The impact of icodextrin on the outcomes of incident peritoneal dialysis patients.

OBJECTIVE: The aim of the study is to investigate the effects of icodextrin on the risks of death, technique failure and the first episode of peritonitis in peritoneal dialysis (PD) patients. METHODS: From medical records of a medical center in Taiwan, a total of 725 newly diagnosed end-stage kidney disease patients receiving PD for at least 90 days from January 1, 2007 to December 31, 2018 were identified. These patients were grouped as 190 icodextrin users and 535 non-users. Users were defined as utilization of icodextrin for ≥ 50% of their PD duration. The use of icodextrin was considered a time-varying exposure in the Cox proportional hazard model. The risks of death, technique failure and the first episode of peritonitis were compared between two cohorts by the end of 2018. RESULTS: Compared to the non-users, the icodextrin users had significant lower risks of mortality (6.5 vs.7.2 per 100 person-years; adjusted HR = 0.62, 95% CI = 0.42-0.91) and technique failure (12.7 vs. 15.2 per 100 person-years; adjusted HR = 0.61, 95% CI = 0.47-0.81), and the first peritonitis episode (5.0 vs. 17.0 per 100 person-years; adjusted HR = 0.22, 95% CI = 0.14-0.35). The risk of peritonitis reduced further in icodextrin users with diabetes and with cardiovascular disease. CONCLUSION: Icodextrin was associated with lower risks of mortality, technique failure, and the first episode of peritonitis.

Tree-structured supervised learning and the genetics of hypertension.

This paper is about an algorithm, FlexTree, for general supervised learning. It extends the binary tree-structured approach (Classification and Regression Trees, CART) although it differs greatly in its selection and combination of predictors. It is particularly applicable to assessing interactions: gene by gene and gene by environment as they bear on complex disease. One model for predisposition to complex disease involves many genes. Of them, most are pure noise; each of the values that is not the prevalent genotype for the minority of genes that contribute to the signal carries a "score." Scores add. Individuals with scores above an unknown threshold are predisposed to the disease. For the additive score problem and simulated data, FlexTree has cross-validated risk better than many cutting-edge technologies to which it was compared when small fractions of candidate genes carry the signal. For the model where only a precise list of aberrant genotypes is predisposing, there is not a systematic pattern of absolute superiority; however, overall, FlexTree seems better than the other technologies. We tried the algorithm on data from 563 Chinese women, 206 hypotensive, 357 hypertensive, with information on ethnicity, menopausal status, insulin-resistant status, and 21 loci. FlexTree and Logic Regression appear better than the others in terms of Bayes risk. However, the differences are not significant in the usual statistical sense.