Complicated intra-abdominal infections in Europe: a comprehensive review of the CIAO study.

The CIAO Study ("Complicated Intra-Abdominal infection Observational" Study) is a multicenter investigation performed in 68 medical institutions throughout Europe over the course of a 6-month observational period (January-June 2012).Patients with either community-acquired or healthcare-associated complicated intra-abdominal infections (IAIs) were included in the study.2,152 patients with a mean age of 53.8 years (range: 4-98 years) were enrolled in the study. 46.3% of the patients were women and 53.7% were men. Intraperitoneal specimens were collected from 62.2% of the enrolled patients, and from these samples, a variety of microorganisms were collectively identified.The overall mortality rate was 7.5% (163/2.152).According to multivariate analysis of the compiled data, several criteria were found to be independent variables predictive of patient mortality, including patient age, the presence of an intestinal non-appendicular source of infection (colonic non-diverticular perforation, complicated diverticulitis, small bowel perforation), a delayed initial intervention (a delay exceeding 24 hours), sepsis and septic shock in the immediate post-operative period, and ICU admission.Given the sweeping geographical distribution of the participating medical centers, the CIAO Study gives an accurate description of the epidemiological, clinical, microbiological, and treatment profiles of complicated intra-abdominal infections (IAIs) throughout Europe.

Use of Two-Part Regression Calibration Model to Correct for Measurement Error in Episodically Consumed Foods in a Single-Replicate Study Design: EPIC Case Study.

In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model.