An empirical antibiotic therapy (EAT) is an important concept and a standard procedure for the treatment of many different types of infections in clinical medicine. Its need is based on the lack of knowledge of the causative agent at an early stage of the disease - usually when the patient presents to health professionals. EAT is based on epidemiological data on most frequently isolated pathogens and their antibiotic resistance pattern for certain diseases (e.g., S. pneumoniae, H. influenzae, and M.catarrhalis for otitis media [middle ear infection]). Identification of the causative agent due to diagnostic procedures (e.g., culture of middle ear fluid) usually leads to an adaptation of the EAT to a pathogen directed therapy, which generally results in narrowing of the therapeutic spectrum. This therapy shift usually aims at reducing selective pressure, which is an important measure to avoid the accumulation of resistant subsets of populations and helps to avoid the spread of resistant pathogens. The adequacy of empirical as well as pathogen-directed use of antibiotics is an important determinant of patient outcomes and may play a role in the emergence of bacterial antibiotic resistance.
Although EAT is a vital and widely applied concept in clinical medicine, it is not yet routinely guided by state-of-the-art technology using the latest evidence and epidemiological data. For clinicians in Switzerland the main source for guiding EAT comes from local guidelines (booklets with in-hospital recommendations), which are often outdated due to an update latency of several years, or time-consuming disseminated internet resources with frequently restricted access.
Therefore, we initiated the project INFECT, an INterface For Empirical antimicrobial ChemoTherapy, aimed at bringing up-to-date bacterial resistance data from bench to bedside. So far, this first prototype has been established as a proof-of-concept, which is planned to be equipped with advanced functionalities in the time coming, including the ability to independently import data from anresis.ch, the Swiss Centre for Antibiotic resistance, into its database. INFECT aims at providing a fast, mobile and easy to use, fully automatic self-updating and platform independent tool to assist empirical treatment choices tailored to the resistance epidemiology in the patients’ geographical region.
In time, INFECT is planned to include automated statistical analysis capabilities through implementation of the statistical software R on our Ubuntu Linux web server.
This will allow the use of statistical algorithms, like automated outbreak detection, resistance pattern recognition, and might even allow resistance forecasting using mathematical models on antibiotic consumption data in the future, while supporting clinicians with an easy-to-use tool for best empirical antimicrobial therapy.
Colour legend and how to use INFECT
Although INFECT is intended to be used to guide and support an optimal empirical antimicrobial therapy, its use does not substitute a thorough investigation of patients’ signs and symptoms, or sound diagnostic and therapeutic reasoning. Although all data is routinely checked for correctness, there is always a possibility of error. Therefore, INFECT specifically DISCLAIMS LIABILITY FOR INCIDENTAL OR CONSEQUENTIAL DAMAGES and assumes no responsibility or liability for any loss or damage suffered by any person as a result of the use or misuse of any of the information or content on this website.
The INFECT team consists of eight highly motivated members (alphabetical order):
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