bluuebunny/crossref_metadata_2025 huggingface dataset:
Whole-Genome Sequencing Reveals Diversity of Carbapenem-Resistant Pseudomonas aeruginosa Collected Through the Emerging Infections Program
Authors: Stanton Richard , Daniels Jonathan , Breaker Erin , Campbell Davina , Lutgring Joseph , Karlsson Maria , Schutz Kyle , Jacob Jesse , Wilson Lucy , Vaeth Elisabeth , Li Linda , Lynfield Ruth , Phipps Erin C. , Hancock Emily , Dumyati Ghinwa , Tsay Rebecca , Cassidy P. Maureen , Mounsey Jacquelyn , Grass Julian , Walters Maroya , Halpin Alison
Published: November 2020

Background: Carbapenem-resistant Pseudomonas aeruginosa (CRPA) is a frequent cause of healthcare-associated infections (HAIs). The CDC Emerging Infections Program (EIP) conducted population and laboratory-based surveillance of CRPA in selected areas in 8 states from August 1, 2016, through July 31, 2018. We aimed to describe the molecular epidemiology and mechanisms of resistance of CRPA isolates collected through this surveillance. Methods: We defined a case as the first isolate of P. aeruginosa resistant to imipenem, meropenem, or doripenem from the lower respiratory tract, urine, wounds, or normally sterile sites identified from a resident of the EIP catchment area in a 30-day period; EIP sites submitted a systematic random sample of isolates to CDC for further characterization. Of 1,021 CRPA clinical isolates submitted, 707 have been sequenced to date using an Illumina MiSeq. Sequenced genomes were classified using the 7-gene multilocus sequence typing (MLST) scheme, and a core genome MLST (cgMLST) scheme was used to determine phylogeny. Antimicrobial resistance genes were identified using publicly available databases, and chromosomal mechanisms of carbapenem resistance were determined using previously validated genetic markers. Results: There were 189 sequence types (STs) among the 707 sequenced genomes (Fig. 1). The most frequently occurring were high-risk clones ST235 (8.5%) and ST298 (4.7%), which were found across all EIP sites. Carbapenemase genes were identified in 5 (<1%) isolates. Overall, 95.6% of the isolates had chromosomal mutations associated with carbapenem resistance: 93.2% had porinD-associated mutations that decrease membrane permeability to the drugs; 24.8% had mutations associated with overexpression of the multidrug efflux pump MexAB-OprM; and 22.9% had mutations associated with overexpression of the endogenous β-lactamase ampC. More than 1 such chromosomal resistance mutation type was present in 37.8% of the isolates. Conclusions: The diversity of the sequence types demonstrates that HAIs caused by CRPA can arise from a variety of strains and that high-risk clones are broadly disseminated across the EIP sites but are a minority of CRPA strains overall. Carbapenem resistance in P. aeruginosa was predominantly driven by chromosomal mutations rather than acquired mechanisms (ie, carbapenemases). The diversity of the CRPA isolates and the lack of carbapenemase genes suggest that this ubiquitous pathogen can readily evolve chromosomal resistance mechanisms, but unlike carbapenemases, these cannot be easily spread through horizontal transfer.

Funding: None

Disclosures: None
Effectiveness of Stewardship Intervention for Urinary Tract Infections in Primary Care: A Difference in Differences Study
Authors: Grigoryan Larissa , Germanos George , Zoorob Roger , Sidani Mohamad , Wang Haijun , Zare Mohammad , Goebel Melanie , Trautner Barbara
Published: November 2020

Adherence to 2011 Infectious Diseases Society of America (IDSA) guidelines for urinary tract infections (UTIs) remains low in primary care. Fluoroquinolones are commonly prescribed to treat uncomplicated cystitis, and most antibiotic prescriptions have durations that exceed current recommendations. We performed a difference-in-differences study to assess the effectiveness of a stewardship intervention in a family medicine clinic at an academic outpatient center from August 2016 to March 2019. During our intervention period, the FDA released 2 additional warnings about the side effects of fluoroquinolones. Methods: The study had 2 sites (intervention and comparison) and 3 periods: baseline, before the intervention, and the intervention. During the first 2 years, we obtained baseline data and performed interviews (preintervention period) exploring provider prescribing decisions for cystitis at both sites. During the intervention period at the intervention site only, we presented an educational lecture including an overview of the IDSA guidelines, definitions for various UTI syndromes and actual clinical examples, and instruction on use of a decision aid. During the audit and feedback phase, providers were contacted once per month in person or by phone to provide follow-up on whether their treatment decision adhered to the IDSA guidelines. We performed a log-binomial regression analysis of the primary outcome, adherence to the IDSA guidelines for management of uncomplicated cystitis, both to antibiotic choice and duration of therapy. Results: We performed 156 audit-and-feedback sessions with 13 providers during the intervention period (March 2018–2019). Patients in both sites were similar in terms of age and Charlson comorbidity index. Adherence to the guidelines for antibiotic choice and duration increased in the intervention period at both sites (Fig. 1). The treatment of cystitis in the intervention period of the intervention site was 11.5 times (95% CI, 6.1–21.6) as likely to be guideline-adherent as the treatment in the baseline period of the comparison site (Fig. 2). Conclusions: Adherence to IDSA guidelines for the choice of antibiotic and duration increased in both intervention and comparison sites. Even though the intervention site started with higher compliance, improvement was also greater in the intervention site. FDA warnings about the side effects of fluoroquinolones released during the intervention period may have contributed to the avoidance of fluoroquinolones at both sites. Our intervention was effective at improving antibiotic choice and duration, so our future plans include incorporating our decision-support algorithm into the electronic medical record.

Funding: This study was supported by Zambon Pharmaceuticals.

Disclosures: None
Firefighters Fighting Germs - Evaluation of a Disinfecting Protocol for Use in the Fire Service
Authors: McGuire-Wolfe Christine
Published: November 2020

Background: Pasco County Fire Rescue (PCFR) is a rapidly growing suburban fire department located in Florida. PCFR employs >500 firefighters (all cross-trained as either emergency medical technicians or paramedics) in 27 stations to provide both emergency medical services (EMS) and fire suppression response. Although multiple studies have established that pathogens are present in both apparatus and stations within the fire service, there is a knowledge gap regarding the effectiveness of cleaning and disinfecting protocols in this specific setting. Methods: In total, 65 high-touch surfaces in 11 vehicles (ambulances and engines) and common areas of 2 fire stations were swabbed before and after disinfection. Vehicle surfaces swabbed included seats, cabinet doors, door handles, stretchers, medical equipment, keyboard, steering wheels, shared headsets and hand rails. Inside the stations, the refrigerator handle, television remote, radio and alarm buttons, door handles, and locker handles were swabbed. Immediately following the initial swab collection, the surfaces were disinfected with hydrogen peroxide wipes and disinfectant cleaner sprayed through an electrostatic system. The same surfaces were then swabbed after disinfection. Colony-forming units (CFUs) were quantified using standard microbiological techniques by a third-party laboratory. Statistical analysis was performed on the resulting bacterial counts using Minitab version 18.1 software. Results: We detected statistically significant decreases in total bacteria, yeast, and mold counts following implementation of this disinfection protocol. The predisinfection mean of bacteria, yeast, and mold counts for all surfaces combined was reduced 96% after disinfection (from 254,637 CFU to 9,392 CFU). Conclusions: Cleaning and disinfection of surfaces in PCFR emergency vehicles and fire station common areas with the agents described above effectively reduced contamination with bacteria, yeast, and mold spores. The PCFR has implemented this disinfection protocol as a tool in eliminating EMS vehicles and the station environment as reservoirs of infection for patients, visitors, and firefighters. Future efforts will include assessing the impact of regular cleaning and disinfection on baseline levels of bacteria, yeast, and mold spores.

Funding: This study was supported by Clorox.

Disclosures: None
Influence of Antibiotic Susceptibility Testing on Antibiotic Choice in Hospital-Acquired and Ventilator-Associated Pneumonia
Authors: Zappernick Taissa , Christian Robbie , Sims Sharanie , Wilson Brigid , Perez Federico , Bonomo Robert , Jump Robin
Published: November 2020

Background: The survival of patients with hospital-acquired pneumonia (HAP) and ventilator-associated pneumonia (VAP) is largely determined by the timely administration of effective antibiotic therapy. Guidelines for the treatment HAP and VAP recommend empiric treatment with broad-spectrum antibiotics and tailoring of antibiotic therapy once results of microbiological testing are available. Objective: We examined the influence of bacterial identification and antibiotic susceptibility testing on antibiotic therapy for patients with HAP or VAP. Methods: We used the US Veterans’ Health Administration (VHA) database to identify a retrospective cohort of patients diagnosed with HAP or VAP between fiscal year 2015 and 2018. We further analyzed patients who were started on empiric antibiotic therapy, for whom microbiological test results from a respiratory sample were available within 7 days and who were alive within 48 hours of sample collection. We used the antibiotic spectrum index (ASI) to compare antibiotics prescribed the day before and the day after availability of bacterial identification and antibiotic susceptibility testing results. Results: We identified 4,669 cases of HAP and VAP in 4,555 VHA patients. The median time from respiratory sample receipt in the laboratory to final result of bacterial identification and antibiotic susceptibility testing was 2.22 days (IQR, 1.31–3.38 days). The most common pathogen was Staphylococcus aureus (n = 994), with methicillin resistance in 58% of those isolates tested. The next most common pathogen was Pseudomonas spp (n = 946 isolates). The susceptibility of antipseudomonal antibiotics, when tested, was as follows: 64% to carbapenems, 74% to cephalosporins, 75% to β-lactam/β-lactamase inhibitors, 69% to fluoroquinolones, and 95% to amikacin. Lactose-fermenting gram-negative bacteria (296 Escherichia coli and 360 Klebsiella pneumoniae) were also common. Among the 3,094 cases who received empiric antibiotic therapy, 607 (20%) had antibiotics stopped the day after antibiotic susceptibility results became available, 920 (30%) had a decrease in ASI, 1,075 (35%) had no change in ASI, and 492 (16%) had an increase in ASI (Fig. 1). Among the 1,098 patients who were not started on empiric antibiotic therapy, only 154 (14%) were started on antibiotic therapy the day after antibiotic susceptibility results became available. Conclusions: Changes in antibiotic therapy occurred in at least two-thirds of cases the day after bacterial identification and antibiotic susceptibility results became available. These results highlight how respiratory cultures can inform the treatment and improve antibiotic stewardship for patients with HAP/VAP.

Funding: This study was supported by Accelerate Diagnostics.

Disclosures: None
Patient-Specific Predictive Antibiogram in Decision Support for Empiric Antibiotic Treatment
Authors: Sengupta Debarka , Singh Vaibhav , Singh Seema , Tewari Dinesh , Kapoor Mudit , Ghosh Debabrata , Sharma Shivam
Published: November 2020

Background: The rising trend of antibiotic resistance imposes a heavy burden on healthcare both clinically and economically (US$55 billion), with 23,000 estimated annual deaths in the United States as well as increased length of stay and morbidity. Machine-learning–based methods have, of late, been used for leveraging patient’s clinical history and demographic information to predict antimicrobial resistance. We developed a machine-learning model ensemble that maximizes the accuracy of such a drug-sensitivity versus resistivity classification system compared to the existing best-practice methods. Methods: We first performed a comprehensive analysis of the association between infecting bacterial species and patient factors, including patient demographics, comorbidities, and certain healthcare-specific features. We leveraged the predictable nature of these complex associations to infer patient-specific antibiotic sensitivities. Various base-learners, including k-NN (k-nearest neighbors) and gradient boosting machine (GBM), were used to train an ensemble model for confident prediction of antimicrobial susceptibilities. Base learner selection and model performance evaluation was performed carefully using a variety of standard metrics, namely accuracy, precision, recall, F1 score, and Cohen κ. Results: For validating the performance on MIMIC-III database harboring deidentified clinical data of 53,423 distinct patient admissions between 2001 and 2012, in the intensive care units (ICUs) of the Beth Israel Deaconess Medical Center in Boston, Massachusetts. From ~11,000 positive cultures, we used 4 major specimen types namely urine, sputum, blood, and pus swab for evaluation of the model performance. Figure 1 shows the receiver operating characteristic (ROC) curves obtained for bloodstream infection cases upon model building and prediction on 70:30 split of the data. We received area under the curve (AUC) values of 0.88, 0.92, 0.92, and 0.94 for urine, sputum, blood, and pus swab samples, respectively. Figure 2 shows the comparative performance of our proposed method as well as some off-the-shelf classification algorithms. Conclusions: Highly accurate, patient-specific predictive antibiogram (PSPA) data can aid clinicians significantly in antibiotic recommendation in ICU, thereby accelerating patient recovery and curbing antimicrobial resistance.

Funding: This study was supported by Circle of Life Healthcare Pvt. Ltd.

Disclosures: None
Similar Mortality in Patients with Invasive and Noninvasive Pneumonia Due to Group B Streptococcus
Authors: Wilson Brigid , Song Sunah , Zappernick Taissa , Briggs Janet , Banks Richard , Jump Robin , Perez Federico
Published: November 2020

Background: Rates of invasive infections caused by caused group B Streptococcus (GBS) are increasing among adults. The burden of noninvasive GBS infections, including pneumonia, has not been well characterized. Here, we compare comorbidities and mortality associated with invasive and noninvasive pneumonia caused by GBS. Methods: Using the Veterans’ Health Administration national data warehouse, we studied a retrospective cohort review of veterans diagnosed with GBS pneumonia between 2008 and 2017. Invasive pneumonia was defined as blood cultures positive for GBS associated with an order for a chest x-ray and an International Classification of Disease (ICD) code for pneumonia. Noninvasive pneumonia was defined as a respiratory culture positive for GBS associated with both an order for a chest x-ray and an ICD code for pneumonia among patients with negative or without blood cultures. Patients with respiratory cultures positive for GBS without either an associated chest x-ray or ICD code for pneumonia were considered colonized. We compared demographics, comorbid conditions, and mortality among patients with invasive and noninvasive GBS pneumonia. Results: Between 2008 and 2017, we detected 706 cases of invasive GBS pneumonia, 1,244 cases of noninvasive GBS pneumonia, and 1,470 cases of respiratory colonization with GBS. Most patients were male (97%), with an average age of 69.0 years (SD, 12.0 years). The prevalence of several comorbid conditions differed between those with invasive and noninvasive disease: diabetes mellitus (61% and 46%, respectively); chronic pulmonary diseases (53% and 65%, respectively); chronic heart disease (58% and 44%, respectively), chronic kidney disease (43% and 27%, respectively). Mortality was similar among those with invasive and noninvasive GBS pneumonia at 30 days (17% and 18%, respectively) and at 1 year (38% and 43%, respectively) (Fig. 1). Conclusions: We identified important differences in underlying comorbid conditions between patients with invasive and noninvasive GBS pneumonia, which may give rise to differences in their clinical presentation. Overall mortality, however, was similar: more than one-third of patients with GBS pneumonia died within 1 year. These findings indicate that noninvasive GBS pneumonia is an important clinical entity.

Funding: This study was supported by Pfizer.

Disclosures: None
Surgical Site Infections with Predominance of Multidrug Resistant in Benin: A Multicenter Study
Authors: YEHOUENOU Carine Laurence , RODRIGUEZ-VILLALOBOS Hector , DALLEUR Olivia , SIMON Anne
Published: November 2020

Background: Surgical site infections remain common and widespread; they contribute to increasing antimicrobial resistance among the etiological agents. Antimicrobial resistance is the ability of a microorganism like bacteria to stop an antimicrobial from working against it. This study was conducted to determine the spectrum of bacterial isolates from surgical site infections and their susceptibility patterns. A secondary outcome was to compare bacterial identification by a local lab and a European one. Methods: This descriptive cross-sectional study was conducted between January and August 2019 in 6 public hospitals in Benin. Pus specimens were processed using standard microbiological procedures, and identification was performed using the analytical profile index (API). Antimicrobial susceptibility testing was performed in Benin following the modified Kirby-Bauer disk-diffusion technique and was confirmed in Belgium by MALDI-TOF mass spectrometry. A second antimicrobial susceptibility test was performed using BD Phoenix automated microbiology system (Becton Dickinson). Clinical data of enrolled patients were obtained from hospital records. Results: The mean age of patients was 32 ± 11 years (range, 18–76). The median time for surgical site infections was 9 postoperative days. Of the 229 patients from whom wound swabs were collected, 195 (85.15%) showed positive aerobic bacterial growth. In total, 164 pathogenic bacteria were isolated, including 41 gram-positive organisms (25%), 78 gram-negative fermentative bacteria (47.5%), and 45 gram-negative nonfermentative bacteria (27.5%). We observed 3 discrepancies between API technique and MALDI-TOF. Two Klebsiella pneumoniae and 1 Pseudomonas spp (API) versus, respectively, Klebsiella varicola and Pseudomonas mendocina (MALDI-TOF). The most prevalent bacterial species were E. coli (31%), followed by S. aureus (25%), Pseudomonas aeruginosa (18%), and Klebsiella pneumoniae (11%). Of the 41 S. aureus, 26 (63,41%) were methicillin-resistant Staphylococcus aureus (MRSA), and 3 of these were carrying both MRSA and induced clindamycin resistance (ICR). Extended-spectrum β-lactamase (ESBL)–producing Enterobacteriaceae were observed in 60 of 78 isolates tested (77%). All of 2 Morganella morgannii and 89% of K. pneumoniae were ESBL producers. Conclusions: Among S. aureus, 2 of 3 were MRSA, whereas almost K. pneumoniae and E. coli were ESBL producers. Three strains are pan–drug resistant in nonfermentative bacteria, and no isolate was susceptible to all antibiotics. These findings are of high interest for better management of patients and control of antimicrobial resistance in Benin.

Funding: This study was supported by Académie de Recherche pour l’Enseignement Supérieur (ARES).

Disclosures: None
Targeted Assessment for Prevention Facility Assessments: The Most Common CAUTI and CLABSI Infection Prevention Gaps
Authors: Snyder Rachel , White Katelyn , Glowicz Janet , Novosad Shannon , Soda Elizabeth , Kuhar David , Sinkowitz-Cochran Ronda
Published: November 2020

Background: The Targeted Assessment for Prevention (TAP) strategy is a quality improvement framework created by the Centers for Disease Control and Prevention (CDC) to facilitate the reduction of healthcare-associated infections (HAIs). TAP facility assessments are a component of the TAP strategy and are completed by staff across the facility to help identify perceptions of and target infection prevention gaps. We have described the gaps most commonly reported by facilities completing TAP facility assessments for catheter-associated urinary tract infections (CAUTIs) and central-line–associated bloodstream infections (CLABSIs). Methods: TAP CAUTI and CLABSI assessments were completed by acute-care facilities across the nation, with CDC technical assistance, from December 2014 to August 2019. Similar questions across 2 versions of CAUTI assessments and 3 versions of CLABSI assessments were combined. Analysis was limited to facilities with ≥10 assessments. Infection prevention gaps were defined as ≥33% respondents answering Unknown, ≥33% respondents answering “no,” or ≥50% of respondents answering “no” and “unknown” or “never” and “rarely” “sometimes” “unknown.” The analysis was completed at the facility level, and the gaps most commonly reported across facilities were identified. Results: In total, 1,942 CAUTI assessments from 42 facilities in 12 states and 1,623 CLABSI assessments from 29 facilities in 11 states were included for analysis. The mean numbers of assessments per facility were 46.2 for CAUTIs and 56.0 for CLABSIs. Across both CAUTIs and CLABSIs, commonly reported perceptions about infection prevention gaps included lack of physician and nurse champions for prevention activities, failure to conduct competency assessments, and inconsistency in select device insertion practices (Fig. 1). For CAUTIs, lack of practices to facilitate timely removal of urinary catheters were also commonly reported, with one-third of facilities reporting inconsistency in use of alerts for catheter removal, 78.6% reporting lack of physician response to these alerts, and 90.5% reporting deficiencies in removing unnecessary catheters in the postanesthesia care unit. For CLABSIs, 79.3% of facilities reported failure to replace central lines within 48 hours after emergent insertion, and 62.1% reported that feedback was not provided to staff on central-line device utilization ratios. Conclusion: For both assessments, absence of CAUTI and CLABSI prevention champions, failure to conduct competency assessments, and inconsistency in performing device insertion practices were commonly reported across facilities. These common gaps have and will continue to inform the development of tools and resources to improve infection prevention practices as well as help to better target the implementation of interventions.

Funding: None

Disclosures: None
The Gut Microbiome and Resistome of Healthy Volunteers are Restructured After Short Courses of Antibiotics
Authors: Anthony Winston , Sukhum Kimberley , Cass Candice , Reske Kimberly , Seiler Sondra , Hink Tiffany , Coon Christopher , D'Souza Alaric , Wang Bin , Sun Sherry , Dubberke Erik , Burnham Carey-Ann , Dantas Gautam , Kwon Jennie H.
Published: November 2020

Background: Antimicrobial exposure is a significant risk factor for the development of antibiotic-resistant organisms (ARO); however, the depth and duration of this impact is not well described. The study goal is to define impact of antibiotics on the gut microbiome of healthy volunteers (HVs). Methods: HVs were randomized to receive either 5 days of levofloxacin (LVX), azithromycin (AZM), cefpodoxime (CPD), or AZM + CPD (Fig. 1). Stool samples were collected at 15 time points per patient before, during, and after antibiotics. Remnant stool samples from the microbiology laboratory were collected from patients admitted to the medical intensive care unit (MICU) as a comparison of the microbiome in a critically ill state. DNA was extracted from samples and was submitted for shotgun sequencing. Relative abundance, resistome, and metabolic pathway abundance of bacterial taxa were determined and statistical analysis conducted in R software. Results: In total, 289 stool specimens from 20 HVs, and 26 remnant stool specimens were obtained from patients admitted from the MICU (Fig. 1). Community diversity and richness decreased in the first week post-ABX for all HVs (P < .01). Linear discriminant analysis identified Bacteroides and Clostridium as taxonomic groups enriched after CPD, while AZM and LVX produced a relative abundance increase in diverse Firmicutes spp. Longitudinal tracking confirmed that after all antibiotics except LVX, HV microbiomes lost species diversity and shifted toward a state similar to that observed in MICU patients (Fig. 2). The gut microbiome of most HVs exhibited resiliency and returned to a higher diversity level similar to their starting point; however, 10% of HVs did not. Moreover, antibiotic-specific increases in resistance markers reveal innate resistance to β-lactams and macrolides within the gut microbiome of the HVs. Finally, HV microbiomes, which shifted toward a MICU-like taxonomic state, also clustered with microbial metabolic profiles from MICU patients.

The HV microbial metabolic profiles were significantly enriched for important biosynthesis pathways producing chorismate and polysaccharides. MICU patient gut microbiomes were enriched for fatty acid regulation and quinolone biosynthesis, and for many degradation pathways important for different aspects of antibiotic resistance such as membrane integrity, alternative respiration, and antibiotic inactivation. Conclusions: Short courses of antibiotics can cause acute and chronic microbiome disruptions in HVs, as evidenced by decreased microbiome diversity and increases in specific innate resistance elements. These data support the need for antimicrobial stewardship to support rationale antibiotic use to prevent gut microbiome disruptions.

Funding: CDC BAA 200-2016-91962

Disclosures: None
Mining Camera Traces to Estimate Interactions Between Healthcare Workers and Patients
Authors: Hasan D. M. Hasibul , Polgreen Philip , Segre Alberto , Simmering Jacob , Pemmaraju Sriram
Published: November 2020

Background: Simulations based on models of healthcare worker (HCW) mobility and contact patterns with patients provide a key tool for understanding spread of healthcare-acquired infections (HAIs). However, simulations suffer from lack of accurate model parameters. This research uses Microsoft Kinect cameras placed in a patient room in the medical intensive care unit (MICU) at the University of Iowa Hospitals and Clinics (UIHC) to obtain reliable distributions of HCW visit length and time spent by HCWs near a patient. These data can inform modeling efforts for understanding HAI spread. Methods: Three Kinect cameras (left, right, and door cameras) were placed in a patient room to track the human body (ie, left/right hands and head) at 30 frames per second. The results reported here are based on 7 randomly selected days from a total of 308 observation days. Each tracked body may have multiple raw segments over the 2 camera regions, which we “stitch” up by matching features (eg, direction, velocity, etc), to obtain complete trajectories. Due to camera noise, in a substantial fraction of the frames bodies display unnatural characteristics including frequent and rapid directional and velocity change. We use unsupervised learning techniques to identify such “ghost” frames and we remove from our analysis bodies that have 20% or more “ghost” frames. Results: The heat map of hand positions (Fig. 1) shows that high-frequency locations are clustered around the bed and more to the patient’s right in accordance with the general medical practice of performing patient exams from their right. HCW visit frequency per hour (mean, 6.952; SD, 2.855) has 2 peaks, 1 during morning shift and 1 during the afternoon shift, with a distinct decrease after midnight. Figure 2 shows visit length (in minutes) distribution (mean, 1.570; SD, 2.679) being dominated by “check in visits” of <30 seconds. HCWs do not spend much time at touching distance from patients during short-length visits, and the fraction of time spent near the patient’s bed seems to increase with visit length up to a point. Conclusions: Using fine-grained data, this research extracts distributions of these critical parameters of HCW–patient interactions: (1) HCW visit length, (2) HCW visit frequency as a function of time of day, and (3) time spent by HCW within touching distance of patient as a function of visit length. To the best of our knowledge, we provide the first reliable estimates of these parameters.

Funding: None

Disclosures: None
Novel Methodology to Measure Preprocedure Antimicrobial Prophylaxis: Integrating Text Mining With Structured Data
Authors: Mull Hillary , Stolzmann Kelly , Kalver Emily , Shin Marlena , Schweizer Marin , Asundi Archana , Mehta Payal , Stanislawski Maggie , Branch-Elliman Westyn
Published: November 2020

Background: Antimicrobial prophylaxis is an evidence-proven strategy for reducing procedure-related infections; however, measuring this key quality metric typically requires manual review, due to the way antimicrobial prophylaxis is documented in the electronic medical record (EMR). Our objective was to combine structured and unstructured data from the Veterans’ Health Administration (VA) EMR to create an electronic tool for measuring preincisional antimicrobial prophylaxis. We assessed this methodology in cardiac device implantation procedures. Methods: With clinician input and review of clinical guidelines, we developed a list of antimicrobial names recommended for the prevention of cardiac device infection. Next, we iteratively combined positive flags for an antimicrobial order or drug fill from structured data fields in the EMR and hits on text string searches of antimicrobial names documented in electronic clinical notes to optimize an algorithm to flag preincisional antimicrobial use with high sensitivity and specificity. We trained the algorithm using existing fiscal year (FY) 2008-15 data from the VA Clinical Assessment Reporting and Tracking-Electrophysiology (CART-EP), which contains manually determined information about antimicrobial prophylaxis. We then validated the performance of the final version of the algorithm using a national cohort of VA patients who underwent cardiac device procedures in FY 2016 or 2017. Discordant cases underwent expert manual review to identify reasons for algorithm misclassification and to identify potential future implementation barriers. Results: The CART-EP dataset included 2,102 procedures at 38 VA facilities with manually identified antimicrobial prophylaxis in 2,056 cases (97.8%). The final algorithm combining structured EMR fields and text-note search results flagged 2,048 of the CART-EP cases (97.4%). Algorithm validation identified antimicrobial prophylaxis in 16,334 of 19,212 cardiac device procedures (87.9%). Misclassifications occurred due to EMR documentation issues. Conclusions: We developed a methodology with high accuracy to measure guideline-concordant use of antimicrobial prophylaxis before cardiac device procedures using data fields present in modern EMRs that does not rely on manual review. In addition to broad applicability in the VA and other healthcare systems with EMRs, this method could be adapted for other procedural areas in which antimicrobial prophylaxis is recommended but comprehensive measurement has been limited to resource-intense manual review.

Funding: None

Disclosures: None
Making a Case for Adjusting NHSN SSI Risk Stratification Classification for Use of Enhanced Electronic Infection Surveillance
Authors: Pearson Meri , Doline Krista
Published: November 2020

Background: A large healthcare system in Georgia went live with an enhanced electronic infection surveillance system in August of 2018. The system was employed at its facilities using a staggered approach. Prior to the implementation of this infection surveillance platform, the healthcare system performed healthcare-associated infection (HAI) surveillance using an in-house culture-based system. The NHSN estimates that culture-based surveillance misses 50%–60% of true surgical site infections (SSIs). Due to the lack of clinical-based detection methods (eg, radiologic imaging), we were unable to appropriately detect all patient harm using the old surveillance system. Method: A retrospective analysis was performed to assess the change in HAI for colon (COLO), abdominal hysterectomy (HYST), hip prosthesis (HPRO), and knee prosthesis (KPRO). SSI cases that met NHSN surveillance criteria were reviewed to determine whether they would have been identified prior to launching the new enhanced electronic surveillance system. Results: Systemwide, 8 of 26 COLO SSIs (31%) and 9 of 18 HYST SSIs (50%) would have not been detected using our old surveillance system. HPRO SSIs and KPRO SSIs identified by our new surveillance system were detected using our old surveillance system, and no change was observed. Conclusion: This analysis showed an increase in COLO SSIs and HYST SSIs from enhanced surveillance. Electronic surveillance systems are not considered as a risk factor in the NHSN annual facility survey that aids in calculating a facility’s standardized infection ratio (SIR). These data help support NHSN consideration of modifying the logistic regression calculation used for the complex SSI models. This revision would allow facilities to compare themselves equitably to those using electronic infection surveillance.

Funding: None

Disclosures: None
Measles Exposure Investigation in a Children’s Hospital Emergency Department— Denver Metropolitan Area, Colorado, 2019
Authors: Richter Ashley
Published: November 2020

Background: On December 14, 3 unvaccinated siblings with recent international travel presented to Children’s Hospital Colorado emergency department (CHCO-ED) with fever, rash, conjunctivitis, coryza, and cough. Measles was immediately suspected; respiratory masks were placed on the patients before they entered an airborne isolation room, and public health officials (PH) were promptly notified. Notably, on December 12, 1 ill sibling presented to CHCO-ED with fever only. We conducted an investigation to confirm measles, to determine susceptibility of potentially exposed ED contacts and healthcare workers (HCWs), and to implement infection prevention measures to prevent secondary cases. Methods: Measles was confirmed using polymerase chain reaction testing. Through medical record review and CHCO-ED unit-leader interviews, we identified patients and HCWs in overlapping ED areas with the first sibling, until 2 hours after discharge. Measles susceptibility was assessed through interviews with adults accompanying pediatric patients and HCW immunity record reviews. Potentially exposed persons were classified as immune (≥1 documented measles-mumps-rubella (MMR) vaccination or serologic evidence of immunity), unconfirmed immune (self-reported MMR or childhood vaccination without documentation), or susceptible (no MMR vaccine history or age <12 months). Susceptibility status directed disease control intervention, and contact follow-up was 21 days. Results: On December 14, all 3 siblings (ages 8–11 years) had laboratory-confirmed measles and were hospitalized. CHCO’s rapid isolation of the 3 cases within 5 minutes after presentation to the ED eliminated the need for exposure assessment on the day of hospitalization. However on December 12, the 1 ill sibling potentially exposed 258 ED contacts (90 patients, 168 accompanying adults) and 22 HCWs. The PH department identified 158 immune contacts (61%), 75 unconfirmed immune contacts (29%), and 19 susceptible contacts (8%); 6 contacts (2%) were lost to follow-up. Overall, 15 susceptible contacts received immune globulin (IG) postexposure prophylaxis and 4 contacts were placed on 21-day quarantine. Unconfirmed immune contacts self-monitored for measles symptoms and were contacted weekly by PH for 21 days. Moreover, 20 immune HCWs monitored symptoms daily; 2 susceptible HCWs were placed on 21-day quarantine. No secondary cases were identified. Conclusions: Rapid measles identification and isolation, high levels (90%) of immunity among contacts, prompt administration of IG, and effective collaboration between PH and CHCO prevented transmission.

Funding: None

Disclosures: None
Comparing Automated Cluster Detection Methods for Carbapenem-Resistant Enterobacteriaceae (CRE): Rule-Based Versus Statistical
Authors: Octaria Rany , Griffith Hannah , Estes Matthew , Wiedeman Caleb , Chan Allison , Kainer Marion
Published: November 2020

Background:
The Tennessee (TN) Department of Health (TDH) has been identifying clusters of reportable conditions using the Electronic Surveillance System for Early Notification of Community-Based Epidemics (ESSENCE), a cluster detection method using space-time scan permutation statistics based on patient ZIP code. CRE are reportable in Tennessee; isolate submission is required for carbapenemase (CP) production and resistance mechanism (eg, KPC gene) testing. The Council for Outbreak Response: Healthcare-Associated Infections (HAI) and Antimicrobial-Resistant (AR) Pathogens (CORHA) released proposed thresholds of reporting CRE to public health. Thresholds vary by healthcare facility type and regional epidemiology. The TDH HAI/AR program currently runs a daily automated SAS code using the CORHA reporting threshold to help public health identify suspect KPC clusters. We evaluated our rule-based CORHA method against 2 space-time statistic-based methods for KPC cluster detection in Tennessee. Methods: Simulations for each cluster detection method were performed using retrospective CP-CRE surveillance data for 2018. Simulations were conducted using (1) CORHA reporting thresholds by facility case count to flag clusters of 2 or more cases within 28 days, (2) ESSENCE using patient residence ZIP code and the earliest of collection date or symptom onset date as is used for other reportable conditions in Tennessee, and (3) a modified space-time statistical method using SaTScan in which reporting facility, rather than a geographic location, was used as space variable to detect within-facility clusters within 1–28 days. We compared the number and overlap of cases and clusters identified with each method. Univariate logistic regression with CORHA flagging as predictor and flagging by each ESSENCE or CORHA method as outcome variables, were used to compare cases tagged by each method pair, respectively. Results: Of 183 KPC CP-CRE cases, 54 (30.6%) were flagged as part of suspect clusters by at least 1 method. Simulations generated 16 alerts (36 cases) using CORHA, 10 clusters (25 cases) using modified SaTScan, and 10 clusters (20 cases) using standard ESSENCE protocol. Among KPC CP-CRE cases flagged by CORHA, 12 (33.3%) were also flagged by modified SaTScan and 2 (5%) by ESSENCE. A case flagged using CORHA method has 5.15 (95% CI, 2.10–12.64) times higher odds of also being flagged by the modified SaTScan method compared to cases not flagged by CORHA. Conclusions: An algorithm based on CORHA thresholds for reporting CRE to public health had strong agreement with modified SaTScan, a space-time method. We intend to explore the extension of the time interval for ESSENCE.

Funding: None

Disclosures: None
Contamination of Healthcare Worker Personal Protective Equipment with MRSA Outside the Intensive Care Unit Setting
Authors: Nadimpalli Gita , Pineles Lisa , Lebherz Karly , Johnson J. Kristie , Calfee David , Miller Loren , Morgan Daniel , Harris Anthony
Published: November 2020

Background: Estimates of contamination of healthcare personnel (HCP) gloves and gowns with methicillin-resistant Staphylococcus aureus (MRSA) following interactions with colonized or infected patients range from 17% to 20%. Most studies were conducted in the intensive care unit (ICU) setting where patients had a recent positive clinical culture. The aim of this study was to determine the rate of MRSA transmission to HCP gloves and gown in non-ICU acute-care hospital units and to identify associated risk factors. Methods: Patients on contact precautions with history of MRSA colonization or infection admitted to non-ICU settings were randomly selected from electronic health records. We observed patient care activities and cultured the gloves and gowns of 10 HCP interactions per patient prior to doffing. Cultures from patients’ anterior nares, chest, antecubital fossa and perianal area were collected to quantify bacterial bioburden. Bacterial counts were log transformed. Results: We observed 55 patients (Fig. 1), and 517 HCP–patient interactions. Of the HCP–patient interactions, 16 (3.1%) led to MRSA contamination of HCP gloves, 18 (3.5%) led to contamination of HCP gown, and 28 (5.4%) led to contamination of either gloves or gown. In addition, 5 (12.8%) patients had a positive clinical or surveillance culture for MRSA in the prior 7 days. Nurses, physicians and technicians were grouped in “direct patient care”, and rest of the HCPs were included in “no direct care group.” Of 404 interactions, 26 (6.4%) of providers in the “direct patient care” group showed transmission of MRSA to gloves or gown in comparison to 2 of 113 (1.8%) interactions involving providers in the “no direct patient care” group (P = .05) (Fig. 2). The median MRSA bioburden was 0 log 10CFU/mL in the nares (range, 0–3.6), perianal region (range, 0–3.5), the arm skin (range, 0-0.3), and the chest skin (range, 0–6.2). Detectable bioburden on patients was negatively correlated with the time since placed on contact precautions (rs= −0.06; P < .001). Of 97 observations with detectable bacterial bioburden at any site, 9 (9.3%) resulted in transmission of MRSA to HCP in comparison to 11 (3.6%) of 310 observations with no detectable bioburden at all sites (P = .03). Conclusions: Transmission of MRSA to gloves or gowns of HCP caring for patients on contact precautions for MRSA in non-ICU settings was lower than in the ICU setting. More evidence is needed to help guide the optimal use of contact precautions for the right patient, in the right setting, for the right type of encounter.

Funding: None

Disclosures: None
Direct Data Mining from the Electronic Medical Record to Assess and Improve Compliance With Infection Prevention Bundles
Authors: Conner Janet , Ivaska Joan
Published: November 2020

Background: Bundles have been proven to reduce the risk of healthcare-associated infections and to provide for rapid recognition and response for the best outcome in patients with sepsis. Each element alone does not provide the statistical significance that all elements together allow. Providing near real-time compliance with bundle measures to clinical staff can drive performance improvement with the bundle during the patient’s hospital stay, resulting in improved clinical care and prevention of infection. Methods: In 2019, 3 clinical initiatives were chartered that applied evidence-based bundles for early identification and treatment of sepsis, prevention of healthcare-associated pneumonia (HAP), and prevention of surgical site infection. The bundle included the following elements: assessment of sepsis, measurement of lactic acid, collection of blood culture, timely administration of antibiotics. The HAP bundle included the following elements: assessment of aspiration risk, elevation of the head of the bed, oral care twice daily and preoperatively, and incentive spirometry postoperatively. And the SSI bundle included the following elements: preoperative CHG bath, appropriate preoperative antibiotic, perioperative glucose control, and perioperative temperature control. A multidisciplinary team developed and implemented dashboards that extracted bundle elements from the electronic medical record (EMR) nightly. Bundle compliance was calculated at the individual element level as well as the aggregate. Bundle failure data were available at the patient level as well as in aggregate by care location and provider, allowing for real-time feedback to staff and creation of improvement plans. An unanticipated benefit was the identification and correction of charting inconsistencies. Results: Collection, aggregation, and analysis of bundle compliance data were displayed in a system dashboard, and data were refreshed nightly. This approach allowed us to display overall bundle compliance at the facility and system level, including a heat map showing each facility’s compliance with the bundle and each associated element. Utilization of an EMR dashboard allowed for performance review on 100% of eligible patients rather than a sample, as occurs with manual review and abstraction processes. Routine review of performance via the dashboards with frontline staff, clinical leaders, medical staff, and executives has resulted in month-by- month improvement in bundle compliance. Conclusions: Direct data mining, data aggregation and analysis, followed by direct feedback to frontline staff, has resulted in steady improvement in overall bundle compliance, compliance with individual bundle components, and standardization of charting in the EMR. This approach has ultimately resulted in better outcomes for sepsis patients, reduction in healthcare-associated pneumonia, and reduction in surgical site infections.

Funding: None

Disclosures: None