1. TREAT – A Decision Support System for Antibiotic Treatment S. Andreassen 1, L.E Kristensen 2, L. Leibovici 3, U. Frank 4, J. H. Jensen 1, H. C. Schønheyder 5 1 Aalborg University, Denmark 2 Judex Datasystems A/S, Denmark 3 Rabin Med. Ctr., Petah-Tiqva, Israel 4 Freiburg Univ. Hosp., Germany 5 Aalborg Hospital, Denmark Supported by an EU 5 th Framework grant (TREAT, IST 1999-11459)

2. Operational project goals 1.Build TREAT - a model of infections and their therapy, based on Causal Probabilistic Nets and on Decision Theory 2.Implement TREAT as a system integrated into the hospital information infrastructure (TREAT-LAB and TREAT-WARD) 3.Test TREAT in 3 countries to show that it can improve diagnosis and treatment of severe infections by reducing the percentage (30-40%) of inappropriate antibiotic treatments to half, thereby reducing the infection related mortality reducing cost of therapy restricting the use of broad-spectrum antibiotics stemming the rise of antibiotic resistance 4.Achieve scientific and commercial dissemination

3. A model of infections A (very) simplified version of the TREAT CPN will be used: 1.to demonstrate the concepts of infection sepsis prognosis (sepsis*) treatment coverage and mortality 2.to demonstrate the value of morphology, Gram stain and motility

4. Two urinary tract pathogens

5. Sepsis = Yes, Treatment = No, Res. Factor = Present (Hosp. Acq.)

6. Likelihood E. Coli * 2

7. Treatment = Gentamicin

8. The value of morphology, Gram stain and motility

9. Gram negative rods isolated from blood culture

10. Urinary tract = Yes, Motility = Peritrichous

11. Databases for local calibrations To adapt the TREAT system to a given hospital, databases are needed for: Infection related mortality Cost of treatments Antibiotics Resistance and cross-resistance Pathogen prevalences (influenced by risk factors, ICD diagnoses)

12. Database for crude mortality Mortality per site of infection dependent on coverage of empirical and semi-empirical treatment

13. Database for costs of treatments considered by TREAT

14. Database for antibiotics

15. Resistance and cross-resistance (Nosocomial E. coli infection)

16. Balance for each antibiotic drug: Benefits: –reduced mortality, morbidity and hospital stay related to coverage, activity at the site of infection, and synergism. Detriments: –cost of drug, administration and monitoring. –side-effects. –ecological costs.

17. Non-interventional study in 813 Danish bacteraemic patients: N=813 Age>65 yrs.61% ICU11% Source: Urinary28% Source: Abdominal23% Source: Lungs13% 30-day fatality20%

18. Non-interventional study in 813 Danish bacteraemic patients: Physician - empirical Physician- semi DSS Coverage59%78%87% Mean cost, $187.5223.5201.0 Mean, side- effects, $ 100.0115.0117.5 Mean, resistance, $ 1626.51874.51511

19. Physician - empirical Physician-semiDSS Abd Abd: No. of regimens 17217 Abd Abd:% of pts. prescribed broad spectrum 2.1%3.1%0% UTI UTI: No. of regimens 18206 UTI UTI:% of pts. prescribed broad spectrum 2.3%3.2%1.6% Non-interventional study in 813 Danish bacteraemic patients:

20. Conclusions : TREAT – a computerised DSS – prescribed appropriate antibiotic treatment more often than the attending physician, while using less broad- spectrum antibiotics at a lesser cost. The use of a causal probabilistic net as the basic model allowed us to combine data from several sources with knowledge; and to calibrate the system to different sites, in different countries. A randomised, controlled trial of the system in 3 countries is due to start in 6 months.