| Literature DB >> 24731220 |
Johannes P Borde1, Klaus Kaier, Michaela Steib-Bauert, Werner Vach, Annette Geibel-Zehender, Hansjörg Busch, Hartmut Bertz, Martin Hug, Katja de With, Winfried V Kern.
Abstract
BACKGROUND: Restricted use of third-generation cephalosporins and fluoroquinolones has been linked to a reduced incidence of hospital-acquired infections with multidrug-resistant bacteria. We implemented an intensified antibiotic stewardship (ABS) programme in the medical service of a university hospital center aiming at a reduction by at least 30% in the use of these two drug classes.Entities:
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Year: 2014 PMID: 24731220 PMCID: PMC3999502 DOI: 10.1186/1471-2334-14-201
Source DB: PubMed Journal: BMC Infect Dis ISSN: 1471-2334 Impact factor: 3.090
Comparison of mean monthly drug use density values expressed as DDD per 100 patient days or RDD per 100 patient days in the medical service pre- and post-intervention
| | ||||||||
|---|---|---|---|---|---|---|---|---|
| Cephalosporins | 20.1 | 14.0 | −30.2% | <0.001 | 16.3 | 10.3 | −36.8% | <0.001 |
| 3° Cephalosporins | 14.4 | 7.4 | −49.0% | <0.001 | 13.1 | 6.4 | −51.3% | <0.001 |
| 1°/2° Cephalosporins | 5.6 | 6.6 | +17.7% | <0.05 | 3.2 | 3.9 | +23.6% | <0.01 |
| Penicillins | 23.1 | 29.6 | +28.3% | <0.001 | 15.4 | 18.2 | +17.9% | <0.001 |
| Piperacillin ± BLI | 9.3 | 9.9 | +5.8% | ns | 10.9 | 11.5 | +5.8% | ns |
| Aminopenicillins + BLI | 6.6 | 11.3 | +69.9% | <0.001 | 2.4 | 4.1 | +70.4% | <0.001 |
| Narrow-spectrum penicillins | 7.1 | 8.5 | +19.1% | ns | 2.1 | 2.5 | +20.3% | ns |
| Carbapenems | 9.5 | 8.3 | −12.7% | <0.01 | 6.9 | 5.8 | −15.7% | <0.001 |
| Fluoroquinolones | 19.6 | 12.0 | −38.5% | <0.001 | 17.7 | 10.1 | −43.2% | <0.001 |
| Aminoglycosides | 0.9 | 0.7 | −18.9% | ns | 0.7 | 0.6 | −19.1% | ns |
| Glycopeptides | 4.3 | 3.9 | −8.6% | ns | 4.3 | 3.9 | −8.6% | ns |
| Tetracyclines | 1.4 | 0.9 | −36.9% | <0.01 | 1.0 | 0.7 | −35.9% | <0.01 |
| Macrolides/clindamycin | 18.1 | 13.7 | −24.4% | <0.001 | 10.3 | 7.9 | −22.8% | 0.001 |
| TOTAL | 110.5 | 94.8 | −14.2% | <0.001 | 86.1 | 68.9 | −19.9% | <0.001 |
Beta-lactamase inhibitor (BLI).
Figure 1Trends in the overall monthly antibiotic use density (expressed as RDD per 100 patient days) in the medical service pre- and post-intervention.
Monthly drug use density (expressed as RDD per 100 patient days) trends in the medical service pre- (baseline) and post-intervention as estimated in a single-level interrupted time-series model (P values in parentheses)
| Baseline | −0.02 | −0.10 | −0.03 | −0.13 |
| trend β1 | (ns) | (<0.001) | (ns) | (0.038) |
| Post-intervention | −0.52 (<0.001) | −0.40 (<0.001) | +0.34 (<0.001) | −1.10 (<0.001) |
| trend change β2 | | | | |
| Intercept β0 | 16.61 (<0.001) | 19.96 (<0.001) | 16.11 (<0.001) | 88.73 (<0.001) |
| N | 63 | 63 | 63 | 63 |
Figure 2Trends in the monthly antibiotic use density (expressed as RDD per 100 patient days) for cephalosporins (blue). Fluoroquinolones (red) and penicillins (green) in the medical service pre- and post-intervention.
Monthly drug use density (expressed as RDD per 100 patient days) trends in hematology-oncology (Hem-Onc) and the medical ICU pre- (baseline) and post-intervention as estimated in a single-level interrupted time-series model; only trend changes that were statistically significant are shown
| Baseline trend β1 | Hem-Onc | −0.05 (ns) | −0.18 (<0.001) | −0.00 (ns) | −0.17 (ns) |
| | Medical ICU | −0.045 (ns) | −0.02 (ns) | −0.49 (<0.001) | −0.31 (ns) |
| Post-intervention | Hem-Onc | −0.41 (<0.001) | −1.16 (<0.001) | +0.17 (ns) | −2.17 (<0.001) |
| trend change β2 | Medical ICU | −1.06 (<0.05) | +0.04 (ns) | +1.83 (<0.001) | −1.11 (ns) |
| Intercept β0 | Hem-Onc | 14.06 (<0.001) | 38.29 (<0.001) | 18.88 (<0.001) | 124.1 (<0.001) |
| | Medical ICU | 26.87 (<0.001) | 13.02 (<0.001) | 40.93 (<0.001) | 148.3 (<0.001) |
| N | 63 | 63 | 63 | 63 |
Monthly drug use density (expressed as RDD per 100 patient days) trends ßin the medical service post-intervention compared to the effects of a hypothetical intervention at six control services according to regression analysis (P values in parentheses)
| | ||||
|---|---|---|---|---|
| Medical service | −0.517 | −0.40 | 0.34 | −1.102 |
| (intervention) | (<0.001) | (<0.001) | (<0.001) | (<0.001) |
| Department A | −0.171 | 0.01 | 0.261 | 0.247 |
| (control) | (0.024) | (ns) | (<0.001) | (0.041) |
| Department B | −0.40 | 0.451 | −0.0579 | 0.0246 |
| (control) | (<0.001) | (<0.001) | (0.1) | (ns) |
| Department C | 0.057 | −0.315 | 0.0501 | −0.332 |
| (control) | (ns) | (<0.001) | (ns) | (0.006) |
| Department D | −0.134 | −0.0431 | 0.0418 | −0.272 |
| (control) | (0.077) | (ns) | (ns) | (0.024) |
| Department E | −0.278 | −0.021 | −0.0305 | −0.534 |
| (control) | (<0.001) | (ns) | (ns) | (<0.001) |
| Department F | −0.426 | 0.002 | 0.206 | −0.454 |
| (control) | (<0.001) | (ns) | (<0.001) | (<0.001) |
| Medical service | −0.292 | −0.414 | 0.261 | −0.882 |
| versus controls* | (<0.001) | (<0.001) | (<0.001) | (<0.001) |
*Stata’s lincom command was applied to simply subtract the average β2 in the control (non-intervention) settings (rows 2–7) from the β2 in the medical service in which the intervention took place.