bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multiscale Modeling of Patient Movement to Determine Effects of Surveillance on Healthcare-Associated Infections
Authors: Lin Gary , Tseng Katie , Martinez Diego , Klein Eili 
Published: November 2020

Background: The transmission of pathogenic organisms in healthcare settings is a major cause of healthcare-associated infections (HAIs). In recent years, infections with carbapenem-resistant Enterobacteriaceae (CRE) have become a significant public health threat, in part because many patients are arriving at the hospital already colonized, and colonization is a major risk factor for infection. Reducing transmission requires understanding how patient movement drives the spread of CRE; however, analysis of this issue has mostly been modeled at a hospital-level without much consideration for the population dynamics that occur outside of the hospital setting and how patients move between healthcare settings. Patients move between hospitals, other healthcare settings, such as long-term care facilities (LTCFs), and the community, all of which pose different colonization risks. Thus, studying each environment in isolation fails to realistically address the consequences of large-scale policy interventions. One such intervention is a statewide electronic registry to track patients who are known to be colonized or have had a CRE infection. Understanding the potential for reducing CRE morbidity and mortality requires consideration of small- and large-scale effects on patients’ movement and transmission. Methods: We developed a multiscale, metapopulation model for hospitals, communities, and LTCFs in the state of Maryland. In our computational simulation, we included a regional- as well as a local-scale model that were informed by the patient-mix data from the Maryland Health Service Cost Review Commission. We examined the impact of implementing a registry compared to less coordinated scenarios. Results: The most effective policy was the implementation of an electronic registry which resulted in 9.6% median reduction in CRE HAIs in Maryland for simulated outcomes (Fig. 1). Other interventions included colonization screening at various or all hospitals and using a predictive algorithm to determine at-risk patients that need to be screened. These interventions only resulted in ~1%–3% reductions in HAIs. We also observed that coupling other interventions with an electronic registry does not aid in reducing more HAIs.Funding: NoneDisclosures: NoneBoxplot of simulated outcomes of all scenarios compared to the baseline scenario. The scenarios include (1) complete screening with no electronic registry, (2) selective screening with no electronic registry, (3) predictive screening with no electronic registry, (4) baseline with an electronic registry, (5) complete screening with an electronic registry, (6) selective screening with an electronic registry, and (7) predictive screening with an electronic registry. Scenarios 5-7 include the same interventions as 1–3 coupled with an electronic registry.
National Frequencies of Administering or Prescribing Immunosuppressive Opioids in US Ambulatory Care Settings: 2006–2016
Authors: Romine James , Ellingson Katherine 
Published: November 2020

Background: Several decades of animal and basic science research have demonstrated that certain opioids have immunosuppressive properties, but the clinical relevance of opioid-related immunosuppression remains unclear. Although experts have called for epidemiologic research to inform clinical practice, prioritization of that research depends partly on a determination of the number of people potentially affected. To date, population-level estimates of administering or prescribing immunosuppressive opioids (ISOs) have not been measured. Our objective was to estimate the overall frequency of ambulatory visits involving ISOs, and to estimate the frequency of these visits among immunocompromised patients. Methods: We used the CDC National Ambulatory Medical Care Survey (NAMCS) and National Hospital Ambulatory Medical Care Survey–Emergency Departments (NHAMCS-ED) data sets (2006–2016) to compute average annual frequencies of patient visits involving ISOs. We accounted for survey sampling design and visit weights using SAS version 9.4 software. We adopted a definition of ISOs from the literature as ‘alone or in-combination’ formulas of codeine, morphine, and fentanyl. We approximated patients’ immunocompromised status by the administering or prescribing of anti-infective drugs, and by chronic conditions indicative of immunocompromised status. We stratified visits with mentions of ISOs by co-occurring clinical-use of anti-infective drugs, and by selected chronic conditions. Results: From 2006 to 2016, annual averages of 7.9% (N = 10,383,000; SE, 447,000) of all ED visits and 1.3% (N = 12,674,000; SE, 558,000) of all outpatient office visits involved the administering or prescribing of 1 or more ISO. Over the same period, coprescribing or administering of anti-infective drugs alongside ISOs occurred during 2.1% (N = 2,782,000; SE, 130,000) of all ED visits, and 0.4% (N = 3,525,000; SE, 219,000) of all outpatient office visits. ED visits by patients with selected chronic conditions who were administered or prescribed ISOs include cancer—499,000 (SE, 39,000), diabetes—1,369,000 (SE, 82,000), and HIV—45,000 (SE, 7,000). Outpatient office visits by patients with selected chronic conditions who were administered or prescribed ISOs include cancer—1,032,000 (SE, 92,000), diabetes—1,802,000 (SE, 142,000), and chronic renal failure—138,000 (SE, 22,000). Conclusions: More than 10 million ED visits and 12 million outpatient office visits involved the clinical use of ISOs on average, from 2006 to 2016. These averages include visits by immunocompromised patients who could potentially benefit from nonimmunosuppressive analgesic alternatives, when appropriate. Until further research is conducted on the clinical relevance of these opioids’ immunosuppressive properties, their use to treat immunocompromised patients may represent unrecognized patterns of inappropriate drug use.Funding: NoneDisclosures: None
NHSN Catheter-Associated Urinary Tract (CAUTI) Definition—Opportunity for Improvement
Authors: Muto Carlene , Rea Kathleen , Piedmont Christie 
Published: November 2020

Background: Urinary tract infections (UTIs) are one of the most common hospital-acquired infections; ~70%–80% are attributable to an indwelling urethral catheter. Daily risk of bacteriuria acquisition varies from 3% to 7% with a catheter. CAUTIs are associated with increased mortality, cost, and inappropriate treatment of asymptomatic bacteriuria which promotes antimicrobial resistance and Clostridium difficile infection. NHSN CAUTI criteria is most commonly met when a patient has a positive urine culture and a fever. Although fever can be associated with many sources, it cannot be excluded from UTI determination even when attributable to another recognized source. Given the high prevalence of bacteriuria in catheterized patients and the many sources of fever, the NHSN definition lacks specificity. Objective: To better classify CAUTI using enhanced criteria to so that appropriate reduction efforts would be utilized. Methods: A retrospective review was conducted to evaluate NHSN-defined CAUTIs from July 2017 to December 2018. Patients with NHSN defined CAUTI were evaluated to determine elements present to meet criteria. Overcaptured (O-CAUTIs) were defined as follows: (1) O-CAUTI 1, a positive culture with fever attributable to an infectious source; (2) O-CAUTI 2, a positive culture with fever attributable noninfectious source; (3) O-CAUTI 3, repeated positive cultures outside the RI period; (4) O-CAUTI 4, a positive culture with symptoms attributable to another source and no fever. Classifications were discussed with the medical and clinical leadership to determine appropriate opportunities for improvement. Results: Overall, 49 NHSN CAUTIs were identified with 11 of 49 (22%) being true CAUTIs and 38 of 49 (78%) O-CAUTI. O-CAUTI 1 was most common, with 17 of 38 (45%). The most frequent attributable source of fever for O-CAUTI 1 (infectious source) was respiratory (7 of 17, 59%) followed by gastrointestinal (6 of 17, 35%). Also, 14 of 38 (37%) were O-CAUTI 2. Central fever was the most frequent source of fever for the noninfectious source (9 of 14, 64%) followed by drug fever (2 of 14, 14%). Of 38 patients, 3 (8%) had both an infectious and noninfectious reason for fever (CAUTI 1 and 2); 4 patients had no fever. Furthermore, 2 were O-CAUTI 3 (repeat culture positive) and 2 were O-CAUTI 4 (1 with hematuria and renal cell carcinoma and 1 with dysuria without leukocytosis). Conclusions: NHSN CAUTI definitions capture UTIs and other events. In FY2018, there were no true CAUTIs in 5 of 12 months (42%). Also, 50% of CDC CAUTIs were not UTI but could lead to inappropriate antibiotic use. Reviewing only CAUTI reduction work in O-CAUTIs prevents the assessment of other appropriate opportunities for improvement.Funding: NoneDisclosures: None
Naturally Emerging Cohorting Behavior of Healthcare Workers and Its Implications for Disease Spread
Authors: Kharkar Abhijeet , Hasan D M Hasibul , Polgreen Philip , Segre Alberto , Sewell Daniel , Pemmaraju Sriram 
Published: November 2020

Background: Mobility patterns of healthcare workers (HCWs) (ie, the spatiotemporal distribution of patient rooms they visit) have a significant impact on the spread of healthcare acquired infections (HAIs). Objective: In this project, we used fine-grained data from a sensor deployment at the medical intensive care unit (MICU) in the University of Iowa Hospitals and Clinics (UIHC) to study the mobility patterns of HCWs and their impact on HAI spread. Methods: We analyzed 10 days of data from a 20-bed MICU sensor deployment. For parameters t1 and t2, each pair of rooms i and j is assigned a weight W(i, j) representing the number of times an HCW spends at least t1 seconds in room i followed by at least t1 seconds in room j, within t2 seconds of each other. W(i, j) is a measure of HCW traffic going from room i to room j; we study the correlation between W(i, j) and the distance between rooms i and j. Additionally, we perform 2 disease-spread simulations: (1) a base simulation, obtained by replaying observed HCW mobility traces and (2) a perturbed simulation, which is the same as the base simulation, except that we replace each HCW who visits a room by a random available HCW. Thus, the perturbed simulation removes correlations in the observed HCW mobility traces. Results: We computed W(i, j) for all room pairs i, j for parameters t1 = 30 seconds and t2 = 1,800 and 3,600 seconds. For nurses, there was a strong negative correlation of between pairwise room distance and the weights W(i, j) (−0.768 for t2 = 1,800; −0.711 for t2 = 3,600), The more distant 2 rooms were, the less they shared nurse traffic. This was not true for physicians (correlation = −0.027 for t2 = 1,800; −0.014 for t2 = 3,600). Figure 1 shows a weight versus distance scatter plot for nurses for t1 = 30 and t2 = 1,800. This spatial correlation has positive implications for disease spread; the base simulation, which preserves these spatial correlations, has between 12% and 55% fewer mean infected patients (>100 replicates) for different simulation parameters compared to the perturbed simulation. Conclusions: Our results, based on fine-grained data, show a “naturally emerging” cohorting behavior of nurses, where nurses are more likely to visit rooms close to each other within a 30–60 minute time window, than rooms further away. Through simulations, this behavior provides substantial protection against disease spread.Funding: NoneDisclosures: None
Opening Doors in the Operating Rooms: An Intervention and Outcome Study
Authors: Doshi Akash , Shadowen Rebecca 
Published: November 2020

Background:
Surgical site infections (SSIs) are a major cause of morbidity and mortality with an estimated cost of $3–10 billion annually in the United States. Laminar air flow in the operating room (OR) is 1 factor in reducing SSIs. Opening the OR door results in interruption of laminar air flow. As a part of annual infection prevention evaluation of our facility, we observed cases in the OR in which we identified excessive unnecessary door openings during surgical cases. We report an intervention in door openings in the OR and the effect on infection rate after surgery. Methods: We conducted an observational analytical study using prospective audit and feedback. Door-opening counters were placed on 4 OR doors. Each day, they were reset and the number was logged for each case by the circulating nurse. A baseline number of door openings was established between April 18, 2019, and May 2, 2019. Subsequently, daily feedback sheets were provided to all persons involved in the previous day’s procedures detailing the rationale to limit unnecessary door openings and the number of door openings that had occurred during the case(s) in which they were specifically involved from May 3, 2019, to June 4, 2019. Analyses of postoperative infection rates compared with historical controls were conducted. Using Stata version 15 statistical software, independent sample t tests were performed to see the difference between control and intervention groups. A CI 95% was set for significance. Results: There were no differences between control and intervention groups with the number of procedures (71 vs 80), OR, duration of procedure, or type of case. Outliers due to vibration of doors triggering the counters were removed, and door stabilizations were performed throughout the study. After removing outliers, there were no differences in control groups and interventions groups (39 vs 43). An independent sample t test showed a significant difference in the mean number of door openings between the control and intervention groups: 32.13 versus 24.84 (P < .05 and P = .0072). There have been no postoperative infections in any of the cases in the study to date compared to an overall annual rate of 1.5% in 2018 at our facility. Conclusions: Prospective audit and feedback to OR staff can reduce the number of unnecessary door openings during operating procedures. The baseline number of door openings from this study was 25 per case. No postoperative infections occurred in the patients receiving surgery in this study.Funding: NoneDisclosures: None
Outcomes of Neutropenic Patients with Clostridium difficile Infection
Authors: Sharma Roopali , Dixit Deepali , Pathickal Sherin , Park Jenny , Lee Bernice , Jain Siddharth , Kairys Joelle , Katchiuri Nino , Park Tae , Narayanan Navneeth , Swamy Siddharth 
Published: November 2020

Background: Data from Clostridium difficile infection (CDI) in neutropenic patients are still scarce. Objective: To assess outcomes of CDI in patients with and without neutropenia. Methods: The study included a retrospective cohort of adult patients at 3 academic hospitals between January 2013 and December 2017. The 2 study arms were neutropenic patients (neutrophil count <500/mm3) and nonneutropenic patients with confirmed CDI episodes. The primary outcome evaluated the composite end point of all-cause in-hospital mortality, intensive care unit (ICU) admissions, and treatment failure at 7 days. The secondary outcome evaluated hospital length of stay. Results: Of 962 unique cases of CDI, 158 were neutropenic (59% men) and 804 were nonneutropenic (46% men). The median age was 57 years (IQR, 44–64) in the neutropenic group and 68 years (IQR, 56–79) in the nonneutropenic group. The median Charlson comorbidity score was 5 (IQR, 3–7.8) and 4 (IQR, 3–5) in the neutropenic and nonneutropenic groups, respectively. Regarding severity, 88.6% versus 48.9% were nonsevere, 8.2% versus 47% were severe, and 3.2% versus 4.1% were fulminant in the neutropenic and nonneutropenic groups, respectively. Also, 63% of patients (60.9% in nonneutropenic, 65.2% in neutropenic) were exposed to proton-pump inhibitors. A combination CDI treatment was required in 53.2% of neutropenic patients and 50.1% of nonneutropenic patients. The primary composite end point occurred in 27% of neutropenic patients versus 22% of nonneutropenic patients (P = .257), with an adjusted odds ratio of 1.30 (95% CI, 0.84–2.00). The median hospital length of stay after controlling for covariates was 21.3 days versus 14.2 days in the neutropenic and nonneutropenic groups, respectively (P < .001). Complications (defined as hypotension requiring vasopressors, ileus, or bowel perforation) were seen in 6.0% of the nonneutropenic group and 4.4% of the neutropenic group (P = .574), with an adjusted odds ratio of 0.61 (95% CI, 0.28–1.45). Conclusions: Neutropenic patients were younger and their cases were less severe; however, they had lower incidences of all-cause in-hospital mortality, ICU admissions, and treatment failure. Hospital length of stay was significantly shorter in the neutropenic group than in the nonneutropenic group.Funding: NoneDisclosures: None
Pitfalls of a Staged Implementation of an Automated Hand Hygiene System: Lessons Learned
Authors: Sisler Lori , Nigh Kathy 
Published: November 2020

Background: Hand hygiene is the first defense against healthcare-associated infections, yet studies show that adherence to hand hygiene still remains low. An academic medical center selected a beacon-based automated hand hygiene reminder system to improve hand hygiene adherence. Accountability is challenging to enforce without a reliable means to measure hand hygiene adherence. The hospital used secret shoppers to observe hand hygiene adherence. This method captures an estimated 0.5%–1.7% of opportunities and may be influenced by the Hawthorne effect. Methods: In November 2018, a phased trial of an electronic hand hygiene reminder system began in 4 intensive care units (ICUs). The system selected used a badge and beacon technology. The badge identifies each care provider and displays colored lights to show adherence status. Beacons are present on the patient’s bed, soap, and hand sanitizer dispenser. These beacons establish a “patient zone” that captures opportunities for hand hygiene. The specialty beds in the ICUs were supposed to remain on the units. A patient transferring to a lower level of care would be placed on another bed or gurney when leaving the ICU. ICU staff were badged for the system. Results: The phased implementation strategy had challenges with beds, badges, and the system. Despite planning, education, and communication, the beds left the ICU area, so the beaconed beds were outside the ICU, and staff did not always wear their assigned badge. There were issues with the system router as well. Unit leadership and the infection control team worked on processes to get beds back into the units. The implementation team decided to provide badges to staff who regularly worked in the ICU to differentiate from consultation groups that came to the ICU (and were not badged). The system routers were plugged in at various places on the units and had become unplugged so information was not sent for reports. Despite these issues, over the year of implementation, the units did achieve an increase in hand hygiene adherence from 48% to 85%. Collectively, the units achieved a 53% reduction in central-line–associated blood stream infection (CLABSI), reducing infections from 13 to 7 and a 35% reduction in methicillin-resistant Staphylococcus aureus (MRSA), reducing infections from 8 to 3 as defined by the NHSN. Conclusions: When implementing a beacon-based, automated hand hygiene system, staged implementation can be challenging. To avoid these challenges, facility-wide implementation is preferable.Funding: NoneDisclosures: None
Predicting Community-Onset Candidemia in an Academic Medical Center Using Machine Learning
Authors: AlZunitan Mohammed , Marra Alexandre , Edmond Michael , Street Nick , Diekema Daniel , Salinas Jorge 
Published: November 2020

Background: Candidemia is a leading cause of bloodstream infections (BSIs), and community-onset candidemia is being recognized as a public health problem. In the era of electronic health records (EHRs), we can use machine learning to detect patterns in patient data that may predict infections. Objective: We aimed to predict community-onset candidemia in patients admitted to the University of Iowa Hospital & Clinics (UIHC) using machine-learning algorithms. Methods: We retrospectively reviewed data for patients admitted to UIHC during 2015–2018. All adult inpatients who had a requested blood culture were included. Candidemia was defined as a blood culture positive for Candida within 48 hours after admission. Variables of interest were extracted from the EHR: age, sex, body mass index, and month of admission. We also included comorbidities upon admission defined by the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM): cardiovascular diseases, neurological disorders, chronic pulmonary disease, dementia, rheumatoid disease, peptic ulcer disease, liver disease, diabetes mellitus, hypothyroidism, renal failure, coagulopathy, obesity, weight loss, fluid and electrolyte disorders, anemia, alcohol abuse, drug abuse, psychiatric diseases, malignancy, and HIV/AIDS. We calculated Charlson and Elixhauser scores based on ICD-10-CM codes. We also included prehospitalization conditions (90 days before admission): Candida-positive cultures from sites other than blood, antibiotics/antifungals, hemodialysis, central lines, corticosteroids, surgeries, and intensive care unit (ICU) admissions. Mode and median imputation were used for missing information. Random forests with resampled training sets were used for prediction, and results were evaluated using 10-fold cross validation. Results: In total, 30,528 adult admissions were extracted; 73 admissions had an episode of candidemia (<1%). Median admission age was 61 years, and nearly half of admissions were female patients (44.7%). Mean BMI was 27.67. The most admissions occurred during the months of March, August, and November. The 3 most common ICD-10-CM codes were diabetes mellitus, hypertension, and cancer. Median Charlson and Elixhauser scores were 1 and 2, respectively. The model used 103 variables. The 3 most predictive variables were Elixhauser score on admission, and characteristics in the 90 days prior to admission were Candida from sites other than blood, use of a central line, and recent use of antibiotics/antifungals. The model’s area under the receiver operating characteristic curve was 0.72. Conclusions: Preadmission patient characteristics predicted community-onset candidemia. Machine-learning models may help detect patients eligible for screening for candidemia and prompt empiric antifungal therapy in high-risk patients in the first 48 hours of their admission.Funding: NoneDisclosures: None