A stochastic model of susceptibility to antibiotic therapy--The effects of cross-resistance and treatment history.
OBJECTIVE
Selection of antibiotic therapy is a complicated process, depending on, among others, the effect of cross-resistance between antibiotics. We propose a model, which incorporates information about treatment history in the form of information on the success or failure of the current treatment and which combines this with data on cross-resistance to predict the susceptibility to future antibiotic treatments, thus providing a systematic basis for revision of antibiotic treatment.


METHODS
The stochastic model was built as a causal probabilistic network (CPN). Data used in the model were based on a bacteriology database including data on patient and episode unique pathogens cultured from a microbiological sample.


RESULTS
In this paper, we develop a CPN that can exploit knowledge about cross-resistance between two consecutive treatments, explore the properties of this CPN and consider how the CPN can be integrated into a complete decision support system for selection of antibiotic therapy.


CONCLUSIONS
The model presented may be useful both as a theoretical tool describing cross-resistance between antibiotics and as a part of complete decision support system for selection of antibiotic therapy.
Zalounina
Alina
A
Center for Model-based Medical Decision Support, Niels Jernes Vej 14, Aalborg University, 9220 Aalborg East, Denmark. az@hst.aau.dk
Paul
Mical
M
Leibovici
Leonard
L
Andreassen
Steen
S
D000900:Anti-Bacterial Agents / Q000627:therapeutic use*; D019992:Databases as Topic; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D024901:Drug Resistance, Multiple, Bacterial; D004360:Drug Therapy, Computer-Assisted; D006801:Humans; D016347:Medical Records Systems, Computerized; D013269:Stochastic Processes; D016896:Treatment Outcome
D000900:Anti-Bacterial Agents