bluuebunny/crossref_metadata_2025_split huggingface dataset:
2424. Shedding of Viable Clostridiodes difficile in Patients Admitted to a Medical Intensive Care Unit
Authors: Young Vincent B , Keidan Micah , Yelin Rachel D , Dangana Thelma E , Bell Pamela B , Rao Krishna , Hayden Mary K 
Published: October 2019

Abstract

Background
Hospitalized patients are at risk of colonization with a range of healthcare-associated bacterial pathogens, including C. difficile. In patients admitted to intensive care units (ICUs), in whom C. difficile infection (CDI) is associated with increased morbidity and mortality. To understand the risk for acquisition of C. difficile and development of CDI, we monitored ICU patients daily for shedding of C. difficile by culture.

Methods
We conducted a secondary analysis of daily rectal/fecal swab samples collected from medical ICU patients of a 720-bed academic medical center in Chicago, IL. Selective culture for C. difficile was performed on swab samples from patients who had 2 or more samples obtained using selective media. Confirmation of putative C. difficile isolates was done by specific PCR assays for the 16S rRNA-encoding gene and the toxin genes tcdA, tcdB, cdtA and cdtB. Clinical testing for CDI was performed using the Xpert® C. difficile PCR assay (Cepheid). Clinical and demographic metadata were collected at bedside and by electronic medical record review.

Results
Culture was attempted on 2106 swab samples from 451 patients (486 ICU admissions) (Figure 1). A mean of 4.33 samples was obtained from each patient. C. difficile was isolated from 211 (10%) samples from 79 patients (Table 1). The first sample was positive by culture for 48 (9.9%) of patient admissions to the ICU. 31 (6.4%) patients who were initially negative by culture had a subsequent sample from which C. difficile was isolated. Persistence of culture-positivity varied from patient to patient (Figure 2). Of 80 patients who were tested for CDI based on physician suspicion, 12 patients had a positive Cepheid PCR test; 9 had diarrhea and were treated for CDI.

Conclusion
Surveillance for shedding of C. difficile by daily culture reveals that patients admitted to the ICU can shed the pathogen intermittently without attributable disease. This can be in the form patients who are admitted carrying the organism as well as those who appear to acquire the organism during their stay. It is unclear whether patient or microbiome factors underlie the differences seen in patterns of shedding. Furthermore, intermittent shedding may reflect multiple episodes of exposure to C. difficile spores and asymptomatic shedding without stable colonization.

Disclosures
All authors: No reported disclosures.
2426. Performance of Statistical Process Control Charts for Detecting Clinically-Significant Increases in Clostridium difficile Infection Rates
Authors: Nehls Nicole , Bilantuono Anthony , Etherton Christian , Ilieş Iulian , Benneyan James C , Baker Arthur W , Anderson Deverick J 
Published: October 2019

Abstract

Background
Clostridium difficile infections (CDIs) are the most common type of healthcare-associated infection in the United States, with an estimated annual incidence of 500,000 cases and excess healthcare costs of $5 billion per year. The prevalence and severity of CDIs have been increasing in recent years, making it of vital importance to detect outbreaks sufficiently early to minimize negative health outcomes. Statistical process control (SPC) methods have proven to be a versatile tool in healthcare, enabling near real-time monitoring of adverse events rates and thereby improving patients’ health. The aim of this study was to investigate the performance of SPC in detecting clinically significant increases in CDI rates.

Methods
We retrospectively analyzed monthly CDI rates at 6 community hospitals in the Duke Infection Control Outreach Network from 2009–2017. Detected CDIs were stratified by surveillance system (LabID or traditional), infection source, recurrence type, and diagnostic test (nucleic acid amplification or enzyme-linked immunosorbent assay). Recurrent and community-associated CDIs were excluded from all analyses. Several variations of Shewhart and exponentially-weighted moving average (EWMA) u-charts were applied to each hospital (Figure 1), including using different baseline types (global, fixed, or rolling) and baseline data streams (hospital or network-wide). To help assess chart performance, epidemiologists determined the clinical relevance (yes/no) of 167 statistical signals generated using earlier iterations of these charts. Performance was quantified via sensitivity, specificity, and accuracy.

Results
EWMA u-charts with global network-wide baselines performed the best (Figure 2), detecting the largest number of clinically relevant signals (56% sensitivity) with high specificity (96%). Charts utilizing network-wide baselines were generally more accurate than those using local hospital data for that purpose (accuracy of 46–72% vs. 43–45%). Similarly, charts with fixed baselines performed better than those with rolling ones (accuracy of 43–62% vs. 43–47%).

Conclusion
SPC charts are easily applicable to CDI surveillance; however, their parameters would need to be optimized to maximize utility.

Disclosures
All authors: No reported disclosures.
2431. When More is Less – Recognizing More Community-onset Clostridium difficile Infections Helps to Dramatically Lower C diff Standardized Infection Ratio (SIR)
Authors: Parada Jorge P , Green Melissa , Wright Dominique , Parilla Deborah , Hedlund Heather , Harrington Amanda , Smith Kevin R 
Published: October 2019

Abstract

Background
It is essential to recognize the true burden of community-onset (CO) Clostridium difficile infection (CDI) in hospital, not only because it prevents late recognition of CO CDI as being classified as a hospital-onset (HO) event, but also to assure appropriate contact precautions and therapeutic measures are deployed in a timely fashion. We recognized that our timely diagnosis of CO-CDI was suboptimal and sought to improve early recognition of CO-CDI.

Methods
We developed an automated daily report for all patients during their first 3 days of hospitalization who had loose stools documented in the nursing flow sheets and no stool sample sent to the lab. This report was automatically pushed out to the unit nurse managers, as well as reviewed by the infection preventionists (IP). Nurse managers alerted staff to acquire a stool sample to send to the lab. If stool testing still was not sent at the time of IP review of these symptomatic cases, then the IP called the nurse caring for the patient to encourage that a stool sample be sent ASAP and before the third hospital day was completed.

Results
We increased early appropriate stool testing for patients with documented loose stools during the first 3 days of hospitalization. Improved early diagnosis and better lab stewardship was associated with a marked increase in CO-CDI (15.6/month in 2015 vs 58.7/month in the last year), as well as a decrease in HO-CDI (22.8/month in 2015 vs 7.4/month last year) (Figure 1). In turn, we saw a remarkable drop in our CDI SIR (2 year pre-intervention SIR = 1.49 vs post-intervention SIR for the last 1.5 years = 0.41) (Figure 2).

Conclusion
After several years of our CDI SIR remaining stubbornly around 1.5, we developed a system of enhanced recognition of patients who had loose stools early in their hospitalization. This aided in better recognition of CDI present on admission, substantially increasing our detection of CO-CDI. We also noted decreases in HO-CDI, presumably secondary to no longer diagnosing patients later in their hospitalization as HO-CDI cases who actually had been admitted with CO-CDI. Better early recognition and isolation of patients with CDI also helped to decrease inadvertent C. difficile transmission in hospital, contributing to decreases in HO-CDI. In turn, we noted a remarkable decrease in our CDI SIR.

Disclosures
All authors: No reported disclosures.
2440. Using a geospatially explicit agent-based model of a regional healthcare network to assess varied antibiotic risk on Clostridioides difficile infection incidence
Authors: Rhea Sarah , Jones Kasey , Bobashev Georgiy , Munoz Breda , Rineer James , Hilscher Rainer , DiBiase Lauren , Sickbert-Bennett Emily , Weber David J 
Published: October 2019

Abstract

Background
Different antibiotic classes are associated with different Clostridioides difficile infection (CDI) risk. The impact of varied antibiotic risk on CDI incidence can be explored using agent-based models (ABMs). ABMs can simulate complete systems (e.g., regional healthcare networks) comprised of discrete, unique agents (e.g., patients) which can be represented using a synthetic population, or model-generated representation of the population. We used an ABM of a North Carolina (NC) regional healthcare network to assess the impact of increasing antibiotic risk ratios (RRs) across network locations on healthcare-associated (HA) and community-associated (CA) CDI incidence.

Methods
The ABM describes CDI acquisition and patient movement across 14 network locations (i.e., nodes) (11 short-term acute care hospitals, 1 long-term acute care hospital, 1 nursing home, and the community). We used a sample of 2 million synthetic NC residents as ABM microdata. We updated agent states (i.e., location, antibiotic exposure, C. difficile colonization, CDI status) daily. We applied antibiotic RRs of 1, 5, 8.9 (original model RR), 15, and 20 to agents across the network to simulate varied risk corresponding to different antibiotic classes. We determined network HA-CDI and CA-CDI incidence and percent mean change for each RR.

Results
In this simulation study, HA-CDI incidence increased with increasing antibiotic risk, ranging from 11.3 to 81.4 HA-CDI cases/100,000 person-years for antibiotic RRs of 1 to 20, respectively. On average, the per unit increase in antibiotic RR was 33% for HA-CDI and 6% for CA-CDI (figure).

Conclusion
We used a geospatially explicit ABM to simulate increasing antibiotic risk, corresponding to different antibiotic classes, and to explore the impact on CDI incidence. The per unit increase in antibiotic risk was greater for HA-CDI than CA-CDI due to the higher probability of receiving antibiotics and higher concentration of agents with other CDI risk factors in the healthcare facilities of the ABM. These types of analyses, which demonstrate the interconnectedness of network healthcare facilities and the associated community served by the network, might help inform targeted antibiotic stewardship efforts in certain network locations.

Disclosures
All authors: No reported disclosures.
2441. Automated, Rapid Detection of Potential Healthcare-Acquired Infection Clusters Based on Microbiology And Patient Geotemporal Data
Authors: Kolde Raivo , Loving Joshua , Sharma Rohit , Carmona Juan J , Doty Alan J , Gross Brian D , van Aggelen Helen C 
Published: October 2019

Abstract

Background
Whole-genome sequencing (WGS) has shown promise in identifying transmissions of healthcare-associated infections (HAIs), but it may be costly to sequence all potential HAIs. By automatically identifying samples likely to be HAIs, WGS can be focused on specific samples. We describe an algorithm that quickly identifies potential HAI clusters by analyzing patient geotemporal and pathogen microbiology data. This approach systematically triages potential HAI investigations to aid infection control professionals (ICPs) in their workflow.

Methods
This novel algorithm within Philips IntelliSpace Epidemiology scores the potential of transmission for pairs of infections. Inputs include microbiology (MB) data (genus- or species-level identification and antimicrobial susceptibility test results) and geotemporal (GT) data (timing of sample collection and shared location stays). From the resulting pairwise scores, clusters of potential HAIs are identified. Leveraging 9 months (June, 2018 – March, 2019) of data from a 900-bed US hospital (i.e., 2825 samples, 1814 patients and 13 organisms—of which a subset of 404 samples had WGS performed concomitantly with MB studies), we evaluated the extent to which this algorithm captures genetically similar sample pairs.

Results
Pairwise scores enrich for genetically similar samples when considering MB data only (odds ratio: 17.3), GT only (odds ratio: 6.1) and a combination of both (odds ratio: 19.8), with highly significant P-values for all (P < 10−16). Considering MB only, 91% of samples group together in potential transmission clusters. With MB and GT data, this fraction drops to 24.6% (694 samples) forming 178 possible clusters, 173 of which contain fewer than ten samples each. The 5 larger clusters contain 40–64 samples each and span multiple units in the hospital.

Conclusion
The proposed system automatically suggests potential HAI clusters. By combining MB and GT data, the number of samples to review is reduced, enabling ICPs to focus their attention and sequencing efforts. By focusing on a targeted group of higher probability clusters, ICPs may be able to increase their efficiency and effectiveness in controlling the spread of HAIs—thus boosting potential for patient safety and amelioration of cost of care.

Disclosures
All authors: No reported disclosures.
2444. Infection Prevention in Home Healthcare: Results from a National Study of Home Health Agencies
Authors: Pogorzelska-Maziarz Monika , Shang Jingjing , Chastain Ashley , Stone Patricia 
Published: October 2019

Abstract

Background
As the population of older Americans with chronic conditions continues to grow, the role of home healthcare (HHC) services in improving care transitions between acute care and independent living has become a national priority. This has led to the development of value-based purchasing (VBP) initiatives, changes in the Centers for Medicare and Medicaid Services’ Home Health Conditions of Participation, and the Joint Commission’s national patient safety goals for HHC. We aimed to describe the infection prevention and control (IPC) infrastructure in US home health agencies (HHA).

Methods
From March to November 2018, we conducted in-depth, phone interviews with 41 staff from 13 HHAs across the United States, including administrators, IPC and quality improvement (QI) personnel, registered nurses and home health aides. In October 2018, we launched a nationwide survey to a random sample of 1,500 HHAs stratified by census region, ownership status and urban/rural location, and achieved a 40% response rate. Transcripts of the qualitative interviews were coded and themes were identified using content analysis. Survey data were analyzed using descriptive statistics.

Results
Themes from the interviews included: 1) Uniqueness of HHC setting, 2) Importance of staff and patient/caregiver education, (3) Care coordination challenges, and, (4) Keys to success and innovation. From the surveys, we found that, at the majority of HHAs, the staff member in charge of IPC had other responsibilities including QI (57%), clinical/administrative/managerial (49%), supervision of clinical services/patient coordination (48%), and education/training (45%). For those staff members in charge of IPC, over a third had received no specific IPC training, and only 5% were certified in IPC. For those staff who received training, the training was provided by external consultants (26%) or a professional society/health department (28%). Respondents cited the most challenging aspect of IPC as collecting/reporting infection data (24%), adherence to/monitoring bag technique (15%) and adequate staff coverage (13%).

Conclusion
This work represents a current snapshot of IPC infrastructure and challenges in US HHC agencies and identifies important barriers to IPC in these settings.

Disclosures
All authors: No reported disclosures.
2445. Risk for Readmission and Mortality Amongst LTACH Residents with New-Onset Healthcare-Associated Infections
Authors: Massey Kyle D , Murphy Melissa , Miano Todd , Binkley Shawn , Morgan Steven C , Jacob Jerry 
Published: October 2019

Abstract

Background
Long-term acute care hospitals (LTACH) provide cost-effective alternatives for stable patients requiring ICU-level care. LTACH patients are at increased risk for adverse outcomes including healthcare-associated infections (HAI). There is a paucity of data describing outcomes associated with HAIs in LTACH patients.

Methods
This was a single-center, retrospective study of LTACH patients over a 3 year period. Patients with an HAI (bloodstream [BSI], pneumonia [PNA], urinary tract [UTI], and Clostridioides difficile [CDI] infections) as defined by NHSN criteria were matched by length of stay (LOS) at the time of inclusion to unexposed patients. Follow-up was 30 days from the date of inclusion. The primary outcome was a composite of unplanned readmission to an acute care hospital or death at the LTACH. Secondary outcomes included all-cause mortality. Patients with HAIs were further evaluated to determine risk factors associated with readmission Antibiotics and cultured organisms were collected. Outcomes were analyzed using Cox proportional hazards model. Variables found to have a P < 0.1 on univariate analysis and those of clinical interest were included in the models.

Results
250 patients were included, 125 in each group. The distribution of HAIs was 40 BSI, 39 UTI, 26 PNA, and 20 CDI. The incidence of the primary outcome and mortality were 26.0% and 11.6% respectively. HAI was associated with increased risk of the primary outcome, but the effect varied over time: Risk increased seven-fold during the first 5 days (HR, 7.47 [95% CI, 2.86–19.42]) but was smaller and non-significant after day five (HR, 1.94 [95% CI, 0.85–4.43]). Mortality was not significantly different between groups (HR, 1.58 [95% CI, 0.74–3.38]). After adjustment, only hypotension (HR, 2.27 [95% CI 1.21–4.27]) and referral hospital LOS > 28 days (HR, 1.97 [95% CI 1.10–3.53]) were associated with readmission. 37% of cultured organisms were multi-drug-resistant. 17% of Enterobacteriaceae were carbapenem resistant. Empiric antibiotics failed to cover in 35% of infections.

Conclusion
HAI was associated with a significant increase in risk of readmission. Exploration of modifiable variables of infection, including hypotension and antibiotic selection, may help to reduce rates of readmission.

Disclosures
All authors: No reported disclosures.
2446. Longitudinal Results and Cost Savings Using a Bundle of C. difficile Infection Prevention Strategies in a Long-Term Care Facility
Authors: Olson Bridget A , Butera Michael L , Ship Noam 
Published: October 2019

Abstract

Background
Long-term care patients are at high risk of C. difficile infections (CDI) due to advanced age, high comorbid illness burden, and frequent antibiotic use. Primary infection prevention of CDI is challenging and not frequently studied. Following a period of high CDI incidence, the Long-Term Care facility at Sharp Coronado Hospital implemented a bundle of strategies.

Methods
Patient census, the incidence of CDI (primary and recurrent cases), transfers to acute care and length-of-stay were collected from Jul 2008 through December 2018. In the first phase, 2010, a bundle of CDI prevention strategies was initiated, including an Antimicrobial Stewardship Program (ASP), reduction of acid suppression, and L. acidophilus and S. boulardii probiotics with antibiotic use. From 2012, there was further refinement of the ASP and the probiotic was changed to capsules of a 3-species combination of Lactobacillus acidophilus CL1285, L. casei LBC80R and L. rhamnosus CLR2, 100 Billion CFU daily. In October 2016, a protocol was put in place delegating authority to pharmacists to add probiotics to all antibiotic courses. The average CDI rates were calculated and compared for each time period. The net cost of CDI was calculated from the number of CDI cases, hospital length-of-stay and probiotic purchases.

Results
The incidence of facility-onset CDI cases decreased significantly with each policy change from 7.6 cases/10,000 patient-days (2008–09), to 2.8 (2010–11, p = 0.028), to 0.91 (2012-Q3 2016, p = 0.0015) and to the present incidence 0.24 (Q4 2016–2018, p = 0.048). The annual cost of facility-onset CDI was $214k initially. The annual cost, including the purchase of probiotic, decreased to $161k with introduction of the bundle, to $57k in switching probiotics, and to $18k with initiation of a probiotic policy.

Conclusion
Implementing a bundle of concurrent infection prevention strategies resulted in a significant reduction in CDI incidence. Refinements to the bundle led to significant reductions in CDI incidence, along with switching the type of probiotic, and delegating ordering authority to pharmacists to ensure probiotic compliance. Cumulatively, there was a 95% decrease in CDI incidence at the Long-Term Care facility and meaningful cost savings with each refinement.

Disclosures
All authors: No reported disclosures.
2449. Early Detection of Candida auris is Essential to Control Spread: Four Effective Active Surveillance Strategies
Authors: Vallabhaneni Snigdha , Zahn Matthew , Epson Erin , ODonnell Kathleen , Horwich-Scholefield Sam , Brooks Richard , Vaeth Elisabeth , Blood Timothy , Shannon D J , Feaster Christine , Leung Vivian , Maloney Meghan , Forsberg Kaitlin , Kallen Alexander , Jackson Brendan R , Walters Maroya S 
Published: October 2019

Abstract

Background
C. auris has been identified from > 1600 US patients. Risk factors include high-acuity post-acute care admissions (e.g., long-term acute care hospitals (LTACHs)), hospitalization abroad, and carbapenemase-producing organism (CPO) colonization. Early detection of C. auris is key to controlling spread. We describe four active surveillance strategies that led to early C. auris identification.

Methods
Based on known risk factors, state health departments used active C. auris surveillance strategies: (1) species identification of yeast from urine cultures from LTACHs, (2) screening patients with a CPO and hospitalization abroad, (3) LTACH C. auris point prevalence surveys (PPS), or (4) admission screening in acute and long-term care settings.

Results
(1)A laboratory in Southern California serving 12 LTACHs began species identification for all Candida from urine cultures, which would have otherwise been discarded because they are assumed to be not clinically significant. Within 5 months, testing of 271 Candida urine isolates identified the region’s first C. auris case, prompting contact tracing and identification of additional cases and facilities. (2) When CPOs were identified in patients with recent hospitalizations outside of the United States, the Maryland Department of Health screened patients for C.auris colonization. Of four screened, one, who received care in Kenya, was C. auris colonized. (3) The Indiana State Department of Health implemented monthly PPS at an LTACH that frequently admits patients transferred from a high prevalence area. Of 38 patients screened, two were colonized. (4) The Connecticut Department of Public Health offers C. auris admission screening for patients who received inpatient care in high prevalence areas; of 12 screened, one C. auris colonized patient was found. Infection control assessments and implementation of infection control measures followed each detection.

Conclusion
Early detection of C. auris is important but is impacted by infrequent yeast species identification and a reservoir of asymptomatic colonized patients. Healthcare facilities and public health jurisdictions can consider adopting one or more of these strategies based on epidemiology and resource availability.

Disclosures
All authors: No reported disclosures.