IntroductionThe management of bloodstream infections especially sepsis is a difficult task. An optimal antibiotic therapy (ABX) is paramount for success. Procalcitonin (PCT) is a well investigated biomarker that allows close monitoring of the infection and management of ABX. It has proven to be a cost-efficient diagnostic tool. In Diagnoses Related Groups (DRG) based reimbursement systems, hospitals get only a fixed amount of money for certain treatments. Thus it's very important to obtain an optimal balance of clinical treatment and resource consumption namely the length of stay in hospital and especially in the Intensive Care Unit (ICU). We investigated which economic effects an optimized PCT-based algorithm for antibiotic management could have.Materials and methodsWe collected inpatient episode data from 16 hospitals. These data contain administrative and clinical information such as length of stay, days in the ICU or diagnoses and procedures. From various RCTs and reviews there are different algorithms for the use of PCT to manage ABX published. Moreover RCTs and meta-analyses have proven possible savings in days of ABX (ABD) and length of stay in ICU (ICUD). As the meta-analyses use studies on different patient populations (pneumonia, sepsis, other bacterial infections), we undertook a short meta-analyses of 6 relevant studies investigating in sepsis or ventilator associated pneumonia (VAP). From this analyses we obtained savings in ABD and ICUD by calculating the weighted mean differences. Then we designed a new PCT-based algorithm using results from two very recent reviews. The algorithm contains evidence from several studies. From the patient data we calculated cost estimates using German National standard costing information for the German G-DRG system.We developed a simulation model where the possible savings and the extra costs for (in average) 8 PCT tests due to our algorithm were brought into equation.ResultsWe calculated ABD savings of -4 days and ICUD reductions of -1.8 days. our algorithm contains recommendations for ABX onset (PCT ≥ 0.5 ng/ml), validation whether ABX is appropriate or not (Delta from day 2 to day 3 ≥ 30% indicates inappropriate ABX) and recommendations for discontinuing ABX (PCT ≤ 0.25 ng/ml).We received 278, 264 episode datasets where we identified by computer-based selection 3, 263 cases with sepsis. After excluding cases with length of stay (LOS) too short to achieve the intended savings, we ended with 1, 312 cases with ICUD and 268 cases without ICUD. Average length of stay of ICU-patients was 27.7 ± 25.7 days and for Non-ICU patients 17.5 ± 14.6 days respectively. ICU patients had an average of 8.8 ± 8.7 ICUD.After applying the simulation model on this population we calculated possible savings of € -1, 163, 000 for ICU-patients and € -36, 512 for Non-ICU patients.DiscussionOur findings concerning the savings from the reduction of ABD are consistent with other publications. Savings ICUD had never been economically evaluated so far. our algorithm is able to possibly set a new ...

The cost of treatments especially in conditions where multiresistant bacteria are involved are a major issue in times where in most developed countries in the world payment systems based on diagnoses-related-groups (DRG) are in place. There is great evidence that especially the length of stay in hospital (LOS), the time in the intensive care unit (ICU-days) and the hours of mechanical ventilation (HMV) are major cost drivers.While established methods of pharmacoeconomical analyses focus on the efficiency of drugs from healthcare system perspective, these data are often not sufficient for improving treatment strategies in a given hospital context.We developed a system that allows the analysis of patients with severe infections on the basis of routine data that is also used for reimbursement. These data contain a lot of information concerning the clinical conditions. By using the ICD-coding we developed an algorithm which allows the detection of patients with infections and gives information on the potential financial outcome of these patients. By using the analysis it is possible to identify subsets of infections and the patient records that had a potentially negative DRG-result, i.e. the costs are higher than the reimbursement. When identified the patient records undergo a peer review, where the clinical situation and the antibiotic therapy are reviewed by medical experts. In case simulations it is possible to find out if a different therapeutic approach, e.g. by different choices in initial (empirical) antibiotic treatment would have caused other outcomes.Data driven analyses together with peer reviews of patient records are a useful tool to examine antibiotic treatment strategies and to establish changes that again can be reviewed on a regular basis. Doing this a continous improvement process can be established in hospitals which can lead to a better balance of clinical and economical outcomes in patients with severe infections. Moreover these analyses are helpful in assessing the literature on economical benefits of new therapies.