High-grade proteinuria as a cardiovascular risk factor in renal transplant recipients

Time-Varying Proteinuria and the Risk of Cardiovascular Disease and Graft Failure in Kidney Transplant Recipients

Introduction: Proteinuria is recognized as an independent risk factor for cardiovascular disease in kidney transplant recipients, but previous studies have not considered the impact of changes in urine protein over time. Research Question and Design: We used time-dependent, multivariable Cox proportional hazards models in this observational cohort study of adult kidney transplant recipients to evaluate whether proteinuria measured by dipstick on random spot urine samples starting from 1-month post-transplant was associated with the risk of major adverse cardiac events and graft loss. Results: A total of 144 major adverse cardiac events, defined as acute myocardial infarction, cerebrovascular accident, revascularization, or all-cause mortality, were observed in 1106 patients over 5728.7 person-years. Any level of proteinuria greater or equal to trace resulted in a two-fold increase in the risk of major adverse cardiac events (hazard ratio 2.00 [95% confidence interval 1.41, 2.84]). This relationship was not found to be dose-dependent (hazard ratios of 2.98, 1.76, 1.63, and 1.54 for trace, 1+, 2+, and 3+ urine protein, respectively). There was an increased risk of graft failure with greater urine protein concentration (hazard ratios 2.22, 2.85, 6.41, and 19.71 for trace, 1+, 2+, and 3+, respectively). Conclusion: Urine protein is associated with major adverse cardiac events and graft loss in kidney transplant recipients. The role of interventions to reduce proteinuria on decreasing the risk of adverse cardiovascular and graft outcomes in kidney transplant recipients requires further study.

Aromatase inhibitors and risk of cardiovascular events in breast cancer patients: a systematic review and meta-analysis

BACKGROUND

Cardiovascular events (CVEs) was considered as one of the primary cause to reduce the quality of life in breast cancer patients with aromatase inhibitors (AIs) treatment, which has not been sufficiently addressed. The aim of this study was to assess the correlation between risk of CVEs and AIs in patients with breast cancer.

METHODS

Included studies were obtained from the databases of Embase, Pubmed, Cochrane Library, Clinical Trials.gov, and reference lists. The main outcome measures were overall incidence, odds ratios (ORs), and 95% confidence intervals (CIs). Furthermore, the association and the risk differences among different tumor types, AIs,ages,or treatment regimens were conducted. Fixed-effect or random-effect models were applied in the statistical analyses according to the heterogeneity. Our analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.

RESULTS

Seventeen studies, which included 44,411 subjects, were included in our analyses. The overall incidence of CVEs in AIs group was 13.02% (95% CI: 8.15-20.17%) and almost all of the high-grade CVEs occurred in patients treated with AIs. The pooled ORs of CVEs was 0.9940 (95% CI: 0.8545-1.1562). Under sub-group analysis, the incidence of CVEs related to exemestane was higher than that of controls (OR = 1.1564, 95% CI: 1.0656-1.2549), but no statistical differences in risk of CVEs were found in other sub-group analysis. No evidence of publication bias was found for incidence of CVEs in our meta-analysis by a funnel plot.

CONCLUSIONS

These results suggest that patients with breast cancer treated with AIs do not have a significant risk of developing CVEs in comparison with the controls, and exemestane might not be considered as the alternative AI to the breast cancer patients from the perspective of CVEs. Further studies are recommended to investigate this association and the risk differences among different tumor types, AIs or treatment regimens.

Prediction of Kidney Graft Rejection Using Artificial Neural Network

OBJECTIVES

Kidney transplantation is the best renal replacement therapy for patients with end-stage renal disease. Several studies have attempted to identify predisposing factors of graft rejection; however, the results have been inconsistent. We aimed to identify prognostic factors associated with kidney transplant rejection using the artificial neural network (ANN) approach and to compare the results with those obtained by logistic regression (LR).

METHODS

The study used information regarding 378 patients who had undergone kidney transplantation from a retrospective study conducted in Hamadan, Western Iran, from 1994 to 2011. ANN was used to identify potential important risk factors for chronic nonreversible graft rejection.

RESULTS

Recipients' age, creatinine level, cold ischemic time, and hemoglobin level at discharge were identified as the most important prognostic factors by ANN. The ANN model showed higher total accuracy (0.75 vs. 0.55 for LR), and the area under the ROC curve (0.88 vs. 0.75 for LR) was better than that obtained with LR.

CONCLUSIONS

The results of this study indicate that the ANN model outperformed LR in the prediction of kidney transplantation failure. Therefore, this approach is a promising classifier for predicting graft failure to improve patients' survival and quality of life, and it should be further investigated for the prediction of other clinical outcomes.