Sex differences in the survival benefit of kidney transplantation: a retrospective cohort study using target trial emulation.

BACKGROUND: Kidney transplantation is the preferred treatment for eligible patients with kidney failure who need renal replacement therapy. However, it remains unclear whether the anticipated survival benefit from kidney transplantation is different for women and men. METHODS: We included all dialysis patients recorded in the Austrian Dialysis and Transplant Registry who were waitlisted for their first kidney transplant between 2000 and 2018. In order to estimate the causal effect of kidney transplantation on 10-year restricted mean survival time, we mimicked a series of controlled clinical trials and applied inverse probability of treatment and censoring weighted sequential Cox models. RESULTS: This study included 4408 patients (33% female) with a mean age of 52 years. Glomerulonephritis was the most common primary renal disease both in women (27%) and men (28%). Kidney transplantation led to a gain of 2.22 years (95% CI 1.88 to 2.49) compared with dialysis over a 10-year follow-up. The effect was smaller in women (1.95 years, 95% CI 1.38 to 2.41) than in men (2.35 years, 95% CI 1.92 to 2.70) due to a better survival on dialysis. Across ages the survival benefit of transplantation over a follow-up of 10 years was smaller in younger women and men and increased with age, showing a peak for both women and men aged about 60 years. CONCLUSIONS: There were few differences in survival benefit by transplantation between females and males. Females had better survival than males on the waitlist receiving dialysis and similar survival to males after transplantation.

Evaluating variable selection methods for multivariable regression models: A simulation study protocol.

Researchers often perform data-driven variable selection when modeling the associations between an outcome and multiple independent variables in regression analysis. Variable selection may improve the interpretability, parsimony and/or predictive accuracy of a model. Yet variable selection can also have negative consequences, such as false exclusion of important variables or inclusion of noise variables, biased estimation of regression coefficients, underestimated standard errors and invalid confidence intervals, as well as model instability. While the potential advantages and disadvantages of variable selection have been discussed in the literature for decades, few large-scale simulation studies have neutrally compared data-driven variable selection methods with respect to their consequences for the resulting models. We present the protocol for a simulation study that will evaluate different variable selection methods: forward selection, stepwise forward selection, backward elimination, augmented backward elimination, univariable selection, univariable selection followed by backward elimination, and penalized likelihood approaches (Lasso, relaxed Lasso, adaptive Lasso). These methods will be compared with respect to false inclusion and/or exclusion of variables, consequences on bias and variance of the estimated regression coefficients, the validity of the confidence intervals for the coefficients, the accuracy of the estimated variable importance ranking, and the predictive performance of the selected models. We consider both linear and logistic regression in a low-dimensional setting (20 independent variables with 10 true predictors and 10 noise variables). The simulation will be based on real-world data from the National Health and Nutrition Examination Survey (NHANES). Publishing this study protocol ahead of performing the simulation increases transparency and allows integrating the perspective of other experts into the study design.

Amyloid-like p53 as prognostic biomarker in serous ovarian cancer-a study of the OVCAD consortium.

TP53 is the most commonly mutated gene in cancer and has been shown to form amyloid-like aggregates, similar to key proteins in neurodegenerative diseases. Nonetheless, the clinical implications of p53 aggregation remain unclear. Here, we investigated the presence and clinical relevance of p53 aggregates in serous ovarian cancer (OC). Using the p53-Seprion-ELISA, p53 aggregates were detected in 46 out of 81 patients, with a detection rate of 84.3% in patients with missense mutations. High p53 aggregation was associated with prolonged progression-free survival. We found associations of overall survival with p53 aggregates, but they did not reach statistical significance. Interestingly, p53 aggregation was significantly associated with elevated levels of p53 autoantibodies and increased apoptosis, suggesting that high levels of p53 aggregates may trigger an immune response and/or exert a cytotoxic effect. To conclude, for the first time, we demonstrated that p53 aggregates are an independent prognostic marker in serous OC. P53-targeted therapies based on the amount of these aggregates may improve the patient's prognosis.