk of all-cause mortality and a 35% increased risk of graft failure. Further studies are needed to determine the exact cause of increased mortality and the mechanism involved in graft failure.

Toward Advancing Long-Term Outcomes of Kidney Transplantation with Artificial Intelligence

After decades of pioneering advances and improvements, kidney transplantation is now the renal replacement therapy of choice for most patients with end-stage kidney disease (ESKD). Despite this success, the high risk of premature death and frequent occurrence of graft failure remain important clinical and research challenges. The current burst of studies and other innovative initiatives using artificial intelligence (AI) for a wide range of analytical and practical applications in biomedical areas seems to correlate with the same trend observed in publications in the kidney transplantation field, and points toward the potential of such novel approaches to address the aforementioned aim of improving long-term outcomes of kidney transplant recipients (KTR). However, at the same time, this trend underscores now more than ever the old methodological challenges and potential threats that the research and clinical community needs to be aware of and actively look after with regard to AI-driven evidence. The purpose of this narrative mini-review is to explore challenges for obtaining applicable and adequate kidney transplant data for analyses using AI techniques to develop prediction models, and to propose next steps in the field. We make a call to act toward establishing the strong collaborations needed to bring innovative synergies further augmented by AI, which have the potential to impact the long-term care of KTR. We encourage researchers and clinicians to submit their invaluable research, including original clinical and imaging studies, database studies from registries, meta-analyses, and AI research in the kidney transplantation field.

Metabolic Consequences of Solid Organ Transplantation

Metabolic complications affect over 50% of solid organ transplant recipients. These include posttransplant diabetes, nonalcoholic fatty liver disease, dyslipidemia, and obesity. Preexisting metabolic disease is further exacerbated with immunosuppression and posttransplant weight gain. Patients transition from a state of cachexia induced by end-organ disease to a pro-anabolic state after transplant due to weight gain, sedentary lifestyle, and suboptimal dietary habits in the setting of immunosuppression. Specific immunosuppressants have different metabolic effects, although all the foundation/maintenance immunosuppressants (calcineurin inhibitors, mTOR inhibitors) increase the risk of metabolic disease. In this comprehensive review, we summarize the emerging knowledge of the molecular pathogenesis of these different metabolic complications, and the potential genetic contribution (recipient +/- donor) to these conditions. These metabolic complications impact both graft and patient survival, particularly increasing the risk of cardiovascular and cancer-associated mortality. The current evidence for prevention and therapeutic management of posttransplant metabolic conditions is provided while highlighting gaps for future avenues in translational research.

Predictors of Survival After Liver Transplantation in Patients With the Highest Acuity (MELD ≥40)

OBJECTIVE

To identify factors that accurately predict 1-year survival for liver transplant recipients with a MELD score ≥40.

BACKGROUND

Although transplant is beneficial for patients with the highest acuity (MELD ≥40), mortality in this group is high. Predicting which patients are likely to survive for >1 year would be medically and economically helpful.

METHODS

The Scientific Registry of Transplant Recipients database was reviewed to identify adult liver transplant recipients from 2002 through 2016 with MELD score ≥40 at transplant. The relationships between 44 recipient and donor factors and 1-year patient survival were examined using random survival forests methods. Variable importance measures were used to identify the factors with the strongest influence on survival, and partial dependence plots were used to determine the dependence of survival on the target variable while adjusting for all other variables.

RESULTS

We identified 5309 liver transplants that met our criteria. The overall 1-year survival of high-acuity patients improved from 69% in 2001 to 87% in 2016. The strongest predictors of death within 1 year of transplant were patient on mechanical ventilator before transplantation, prior liver transplant, older recipient age, older donor age, donation after cardiac death, and longer cold ischemia.

CONCLUSIONS

Liver transplant outcomes continue to improve even for patients with high medical acuity. Applying ensemble learning methods to recipient and donor factors available before transplant can predict survival probabilities for future transplant cases. This information can be used to facilitate donor/recipient matching and to improve informed consent.

Prognostic value for long-term graft survival of estimated glomerular filtration rate and proteinuria quantified at 3 months after kidney transplantation

Background

The estimated glomerular filtration rate (eGFR) measured at 1 year is the usual benchmark applied in kidney transplantation (KT). However, acting on earlier eGFR values could help in managing KT during the first post-operative year. We aimed to assess the prognostic value for long-term graft survival of the early (3 months) quantification of eGFR and proteinuria following KT.

Methods

The 3-, 6- and 12-month eGFR using the Modified Diet in Renal Disease equation (eGFR_MDRD) was determined and proteinuria was measured in 754 patients who underwent their first KT between 2000 and 2010 (with a mean follow-up of 8.3 years) in our centre. Adjusted associations with graft survival were estimated using a multivariable Cox model. The predictive accuracy was estimated using the C-index and net reclassification index. These same analyses were measured in a multicentre validation cohort of 1936 patients.

Results

Both 3-month eGFR_MDRD and proteinuria were independent predictors of return to dialysis (all P < 0.05) and there was a strong correlation between eGFR at 3 and 12 months (Spearman’s ρ = 0.76). The predictive accuracy of the 3-month eGFR was within a similar range and did not differ significantly from the 12-month eGFR in either the derivation cohort [C-index 62.6 (range 57.2–68.1) versus 66.0 (range 60.1–71.9), P = 0.41] or the validation cohort [C-index 69.3 (range 66.4–72.1) versus 71.7 (range 68.7–74.6), P = 0.25].

Conclusion

The 3-month eGFR was a valuable predictor of the long-term return to dialysis whose predictive accuracy was not significantly less than that of the 12-month eGFR in multicentre cohorts totalling >2500 patients. Three-month outcomes may be useful in randomized controlled trials targeting early therapeutic interventions.