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Prescribing metrics, cost, and surrogate markers are often used to describe the value of antimicrobial stewardship (AMS) programs. However, process measures are only indirectly related to clinical outcomes and may not represent the total effect of an intervention. We determined the global impact of a multifaceted AMS initiative for hospitalized adults with common infections.
Single center, quasi-experimental study.
Hospitalized adults with urinary, skin, and respiratory tract infections discharged from family medicine and internal medicine wards before (January 2017–June 2017) and after (January 2018–June 2018) an AMS initiative on a family medicine ward were included. A series of AMS-focused initiatives comprised the development and dissemination of: handheld prescribing tools, AMS positive feedback cases, and academic modules. We compared the effect on an ordinal end point consisting of clinical resolution, adverse drug events, and antimicrobial optimization between the preintervention and postintervention periods.
In total, 256 subjects were included before and after an AMS intervention. Excessive durations of therapy were reduced from 40.3% to 22% (P < .001). Patients without an optimized antimicrobial course were more likely to experience clinical failure (OR, 2.35; 95% CI, 1.17–4.72). The likelihood of a better global outcome was greater in the family medicine intervention arm (62.0%, 95% CI, 59.6–67.1) than in the preintervention family medicine arm.
Collaborative, targeted feedback with prescribing metrics, AMS cases, and education improved global outcomes for hospitalized adults on a family medicine ward.
Bloodstream infections due to methicillin-resistant Staphylococcus aureus (MRSA) have been associated with significant risk of in-hospital mortality. The acute physiology and chronic health evaluation (APACHE) II score was developed and validated for use among intensive care unit (ICU) patients, but its utility among non-ICU patients is unknown. The aim of this study was to determine the ability of APACHE II to predict death at multiple time points among ICU and non-ICU patients with MRSA bacteremia.
Retrospective cohort study.
Secondary analysis of data from 200 patients with MRSA bacteremia at 2 hospitals.
Logistic regression models were constructed to predict overall in-hospital mortality and mortality at 48 hours, 7 days, 14 days, and 30 days using APACHE II scores separately in ICU and non-ICU patients. The performance of APACHE II scores was compared with age adjustment alone among all patients. Discriminatory ability was assessed using the c-statistic and was compared at each time point using X2 tests. Model calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test.
APACHE II was a significant predictor of death at all time points in both ICU and non-ICU patients. Discrimination was high in all models, with c-statistics ranging from 0.72 to 0.84, and was similar between ICU and non-ICU patients at all time points. APACHE II scores significantly improved the prediction of overall and 48-hour mortality compared with age adjustment alone.
The APACHE II score may be a valid tool to control for confounding or for the prediction of death among ICU and non-ICU patients with MRSA bacteremia.
To create a clinical tool based on institution-specific risk factors to estimate the probability of carbapenem resistance among Pseudomonas aeruginosa isolates obtained from infected patients. By better estimating the probability of carbapenem resistance on the basis of patient-specific factors, clinicians can refine their empirical therapy for P. aeruginosa infections and potentially maximize clinical outcomes by increasing the likelihood of appropriate empirical antimicrobial therapy.
A retrospective, cross-sectional study.
Tertiary care academic hospital.
All adult inpatients who had a respiratory tract infection due to P. aeruginosa between January 2001 and June 2005.
Data on demographic characteristics, antibiotic history, and microbiology were collected. Log-binomial regression was employed to identify predictors of carbapenem resistance among P. aeruginosa isolates and to devise the clinical prediction tool.
Among 351 patients with P. aeruginosa infection, 44% were infected with carbapenem-resistant P. aeruginosa strains. Independent predictors of carbapenem resistance were prior receipt of mechanical ventilation for 11 days or more, prior exposure to fluoroquinolones for 3 days or more, and prior exposure to carbapenems for 3 days or more.
With carbapenem resistance rates among P. aeruginosa isolates on the rise at our institution, the challenge was to identify patients for whom carbapenems would remain an effective empirical agent, as well as the patients at greatest risk for infection with carbapenem-resistant strains. The clinical prediction tool accurately estimated carbapenem resistance among this risk-stratified cross-sectional study of patients with P. aeruginosa infection. This tool may be an effective way for clinicians to refine their selection of empirical antibiotic therapy and to maximize clinical outcomes by increasing the likelihood of appropriate antibiotic treatment.
To identify institution-specific risk factors for MRSA bacteremia and develop an objective mechanism to estimate the probability of methicillin resistance in a given patient with Staphylococcus aureus bacteremia (SAB).
A cohort study was performed to identify institution-specific risk factors for MRSA. Logistic regression was used to model the likelihood of MRSA A stepwise approach was employed to derive a parsimonious model. The MRSA prediction tool was developed from the final model.
A 279-bed, level 1 trauma center.
Between January 1, 1999, and June 30, 2001, 494 patients with clinically significant episodes of SAB were identified.
The MRSA rate was 45.5%. Of 18 characteristics included in the logistic regression, the only independent features for MRSA were prior antibiotic exposure (OR, 9.2; CI95, 4.8 to 17.9), hospital onset (OR, 3.0; CI95, 1.9 to 4.9), history of hospitalization (OR, 2.5; CI95, 1.5 to 3.8), and presence of decubitus ulcers (OR, 2.5; CI95, 1.2 to 4.9). The prediction tool was derived from the final model, which was shown to accurately reflect the actual MRSA distribution in the cohort.
Through multivariate modeling techniques, we were able to identify the most important determinants of MRSA at our institution and develop a tool to predict the probability of methicillin resistance in a patient with SAB. This knowledge can be used to guide empiric antibiotic selection. In the era of antibiotic resistance, such tools are essential to prevent indiscriminate antibiotic use and preserve the longevity of current antimicrobials.
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