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ViResiST
- 1. Project ViResiST José-María López-Lozano Dominique L. Monnet Hospital Vega Baja Orihuela-Alicante (Spain) Statens Serum Institut Copenhagen (Denmark)
- 3. Participating Hospitals No. beds
- 4. SCA EXPORT EXCEL ACCESS RESULTS TIME SERIES ANALISYS EXCEL MACRO Automatic process Semi-automatic process ANTIBIOTIC PROFILE SERIES ISOLATE SERIES RESISTANCE SERIES ANTIBIOTIC USE SERIES ACCESS
- 5. Expected resistance percentage for each microorganism-antibiotic combination Expected % of imipenem-resistant P.aeruginosa for current month
- 6. Expected microorganism in concrete specimen at a concrete service
- 7. Specimen Microorg. Antibiotic Setting Save graphics Saving results (Excel format) Evolution of monthly percentage of resistance Number of resistant isolates Total number of isolates
- 8. Evolution of the hospital antimicrobial use Monthly no. DDD per 1,000 patient-days
- 9. Antibiotic use in the community Monthly no. DDD/1,000 inhab.-days
- 10. Resistance series comparison among several hospitals or community centers
- 11. Comparing antimicrobial use among different hospitals
- 12. Combined evolution of antimicrobial use in several primary health care regions
- 13. Comparing resistance and antibiotic use series Monthly hospital erythromycin use Monthly hospital clarithromycin use Monthly % erythromycin-resistant coag.-negative staph.
Editor's Notes
- ¿Qué es el programa ViResiST? La palabra ViResiST es un acrónimo que hace referencia al tipo de metodología estadística que se utiliza en nuestra red de vigilancia de la resistencia, el uso de antibióticos y la relación existente entre ellos. ¿Qué objetivos persigue el proyecto? Comprender mejor y analizar la relación entre uso de antibióticos y resistencia, dado que: - Los datos de resistencia y uso de antibióticos están autocorrelacionados - Se tiene en cuenta el intervalo entre uso y resistencia - Se puede medir la magnitud del efecto de uno sobre la otra - Se pueden incorporar varios antibióticos en el modelo
- The project is based on the collaboration between several centers that send its data to the Coordinating Center where some Statistical analysis are made using SCA software. Results are returned to Participating Centers
- The first usefulness of the computer application is a look up table with the results of the ARIMA models fitted on each series of resistance. This look up table gather the expected resistance for the current quarter as percentages. This information can be used by the clinician at the time of the empiric therapy, when he suspect that the patient has an infection caused by a certain microorganism and he must decided among various antibiotics. Once the microorganism is known, the look up table also give the possibility of choosing the antibiotic with the least risk for resistance selection.
- This application computes the expected probability to each microorganism in each specimen isolated in a concrete service. The interest of this information is on the possibility to guide the empirical therapy in our local setting. In our example, 57% of the blood specimen in surgical services could be SCN (Coagulase negative staplilococcus), 10%, S.aureus, 6% E.coli, and so on. If we know the probability for each microorganism we can choose the antibiotic presenting less resistance for this bacteria in OUR hospital, improving our success probabilities. We expect to include the risk for selection of resistance for each antibiotic as a output of the Transfer Function analysis
- The program is an interactive application that allows to select different variables of interest. Selection is done by means of several menus. The program allows, also, to save our data and graphics. In the example we compare ciproflaxin resistance in E.coli isolated in adults people with the same microorganisms isolated in children. Because quinolones are rarely used in children, the observed resistance can be explained by the transmission from adults (at home) or from contaminated food.
- Whe can see the evolution in the use of individual antibiotics or therapeutic groups, for the overall hospital or for diferent services. Example in the use of cephalosporin in hospital A. Whe can see the increase of use from 1997. The use is greater in winter. The left table shows dates of use that was used to make the graphic like DDD / 1000 patients-day
- As in the hospital case, the program allow also to explore the antimicrobial community use, in this case as DDD/1000 inhabitant-days. The example show the strong seasonality pattern of amoxicillin-clavulanic acid in a Spanish Health Area
- In the same screen we can compare resistance percentage evolution observed at several number of primary health care areas. The figure at the bottom shows comparative evolution of penicillin resistant or intermediate Streptococcus pneumoniae hospital and community strains in four Health Areas. It can be observed the great existing difference among C and E hospitals (Health Areas) on the one hand, and A and R hospitals on the other hand. You can see also a certain seasonal pattern in C Hospital resistance performance with a resistance increase during Autumn months. These differences in resistance level and in seasonal variation lead us to statistically compare it with antibiotic use in Primary Health Care.
- In the example we can see the fluorquinolones use in several hospitals
- The same comparison as in hospitals can be made for community (different Health Areas)
- It is possible to match in the same graphic monthly percentage of resistance and monthly use of a certain antibiotic in a certain hospital or Primary Health Care area. This look allow us to appreciate the concomitant development of both parameters before proceeding to a statistical analysis.