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Most human cytomegalovirus (HCMV) genes are highly conserved in sequence among strains, but some exhibit a substantial degree of variation. Two of these genes are UL146, which encodes a CXC chemokine, and UL139, which is predicted to encode a membrane glycoprotein. The sequences of these genes were determined from a collection of 184 HCMV samples obtained from Africa, Australia, Asia, Europe, and North America. UL146 is hypervariable throughout, whereas variation in UL139 is concentrated in a sequence encoding a potentially highly glycosylated region. The UL146 sequences fell into 14 genotypes, as did all previously reported sequences. The UL139 sequences grouped into 8 genotypes, and all previously reported sequences fell into a subset of these. There were minor differences among continents in genotypic frequencies for UL146 and UL139, but no clear geographical separation, and identical nucleotide sequences were represented among communities distant from each other. The frequent detection of multiple genotypes indicated that mixed infections are common. For both genes, the degree of divergence was sufficient to preclude reliable sequence alignments between genotypes in the most variable regions, and the mode of evolution involved in generating the genotypes could not be discerned. Within genotypes, constraint appears to have been the predominant mode, and positive selection was detected marginally at best. No evidence was found for linkage disequilibrium. The emerging scenario is that the HCMV genotypes developed in early human populations (or even earlier), becoming established via founder or bottleneck effects, and have spread, recombined and mixed worldwide in more recent times.

Background: Accurate prediction of patient survival from the time of starting renal replacement therapy (RRT) is desirable, but previously published predictive models have low accuracy. We have attempted to overcome limitations of previous studies by conducting an ambidirectional inception cohort study in patients on RRT from centres throughout Europe. A conventional multivariate regression (MVR) model, a self-learning rule-based model (RBM) and a simple co-morbidity score [the Charlson score modified for renal disease (MCS)] were compared., Methods: In 1996, all 3640 dialysis centres registered with the ERA-EDTA were invited to identify all patients on RRT for end-stage renal failure (ESRF) who died during the 28 days of February 1997 (training cohort) and all patients who started RRT in the same period (validation cohort). Fifty-four clinical and laboratory variables from the time of starting RRT were collected in both cohorts using a two-page questionnaire. The data from the training cohort were given to statisticians at the Amsterdam Academic Medical Centre to create the MVR model and to engineers in Strathclyde University to create the RBM. They were then given the baseline data from patients in the validation cohort to predict how long each patient would survive. Follow-up questionnaires were sent to the centre of each patient in the validation cohort to determine actual survival., Results: A total of 2310 patients from 793 centres in 37 countries in the ERA-EDTA area were used to construct and validate the models. For predicting 1-year survival, the RBM had the highest positive predictive value (PPV) (84.2%), the MVR model had the highest negative predictive value (NPV) (47%) and the RBM had the highest likelihood ratio (1.59). For predicting 5-year survival, the MCS had the highest PPV (79.4%), the RBM had the highest NPV (74.3%) and the MCS had the highest likelihood ratio (7.0). The proportion of explained variance in survival for MCS, MVR and RBM was 14.6, 12.9 and 3.95%, respectively., Conclusion: Using the ambidirectional inception cohort design of this ERA-EDTA Registry survey, we have been able to create and validate two novel instruments to predict survival in patients starting RRT and compare them with a simple scoring model. The models tended to predict 5-year survival with more accuracy than 1-year survival. Examples of potential applications include informing clinical decision making about the likely benefit of starting RRT and listing for transplantation, adjusting for baseline risk in comparative studies and identifying specific risk groups to participate in clinical trials.