bluuebunny/crossref_metadata_2025_split huggingface dataset:
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: NoneDisclosures: 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 200-2016-91962Disclosures: 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: NoneDisclosures: 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: NoneDisclosures: 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: NoneDisclosures: 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: NoneDisclosures: 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: NoneDisclosures: 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: NoneDisclosures: None