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Objective: To determine patient-specific risk factors and clinical outcomes associated with contaminated blood cultures. Design: A single-center, retrospective case-control risk factor and clinical outcome analysis performed on inpatients with blood cultures collected in the emergency department, 2014–2018. Patients with contaminated blood cultures (cases) were compared to patients with negative blood cultures (controls). Setting: A 509-bed tertiary-care university hospital. Methods: Risk factors independently associated with blood-culture contamination were determined using multivariable logistic regression. The impacts of contamination on clinical outcomes were assessed using linear regression, logistic regression, and generalized linear model with γ log link. Results: Of 13,782 blood cultures, 1,504 (10.9%) true positives were excluded, leaving 1,012 (7.3%) cases and 11,266 (81.7%) controls. The following factors were independently associated with blood-culture contamination: increasing age (adjusted odds ratio [aOR], 1.01; 95% confidence interval [CI], 1.01–1.01), black race (aOR, 1.32; 95% CI, 1.15–1.51), increased body mass index (BMI; aOR, 1.01; 95% CI, 1.00–1.02), chronic obstructive pulmonary disease (aOR, 1.16; 95% CI, 1.02–1.33), paralysis (aOR 1.64; 95% CI, 1.26–2.14) and sepsis plus shock (aOR, 1.26; 95% CI, 1.07–1.49). After controlling for age, race, BMI, and sepsis, blood-culture contamination increased length of stay (LOS; β = 1.24 ± 0.24; P < .0001), length of antibiotic treatment (LOT; β = 1.01 ± 0.20; P < .001), hospital charges (β = 0.22 ± 0.03; P < .0001), acute kidney injury (AKI; aOR, 1.60; 95% CI, 1.40–1.83), echocardiogram orders (aOR, 1.51; 95% CI, 1.30–1.75) and in-hospital mortality (aOR, 1.69; 95% CI, 1.31–2.16). Conclusions: These unique risk factors identify high-risk individuals for blood-culture contamination. After controlling for confounders, contamination significantly increased LOS, LOT, hospital charges, AKI, echocardiograms, and in-hospital mortality.

OBJECTIVE: To determine the association of medical marijuana legalization with prescription opioid utilization. METHODS: A 10% sample of a nationally representative database of commercially insured population was used to gather information on opioid use, chronic opioid use, and high-risk opioid use for the years 2006-2014. Adults with pharmacy and medical benefits for the entire calendar year were included in the population for that year. Multilevel logistic regression analysis, controlling for patient, person-year, and state-level factors, were used to determine the impact of medical marijuana legalization on the three opioid use measures. Subgroup analysis among cancer-free adults and cancer-free adults with at least one chronic non-cancer pain condition in the particular year were conducted. Alternate regression models were used to test the robustness of our results including a fixed effects model, an alternate definition for start date for medical marijuana legalization, a person-level analysis, and a falsification test. RESULTS: The final sample included a total of 4,840,562 persons translating into 15,705,562 person years. Medical marijuana legalization was found to be associated with a lower odds of any opioid use: OR = 0.95 (0.94-0.96), chronic opioid use: OR = 0.93 (0.91-0.95), and high-risk opioid use: OR = 0.96 (0.94-0.98). The findings were similar in both the subgroup analyses and all the sensitivity analyses. The falsification tests showed no association between medical marijuana legalization and prescriptions for antihyperlipidemics (OR = 1.00; CI 0.99-1.01) or antihypertensives (OR = 1.00; CI 0.99-1.01). CONCLUSIONS: In states where marijuana is available through medical channels, a modestly lower rate of opioid and high-risk opioid prescribing was observed. Policy makers could consider medical marijuana legalization as a tool that may modestly reduce chronic and high-risk opioid use. However, further research assessing risk versus benefits of medical marijuana legalization and head to head comparisons of marijuana versus opioids for pain management is required.

Background Accelerate Pheno blood culture detection system (AXDX) provides identification (ID) and antimicrobial susceptibility testing (AST) results within 8h of blood culture growth. Limited data exists regarding its clinical impact. Other rapid platforms coupled with antimicrobial stewardship program (ASP) real-time notification (RTN) have shown improved length of stay (LOS) in bacteremia Methods A single-center, quasi-experimental study of adult bacteremic inpatients before/after AXDX implementation was conducted comparing clinical outcomes from 1 historical and 2 intervention cohorts (AXDX and AXDX+RTN). Primary outcome was LOS. Results Of 830 bacteremic episodes, 188 (77%) of 245 historical and 308 (155 AXDX, 153 AXDX+RTN; 65%) of 585 intervention episodes were included. Median LOS was shorter with AXDX (6.3d) and AXDX+RTN (6.7d) compared to historical (8.1d; P=0.001). Achievement of optimal therapy (AOT) was more frequent (93.6% and 95.4%) and median time to optimal therapy (TTOT) was faster (1.3d and 1.4d) in AXDX and AXDX+RTN compared to historical (84.6%, P≤0.001 and 2.4d; P≤0.001) respectively. Median antimicrobial days of therapy (DOT) was shorter in both intervention arms compared to historical (6d each vs 7d; P=0.011). Median LOS benefit was most pronounced in patients with coagulase negative Staphylococcus bacteremia (5.5d and 4.5d vs 7.2d; P=0.003) in AXDX, AXDX+RTN, and historical cohorts respectively. Conclusions LOS, AOT, TTOT, and total DOT significantly improved after AXDX implementation. Addition of RTN did not show further improvement over AXDX and an already active ASP. These results suggest AXDX can be integrated into healthcare systems with an active ASP even without the resources to include RTN.

Background: To assess the risk of lymphedema associated with the use of calcium channel blockers (CCB) among breast cancer patients.Methods: A nested case-control study of adult female breast cancer patients receiving an antihypertensive agent was conducted using administrative claims data between 2007 and 2015. Cases were patients with lymphedema who were matched to 5 controls based on nest entry date (AE180 days), age (AE5 years), number of hypertensive drug classes, Charlson Comorbidity Index (CCI), thiazide exposure, and insurance type. Exposure to CCBs and covariates was identified in the 180-day period prior to event date. Conditional logistic regression was used to assess the impact of exposure among cases and controls.Results: A total of 717 cases and 1,681 matched controls were identified. After matching on baseline characteristics, mastectomy (7.8% vs. 4.8%; P ¼ 0.0039), exposure to radiotherapy (27.1% vs. 21.7%; P ¼ 0.0046), taxanebased chemotherapy (11.7% vs. 7.4%; P ¼ 0.0007), anthracycline-based chemotherapy (6.0% vs. 3.6%; P ¼ 0.0073), CCB use (28.3% vs. 23.3%; P ¼ 0.0087), and CCI ) 19.8% vs. 12.7%; P < 0.0001; score of 4 or above) were all higher in cases during the 180 days prior to the event date. In the adjusted analysis, CCB exposure was significantly associated with increased risk of lymphedema (OR ¼ 1.320; 95% confidence interval, 1.003-1.737).Conclusions: CCB use was significantly associated with the development of lymphedema in breast cancer patients.Impact: CCBs should be avoided or used with caution in breast cancer patients to reduce the risk for developing lymphedema.