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.

Complicated intra-abdominal infections in Europe: preliminary data from the first three months of the CIAO Study

The CIAO Study is a multicenter observational study currently underway in 66 European medical institutions over the course of a six-month study period (January-June 2012).This preliminary report overviews the findings of the first half of the study, which includes all data from the first three months of the six-month study period.Patients with either community-acquired or healthcare-associated complicated intra-abdominal infections (IAIs) were included in the study.912 patients with a mean age of 54.4 years (range 4-98) were enrolled in the study during the first three-month period. 47.7% of the patients were women and 52.3% were men. Among these patients, 83.3% were affected by community-acquired IAIs while the remaining 16.7% presented with healthcare-associated infections. Intraperitoneal specimens were collected from 64.2% of the enrolled patients, and from these samples, 825 microorganisms were collectively identified.The overall mortality rate was 6.4% (58/912). According to univariate statistical analysis of the data, critical clinical condition of the patient upon hospital admission (defined by severe sepsis and septic shock) as well as healthcare-associated infections, non-appendicular origin, generalized peritonitis, and serious comorbidities such as malignancy and severe cardiovascular disease were all significant risk factors for patient mortality.White Blood Cell counts (WBCs) greater than 12,000 or less than 4,000 and core body temperatures exceeding 38°C or less than 36°C by the third post-operative day were statistically significant indicators of patient mortality.

MGOF: Stata module to perform goodness-of-fit tests for multinomial data

mgof computes goodness-of-fit tests for the distribution of a discrete (categorical, multinomial) variable. The default is to perform classical large sample chi-squared approximation tests based on Pearson's X2 statistic and the log likelihood ratio (G2) statistic or a statistic from the Cressie-Read family. Alternatively, mgof computes exact tests using Monte Carlo methods or exhaustive enumeration. A Kolmogorov-Smirnov test for discrete data is also provided. The moremata package, also available from SSC, is required.

Multinomial goodness-of-fit: large sample tests with survey design correction and exact tests for small samples

A new Stata command called -mgof- is introduced. The command is used to compute distributional tests for discrete (categorical, multinomial) variables. Apart from classic large sample $\chi^2$-approximation tests based on Pearson's $X^2$, the likelihood ratio, or any other statistic from the power-divergence family (Cressie and Read 1984), large sample tests for complex survey designs and exact tests for small samples are supported. The complex survey correction is based on the approach by Rao and Scott (1981) and parallels the survey design correction used for independence tests in -svy:tabulate-. The exact tests are computed using Monte Carlo methods or exhaustive enumeration. An exact Kolmogorov-Smirnov test for discrete data is also provided.