Background: Obesity is a major public health problem associated with significant medical complications. Main body: This review examines 8 primary diseases: type 2 diabetes, hypertension, dementia, non-alcoholic fatty liver disease, polycystic ovarian syndrome, dyslipidemia, cancer, and their manifestations in obese patients. A total of 39 articles were used for this review. The authors conducted limited review, searching PubMed and Google Scholar databases using a combination of key words “COVID-19” or “SARS-COV2”, “type 2 diabetes”, “hypertension”, “dementia”, “non-alcoholic fatty liver disease”, “polycystic ovarian syndrome”, “dyslipidemia”, “cancer”, and “obesity”. No specific date limitation was used. Obesity exacerbates many medical conditions and has recently been identified as an independent risk factor for COVID-19 severity. This sets obesity at the pinnacle of all disease complications. The long-term impact of obesity ranges from financial burden on the health system, lower life expectancy, and reduced survival rates. Conclusion: Obesity is an important modifiable risk factor. There is the need for healthcare providers to understand the medical complications associated with obesity to optimize patient care.

This study evaluates the agreement between emergency physician (EP) assessment of diastolic dysfunction (DD) by a simplified approach using average peak mitral excursion velocity (eʹA) and an independent cardiologist’s diagnosis of DD by estimating left atrial (LA) pressure using American Society of Echocardiography (ASE) guidelines. This was a secondary analysis of 48 limited bedside echocardiograms (LBE) performed as a part of a research study of patients presenting to the Emergency Department (ED) with elevated blood pressure but without decompensated heart failure. EPs diagnosed DD based on eʹA < 9 cm/s alone. A blinded board-certified cardiologist reviewed LBEs to estimate LA filling pressures following ASE guidelines. An unweighted kappa measure was calculated to determine agreement between EP and cardiologist. Six LBEs were deemed indeterminate by the cardiologist and excluded from the analysis. Agreement was reached in 41 out of 48 cases (85.4%). The unweighted kappa coefficient was 0.74 (95% CI 0.57–0.92). EPs identified 18 out of 20 LBEs diagnosed with diastolic dysfunction by the cardiologist. There is a good agreement between (eʹA) by EP and cardiologist interpretation of LBEs. Future studies should investigate this simplified approach as a one-step method of screening for LV diastolic dysfunction in the ED.

Social determinants of health (SDoH) contribute to disparities in SARS-CoV-2 infection, but their associations with long COVID are unknown. To determine associations between SDoH at the time of SARS-CoV-2 infection and risk for long COVID. Prospective observational cohort study. 33 states plus Washington, DC, and Puerto Rico. Adults (aged ≥18 years) enrolled in RECOVER-Adult (Researching COVID to Enhance Recovery) between October 2021 and November 2023 who were within 30 days of SARS-CoV-2 infection; completed baseline SDoH, comorbidity, and pregnancy questionnaires; and were followed prospectively. Social risk factors from SDoH baseline questionnaires, ZIP code poverty and household crowding measures, and a weighted score of 11 or higher on the Long COVID Research Index 6 months after infection. Among 3787 participants, 418 (11%) developed long COVID. After adjustment for demographic characteristics, pregnancy, disability, comorbidities, SARS-CoV-2 severity, and vaccinations, financial hardship (adjusted marginal risk ratio [ARR], 2.36 [95% CI, 1.97 to 2.91]), food insecurity (ARR, 2.36 [CI, 1.83 to 2.98]), less than a college education (ARR, 1.60 [CI, 1.30 to 1.97]), experiences of medical discrimination (ARR, 2.37 [CI, 1.94 to 2.83]), skipped medical care due to cost (ARR, 2.87 [CI, 2.22 to 3.70]), and lack of social support (ARR, 1.79 [CI, 1.50 to 2.17]) were associated with increased risk for long COVID. Living in ZIP codes with the highest (vs. lowest) household crowding was also associated with greater risk (ARR, 1.36 [CI, 1.05 to 1.71]). Selection bias may influence observed associations and generalizability. Participants with social risk factors at the time of SARS-CoV-2 infection had greater risk for subsequent long COVID than those without. Future studies should determine whether social risk factor interventions mitigate long-term effects of SARS-CoV-2 infection. National Institutes of Health.

Background: During the COVID-19 pandemic, novel digital health technologies have the potential to improve our understanding of SARS-CoV-2 and COVID-19, improve care delivery, and produce better health outcomes. The National Institutes of Health called on digital health leaders to contribute to a high-quality data repository that will support researchers to make discoveries that are otherwise not possible with small, limited data sets. Objective: To this end, we seek to develop a COVID-19 digital biomarker for early detection of physiological exacerbation or decompensation. We propose the development and validation of a COVID-19 decompensation Index (CDI) in a 2-phase study that builds on existing wearable biosensor-derived analytics generated by physIQ’s end-to-end cloud platform for continuous physiological monitoring with wearable biosensors. This effort serves to achieve two primary objectives: (1) to collect adequate data to help develop the CDI and (2) to collect rich deidentified clinical data correlating with outcomes and symptoms related to COVID-19 progression. Our secondary objectives include evaluation of the feasibility and usability of pinpointIQ, a digital platform through which data are gathered, analyzed, and displayed. Methods: This is a prospective, nonrandomized, open-label, 2-phase study. Phase I will involve data collection for the digital data hub of the National Institutes of Health as well as data to support the preliminary development of the CDI. Phase II will involve data collection for the hub and contribute to continued refinement and validation of the CDI. While this study will focus on the development of a CDI, the digital platform will also be evaluated for feasibility and usability while clinicians deliver care to continuously monitored patients enrolled in the study. Results: Our target CDI will be a binary classifier trained to distinguish participants with and those without decompensation. The primary performance metric for CDI will be the area under the receiver operating characteristic curve with a minimum performance criterion of ≥0.75 (α=.05; power [1–β]=0.80). Furthermore, we will determine the sex or gender and race or ethnicity of the participants, which would account for differences in the CDI performance, as well as the lead time—time to predict decompensation—and its relationship with the ultimate disease severity based on the World Health Organization COVID-19 ordinal scale. Conclusions: Using machine learning techniques on a large data set of patients with COVID-19 could provide valuable insights into the pathophysiology of COVID-19 and a digital biomarker for COVID-19 decompensation. Through this study, we intend to develop a tool that can uniquely reflect physiological data of a diverse population and contribute to high-quality data that will help researchers better understand COVID-19. Clinical Trial: ClinicalTrials.gov NCT04575532; https://www.clinicaltrials.gov/ct2/show/NCT04575532

Importance: Hypertension is a leading risk factor for cardiovascular diseases and is often undiagnosed. Emergency department (ED) visits serve as critical access points within health care and present a unique opportunity for hypertension screening and intervention.Objective: To evaluate the effectiveness of an Education and mHealth Empowerment (E2) intervention compared with usual care in reducing systolic blood pressure (SBP) among patients with elevated BP discharged from the ED.Design, Setting, and Participants: This randomized clinical trial enrolled participants who presented to an urban academic medical center ED for any indication and had elevated blood pressure (≥140/90 mm Hg and ≤180/110 mm Hg). Eligible participants who were discharged from the ED were enrolled between February 12, 2019, and March 31, 2023, and were randomized to receive either usual care or the intervention with follow-up visits at 3 and 6 months.Interventions: Usual care involved standard hypertension discharge instructions with a referral for outpatient follow-up. The E2 intervention involved a 3-prong approach, which included a brief Post-Acute Care Hypertension consultation (PACHT-c) with a clinical pharmacist or an advanced practice nurse, a smartphone-enabled BP monitoring kit (Withings device and mobile app) for daily self-monitoring along with behavior change text messages, and primary care referral.Main Outcomes and Measures: The primary outcome was the mean change in SBP (mm Hg) from baseline to 6 months.Results: Of the 574 participants enrolled, mean (SD) age was 51.1 (12.5) years, and 323 (56%) were female; 413 were Black (72%), 115 were Hispanic or Latino (20%), 27 were White (5%), and 19 were other race and ethnicity (3%), which included Asian, American Indian, and other racial or ethnic groups. Of the 413 patients with BP data at 6 months, the E2 intervention group (n = 210) showed a greater mean reduction in SBP (mean difference, 4.9 mm Hg; 95% CI, 0.8-9.0 mm Hg; P = .02) compared with the usual-care group (n = 203). A similar proportion of patients achieved BP less than or equal to 140/90 mm Hg at 6 months in the intervention arm (42.9% [90 of 210]) and the control arm (36.9% [75 of 203]; P = .22).Conclusions and Relevance: In this single-center randomized clinical trial, a multicomponent intervention directed at patients in the ED who have elevated BP was associated with greater reduction in SBP at 6 months. Identifying patients who present to the ED with hypertension may be a viable strategy to improve BP management.Trial Registration: ClinicalTrials.gov Identifier: NCT03749499