Assessing Electronic Health Literacy at an Urban Academic Hospital.

BACKGROUND: Residents of the Bronx suffer marked health disparities due to socioeconomic and other factors. The coronavirus disease 2019 pandemic worsened these health outcome disparities and health care access disparities, especially with the abrupt transition to online care. OBJECTIVES: This study classified electronic health literacy (EHL) among patients at an urban, academic hospital in the Bronx, and assessed for associations between EHL levels and various demographic characteristics. METHODS: We designed a cross-sectional, observational study in adults 18 years or older presenting to the Montefiore Einstein Center for Cancer Care (MECCC) Department of Radiation Oncology or the Montefiore Department of Medicine in the Bronx. We assessed EHL using the eHealth Literacy Scale (eHEALS) survey, a previously validated tool, and our newly developed eHealth Literacy Objective Scale-Scenario Based (eHeLiOS-SB) tool. RESULTS: A total of 97 patients recruited from the MECCC and Department of Medicine participated in this study. There was a statistically significant association between age and EHL as assessed by both eHEALS and eHeLiOS-SB, with older adults having lower EHL scores. Additionally, a question designed to assess general attitudes toward digital health technologies found that most participants had a positive attitude toward such applications. CONCLUSION: Many patients, especially older adults, may require additional support to effectively navigate telehealth. Further research is warranted to optimize telemedicine strategies in this potentially-marginalized population and ultimately to create telehealth practices accessible to patients of all ages and demographics.

Development and Validation of a Respiratory-Responsive Vocal Biomarker-Based Tool for Generalizable Detection of Respiratory Impairment: Independent Case-Control Studies in Multiple Respiratory Conditions Including Asthma, Chronic Obstructive Pulmonary Disease, and COVID-19.

BACKGROUND: Vocal biomarker-based machine learning approaches have shown promising results in the detection of various health conditions, including respiratory diseases, such as asthma. OBJECTIVE: This study aimed to determine whether a respiratory-responsive vocal biomarker (RRVB) model platform initially trained on an asthma and healthy volunteer (HV) data set can differentiate patients with active COVID-19 infection from asymptomatic HVs by assessing its sensitivity, specificity, and odds ratio (OR). METHODS: A logistic regression model using a weighted sum of voice acoustic features was previously trained and validated on a data set of approximately 1700 patients with a confirmed asthma diagnosis and a similar number of healthy controls. The same model has shown generalizability to patients with chronic obstructive pulmonary disease, interstitial lung disease, and cough. In this study, 497 participants (female: n=268, 53.9%; <65 years old: n=467, 94%; Marathi speakers: n=253, 50.9%; English speakers: n=223, 44.9%; Spanish speakers: n=25, 5%) were enrolled across 4 clinical sites in the United States and India and provided voice samples and symptom reports on their personal smartphones. The participants included patients who are symptomatic COVID-19 positive and negative as well as asymptomatic HVs. The RRVB model performance was assessed by comparing it with the clinical diagnosis of COVID-19 confirmed by reverse transcriptase-polymerase chain reaction. RESULTS: The ability of the RRVB model to differentiate patients with respiratory conditions from healthy controls was previously demonstrated on validation data in asthma, chronic obstructive pulmonary disease, interstitial lung disease, and cough, with ORs of 4.3, 9.1, 3.1, and 3.9, respectively. The same RRVB model in this study in COVID-19 performed with a sensitivity of 73.2%, specificity of 62.9%, and OR of 4.64 (P<.001). Patients who experienced respiratory symptoms were detected more frequently than those who did not experience respiratory symptoms and completely asymptomatic patients (sensitivity: 78.4% vs 67.4% vs 68%, respectively). CONCLUSIONS: The RRVB model has shown good generalizability across respiratory conditions, geographies, and languages. Results using data set of patients with COVID-19 demonstrate its meaningful potential to serve as a prescreening tool for identifying individuals at risk for COVID-19 infection in combination with temperature and symptom reports. Although not a COVID-19 test, these results suggest that the RRVB model can encourage targeted testing. Moreover, the generalizability of this model for detecting respiratory symptoms across different linguistic and geographic contexts suggests a potential path for the development and validation of voice-based tools for broader disease surveillance and monitoring applications in the future.

e-Health Literacy Rates and Applicability of a Patient-Facing Mobile App Intervention in a Predominantly African-American and Latinx Cohort with Type 2 Diabetes

Introduction: Racial and ethnic minorities have a higher burden of type 2 diabetes in part due to limited access to health-promoting resources and self-management tools. We developed and implemented a patient-centric mobile application, DiabetesXcel, to provide guideline-based education (via animated videos) and adherence support via goal setting and tailored push notifications. Methods: Patients provided verbal or e-consent via REDCap generated e-mail links virtually (in-light of COVID-19) and completed baseline sociodemographic and e-health literacy (EHL) surveys (eHEALs) ;and DQoL (Diabetes quality of life) , DSMQ (Diabetes Self-Management Questionnaire) and DKQ-24 (Diabetes Knowledge Questionnaire) . Participants were guided on downloading the app and its functionality was explained in detail. Laboratory data were obtained by chart review from the Epic EHR. Results: Of the 50 patients, mean age was 46.9 (± 9.2) years, 70% were female, and 82% identified as Blacks and Hispanics. Overall, the cohort, with an average 13.1 ± 8.3 years since diagnosis, had high prevalence of complications (60%) , and poor glycemic control (HbA1C > 7.5, 58%) . At baseline, scores of satisfaction (22.6 ± 6.9) , worry (10.9 ± 8.4) , impact (15.5 ± 5.2) and total DQoL scores (48.9 ± 15.6) indicated low quality of life (QoL) . DSMQ (21.8 ± 6.9) and DKQ (18.4 ± 3) were low across patients with and without complications (p= 0.5 and 0.8, respectively) . Interestingly, 88% had moderate-good EHL scores. Conclusion: Most patients in our cohort demonstrated good e-health literacy scores. We expect that our mobile health application will promote diabetes education and self-management skills in our underserved minority community and favorably impact clinical outcomes. We eventually plan to disseminate the app and promote its uptake across marginalized populations on the national level.

Elevated plasma ICAM1 levels predict 28-day mortality in cirrhotic patients with COVID-19 or bacterial sepsis.

BACKGROUND & AIMS: Endothelial injury and dysfunction play a detrimental role in the pathogenesis of infections. Endothelium-related molecules have been reported as potential diagnostic and/or prognostic biomarkers of infection. The prognostic value of these biomarkers in patients with cirrhosis and infections remains elusive. METHODS: In this study, we investigated the performance of key soluble endothelial injury biomarkers, including intercellular adhesion molecule 1 (ICAM1), von Willebrand factor (vWF), vascular endothelial growth factor receptor 1 (VEGFR1), and angiopoietin 1 and 2 (Ang1, 2) as mortality predictors in patients with cirrhosis and severe COVID-19 or bacterial sepsis. RESULTS: A total of 66 hospitalized patients (admitted to the COVID-19 ward or liver intensive care unit [ICU]) were included. Twenty-two patients had COVID-19 alone, while 20 patients had cirrhosis plus COVID-19. Twenty-four patients had cirrhosis plus bacterial sepsis. Among patients with cirrhosis, the most common aetiology of liver disease was alcohol. ICAM1 was increased (p = 0.003) while VEGFR1 (p <0.0001) and Ang1 (p <0.0001) were reduced in patients with COVID-19 and cirrhosis, compared to patients with COVID-19 alone. Endothelial biomarker levels did not differ significantly between patients with cirrhosis and severe COVID-19 or bacterial sepsis in the ICU. In these patients, ICAM1 levels significantly and independently predicted mortality (hazard ratio 3.24; 95% CI 1.19-8.86) along with model for end-stage liver disease (MELD) score, renal and coagulation failures. The AUC for ICAM1 was 0.74, MELD was 0.60 and combined ICAM1 and MELD was 0.70. ICAM1 also positively correlated with the composite organ failure scores recorded 3-5 days post ICU admission (CLIF-OF and SOFA) in this subgroup of patients. CONCLUSION: The study indicates that in patients with cirrhosis, elevated plasma ICAM1 serves as an independent predictor of severe COVID-19- or sepsis-associated 28-day mortality. LAY SUMMARY: Bacterial sepsis and COVID-19 lead to increased mortality in patients with cirrhosis. In this study, we demonstrate that high plasma levels of ICAM1, an endothelial injury biomarker, is one of the important factors predicting mortality in critically ill cirrhotic patients with severe COVID-19 or bacterial sepsis.

A Recombinant Fragment of Human Surfactant Protein D Binds Spike Protein and Inhibits Infectivity and Replication of SARS-CoV-2 in Clinical Samples.

Coronavirus disease (COVID-19) is an acute infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Human SP-D (surfactant protein D) is known to interact with the spike protein of SARS-CoV, but its immune surveillance against SARS-CoV-2 is not known. The current study aimed to examine the potential of a recombinant fragment of human SP-D (rfhSP-D) as an inhibitor of replication and infection of SARS-CoV-2. The interaction of rfhSP-D with the spike protein of SARS-CoV-2 and human ACE-2 (angiotensin-converting enzyme 2) receptor was predicted via docking analysis. The inhibition of interaction between the spike protein and ACE-2 by rfhSP-D was confirmed using direct and indirect ELISA. The effect of rfhSP-D on replication and infectivity of SARS-CoV-2 from clinical samples was assessed by measuring the expression of RdRp gene of the virus using quantitative PCR. In silico interaction studies indicated that three amino acid residues in the receptor-binding domain of spike protein of SARS-CoV-2 were commonly involved in interacting with rfhSP-D and ACE-2. Studies using clinical samples of SARS-CoV-2-positive cases (asymptomatic, n = 7; symptomatic, n = 8) and negative control samples (n = 15) demonstrated that treatment with 1.67 µM rfhSP-D inhibited viral replication by ∼5.5-fold and was more efficient than remdesivir (100 µM) in Vero cells. An approximately two-fold reduction in viral infectivity was also observed after treatment with 1.67 µM rfhSP-D. These results conclusively demonstrate that the rfhSP-D mediated calcium independent interaction between the receptor-binding domain of the S1 subunit of the SARS-CoV-2 spike protein and human ACE-2, its host cell receptor, and significantly reduced SARS-CoV-2 infection and replication in vitro.

The Enigma of Endothelium in COVID-19.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2) has affected millions of people globally. Clinically, it presents with mild flu-like symptoms in most cases but can cause respiratory failure in high risk population. With the aim of unearthing newer treatments, scientists all over the globe are striving hard to comprehend the underlying mechanisms of COVID-19. Several studies till date have indicated a dysregulated host immune response as the major cause of COVID-19 induced mortality. In this Perspective, we propose a key role of endothelium, particularly pulmonary endothelium in the pathogenesis of COVID-19. We draw parallels and divergences between COVID-19-induced respiratory distress and bacterial sepsis-induced lung injury and recommend the road ahead with respect to identification of endothelium-based biomarkers and plausible treatments for COVID-19.