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Background: Cystic echinococcosis (CE) is a globally occurring zoonosis, whereas alveolar echinococcosis (AE) is endemic only in certain parts of the Northern Hemisphere. The socioeconomic impact of human echinococcosis has been shown to be considerable in highly endemic regions. However, detailed data on direct healthcare-related costs associated with CE and AE are scarce for high income countries. The aim of this study was to evaluate direct costs of human disease caused by CE and AE in Austria., Methods: Clinical data from a registry maintained at a national reference center for echinococcosis at the Medical University of Vienna were obtained for the years 2012-2014. These data were used in conjunction with epidemiological data from Austria's national disease reporting system and diagnostic reference laboratory for echinococcosis to assess nationwide costs attributable to CE and AE., Results: In Austria, total modelled direct costs were 486,598€ (95%CI 341,825€ - 631,372€) per year for CE, and 683,824€ (95%CI 469,161€ - 898,486€) for AE. Median costs per patient with AE from diagnosis until the end of a 10-year follow-up period were 30,832€ (25th- 75th percentile: 23,197€ - 31,220€) and 62,777€ (25th- 75th percentile: 60,806€ - 67,867€) for inoperable and operable patients, respectively. Median costs per patients with CE from diagnosis until end of follow-up after 10 years were 16,253€ (25th- 75th percentile: 8,555€ - 24,832€) and 1,786€ (25th- 75th percentile: 736€ - 2,146€) for patients with active and inactive cyst stages, respectively. The first year after inclusion was the most cost-intense year in the observed period, with hospitalizations and albendazole therapy the main contributors to direct costs., Conclusions: This study provides detailed information on direct healthcare-related costs associated with CE and AE in Austria, which may reflect trends for other high-income countries. Surgery and albendazole therapy, due to surprisingly high drug prices, were identified as important cost-drivers. These data will be important for cost-effectiveness analyses of possible prevention programs., Competing Interests: The authors have declared that no competing interests exist.

Background: Cancer patients are at high risk of developing venous thromboembolism (VTE). Red cell distribution width (RDW) has been reported to be associated with arterial and venous thrombosis and mortality in several diseases. Here, we analyzed the association between RDW and other red blood cell (RBC) parameters with risk of VTE and mortality in patients with cancer., Methods: RBC parameters were measured in 1840 patients with cancers of the brain, breast, lung, stomach, colon, pancreas, prostate, kidney; lymphoma, multiple myeloma and other tumor sites, that were included in the Vienna Cancer and Thrombosis Study (CATS), which is an ongoing prospective, observational cohort study of patients with newly diagnosed or progressive cancer after remission. Primary study outcome is occurrence of symptomatic VTE and secondary outcome is death during a maximum follow-up of 2 years., Results: During a median follow-up of 706 days, 131 (7.1%) patients developed VTE and 702 (38.2%) died. High RDW (>16%) was not associated with a higher risk of VTE in the total study cohort; in competing risk analysis accounting for death as competing variable the univariable subhazard ratio (SHR) was 1.34 (95% confidence interval [CI]: 0.80-2.23, p = 0.269). There was also no significant association between other RBC parameters and risk of VTE. High RDW was associated with an increased risk of mortality in the total study population (hazard ratio [HR, 95% CI]: 1.72 [1.39-2.12], p<0.001), and this association prevailed after adjustment for age, sex, hemoglobin, leukocyte and platelet count (HR [95% CI]: 1.34 [1.06-1.70], p = 0.016)., Conclusions: RDW and other RBC parameters were not independently associated with risk of VTE in patients with cancer and might therefore not be of added value for estimating risk of VTE in patients with cancer. We could confirm that high RDW is an independent predictor of poor overall survival in cancer.

Background: Bacteraemia is a frequent and severe condition with a high mortality rate. Despite profound knowledge about the pre-test probability of bacteraemia, blood culture analysis often results in low rates of pathogen detection and therefore increasing diagnostic costs. To improve the cost-effectiveness of blood culture sampling, we computed a risk prediction model based on highly standardizable variables, with the ultimate goal to identify via an automated decision support tool patients with very low risk for bacteraemia., Methods: In this retrospective hospital-wide cohort study evaluating 15,985 patients with suspected bacteraemia, 51 variables were assessed for their diagnostic potency. A derivation cohort (n = 14.699) was used for feature and model selection as well as for cut-off specification. Models were established using the A2DE classifier, a supervised Bayesian classifier. Two internally validated models were further evaluated by a validation cohort (n = 1,286)., Results: The proportion of neutrophile leukocytes in differential blood count was the best individual variable to predict bacteraemia (ROC-AUC: 0.694). Applying the A2DE classifier, two models, model 1 (20 variables) and model 2 (10 variables) were established with an area under the receiver operating characteristic curve (ROC-AUC) of 0.767 and 0.759, respectively. In the validation cohort, ROC-AUCs of 0.800 and 0.786 were achieved. Using predefined cut-off points, 16% and 12% of patients were allocated to the low risk group with a negative predictive value of more than 98.8%., Conclusion: Applying the proposed models, more than ten percent of patients with suspected blood stream infection were identified having minimal risk for bacteraemia. Based on these data the application of this model as an automated decision support tool for physicians is conceivable leading to a potential increase in the cost-effectiveness of blood culture sampling. External prospective validation of the model's generalizability is needed for further appreciation of the usefulness of this tool.