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“Big data” and Gram-negative Resistance: A Multiple Logistic Regression Model Using EMR Data to Predict Carbapenem Resistance in Patients with Klebsiella pneumoniae Bloodstream Infection

Abstract Background The timely identification of carbapenem resistance is essential in the management of patients with Klebsiella pneumoniae bloodstream infection (BSI). An algorithm using electronic medical record (EMR) data to quickly predict resistance could potentially help guide therapy until more definitive resistance testing results are available. Methods All cases of K. pneumoniae BSI at Mount Sinai Hospital from September 2012 through September 2016 were identified. Cases of persistent BSI or recurrent BSI within 2 weeks were included only once. Patients with recurrent BSI after more than 2 weeks of negative blood cultures were considered distinct cases and included more than once. Carbapenem resistance was defined as an imipenem minimum inhibitory concentration of ≥2 μg/ml. Extensive EMR data for each patient were compiled into a relational database using SQLite. Possible risk factors for carbapenem resistance were queried from the database and analyzed via univariate methods. Significant factors were then entered into a multiple logistic regression model in a forward stepwise approach using SPSS. Results A total of 613 cases of K. pneumoniae BSI were identified in 540 unique patients. The overall incidence of imipenem resistance was 10% (61 cases). Significant markers of resistance included in the final model were (1) prior colonization with imipenem-resistant Klebsiella pneumoniae; (2) hospital unit (defined as high-risk unit, low-risk unit, and emergency department); (3) total inpatient days in the previous 5 years; (4) total days of oral or parenteral antibiotics in the past 2 years; and (5) age >60 years old (Figure 1). The model generated a receiver operating characteristic curve with an area under the curve of 0.75 (Figure 2). At a cut point of 0.083, the model correctly predicted 72% of imipenem-resistant cases while incorrectly labeling 32% of susceptible cases as resistant (Sn = 72%, Sp = 63%, Figure 3). Conclusion A multiple logistic regression model using EMR data can generate immediate, clinically useful predictions of carbapenem resistance in patients with K. pneumoniae BSI. Larger data sets are needed to improve and validate these findings.Figure 1. Algorithm variablesFigure 2. Receiver operating characteristic curveFigure 3. Classification table Disclosures All authors: No reported disclosures.


Background. The recommended duration of antibiotic treatment for
Enterobacteriaceae bacteremia is between 7 and 14 days. We compared the clinical outcomes of patients receiving short-course (6-10 days) vs prolonged-course (11-15 days) antibiotic therapy for Enterobacteriaceae bacteremia.
Methods. A retrospective cohort study was conducted at The Johns Hopkins Hospital, The University of Maryland Medical Center, and The Hospital of the University of Pennsylvania including patients with monomicrobial Enterobacteriaceae bacteremia treated with in vitro active antibiotic therapy in the range of 6-15 days between 2008 and 2014. 1:1 nearest neighbor propensity score matching without replacement was performed, prior to regression analysis, to estimate the risk of all-cause mortality within 30 days after the end of antibiotic treatment for patients receiving short vs. prolonged durations of antibiotic therapy. Secondary outcomes included Clostridium difficile infection (CDI) and the emergence of multidrug-resistant Gram-negative (MDRGN) bacteria within 30 days after the end of antibiotic therapy.
Results. A total of 1,769 patients met eligibility criteria. There were 385 matched pairs who were well-balanced on baseline characteristics. The median duration of therapy in the short-course group and prolonged-course group was 8 days (interquartile range (IQR) 7-9 days) and 15 days (IQR 13-15 days), respectively. No difference in allcause mortality between short-and prolonged-course treatment groups was observed (adjusted hazard ratio [aHR] 1.00; 95% CI 0.62-1.63). Rates of CDI were similar between the treatment groups (OR 1.17; 95% CI 0.39-3.51). There was a non-significant protective effect of short-course antibiotic therapy on the emergence of MDRGN bacteria (OR 0.59; 95% CI 0.32-1.09 P = 0.09).
Conclusion. Short courses of antibiotic therapy yields similar clinical outcomes to prolonged courses of antibiotic therapy for Enterobacteriaceae bacteremia, and may protect against subsequent MDRGN emergence.
Disclosures. All authors: No reported disclosures. Background. Enterococcal bloodstream infections (EBSI) have been attributed with significant morbidity and mortality. The objective of this study was to determine whether IDC is associated with improved mortality in patients hospitalized with EBSI.

Infectious Disease Consultation Is Associated with Decreased Mortality with
Methods. This is a cross-sectional study of patients admitted to the University of Alabama Health System between January 1, 2015 and June 30, 2016 who had EBSI. Patients who died within 2 days of hospitalization were excluded. Categorical variables were analyzed with chi-square or Fisher's exact test and continuous variables were analyzed with a t-test or Wilcoxon rank-sums test when appropriate. A P-value < 0.05 was considered significant. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CI) for factors associated with 30-day in-hospital mortality.
Conclusion. IDC was associated with reduced 30-day in-hospital mortality in patients with EBSI. Although further investigation is warranted, the results of this study suggest that early involvement of ID specialists in EBSI may lead to better outcomes.
Disclosures. All authors: No reported disclosures. S32 • OFID 2017:4 (Suppl 1) • Oral Abstracts within 2 weeks were included only once. Patients with recurrent BSI after more than 2 weeks of negative blood cultures were considered distinct cases and included more than once. Carbapenem resistance was defined as an imipenem minimum inhibitory concentration of ≥2 μg/ml. Extensive EMR data for each patient were compiled into a relational database using SQLite. Possible risk factors for carbapenem resistance were queried from the database and analyzed via univariate methods. Significant factors were then entered into a multiple logistic regression model in a forward stepwise approach using SPSS.

Results.
A total of 613 cases of K. pneumoniae BSI were identified in 540 unique patients. The overall incidence of imipenem resistance was 10% (61 cases). Significant markers of resistance included in the final model were (1) prior colonization with imipenem-resistant Klebsiella pneumoniae; (2) hospital unit (defined as high-risk unit, low-risk unit, and emergency department); (3) total inpatient days in the previous 5 years; (4) total days of oral or parenteral antibiotics in the past 2 years; and (5) age >60 years old (Figure 1). The model generated a receiver operating characteristic curve with an area under the curve of 0.75 (Figure 2). At a cut point of 0.083, the model correctly predicted 72% of imipenem-resistant cases while incorrectly labeling 32% of susceptible cases as resistant (Sn = 72%, Sp = 63%, Figure 3).

Conclusion.
A multiple logistic regression model using EMR data can generate immediate, clinically useful predictions of carbapenem resistance in patients with K. pneumoniae BSI. Larger data sets are needed to improve and validate these findings.   Background. Detection and identification of microorganism(s) involved in periprosthetic joint infection (PJI) can inform surgical management and directed antibiotic therapy. Metagenomic shotgun sequencing is a powerful tool with the potential to change how many PJIs are diagnosed as it allows direct detection and identification of pathogens in clinical specimens. In the largest series to date, we utilized a metagenomics-based approach applied to SF to define potential microbial etiologies of failed total knee arthroplasties (TKAs).

Direct Detection and Identification of Prosthetic Joint Pathogens in Synovial Fluid (SF) by Metagenomic Shotgun Sequencing
Methods. Synovial fluid was collected from 112 failed TKAs [74 PJI and 38 aseptic implant failure (AF)] via preoperative arthrocentesis. Cell count and differential, standardized culture and DNA-based metagenomic shotgun sequencing were performed. Human DNA was depleted using the MolYsis basic kit prior to DNA extraction, whole genome amplification, and sequencing. Taxonomic assignment of reads and pathogen identification was achieved using a pipeline incorporating k-mer-and marker gene-based classification software. A scheme for analysis and filtration of false-positives was created and applied, incorporating cut-offs for the number of reads, quality scores, and coverage across a reference genome. Patients were classified as having PJI using the IDSA criteria and expert review. Analyses were recorded as percent agreement, with 95% confidence intervals (CI), of metagenomics to SF culture.
Conclusion. Metagenomic shotgun sequencing performed on SF can be used to diagnose PJI and may be particularly useful for culture-negative PJI.
Disclosures. R. Patel, ASM: Board Member, None; CD Diagnostics, BioFire, Curetis, Merck, Hutchison Biofilm Medical Solutions, Accelerate Diagnostics, Allergan, and The Medicines Company: Grant Investigator, Grant recipient; Curetis: Consultant, Monies paid to my employer; A patent on Bordetella pertussis/parapertussis PCR issued, a patent on a device/method for sonication with royalties paid by Samsung to Mayo Clinic, and a patent on an anti-biofilm substance issued: Patents, Patents, any money is paid to my employer; Actelion: DSMB, Money paid to my employer; ASM and IDSA: Editor's stipends, Editor's stipends; NBME, Up-to-Date and the Infectious Diseases Board Review Course: NBME, Up-to-Date and the Infectious Diseases Board Review Course, Honoraria; Roche, ASM, and IDSA: Travel reimbursement, Travel reimbursement