Kevin Hsueh

Outpatient antibiotic prescription trends in the United States: A national cohort study

OBJECTIVETo characterize trends in outpatient antibiotic prescriptions in the United StatesDESIGNRetrospective ecological and temporal trend study evaluating outpatient antibiotic prescriptions from 2013 to 2015SETTINGNational administrative claims data from a pharmacy benefits manager PARTICIPANTS. Prescription pharmacy beneficiaries from Express Scripts Holding CompanyMEASUREMENTSAnnual and seasonal percent change in antibiotic prescriptionsRESULTSApproximately 98 million outpatient antibiotic prescriptions were filled by 39 million insurance beneficiaries during the 3-year study period. The most commonly prescribed antibiotics were azithromycin, amoxicillin, amoxicillin/clavulanate, ciprofloxacin, and cephalexin. No significant changes in individual or overall annual antibiotic prescribing rates were found during the study period. Significant seasonal variation was observed, with antibiotics being 42% more likely to be prescribed during February than September (peak-to-trough ratio [PTTR], 1.42; 95% confidence interval [CI], 1.39-1.61). Similar seasonal trends were found for azithromycin (PTTR, 2.46; 95% CI, 2.44-3.47), amoxicillin (PTTR, 1.52; 95% CI, 1.42-1.89), and amoxicillin/clavulanate (PTTR, 1.78; 95% CI, 1.68-2.29).CONCLUSIONSThis study demonstrates that annual national outpatient antibiotic prescribing practices remained unchanged during our study period. Furthermore, seasonal peaks in antibiotics generally used to treat viral upper respiratory tract infections remained unchanged during cold and influenza season. These results suggest that inappropriate prescribing of antibiotics remains widespread, despite the concurrent release of several guideline-based best practices intended to reduce inappropriate antibiotic consumption; however, further research linking national outpatient antibiotic prescriptions to associated medical conditions is needed to confirm these findings.Infect Control Hosp Epidemiol 2018;39:584-589.

Effective Antibiotic Conservation by Emergency Antimicrobial Stewardship During a Drug Shortage.

We present the first description of an antimicrobial stewardship program (ASP) used to successfully manage a multi-antimicrobial drug shortage. Without resorting to formulary restriction, meropenem utilization decreased by 69% and piperacillin-tazobactam by 73%. During the shortage period, hospital mortality decreased (P=.03), while hospital length of stay remained unchanged. Infect Control Hosp Epidemiol 2017;38:356-359.

Pharmacokinetic drug evaluation of tedizolid for the treatment of skin infections

ABSTRACT Introduction: Tedizolid is indicated for the treatment of acute bacterial skin and skin structure infections (ABSSSI). Although tedizolid shares many similar properties with linezolid, another oxazolidinone used to treat ABSSSI, the two antibiotics have several key differences. Areas covered: This review provides a detailed summary of the overall pharmacodynamics, pharmacokinetics, clinical efficacy, and safety of tedizolid for the treatment of ABSSSI. Expert opinion: Compared to other antibiotics used for ABSSSI, tedizolid has several advantages. Tedizolid has a long half-life, allowing for once daily dosing. Tedizolid also has broad spectrum of activity against Gram-positive pathogens, including methicillin-resistant Staphylococcus aureus, Coagulase-negative Staphylococci, and Enterococci – including isolates demonstrating resistance to linezolid. It is available in both oral and intravenous formulations, and, has outstanding oral bioavailability, allowing for oral-step down therapy. There is also some evidence that, tedizolid has fewer significant interactions with serotonin reuptake inhibitors or monoamine oxidase inhibitors than linezolid. Finally, thrombocytopenia may occur less often with tedizolid than linezolid. However, these benefits must be weighed against the financial cost of tedizolid and the availability of alternative antibiotic choices.

Creation and Assessment of a Clinical Predictive Calculator and Mortality Associated With Candida krusei Bloodstream Infections

Abstract Background Candida krusei bloodstream infection (CK BSI) is associated with high mortality, but whether this is due to underlying comorbidities in affected patients or the organism itself is unknown. Identifying patient characteristics that are associated with CK BSI is crucial for clinical decision-making and prognosis. Methods We conducted a retrospective analysis of hospitalized patients with Candida BSI at our institution between 2002 and 2015. Data were collected on demographics, comorbidities, medications, procedures, central lines, vital signs, and laboratory values. Multivariable logistic and Cox regression were used to identify risk factors associated with CK and mortality, respectively. Results We identified 1873 individual patients who developed Candida BSI within the study period, 59 of whom had CK BSI. CK BSI was predicted by hematologic malignancy, gastric malignancy, neutropenia, and the use of prophylactic azole antifungals, monoclonal antibodies, and β-lactam/β-lactamase inhibitor combinations. The C-statistic was 0.86 (95% confidence interval, 0.81–0.91). The crude mortality rates were 64.4% for CK BSI and 41.4% for non-CK BSI. Although CK was associated with higher mortality in univariable Cox regression, this relationship was no longer significant with the addition of the following confounders: lymphoma, neutropenia, glucocorticoid use, chronic liver disease, and elevated creatinine. Conclusions Six patient comorbidities predicted the development of CK BSI with high accuracy. Although patients with CK BSI have higher crude mortality rates than patients with non-CK BSI, this difference is not significant when accounting for other patient characteristics.

Risk Factors and Mortality Associated with Candida krusei Bloodstream Infections

Abstract Background Candida krusei (CK) candidemia is associated with high mortality, but whether this is due to underlying comorbidities in affected patients or the organism itself is unknown. We analyzed factors associated with C. krusei candidemia and its outcomes. Methods A retrospective analysis of hospitalized patients with candidemia was conducted at our institution between 2002 and 2015. Data were collected on demographics, comorbidities, medications, procedures, central lines, vital signs, and labs. Univariable logistic and Cox regression were used to identify potential risk factors associated with CK and mortality, respectively. Multivariable analyses were then constructed parsimoniously from these variables. Results Of 1,873 candidemia events, 59 were due to CK. In multivariable analysis, CK candidemia was predicted by hematologic malignancy (OR 8.9, 95% CI [4.1, 19.7]), stomach cancer (OR 14.6, 95% CI [2.9, 72.5]), absolute neutrophil count (OR 2.4, 95% CI [1.2, 4.8]), and the use of prophylactic azole antifungals (OR 2.2, 95% CI [1.1, 4.3]), monoclonal antibodies (OR 5.7, 95% CI [2.0, 15.8]), and penicillin β-lactamase inhibitors (OR 2.5, 95% CI [1.3, 4.8]). The C-statistic was 0.86 (95% CI [0.81, 0.91]). The crude mortality rates were 86.4% for CK candidemia and 63.6% for non-CK candidemia. Although CK was associated with higher mortality in univariable Cox regression (Figure 1, HR 1.8, 95% CI [1.3, 2.4]), this relationship was no longer significant (HR 1.2, 95% CI [0.8, 1.7]) with the addition of the following confounders: hematologic malignancy (HR 0.9, 95% CI [0.7, 1.1]), absolute neutrophil count (HR 1.7, 95% CI [1.4, 2.2]), stomach cancer (HR 1.0, 95% CI [0.5, 1.9]), coagulopathy (HR 1.0, 95% CI [0.9, 1.2], and prophylactic corticosteroids (HR 1.4, 95% CI [1.2, 1.7] (Figure 2). Conclusion A similar set of patient characteristics is associated with both CK infection and increased mortality, suggesting that patients with CK candidemia are at higher risk of mortality due to underlying illness rather than organism-specific mechanisms.Figure 1. Univariable 90-day survival analysis stratified by CK (red) versus non-CK (blue) candidemia.Figure 2. Multivariable 90-day survival analysis stratified by CK (red) vs non-CK (blue) candidemia. Disclosures W. Powderly, Merck: Grant Investigator and Scientific Advisor, Consulting fee and Research grant Gilead: Scientific Advisor, Consulting fee Astellas: Grant Investigator, Research grant A. Spec, Astellas Pharma US, Inc.: Grant Investigator, Research grant

Risk Predictive Model for 90-Day Mortality in Candida Bloodstream Infections

Abstract Background Candida bloodstream infections (CBSI) continue to be associated with high mortality, despite changes in antifungal treatment and diagnostics. Methods All patients age 18 or greater with a first episode of CBSI by blood culture from 1/2002 to 1/2015, admitted to Barnes-Jewish Hospital, a tertiary referral hospital in St. Louis, MO, were included. We collected data on demographics, comorbidities, laboratory values, vital signs, indwelling devices, and medical treatments of interest from the electronic medical record. We analyzed the potential predictor variables using univariate logistic regression. Variables associated with mortality were considered for model inclusion. The final model was built using multivariable binary logistic regression. A predictive equation was created, and a receiver–operator curve (ROC) was calculated to determine the appropriate cut-off points and c-statistic. Results Of the 1873 episodes of CBSI identified, 789 (42%) resulted in death in 90 days. The variables included in this model were age (40–49: OR 0.463, 95% CI 0.291–0.736; 50–69: 0.542, 0.342–0.860; ≥70: 0.560, 0.400–0.785); history of CAD (1.616, 1.171–2.230), chronic liver disease (2.247, 1.327–3.806); maximum heart rate (1.496, 1.126–1.989) and temperature (0.537, 0.408–0.708); AST (1.817, 1.343–2.459) and platelet count (1.563, 1.178–2.073); the presence of ventilator (1.847, 1.321–2.582), urinary catheter (1.365, 1.008–1.847), two or more central lines (1.658, 1.020–2.694); removal of lines after positive culture (0.259, 0.181–0.370); ophthalmology consult during admission (0.441, 0.329–0.592); thoracentesis/chest tube (3.827, 1.550–9.448); diagnosis of secondary malignancy (2.131, 1.488–3.053); whether antimetabolites (2.119, 1.353–3.318), dapsone (4.507, 1.450–14.012), linezolid (1.605, 1.059–2.435), quinolones (1.384, 0.998–1.920) were ordered 90 days before positive culture. An ROC curve was calculated with an internal c-statistic of 0.806. Conclusion We created a risk predictive model for 90-day mortality in patients with CBSI, with 81% probability of predicting mortality. This model can lead to development of point-of-care applications to aid decision-making regarding escalation/de-escalation of care. Disclosures W. Powderly, Merck: Grant Investigator and Scientific Advisor, Consulting fee and Research grant Gilead: Scientific Advisor, Consulting fee Astellas: Grant Investigator, Research grant A. Spec, Astellas: Grant Investigator, Grant recipient