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Burkholderia pseudomallei is a gram-negative aerobic bacillus and the etiologic agent of melioidosis (1). The clinical signs and symptoms of melioidosis are varied, and subclinical infection can occur with or [...]

Background Monitoring antimicrobial use and resistance are key components of initiatives to promote antimicrobial stewardship and prevent antimicrobial-resistant infections. In this surveillance study, we evaluated trends in resistance among healthcare-associated P. aeruginosa isolates and potential associations with antimicrobial consumption. Methods We established a retrospective cohort of P. aeruginosa isolates collected ≥48 hours after inpatient admission at a 1,300-bed academic medical center from July 1, 2013 to July 31, 2018. We included isolates from all clinical cultures and retained the first isolate for a patient encounter. We defined the multidrug-resistant (MDR) status in accordance with the phenotype definitions established by the Centers for Disease Control and Prevention. We calculated the monthly percentage of class-specific resistance and MDR status among isolates. We measured monthly antimicrobial consumption as days of therapy per 1,000 patient-days. To evaluate potential associations between identified trends in resistance and antimicrobial use, we constructed autoregressive integrated moving average models (ARIMA) with transfer functions. Results Of 1,897 isolates included in the analysis, 303 (16.0%) were classified as MDR P. aeruginosa. The rate of healthcare-associated P. aeruginosa infections and percent of MDR isolates remained stable over the five-year study period. However, we identified trends in resistance to specific antimicrobial classes: there was a significant increase in resistance to antipseudomonal carbapenems, while resistance to aminoglycosides and extended-spectrum cephalosporins decreased. Using the ARIMA modeling strategy, bivariable analyses of resistance and antimicrobial use revealed that carbapenem-resistant P. aeruginosa was positively correlated with the use of antipseudomonal carbapenems at a 1-month lag and ertapenem at a 5-month lag. Conclusion Risk assessments that only measure rates of MDR organisms may miss underlying trends in class resistance. Increasing carbapenem resistance despite a stable proportion of MDR isolates highlights a critical area for continued monitoring and antimicrobial stewardship initiatives targeted at carbapenem use in our hospital. Disclosures All authors: No reported disclosures. [ABSTRACT FROM AUTHOR]

Background Predictive models for empiric antibiotic prescribing often estimate the probability of infection with multidrug-resistant organisms. In this work, we developed models to predict coverage of specific treatment regimens to better target antibiotics to high- and low-risk patients. Methods We established a retrospective cohort of adults admitted to the ICU in a 1,300-bed teaching hospital from November 1, 2011 to June 30, 2016. We included patients with a diagnosis of pneumonia and positive respiratory culture collected during their ICU stay. We collected demographics, comorbidities, and medical history from the electronic health record. We evaluated three penalized regression methods for predicting infection susceptibility to 11 treatment regimens: least absolute selection and shrinkage operator (LASSO), minimax concave penalty (MCP), and smoothly clipped absolute deviation (SCAD). We developed models for susceptibility prediction at two stages of the diagnostic process: for all pathogenic bacteria and for infections with Gram-negative organisms only. We selected final models based on higher area under the receiver operating characteristic (AUROC), acceptable goodness of fit, lower variability of the AUROCs in the cross-validation run, and fewer predictors. Results Among 1,917 cases of pneumonia, 54 different pathogens were identified. The most frequently isolated organisms were: Pseudomonas aeruginosa (16.6%), methicillin-resistant Staphylococcus aureus (16.1%), and Staphylococcus aureus (13.5%). Frequently selected variables included age, Elixhauser score, tracheostomy status, recent antimicrobial use, and prior infection with a carbapenem-resistant organism. All final models used MCP or SCAD methods. Point estimates for the AUROCs in the training set ranged from 0.70 to 0.80, and estimates in the internal validation set ranged from 0.64 to 0.77. Conclusion MCP and SCAD outperformed LASSO. For some regimens, models predicted infection susceptibility with fair accuracy. These models have potential to help antibiotic stewardship efforts to better target appropriate antibiotic use. Disclosures All authors: No reported disclosures. [ABSTRACT FROM AUTHOR]

Background Providers use institutional recommendations, national guidelines, and antibiograms to decide on empiric antibiotics. As local antibiograms are most effective after organisms are known, we sought to use local microbiology and clinical data to develop predictive models for antibiotic coverage prior to identifying the organism. We focused on Gram-negative organisms as they are common urinary pathogens and are often the cause of sepsis originating in the urinary tract. As such, they are important to cover in hospitalized patients with urinary tract infections (UTI). Methods Hospitalized patients, with a diagnosis of UTI and a positive urine culture in the first 48 hours were included. Gram-positive organisms, yeast, and cultures without susceptibilities were excluded. Unknown susceptibilities were filled in using expert-derived rules. Clinical information from electronic health record (EHR) data were extracted on each patient. Penalized logistic regression with 10-fold cross validation was used to develop final models for coverage for five antibiotics (cefazolin, ceftriaxone, ciprofloxacin, cefepime, piperacillin–tazobactam). Final models were chosen based on their discrimination, calibration, and number of predictors, and then tested on a held-out validation dataset. Results Included were 5,096 patients (80% training; 20% validation). Coverage ranged from 65% for cefazolin to 90% for cefepime. Positive blood cultures were present in 544 (11%) with 388 (71%), including a urinary pathogen. In the first 24 hours, 2329 (46%) were hypotensive, 2179 (43%) had a respiratory rate > 22, 2049 (40%) had a WBC > 12, 1079 (21%) were febrile, and 584 (11%) required ICU care. Final model covariates included demographics, antibiotic exposure, prior resistant pathogens, and antibiotic allergies. The five predictive models had a point-estimate for the area under the ROC on the validation set that ranged from 0.70 for ciprofloxacin to 0.73 for ceftriaxone. Conclusion In this cohort of moderate to high acuity hospitalized patients with Gram-negative urinary pathogens, we used EHR data to develop 5 models that predict antibiotic coverage which could be used to support empiric prescribing. These models performed well in a held-out validation set. Disclosures All authors: No reported disclosures. [ABSTRACT FROM AUTHOR]

We undertook a systematic review to examine rectal Chlamydia trachomatis (Ct) and Neisseria gonorrhoeae (Ng) infections in women and men who have sex with men (MSM). English-language publications measuring rectal Ct or Ng prevalence using nucleic acid amplification tests were eligible. Searching multiple electronic databases, we identified 115 eligible reports published between January 2000 and November 2016. Overall, the prevalence of rectal Ct (9%) was higher than that of rectal Ng (4.7%). Rectal Ct prevalence was similar in MSM (9%) and women (9.2%), whereas rectal Ng prevalence was higher in MSM (6.1%) than in women (1.7%). Generally, rectal Ct prevalence was similar in sexually transmitted disease clinics (9.1%) and nonsexual health clinics (8.6%), whereas rectal Ng prevalence was somewhat lower in sexually transmitted disease clinics (4.5%) than in nonsexual health clinics (6%). These infections seem to be relatively common across a range of populations and clinical settings, highlighting the need for additional research on these preventable, treatable conditions. [ABSTRACT FROM AUTHOR]