Surgical volume and conversion rate in laparoscopic hysterectomy:Does volume matter? A multicenter retrospective cohort study

Background A multicenter, retrospective, cohort study was conducted in the Netherlands. The aim was to evaluate whether surgical volume of laparoscopic hysterectomies (LHs) performed by proven skilled gynecologists had an impact on the conversion rate from laparoscopy to laparotomy. Methods In 14 hospitals, all LHs performed by 19 proven skilled gynecologists between 2007 and 2010 were included in the analysis. Surgical volume, conversion rate and type of conversion (reactive or strategic) were retrospectively assessed. To estimate the impact of surgical volume on the conversion rate, logistic regressions were performed. These regressions were adjusted for patient's age, Body Mass Index (BMI), ASA classification, previous abdominal surgery and the indication (malignant versus benign) for the LH. Results During the study period, 19 proven skilled gynecologists performed a total of 1051 LHs. Forty percent of the gynecologists performed over 20 LHs per year (median 17.3, range 5.4-49.5). Conversion to laparotomy occurred in 5.0% of all LHs (53 of 1051); 38 (3.6%) were strategic and 15 (1.4%) were reactive conversions. Performing over 20 LHs per year was significantly associated with a lower overall conversion rate (ORadjusted 0.43, 95% CI 0.24-0.77), a lower strategic conversion rate (ORadjusted 0.32, 95% CI 0.16-0.65), but not with a lower reactive conversion rate (ORadjusted 0.96, 95% CI 0.33-2.79). Conclusion A higher annual surgical volume of LHs by proven skilled gynecologists is inversely related to the conversion rate to laparotomy, and results in a lower strategic conversion rate

Genomic Analysis of QTLs and Genes Altering Natural Variation in Stochastic Noise

Quantitative genetic analysis has long been used to study how natural variation of genotype can influence an organism's phenotype. While most studies have focused on genetic determinants of phenotypic average, it is rapidly becoming understood that stochastic noise is genetically determined. However, it is not known how many traits display genetic control of stochastic noise nor how broadly these stochastic loci are distributed within the genome. Understanding these questions is critical to our understanding of quantitative traits and how they relate to the underlying causal loci, especially since stochastic noise may be directly influenced by underlying changes in the wiring of regulatory networks. We identified QTLs controlling natural variation in stochastic noise of glucosinolates, plant defense metabolites, as well as QTLs for stochastic noise of related transcripts. These loci included stochastic noise QTLs unique for either transcript or metabolite variation. Validation of these loci showed that genetic polymorphism within the regulatory network alters stochastic noise independent of effects on corresponding average levels. We examined this phenomenon more globally, using transcriptomic datasets, and found that the Arabidopsis transcriptome exhibits significant, heritable differences in stochastic noise. Further analysis allowed us to identify QTLs that control genomic stochastic noise. Some genomic QTL were in common with those altering average transcript abundance, while others were unique to stochastic noise. Using a single isogenic population, we confirmed that natural variation at ELF3 alters stochastic noise in the circadian clock and metabolism. Since polymorphisms controlling stochastic noise in genomic phenotypes exist within wild germplasm for naturally selected phenotypes, this suggests that analysis of Arabidopsis evolution should account for genetic control of stochastic variance and average phenotypes. It remains to be determined if natural genetic variation controlling stochasticity is equally distributed across the genomes of other multi-cellular eukaryotes