The aim of the present study was to gain national data on the clinical and microbiological characteristics of community-acquired infections in the Faroe Islands and to compare these data with data from other geographical areas. A prospective, observational study involving all patients > = 16 years admitted at the Department of Medicine at the National Hospital, Torshavn, Faroe Islands from October 2013 until April 2015. Of 5279 admissions, 1054 cases were with community-acquired infection and were included in the study. Out of these 1054 cases, 471 did not meet the criteria for SIRS (Systemic Inflammatory Response Syndrome), while the remaining 583 cases had sepsis. Mean age was 68 years. At least one comorbidity was found in 80% of all cases. Documented infections were present in 75%, and a plausible pathogen was identified in 29% of all cases. The most common gram-positive pathogen was Staphylococcus aureus, and the most frequent gram-negative pathogen was Escherichia coli. The most common focus of infection was lower respiratory tract, followed by urinary tract, and skin-soft tissue/bone-joint. Bacteremia was found in 10% of the cases. In community-acquired infections in hospitalized patients in the Faroe Islands the lower respiratory tract and the urinary tract were the most frequent foci of infection. Gram-negative pathogens and Escherichia coli were the most frequent pathogens in infection without Systemic Inflammatory Response Syndrome, in sepsis and in bacteremia. Our data on clinical characteristics and microbiological etiology provide new information which may be used to develop local guidelines for the managing of patients admitted with community-acquired infections.

The aim of this study was to collect data and analyze mortality among patients hospitalized with community-acquired infections in the Faroe Islands. A prospective observational study was conducted in the Medical Department of the National Hospital of the Faroe Islands from October 2013 to April 2015. Cumulative all-cause, in-hospital, short-term, intermediate-term and long-term mortality rates were calculated. Kaplan–Meier survival curves comparing infection-free patients with infected patients of all severities and different age groups are presented. A log-rank test was used to compare groups. Mortality hazard ratios were calculated for subgroups using Cox regression multivariable models. There were 1309 patients without infection and 755 patients with infection. There were 51% female and 49% male patients. Mean age was 62.73 ± 19.71. Cumulative all-cause mortality and in-hospital mortality were highest in more severe forms of infection. This pattern remained the same for short-term mortality in the model adjusted for sex and age, while there were no significant differences among the various infection groups in regard to intermediate- or long-term survival after adjustment. Overall and short-term mortality rates were highest among those with severe manifestations of infection and those with infection compared to infection-free patients.

Background: Patients with malignant diseases receiving chronic opioid therapy are particularly susceptible to drug–drug interactions because of extensive polypharmacy, multimodal anticancer treatment, supportive care, and comorbidities. This study aimed to determine the prevalence of potentially clinically relevant drug–drug interactions and to identify factors associated with their occurrence. Methods: This exploratory observational cross-sectional pilot study included 49 adult oncology patients receiving chronic opioid therapy. Complete medication regimens were screened using the Medscape Drug Interaction Checker and Lexicomp. Lexicomp category D and X interactions were analysed descriptively. Because category D interactions were nearly universal and category X interactions were rare, the presence of at least one Medscape “Serious—Use Alternative” interaction was used pragmatically as the binary outcome for regression modelling. Potential predictors were assessed using univariable binary logistic regression and a parsimonious adjusted model with covariates selected using a clinically informed approach. Because of quasi-complete separation, Firth’s penalized-likelihood logistic regression was used as the primary adjusted analysis. Results: Lexicomp category D interactions were identified in 48 of 49 patients (98.0%), whereas category X interactions were present in 2 patients (4.1%). Medscape serious interactions were detected in 33 patients (67.3%). In the Firth-adjusted model, female sex was associated with lower odds of a Medscape serious interaction (adjusted OR 0.12, 95% CI 0.02–0.52), while cardiovascular disease was associated with higher odds (adjusted OR 5.16, 95% CI 1.27–26.31). Stage IV disease showed a positive but statistically non-significant association, and total medication count was not independently associated with the outcome in sensitivity analysis. Because of the small pilot sample and the resulting wide confidence intervals, these findings should be interpreted as exploratory and hypothesis-generating. Conclusions: Oncology patients receiving chronic opioid therapy had a high burden of potentially clinically relevant drug–drug interactions, predominantly Lexicomp category D interactions, warranting consideration of therapy modification and individualized monitoring rather than absolute avoidance. Regular medication review, use of complementary interaction databases, and individualized clinical assessment may improve pharmacotherapy safety in this vulnerable population.