A multicenter analysis of the clinical microbiology and antimicrobial usage in hospitalized patients in the US with or without COVID-19.

BACKGROUND: Past respiratory viral epidemics suggest that bacterial infections impact clinical outcomes. There is minimal information on potential co-pathogens in patients with coronavirus disease-2019 (COVID-19) in the US. We analyzed pathogens, antimicrobial use, and healthcare utilization in hospitalized US patients with and without severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). METHODS: This multicenter retrospective study included patients with > 1 day of inpatient admission and discharge/death between March 1 and May 31, 2020 at 241 US acute care hospitals in the BD Insights Research Database. We assessed microbiological testing data, antimicrobial utilization in admitted patients with ≥24 h of antimicrobial therapy, and length of stay (LOS). RESULTS: A total of 141,621 patients were tested for SARS-CoV-2 (17,003 [12.0%] positive) and 449,339 patients were not tested. Most (> 90%) patients tested for SARS-CoV-2 had additional microbiologic testing performed compared with 41.9% of SARS-CoV-2-untested patients. Non-SARS-CoV-2 pathogen rates were 20.9% for SARS-CoV-2-positive patients compared with 21.3 and 27.9% for SARS-CoV-2-negative and -untested patients, respectively. Gram-negative bacteria were the most common pathogens (45.5, 44.1, and 43.5% for SARS-CoV-2-positive, -negative, and -untested patients). SARS-CoV-2-positive patients had higher rates of hospital-onset (versus admission-onset) non-SARS-CoV-2 pathogens compared with SARS-CoV-2-negative or -untested patients (42.4, 22.2, and 19.5%, respectively), more antimicrobial usage (68.0, 45.2, and 25.1% of patients), and longer hospital LOS (mean [standard deviation (SD)] of 8.6 [11.4], 5.1 [8.9], and 4.2 [8.0] days) and intensive care unit (ICU) LOS (mean [SD] of 7.8 [8.5], 3.6 [6.2], and 3.6 [5.9] days). For all groups, the presence of a non-SARS-CoV-2 pathogen was associated with increased hospital LOS (mean [SD] days for patients with versus without a non-SARS-CoV-2 pathogen: 13.7 [15.7] vs 7.3 [9.6] days for SARS-CoV-2-positive patients, 8.2 [11.5] vs 4.3 [7.9] days for SARS-CoV-2-negative patients, and 7.1 [11.0] vs 3.9 [7.4] days for SARS-CoV-2-untested patients). CONCLUSIONS: Despite similar rates of non-SARS-CoV-2 pathogens in SARS-CoV-2-positive, -negative, and -untested patients, SARS-CoV-2 was associated with higher rates of hospital-onset infections, greater antimicrobial usage, and extended hospital and ICU LOS. This finding highlights the heavy burden of the COVID-19 pandemic on healthcare systems and suggests possible opportunities for diagnostic and antimicrobial stewardship.

Blood Culture Turnaround Time in U.S. Acute Care Hospitals and Implications for Laboratory Process Optimization.

The rapid identification of blood culture isolates and antimicrobial susceptibility test (AST) results play critical roles for the optimal treatment of patients with bloodstream infections. Whereas others have looked at the time to detection in automated culture systems, we examined the overall time from specimen collection to actionable test results. We examined four points of time, namely, blood specimen collection, Gram stain, organism identification (ID), and AST reports, from electronic data from 13 U.S. hospitals for the 11 most common, clinically significant organisms in septic patients. We compared the differences in turnaround times and the times from when specimens were collected and the results were reported in the 24-h spectrum. From January 2015 to June 2016, 165,593 blood specimens were collected, of which, 9.5% gave positive cultures. No matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry was used during the study period. Across the 10 common bacterial isolates (n = 6,412), the overall median (interquartile range) turnaround times were 0.80 (0.64 to 1.08), 1.81 (1.34 to 2.46), and 2.71 (2.46 to 2.99) days for Gram stain, organism ID, and AST, respectively. For all positive cultures, approximately 25% of the specimens were collected between 6:00 a.m. and 11:59 a.m. In contrast, more of the laboratory reporting times were concentrated between 6:00 a.m. and 11:59 a.m. for Gram stain (43%), organism ID (78%), and AST (82%), respectively (P < 0.001). The overall average turnaround times from specimen collection for Gram stain, organism ID, and AST were approximately 1, 2, and 3 days, respectively. The laboratory results were reported predominantly in the morning hours. Laboratory automation and work flow optimization may play important roles in reducing the microbiology result turnaround time.

Relationships between creatinine increase and mortality rates in patients given vancomycin in 76 hospitals: The increasing role of infectious disease pharmacists.

PURPOSE: Vancomycin is a commonly used antimicrobial with the potential for renal toxicity. We evaluated vancomycin duration, changes in renal function after vancomycin initiation ("post-vancomycin" renal function changes), and associated mortality risk among hospitalized patients. METHODS: We analyzed data from 76 hospitals and excluded patients with a baseline serum creatinine concentration (SCr) of >3.35 mg/dL. We estimated mortality risk relative to vancomycin duration and the magnitude of post-vancomycin SCr change, controlling for demographics, baseline SCr, underlying diseases, clinical acuity, and comorbidities. RESULTS: Among 128,993 adult inpatients treated with vancomycin, 49.0% did not experience SCr elevation. Among the remaining patients, 26.0%, 11.4%, 8.8% and 4.8% experienced increases in post-vancomycin SCr of 1% to 20%, 21% to 40%, 41% to 100%, and greater than 100%, respectively. Compared to mortality risk among patients with a vancomycin therapy duration between 4 and 5 days (the lowest-mortality group), longer vancomycin therapy duration was not independently associated with higher mortality risk after adjusting for confounders. In contrast, there was a graded relationship between post-vancomycin SCr elevation and mortality. Multivariable adjusted mortality odds ratios ranged from 1.60 to 13.66, corresponding to SCr increases of 10% and greater than 200%, respectively. CONCLUSION: Half of patients given vancomycin did not experience SCr elevation and had the lowest mortality, suggesting that vancomycin can be used safely if renal function is stabilized. In the large study cohort, vancomycin duration itself was not an independent predictor of mortality. Post-vancomycin SCr elevation appeared to be a driver of in-hospital mortality. Even a 10% post-vancomycin SCr increase was associated with an increased mortality risk. This finding stresses the importance of closely monitoring renal function and may support the value of pharmacokinetic dosing.

Effect of Inadequate Empiric Antibacterial Therapy on Hospital Outcomes in SARS-CoV-2-Positive and -Negative US Patients With a Positive Bacterial Culture: A Multicenter Evaluation From March to November 2020.

BACKGROUND: Increased utilization of antimicrobial therapy has been observed during the coronavirus disease 2019 pandemic. We evaluated hospital outcomes based on the adequacy of antibacterial therapy for bacterial pathogens in US patients. METHODS: This multicenter retrospective study included patients with ≥24 hours of inpatient admission, ≥24 hours of antibiotic therapy, and discharge/death from March to November 2020 at 201 US hospitals in the BD Insights Research Database. Included patients had a test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and a positive bacterial culture (gram-positive or gram-negative). We used generalized linear mixed models to evaluate the impact of inadequate empiric therapy (IET), defined as therapy not active against the identified bacteria or no antimicrobial therapy in the 48 hours following culture, on in-hospital mortality and hospital and intensive care unit length of stay (LOS). RESULTS: Of 438 888 SARS-CoV-2-tested patients, 39 203 (8.9%) had positive bacterial cultures. Among patients with positive cultures, 9.4% were SARS-CoV-2 positive, 74.4% had a gram-negative pathogen, 25.6% had a gram-positive pathogen, and 44.1% received IET for the bacterial infection. The odds of mortality were 21% higher for IET (odds ratio [OR], 1.21; 95% CI, 1.10-1.33; P < .001) compared with adequate empiric therapy. IET was also associated with increased hospital LOS (LOS, 16.1 days; 95% CI, 15.5-16.7 days; vs LOS, 14.5 days; 95% CI, 13.9-15.1 days; P < .001). Both mortality and hospital LOS findings remained consistent for SARS-CoV-2-positive and -negative patients. CONCLUSIONS: Bacterial pathogens continue to play an important role in hospital outcomes during the pandemic. Adequate and timely therapeutic management may help ensure better outcomes.

Contributing Factors to the Clinical and Economic Burden of Patients with Laboratory-Confirmed Carbapenem-Nonsusceptible Gram-Negative Urinary Tract Infections.

PURPOSE: We explored patient- and hospital-level predictor variables for worse clinical and economic outcomes in carbapenem-nonsusceptible urinary tract infections (UTIs). PATIENTS AND METHODS: We used electronic data (January 2013-September 2015; 78 US hospitals) from a large multicenter clinical database. Nonduplicate gram-negative isolates were considered carbapenem-nonsusceptible if they had resistant/intermediate susceptibility. Potential predictors of outcomes (mortality, 30-day readmissions, length of stay [LOS], hospital total cost, and net gain/loss per case) were examined using generalized linear mixed models. Significant predictors were identified based on statistical significance and model goodness-of-fit criteria. RESULTS: A total of 1439 carbapenem-nonsusceptible urine cases were identified. The mortality rate was 5.5%; the hospital readmission rate was 25.0%. Mean (standard deviation [SD]) LOS, total cost, and loss per case were 12 (14) days, $21,502 ($37,172), and $5828 ($26,540), respectively. Hospital-onset (vs community-onset) infection significantly impacted all outcomes: mortality (odds ratio [OR], 2.21; 95% confidence interval [CI], 1.19-4.11; P=.01), 30-day readmissions (OR, 2.35; 95% CI, 1.49-3.71; P<.001), LOS (25.7 vs 10.2 days; P<.001), hospital total cost ($67,810 vs $22,141; P<.001), and loss per case (-$28,054 vs -$10,809; P<.001). Mechanical ventilation/intensive care unit status, neoplasms, and other underlying diseases were also common predictors for worse outcomes overall; polymicrobial infection was significantly associated with worse economic outcomes. Other key predictors were >1 prior hospitalization for 30-day readmissions, high Acute Laboratory Risk of Mortality Score for mortality, LOS, cost, and hospital teaching status for cost. CONCLUSION: Hospital-onset infections, polymicrobial infections, higher clinical severity, and underlying diseases are key predictors for worsened overall burden of carbapenem-nonsusceptible gram-negative UTIs.

Contributing Factors to the Clinical and Economic Burden of Patients with Laboratory-Confirmed Carbapenem-Nonsusceptible Gram-Negative Respiratory Infections.

PURPOSE: This study examined patient- and hospital-level predictor variables that contribute to worse clinical and economic outcomes in patients with carbapenem-nonsusceptible respiratory infections. PATIENTS AND METHODS: Electronic data (January 2013 to September 2015) were from 78 US hospitals. Nonduplicate, gram-negative respiratory isolates were considered carbapenem-nonsusceptible if they tested resistant/intermediate to imipenem, meropenem, doripenem, or ertapenem. Potential predictors of outcomes (in-hospital mortality, 30-day readmission, length of stay [LOS], hospital total cost, and net gain/loss per patient) were examined using univariate analysis and generalized linear mixed models. Statistical significance and model goodness-of-fit criteria were used to identify significant predictors. RESULTS: A total of 1488 carbapenem-nonsusceptible respiratory patients were identified. Overall, the mortality rate was 13.7%, 30-day readmission rate was 20.6%, mean LOS was 20 days, mean total cost was $54,158, and mean net loss was $139 per patient. Our models showed that hospital-onset infection, higher clinical severity, mechanical ventilation/intensive care unit status, polymicrobial infection, and underlying diseases were all significant predictors for mortality, LOS, and total cost. Hospital-onset infections were also associated with a significantly greater net loss (P≤.01), and underlying disease significantly impacted readmissions (P=.03). The number of prior admissions, hospital characteristics, and payer type were also found to significantly impact measured outcomes. CONCLUSION: Carbapenem-nonsusceptible respiratory infections are associated with a considerable clinical and economic burden. The impact of hospital-onset infections on both clinical and economic outcomes highlights the continued need for action on this modifiable risk factor through antimicrobial stewardship and optimal therapy, thereby reducing the burden in this patient population.

Attributable burden in patients with carbapenem-nonsusceptible gram-negative respiratory infections.

OBJECTIVE: We aimed to describe the clinical and economic burden attributable to carbapenem-nonsusceptible (C-NS) respiratory infections. METHODS: This retrospective matched cohort study assessed clinical and economic outcomes of adult patients (aged ≥18 years) who were admitted to one of 78 acute care hospitals in the United States with nonduplicate C-NS and carbapenem-susceptible (C-S) isolates from a respiratory source. A subset analysis of patients with principal diagnosis codes denoting bacterial pneumonia or other diagnoses was also conducted. Isolates were classified as community- or hospital-onset based on collection time. A generalized linear mixed model method was used to estimate the attributable burden for mortality, 30-day readmission, length of stay (LOS), cost, and net gain/loss (payment minus cost) using propensity score-matched C-NS versus C-S cohorts. RESULTS: For C-NS cases, mortality (25.7%), LOS (29.4 days), and costs ($81,574) were highest in the other principal diagnosis, hospital-onset subgroup; readmissions (19.4%) and net loss (-$9522) were greatest in the bacterial pneumonia, hospital-onset subgroup. Mortality and readmissions were not significantly higher for C-NS cases in any propensity score-matched subgroup. Significant C-NS-attributable burden was found for both other principal diagnosis subgroups for LOS (hospital-onset: 3.7 days, P = 0.006; community-onset: 1.5 days, P<0.001) and cost (hospital-onset: $12,777, P<0.01; community-onset: $2681, P<0.001). CONCLUSIONS: Increased LOS and cost burden were observed in propensity score-matched patients with C-NS compared with C-S respiratory infections; the C-NS-attributable burden was significant only for patients with other principal diagnoses.