When Infections Are Found: A Qualitative Study Characterizing Best Management Practices for Central Line-Associated Bloodstream Infection and Catheter-Associated Urinary Tract Infection Performance Monitoring and Feedback.

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.

Describing the monthly variability of hospital-onset Clostridioides difficile during early coronavirus disease 2019 (COVID-19) using electronic health record data.

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.

Predictive Models for Very Preterm Birth: Developing a Point-of-Care Tool.

OBJECTIVE: The objective of this study was to create three point-of-care predictive models for very preterm birth using variables available at three different time points: prior to pregnancy, at the end of the first trimester, and mid-pregnancy. STUDY DESIGN: This is a retrospective cohort study of 359,396 Ohio Medicaid mothers from 2008 to 2015. The last baby for each mother was included in the final dataset. Births prior to 22 weeks were excluded. Multivariable logistic regression was used to create three models. These models were validated on a cohort that was set aside and not part of the model development. The main outcome measure was birth prior to 32 weeks. RESULTS: The final dataset contained 359,396 live births with 6,516 (1.81%) very preterm births. All models had excellent calibration. Goodness-of-fit tests suggested strong agreement between the probabilities estimated by the model and the actual outcome experience in the data. The mid-pregnancy model had acceptable discrimination with an area under the receiver operator characteristic curve of approximately 0.75 in both the developmental and validation datasets. CONCLUSION: Using data from a large Ohio Medicaid cohort we developed point-of-care predictive models that could be used before pregnancy, after the first trimester, and in mid-pregnancy to estimate the probability of very preterm birth. Future work is needed to determine how the calculator could be used to target interventions to prevent very preterm birth. KEY POINTS: · We developed predictive models for very preterm birth.. · All models showed excellent calibration.. · The models were integrated into a risk calculator..

Novel Visualization of Clostridium difficile Infections in Intensive Care Units.

BACKGROUND: Accurate and timely surveillance and diagnosis of healthcare-facility onset Clostridium difficile infection (HO-CDI) is vital to controlling infections within the hospital, but there are limited tools to assist with timely outbreak investigations. OBJECTIVES: To integrate spatiotemporal factors with HO-CDI cases and develop a map-based dashboard to support infection preventionists (IPs) in performing surveillance and outbreak investigations for HO-CDI. METHODS: Clinical laboratory results and Admit-Transfer-Discharge data for admitted patients over two years were extracted from the Information Warehouse of a large academic medical center and processed according to Center for Disease Control (CDC) National Healthcare Safety Network (NHSN) definitions to classify Clostridium difficile infection (CDI) cases by onset date. Results were validated against the internal infection surveillance database maintained by IPs in Clinical Epidemiology of this Academic Medical Center (AMC). Hospital floor plans were combined with HO-CDI case data, to create a dashboard of intensive care units. Usability testing was performed with a think-aloud session and a survey. RESULTS: The simple classification algorithm identified all 265 HO-CDI cases from 1/1/15-11/30/15 with a positive predictive value (PPV) of 96.3%. When applied to data from 2014, the PPV was 94.6% All users "strongly agreed" that the dashboard would be a positive addition to Clinical Epidemiology and would enable them to present Hospital Acquired Infection (HAI) information to others more efficiently. CONCLUSIONS: The CDI dashboard demonstrates the feasibility of mapping clinical data to hospital patient care units for more efficient surveillance and potential outbreak investigations.

Bacterial and viral co-infections complicating severe influenza: Incidence and impact among 507 U.S. patients, 2013-14.

BACKGROUND: Influenza acts synergistically with bacterial co-pathogens. Few studies have described co-infection in a large cohort with severe influenza infection. OBJECTIVES: To describe the spectrum and clinical impact of co-infections. STUDY DESIGN: Retrospective cohort study of patients with severe influenza infection from September 2013 through April 2014 in intensive care units at 33 U.S. hospitals comparing characteristics of cases with and without co-infection in bivariable and multivariable analysis. RESULTS: Of 507 adult and pediatric patients, 114 (22.5%) developed bacterial co-infection and 23 (4.5%) developed viral co-infection. Staphylococcus aureus was the most common cause of co-infection, isolated in 47 (9.3%) patients. Characteristics independently associated with the development of bacterial co-infection of adult patients in a logistic regression model included the absence of cardiovascular disease (OR 0.41 [0.23-0.73], p=0.003), leukocytosis (>11K/µl, OR 3.7 [2.2-6.2], p<0.001; reference: normal WBC 3.5-11K/µl) at ICU admission and a higher ICU admission SOFA score (for each increase by 1 in SOFA score, OR 1.1 [1.0-1.2], p=0.001). Bacterial co-infections (OR 2.2 [1.4-3.6], p=0.001) and viral co-infections (OR 3.1 [1.3-7.4], p=0.010) were both associated with death in bivariable analysis. Patients with a bacterial co-infection had a longer hospital stay, a longer ICU stay and were likely to have had a greater delay in the initiation of antiviral administration than patients without co-infection (p<0.05) in bivariable analysis. CONCLUSIONS: Bacterial co-infections were common, resulted in delay of antiviral therapy and were associated with increased resource allocation and higher mortality.

Severe Influenza in 33 US Hospitals, 2013-2014: Complications and Risk Factors for Death in 507 Patients.

BACKGROUND: Influenza A (H1N1) pdm09 became the predominant circulating strain in the United States during the 2013-2014 influenza season. Little is known about the epidemiology of severe influenza during this season. METHODS: A retrospective cohort study of severely ill patients with influenza infection in intensive care units in 33 US hospitals from September 1, 2013, through April 1, 2014, was conducted to determine risk factors for mortality present on intensive care unit admission and to describe patient characteristics, spectrum of disease, management, and outcomes. RESULTS: A total of 444 adults and 63 children were admitted to an intensive care unit in a study hospital; 93 adults (20.9%) and 4 children (6.3%) died. By logistic regression analysis, the following factors were significantly associated with mortality among adult patients: older age (>65 years, odds ratio, 3.1 [95% CI, 1.4-6.9], P=.006 and 50-64 years, 2.5 [1.3-4.9], P=.007; reference age 18-49 years), male sex (1.9 [1.1-3.3], P=.031), history of malignant tumor with chemotherapy administered within the prior 6 months (12.1 [3.9-37.0], P<.001), and a higher Sequential Organ Failure Assessment score (for each increase by 1 in score, 1.3 [1.2-1.4], P<.001). CONCLUSION: Risk factors for death among US patients with severe influenza during the 2013-2014 season, when influenza A (H1N1) pdm09 was the predominant circulating strain type, shifted in the first postpandemic season in which it predominated toward those of a more typical epidemic influenza season.