Faculty Bibliography

OBJECTIVE:Global migration and linguistic diversity are at record highs, making healthcare language barriers more prevalent. Nurses, often the first contact with patients in the healthcare system, can improve outcomes including safety and satisfaction through how they manage language barriers. This review aimed to explore how research has examined the nursing workforce with respect to language barriers. METHODS:A systematic scoping review of the literature was conducted using four databases. An iterative coding approach was used for data analysis. Study quality was appraised using the CASP checklists. RESULTS:48 studies representing 16 countries were included. Diverse healthcare settings were represented, with the inpatient setting most commonly studied. The majority of studies were qualitative. Coding produced 4 themes: (1) Interpreter Use/Misuse, (2) Barriers to and Facilitators of Quality Care, (3) Cultural Competence, and (4) Interventions. CONCLUSION/CONCLUSIONS:Generally, nurses noted like experiences and applied similar strategies regardless of setting, country, or language. Language barriers complicated care delivery while increasing stress and workload. PRACTICE IMPLICATIONS/CONCLUSIONS:This review identified gaps which future research can investigate to better support nurses working through language barriers. Similarly, healthcare and government leaders have opportunities to enact policies which address bilingual proficiency, workload, and interpreter use.

BACKGROUND:Little is known about long-term recovery from severe COVID-19 disease. Here, we characterize overall health, physical health, and mental health of patients 1 month after discharge for severe COVID-19. METHODS:This was a prospective single health system observational cohort study of patients ≥ 18 years hospitalized with laboratory-confirmed COVID-19 disease who required at least 6 l of oxygen during admission, had intact baseline cognitive and functional status, and were discharged alive. Participants were enrolled between 30 and 40 days after discharge. Outcomes were elicited through validated survey instruments: the PROMIS® Dyspnea Characteristics and PROMIS® Global Health-10. RESULTS:A total of 161 patients (40.6% of eligible) were enrolled; 152 (38.3%) completed the survey. Median age was 62 years (interquartile range [IQR], 50-67); 57 (37%) were female. Overall, 113/152 (74%) participants reported shortness of breath within the prior week (median score 3 out of 10 [IQR 0-5]), vs 47/152 (31%) pre-COVID-19 infection (0, IQR 0-1), p < 0.001. Participants also rated their physical health and mental health as worse in their post-COVID state (43.8, standard deviation 9.3; mental health 47.3, SD 9.3) compared to their pre-COVID state, (54.3, SD 9.3; 54.3, SD 7.8, respectively), both p < 0.001. Physical and mental health means in the general US population are 50 (SD 10). A total of 52/148 (35.1%) patients without pre-COVID oxygen requirements needed home oxygen after hospital discharge; 20/148 (13.5%) reported still using oxygen at time of survey. CONCLUSIONS:Patients with severe COVID-19 disease typically experience sequelae affecting their respiratory status, physical health, and mental health for at least several weeks after hospital discharge.

BACKGROUND:Hemophagocytic lymphohistiocytosis (HLH) is a rare and aggressive syndrome of excessive cytokine requiring prompt recognition and aggressive therapy. AIMS/OBJECTIVE:We aimed to systematically characterize HLH in moderate-to-severe inflammatory bowel disease (IBD). METHODS:We performed a systematic review of the literature (PubMED; EMBASE) and FDA Adverse Event Reporting System in accordance with the PRISMA statement. Use of biologics was used as a surrogate definition for disease severity (consistent with usual and contemporary clinical management), to enable identification of rare HLH cases with the highest fidelity. RESULTS:58 cases of HLH occurring in IBD patients are known (mean age: 26.0 years, 70% male, 83% with Crohn's disease, mean disease duration 7.0 years). 34.5% of patients were undergoing induction therapy at HLH diagnosis. All cases occurred on patients exposed to anti-TNF agents, but cases with anti-integrin or anti-IL-12/23 exposure were reported. 2/3 of cases did not report prior AZA/6MP exposure. Underlying opportunistic infection or lymphoma was found in > 80% of cases. Survival was 70% if promptly recognized and treated. Five patients restarted biologics after HLH resolved, and one patient developed recurrent HLH. CONCLUSIONS:HLH is rare among IBD patients exposed to biologic therapy. Most cases had an identifiable infection or malignancy at the time of diagnosis as well as history of immunomodulator use. Risk factors may include younger age, male gender, presence of Crohn's disease, and induction phase of treatment. Our study is not intended to assess risk of HLH with specific IBD therapies.

Objectives/UNASSIGNED:The aim of this study was to test the mediating role of perceived discrimination and stress on associations between perceived neighborhood social environment (PNSE) and TV viewing. Methods/UNASSIGNED:Baseline data were used for 4716 participants (mean age = 55.1 y; 63.4% female) in the Jackson Heart Study (JHS), a large prospective cohort study of African Americans in Jackson, Mississippi. One binary TV viewing outcome was created: ≥4 h/day versus <4 h/day. PNSE variables included neighborhood violence, problems (higher value = more violence/problems), and social cohesion (higher value = more cohesion). Mediators included perceived lifetime discrimination, daily discrimination, and chronic stress (higher value = greater discrimination/stress). Multivariable regression was used with bootstrap-generated 95% bias-corrected confidence intervals (BC CIs) to test for mediation adjusting for demographics, health-related and psychosocial factors, and population density. Results/UNASSIGNED: = 1.01, 1.10, respectively). Daily discrimination was neither directly nor indirectly associated with TV viewing. Conclusions/UNASSIGNED:Each PNSE variable was indirectly associated with TV viewing via lifetime discrimination and perceived stress, but not with daily discrimination among JHS participants. Unexpected directionality of mediating effects of lifetime discrimination and chronic stress should be replicated in future studies. Further research is also needed to pinpoint effective community efforts and physical environmental policies (e.g., installing bright street lights, community policing) to reduce adverse neighborhood conditions and psychosocial factors, and decrease TV viewing and subsequent cardiovascular disease risk.

BACKGROUND:Child maltreatment is a leading cause of pediatric morbidity and mortality. We previously reported on development and implementation of a child abuse clinical decision support system (CA-CDSS) in the Cerner electronic health record (EHR). Our objective was to develop a CA-CDSS in two different EHRs. METHODS:Using the CA-CDSS in Cerner as a template, CA-CDSSs were developed for use in four hospitals in the Northwell Health system who use Allscripts and two hospitals in the University of Wisconsin health system who use Epic. Each system had a combination of triggers, alerts and child abuse-specific order sets. Usability evaluation was done prior to launch of the CA-CDSS. RESULTS:Over an 18-month period, a CA-CDSS was embedded into Epic and Allscripts at two hospital systems. The CA-CDSSs vary significantly from each other in terms of the type of triggers which were able to be used, the type of alert, the ability of the alert to link directly to child abuse-specific order sets and the order sets themselves. CONCLUSIONS:Dissemination of CA-CDSS from one EHR into the EHR in other health care systems is possible but time-consuming and needs to be adapted to the strengths and limitations of the specific EHR. Site-specific usability evaluation, buy-in of multiple stakeholder groups and significant information technology support are needed. These barriers limit scalability and widespread dissemination of CA-CDSS.

OBJECTIVES/OBJECTIVE:The aim of this study was to evaluate whether machine learning (ML) of noncontrast computed tomographic (CT) and clinical variables improves the prediction of atherosclerotic cardiovascular disease (ASCVD) and coronary heart disease (CHD) deaths compared with coronary artery calcium (CAC) Agatston scoring and clinical data. BACKGROUND:The CAC score provides a measure of the global burden of coronary atherosclerosis, and its long-term prognostic utility has been consistently shown to have incremental value over clinical risk assessment. However, current approaches fail to integrate all available CT and clinical variables for comprehensive risk assessment. METHODS:The study included data from 66,636 asymptomatic subjects (mean age 54 ± 11 years, 67% men) without established ASCVD undergoing CAC scanning and followed for cardiovascular disease (CVD) and CHD deaths at 10 years. Clinical risk assessment incorporated the ASCVD risk score. For ML, an ensemble boosting approach was used to fit a predictive classifier for outcomes, followed by automated feature selection using information gain ratio. The model-building process incorporated all available clinical and CT data, including the CAC score; the number, volume, and density of CAC plaques; and extracoronary scores; comprising a total of 77 variables. The overall proposed model (ML all) was evaluated using a 10-fold cross-validation framework on the population data and area under the curve (AUC) as metrics. The prediction performance was also compared with 2 traditional scores (ASCVD risk and CAC score) and 2 additional models that were trained using all the clinical data (ML clinical) and CT variables (ML CT). RESULTS:The AUC by ML all (0.845) for predicting CVD death was superior compared with those obtained by ASCVD risk alone (0.821), CAC score alone (0.781), and ML CT alone (0.804) (p < 0.001 for all). Similarly, for predicting CHD death, AUC by ML all (0.860) was superior to the other analyses (0.835 for ASCVD risk, 0.816 for CAC, and 0.827 for ML CT; p < 0.001). CONCLUSIONS:The comprehensive ML model was superior to ASCVD risk, CAC score, and an ML model fitted using CT variables alone in the prediction of both CVD and CHD death.

The posthospital discharge period is vulnerable for patients with coronavirus disease 2019 (COVID-19). The authors implemented a COVID-19 discharge pathway in the electronic medical record for UCHealth, a 12-hospital health care system, including an academic medical center (University of Colorado Hospital [UCH]), to improve patient safety by standardizing discharge processes for COVID-19 patients. There were 3 key elements: (1) building consensus on discharge readiness criteria, (2) summarizing discharge criteria for disposition locations, and (3) establishing primary care follow-up protocols. The discharge pathway was opened 821 times between April 20, 2020, and June 7, 2020. Of the 436 patients discharged from the hospital medicine service at UCH from April 20, 2020, and June 7, 2020, 18 (4%) were readmitted and 13 (3%) had a 30-day emergency department visit. The main trend observed was venous thromboembolism. This pathway allowed real-time integration of clinical guidelines and complex disposition requirements, decreasing cognitive burden and standardizing care for a complex population.