INTRODUCTION Prevalence study is simple, cheap and fast method that provides information about hospital-acquired infections (HAI). OBJECTIVE To assess the HAI prevalence at different surgical departments in the Clinical Center of Banjaluka. METHOD A point prevalence study design was used. All patients (N = 174) hospitalized on July, 1st, 1999 were included in the study. The study was performed by following the CDC guidelines. All patients with clinically manifested infections on the day of study in all surgical departments were recorded. infections of more than one site in the same patient were considered separate infections. RESULTS The overall prevalence of patients with HAI was 16.1% (28/174; 95%CI = 10.4-21.6) and the overall HAI prevalence was 18.4% (32/174). Surgical-site infections were the most prevalent (6.3%) followed by skin/soft tissue infections (4.6%) and urinary tract infections (4.0%). More than two thirds (87.5%) of HAI were microbiologically documented. The most commonly isolated microorganisms were Pseudomonas aeruginosa (29.7%), Enterobacter spp. (24.3%) and Staphylococcus aureus (16.2%). CONCLUSION This point prevalence study clearly showed the magnitude of HAI problem at different surgical departments in the Clinical Center of Banjaluka, the most prevalent anatomic localizations of HAI, and the most common causes.

Experimental evidence exists that especially the heterogeneity in contrast enhancement as evaluated by dynamic contrast enhanced MRI (DCE-MRI) is a predictive feature for treatment outcome in a variety of tumor types. In this study it is investigated whether texture descriptors derived from DCE-MRI based heuristic feature maps are suitable to quantify the heterogeneity in contrast uptake. An automated analysis method is proposed that for each voxel first partitions the signal intensity curve into different temporal regions indicating different stages of enhancement. Within these regions, heuristic features describing the contrast dynamics are estimated. The corresponding features maps are used as the basis for texture analysis, based on cooccurrence matrices, to assess tumor contrast uptake heterogeneity. The method has been applied in pre- and post treatment DCE-MRI data in ten patients with soft tissue sarcomas who underwent isolated limb perfusion. The correspondence between texture measures and the heterogeneity in contrast uptake as visually assessed by a radiologist has been evaluated, and the ability of the texture measures to discriminate between two treatment outcome classes has been assessed. The preliminary results suggest that some of the proposed texture measures are suitable to quantify the heterogeneity in contrast uptake in tumor tissue