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We examined whether we could develop models based on data provided to the United States Renal Data System (USRDS) to accurately predict survival. Records were obtained from patients beginning dialysis in 1990 through 2007. We developed linear and neural network models and optimized the fit of these models to the actual time to death. Next, we examined whether we could accurately predict survival in a dataset containing censored and uncensored patients. The results with these models were contrasted with those obtained with a Cox proportional hazards model fit to the entire dataset. The average C statistic over a 6-month to 10-year time range achieved with these models was approximately 0.7891 (linear model), 0.7804 (transformed dataset linear model), 0.7769 (neural network model), 0.7774 (transformed dataset neural network model), 0.8019 (Cox model), and 0.7970 (transformed dataset Cox model). When we used the Cox proportional hazards model, superior C statistic results were found at time points between 2 and 10 years but at earlier time points, the Cox model was slightly inferior. These results suggest that data provided to the USRDS can allow for predictive models which have a high degree of accuracy years following the initiation of dialysis.

In this tribute to the 2015 recipient of the International Association for Conflict Management Lifetime Achievement Award Winner, we celebrate the work of J. Keith Murnighan. Each of us highlights a unique contribution of his research to a different area in the field of organizational behavior. The four areas we discuss are behavioral economics, experimental design, diversity, and ethics. We conclude our discussion of the four research areas by highlighting common themes suggested by this work. The article closes by giving Keith Murnighan “the final say” to reflect on our article and to impart his words of wisdom to doctoral students and scholars at different stages of their career.

Leprosy is an infectious disease caused by the obligate intracellular pathogen Mycobacterium leprae and remains endemic in many parts of the world. Despite several major studies on susceptibility to leprosy, few genomic loci have been replicated independently. We have conducted an association analysis of more than 1,500 individuals from different case-control and family studies, and observed consistent associations between genetic variants in both TLR1 and the HLA-DRB1/DQA1 regions with susceptibility to leprosy (TLR1 I602S, case-control P = 5.7 x 10(-8), OR = 0.31, 95% CI = 0.20-0.48, and HLA-DQA1 rs1071630, case-control P = 4.9 x 10(-14), OR = 0.43, 95% CI = 0.35-0.54). The effect sizes of these associations suggest that TLR1 and HLA-DRB1/DQA1 are major susceptibility genes in susceptibility to leprosy. Further population differentiation analysis shows that the TLR1 locus is extremely differentiated. The protective dysfunctional 602S allele is rare in Africa but expands to become the dominant allele among individuals of European descent. This supports the hypothesis that this locus may be under selection from mycobacteria or other pathogens that are recognized by TLR1 and its co-receptors. These observations provide insight into the long standing host-pathogen relationship between human and mycobacteria and highlight the key role of the TLR pathway in infectious diseases.

The expression (Estimated - Predicted)/Predicted creatinine generation {(E-P)IP} has been proposed as an index of compliance in continuous peritoneal dialysis (CPD). We attempted to define an (E-P)IP value that can be used as a cut-off for non-compliance and to characterize the relation of (E-P)IP to serum albumin. In 324 clearance studies, (E-P)/P had a normal distribution with a mean of +0.094 and an SD of 0.357. In these studies, there was a weak correlation between (E-P)/P and serum albumin (r=0.12, P2weekly (P

The association between azotemic index dialyzer clearances and hematocrit was investigated in a 63-year-old dialysis-dependent man with acquired renal cysts. During 43 months of hemodialysis, hematocrit rose from 27.3 to 65.0 vol%, as a consequence of high serum erythropoietin levels. Concomitantly, dry weight also increased from 116.8 to 140.8 kg. Both hematocrit and dry weight correlated with: (a) urea reduction ratio, (b) creatinine reduction ratio (CRR), and (c) KT/V urea. All correlations were negative. Stepwise regression showed that only hematocrit was an independent correlate of the CRR (CRR = 0.662 - 007∗ Hct, R2= 0.770); whereas, both hematocrit (Hct) and weight (W) were independent correlates of KT/V urea (KT/V = 2.070 - 0.005∗Hct - 0.009∗W, R2= 0.721). In addition to creatinine clearance, urea clearance through the dialyzer is reduced by a rising hematocrit. The effect of hematocrit on urea clearance is relatively small. Therefore, it requires large changes in hematocrit in order to be detected