Courtney Hebert’s research while affiliated with The Ohio State University and other places

Objective Environmental features of a patient’s room depend on the patient’s level of acuity and their clinical manifestations upon admission and during their hospital stay. In this study, we wish to apply statistical methodology to explore the association between room features and hospital onset infections caused by Clostridioides difficile (HO-CDI) while accounting for room assignment. Method We conducted a nested case–control study using retrospective electronic health record (EHR) data of patients hospitalized at the Ohio State University Wexner Medical Center (OSUWMC) between January 2019 and April 2021. We collected clinical information and combined that with room-based information, collected as surveys. Data were analyzed to assess the association between room factors and HO-CDI. Results 2427 patients and 968 unique rooms were included in the study. Results indicated protective effects for rooms with cubical curtains near the patient (OR = 0.705, 95% CI = 0.549–0.906), rooms with separate shower units (OR = 0.674, 95% CI = 0.528–0.860), rooms with wall-mounted toilets (OR = 0.749, 95% CI = 0.592–0.950), rooms with sliding bathroom doors (OR = 0.593, 95% CI = 0.432–0.816), and sliding door knobs (OR = 0.593, 95% CI = 0.431-0.815). Rooms with manual paper towel dispensers had increased odds of HO-CDI (OR = 1.334, 95% CI = 1.053–1.691) compared to those with automatic towel dispensers. Conclusion Results suggest possible association between specific room features and HO-CDI, which could be further investigated with techniques like environmental sampling. Moreover, findings from the study offer valuable insights for targeted intervention measures.

Background The duration of antibiotic therapy post cardiac intervention (PCI) for treatment of infective endocarditis (IE) remains controversial despite current clinical guidelines. There is paucity of data on the PCI antibiotic duration relating to pathogens, presence of an infected cardiovascular implantable electronic device (CIED), vegetation size, and duration of bacteremia. Due to these limitations, antibiotic therapy may be extended although the impact in clinical practice remains unknown. Methods This was a retrospective study comparing outcomes in patients with IE admitted to The Ohio State Wexner Medical Center who underwent valve replacement and/or CIED extraction from January 2013 to November 2017 based on whether the course of antibiotic therapy was restarted PCI (extended group) or if antibiotics prior to cardiac intervention were included in the total antibiotic duration (continuation group). Patients who completed the full course of antibiotics prior to cardiac intervention were excluded. The primary outcome was a composite of one-year infection-related mortality and IE relapse PCI. The proportional win-fractions regression model was used to compare the composite outcome between the groups. Results 235 patients met study inclusion criteria, 182 in the extended group and 53 in the continuation group. Patient demographics are shown in table 1. 91.5% of patients had positive blood cultures (n = 215) with a median bacteremia duration of 4 days with the most common pathogen being Staphylococcus aureus as shown in figure 1. 79.6% of patients had OR cultures sent (n = 187) shown in table 2. The average antibiotic duration PCI was 39.7 days. 5 patients had bacteremia at the time of surgery. The study revealed no statistically significant difference between the two groups in the primary outcome (Win Ratio 1.59, CI: 0.80-3.15, p-value 0.18) shown in table 3. Conclusion For patients with IE requiring surgical intervention, the outcome of one year mortality and infection relapse was not significantly different between the two groups after adjusting for potential confounders. This study suggests that extending antibiotics may not be associated with improved survival or infection relapse, however prospective studies are needed to confirm these findings. Disclosures All Authors: No reported disclosures

Background Despite the growth of digital healthcare data, surveillance for healthcare associated infections (HAIs) often consists of reviewing cases using lists, charts, or tables. For infections, such as C.diff which can be transmitted patient-to-environment and patient-to-patient, it can be useful to visualize the location of these infections to detect spatial clustering. Here we discuss the development of a web application (GeoHAI) for infection preventionists (IPs) to visualize C.diff in rooms over time and quantify the burden of C.diff in these rooms.Figure 1:Screenshot of GeoHAI application (Development environment with simulated case data) This screenshot shows a view of a floor within one of the hospital buildings. Each circle in a room relates to a patient who was in that room on the chosen date. Clicking on a circle will give details on that patient (Patient ID, admit/discharge date, room number, date room was entered and exited, as well as whether the patient had C. diff (HO- or CO-CDI) and what was the date of the positive test). Orange circles indicate CO-CDI, red are HO-CDI. Shading corresponds to the cumulative number of hours patients with C. diff (HO- or CO-CDI) were in each room for the previous 30 days. The user can also look at the shading for 90 days or 365 days by choosing one of those options in the upper left-hand corner. The shading is intended to give a sense of overall burden of C. diff in each room. The list of floors on the left panel allows the user to switch between floors, and boxes in this area give a preview of high interest rooms on that floor. The building can be selected from the drop-down box in the upper right corner. Methods Development of GeoHAI consisted of (1) processing clinical and geographic data; (2) software development; and (3) deployment within the medical center (MC). (1) Floor plan data was converted to GeoJSON files which contain coordinates to draw polygons corresponding to rooms within the hospital. Rooms were given a spaceID, a unique number for each building-floor-room. Clinical and patient location data include patient ID, admission/discharge date, C.diff result, and entry/exit times for room transfers. We wrote R code to clean the data and identify hospital onset vs community onset C.diff (HO-CDI, CO-CDI), to link patient room to spaceID, and to calculate the historical burden of C.diff in each room, defined as the cumulative number of hours that patients with active C.diff spent in each room. (2) With IP input, we created design specifications which were used to develop the application. Node-RED was used to load clean, clinical data from CSV files into the application database. (3) IT worked with software developers to host the application on the MC’s cloud servers using Azure DevOps. Results GeoHAI is accessed by authorized users in development and production environments on the MC network. Users choose from 42 unique floors in 6 buildings, can select a patient within a room to see if they had C.diff during their stay, the date of C.diff, and room entry/exit date. The degree of room shading correlates to the cumulative hours patients with C.diff were in a room (Figure 1). New data can be loaded via an intuitive web interface. Conclusion GeoHAI displays C.diff cases in a large academic hospital. We next plan to study how GeoHAI impacts HAI investigation, to expand GeoHAI features, and to add additional HAIs. Disclosures All Authors: No reported disclosures

Background: Clostridioides difficile PCR is extremely sensitive but cannot differentiate colonization versus active disease. Over diagnosis of C. difficile infection (CDI) has negative consequences including overuse of antibiotics targeting C. difficile, increased hospital-acquired (HA)-CDI rates, and increased healthcare costs. We describe the implementation of a Clinical Decision Support tool embedded in the C. difficile order and the result on testing, HA-CDI rates and healthcare costs. Methods: The C. difficile order was updated in June 2023 with 4 dynamic questions that reflex if specific criteria are identified in the electronic medical record in the prior 24 hours: less than 3 loose stools documented, receipt of laxative, opioid antagonist, oral contrast, or tube feed initiation. If any criteria are identified, an embedded alert triggers and the provider must choose “yes, high clinical suspicion” or “no (exit and cancel order)” in addition to providing an order indication. All inpatient C. difficile tests were reviewed from July 1 to Sept 30, 2022 (pre-update) and July 1 to Sept 30, 2023 (post-update). An order rate was calculated per 10,000 patient days as well as HA-CDI rate. Cost analysis was completed using direct lab costs and published costs of $35,000 per HA-CDI. Results of the order questions were reviewed post-update. Incident rate comparison was completed using medcalc. Results: Pre-update, 1147 tests were conducted, with an order rate of 104.3. Post-update, 919 tests were performed, with an order rate of 86.6. The positivity rate was 16.1% pre-update and 14.7% post-update. The incidence rate difference was 0.00177 (P 15 (145, 16%). 166 (18%) patients who received laxatives (18 positive, positivity rate 11%) were still tested. Conclusion: Implementation of a dynamic order led to a significant reduction in the total number of C. difficile PCR tests performed with associated reduction in HA-CDI and cost savings. Despite this, patients receiving laxatives were still being tested for C. difficile, highlighting the need for ongoing education and feedback. These results support the use of dynamic ordering for diagnostic stewardship, which can benefit both patients and hospitals.

Healthcare-associated infections (HAIs) remain a significant patient safety problem that can lead to illness and death, despite the implementation of clinical bundles to prevent HAIs. Management practices can support HAI prevention, but their role in HAI performance monitoring and feedback is not well understood. To address this knowledge gap, we previously conducted semi-structured interviews with staff at 18 hospitals to examine the role of management practices around the prevention of central line-associated bloodstream infections (CLABSIs) and catheter-associated urinary tract infections (CAUTIs). Interview transcripts were analyzed to identify themes related to HAI performance monitoring and feedback. The current analysis focuses on 10 higher-performing hospitals that were successful in preventing CLABSIs and CAUTIs. These institutions had robust practices including timely event analysis, leadership engagement, and multidisciplinary participation in HAI reviews. Across these sites, we found common goals including investigating HAIs without blame and identifying opportunities for improvement. Management practices such as timely analysis of HAIs, collaboration between facility leadership and multidisciplinary team members, and a focus on identifying the failure of a procedure or protocol, rather than the failure of staff members, are all approaches that can support infection prevention efforts. These management practices may be especially important as hospitals attempt to address increases in CLABSI and CAUTI rates that may have occurred during the coronavirus pandemic.

Background Drug use associated Infective Endocarditis (DU-IE) has been noted to be increasing over the last decade(Clinical Infectious Diseases® 2020;71(7):1664–7). In this study we sought to assess the unique aspects of DU-IE compared to IE not associated with drug use. We undertook an in-depth descriptive study of patients who presented with IE over the course of 5 years at one large academic medical center. Table 1 Methods This was a retrospective chart review of patients with IE admitted to the Ohio State Wexner Medical center between 2013 and 2017. Prisoners, patients < 18 or > 89 years of age, and pregnant patients were excluded from the study. The first admission during the study period in those with definitive or probable endocarditis by Duke criteria, were included. Persons who inject drugs (PWID) with IE were compared with those with IE without documentation of drug use. DU was determined by both ICD coding and manual chart review. Given the discrepancy between the two, chart review was used for the analysis. Differences in proportion were compared using Fisher’s exact, and continuous variables using student’s t-test. Results Overall, 713 patients met inclusion criteria. Approximately half (45.6%) of the patients had DU-IE, this proportion generally increased over the 5 years, with 59.8% of the cases being DU-IE in 2017. DU-IE patients were more likely to be younger, female, and have a lower Charlson comorbidity score. DU-IE patients were significantly more likely to have MRSA, Gram Negatives and Candida species, whereas non DU-IE was more likely to have Coagulase negative staph species. DU-IE patients were less likely to have a transesophageal echocardiogram, and unsurprisingly were more likely to have tricuspid valve involvement, and pulmonary septic emboli. Conclusion The patient population with DU-IE compared to non-DU IE differ from one another, and further studies are required to better understand long-term clinical outcomes in the treatment between each group. Disclosures All Authors: No reported disclosures

Objective To assess the relative risk of hospital-onset Clostridioides difficile (HO-CDI) during each month of the early coronavirus disease 2019 (COVID-19) pandemic and to compare it with historical expectation based on patient characteristics. Design This study used a retrospective cohort design. We collected secondary data from the institution’s electronic health record (EHR). Setting The Ohio State University Wexner Medical Center, Ohio, a large tertiary healthcare system in the Midwest. Patients or participants All adult patients admitted to the inpatient setting between January 2018 and May 2021 were eligible for the study. Prisoners, children, individuals presenting with Clostridioides difficile on admission, and patients with <4 days of inpatient stay were excluded from the study. Results After controlling for patient characteristics, the observed numbers of HO-CDI cases were not significantly different than expected. However, during 3 months of the pandemic period, the observed numbers of cases were significantly different from what would be expected based on patient characteristics. Of these 3 months, 2 months had more cases than expected and 1 month had fewer. Conclusions Variations in HO-CDI incidence seemed to trend with COVID-19 incidence but were not fully explained by our case mix. Other factors contributing to the variability in HO-CDI incidence beyond listed patient characteristics need to be explored.

Background The Infant Mortality Research Partnership in Ohio is working to help pregnant individuals and families on Medicaid who are at risk for infant mortality and preterm birth. As part of this initiative, researchers at The Ohio State University worked to develop a patient-facing mobile app, OHiFamily, targeted toward, and created for, this population. To address the social determinants of health that can affect maternal and infant health, the app provides curated information on community resources, health care services, and educational materials in a format that is easily accessible and intended to facilitate contact between families and resources. The OHiFamily app includes 3 distinct features, that is, infant care logging (eg, feeding and diaper changes), curated educational resources, and a link to the curated Ohio resource database (CORD). This paper describes the development and assessment of the OHiFamily app as well as CORD. Objective This study aimed to describe the development of the OHiFamily mobile app and CORD and the qualitative feedback received by the app’s intended audience. Methods The researchers performed a landscape analysis and held focus groups to determine the resources and app features of interest to Ohio families on Medicaid. Results Participants from several focus groups were interested in an app that could offer community resources with contact information, information about medical providers and information and ways to contact them, health tips, and information about pregnancy and infant development. Feedback was provided by 9 participants through 3 focus group sessions. Using this feedback, the team created a curated resource database and mobile app to help users locate and access resources, as well as access education materials and infant tracking features. Conclusions OHiFamily offers a unique combination of features and access to local resources for families on Medicaid in Ohio not seen in other smartphone apps.

BACKGROUND The Infant Mortality Research Partnership in Ohio is working to help women and families on Medicaid who are at risk for infant mortality and preterm birth. As part of this initiative, the researchers at XXX University worked to develop a patient-facing mobile application, OHiFamily, targeted towards, and created for, this population. The OHiFamily application aims to reduce preterm birth rates, improve maternal and infant health, and decrease infant mortality. It hopes to achieve this by providing curated information on community resources and educational sites, promoting healthy behaviors and self-care, and facilitating access to healthcare services. OBJECTIVE To describe the development of the OHiFamily mobile application and the qualitative feedback received by the application’s intended audience. METHODS The researchers conducted needs assessments, performed a landscape analysis, and held focus groups to determine resources and application features of interest to Ohio families on Medicaid. RESULTS Participants from the focus groups were interested in an application that could offer community resources with contact information, information about medical providers and how to contact them, health tips, and information about pregnancy and infant development. We had 9 participants give feedback through 3 focus group sessions and summarized the data. Using these data, the team created a curated resource database and mobile application to help users locate and access resources, as well as access education materials and infant tracking features. CONCLUSIONS OHiFamily offers a unique combination of features and access to local resources for families on Medicaid in Ohio not seen in other smartphone applications. The goal of this application is to help families meet their unique needs and get connected to organizations which can help them mitigate risk.