Aims To explore drug exposure, frequency of adverse drug reactions (ADRs), types of ADRs, predisposing risk factors and ADR-related excess hospital stay in medical inpatients. Methods Structured data regarding patient characteristics, 'events' (symptoms, laboratory results), diagnoses (ICD10) and drug therapy were collected using a computer-supported data entry system and an interface for data retrieval from electronic patient records. ADR data were collected by 'event monitoring' to minimize possible bias by the drug monitor. The causality of each event was assessed in relation to disease(s) and drug therapy. Results The analysis included 4331 (100%) hospitalizations. The median observation period was 8 days. The median number of different drugs administered per patient and day was 6 and varied between 4 (Q 1 ) and 9 (Q 3 ) different drugs in 50% of all hospital days. In 41% of all hospitalizations at least one disease-unrelated event could be possibly attributed to drug therapy. Clinically relevant ADRs occurred in 11% of all hospitalizations. In 3.3% of all hospitalizations ADRs were the cause of hospital admission. The incidence of possibly ADR-related deaths was 1.4‰. Factors predisposing for clinically relevant ADRs were female gender and polypharmacy. ADR-related excess hospital stay accounted for 8.6% of hospital days. Conclusions These data demonstrate the feasibility of the developed 'event monitoring' system for quantitative analysis of ADRs in medical inpatients. With increasing numbers of recorded patients the pharmacoepidemiological database provides a valuable tool to study specific questions regarding drug efficacy and safety in hospitalized patients.

Device-related infections are difficult to treat with antibiotics alone. Standard susceptibility tests do not correlate with treatment success. Therefore, the utility of a pharmacokinetic in vitro model has been evaluated in comparison with the tissue-cage infection model in guinea pigs. The bactericidal activity of 28 treatment regimens has been studied by using three different test strains. In vitro efficacy was defined as reduction in the number of suspended or adherent bacteria, and in vivo efficacy was defined as reduction in the number of bacteria in tissue-cage fluid. Test results between the two models (in vivo and in vitro) correlated well, with correlation coefficients of 0.85 for in vivo efficacy versus in vitro efficacy against suspended bacteria and 0.72 for in vivo efficacy versus in vitro efficacy against adherent bacteria (P < 0.05) for Staphylococcus aureus, 0.96 and 0.82 (P < 0.05) for Staphylococcus epidermidis, and 0.89 and 0.97 for Escherichia coli, respectively. In contrast, standard susceptibility tests, ratios of MICs to trough or peak levels, ratios of the area under the curve to the MIC, or time above the MIC were not predictive for therapeutic outcome in either the in vitro or in vivo model. In both models, the bactericidal activity levels with combination regimens were significantly higher than those with single-drug regimens (P < 0.001). Furthermore, rifampin combinations with either vancomycin, teicoplanin, fleroxacin, or ciprofloxacin were significantly more bactericidal against adherent bacteria than netilmicin combinations with vancomycin or daptomycin (P < 0.01). Thus, in vivo verification of the pharmacokinetic in vitro model correlated well with the animal model. The in vitro model offers an alternative to the animal model in experiments that screen and assess antibiotic regimens against devicerelated infections.

No abstract

No abstract