Clinical Features and Outcomes of Hospitalized Adult Patients With Breakthrough COVID-19 Infections: A Propensity-Score-Matched Observational Study.

In this study, we aimed to evaluate the impact of vaccination on intensive care unit (ICU) admission and in-hospital mortality among breakthrough coronavirus disease 2019 (COVID-19) infections. A total of 3,351 adult patients hospitalized with COVID-19 in the Memorial Healthcare System (Hollywood, Florida) between June 1 and September 20, 2021, were included; 284 (8.5%) were fully vaccinated. A propensity-score-matched analysis was conducted to compare fully vaccinated patients with unvaccinated controls. Propensity scores were calculated on the basis of variables associated with vaccination status. A 1:1 matching ratio was applied using logistic regression models, ensuring balanced characteristics between the two groups. The matched samples were then subjected to multivariate analysis. Among breakthrough infections, vaccinated patients demonstrated lower incidences of ICU admission (10.3% vs. 16.4%; P = 0.042) and death (12.2% vs. 18.7%; P = 0.041) than the matched controls. Risk-adjusted multivariate analysis demonstrated a significant inverse association between vaccination and ICU admission (odds ratio = 0.52, 95% confidence interval: 0.31, 0.89; P = 0.019) as well as in-hospital mortality (odds ratio = 0.57, 95% confidence interval: 0.34, 0.94; P = 0.027). Vaccinated individuals experiencing breakthrough infections had significantly lower risks of ICU admission and in-hospital mortality. These findings highlight the benefits of COVID-19 vaccines in reducing severe outcomes among patients with breakthrough infections.

Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in south Florida.

Introduction: The SARS-CoV-2 (COVID-19) pandemic has created substantial health and economic burdens in the US and worldwide. As new variants continuously emerge, predicting critical clinical events in the context of relevant individual risks is a promising option for reducing the overall burden of COVID-19. This study aims to train an AI-driven decision support system that helps build a model to understand the most important features that predict the "mortality" of patients hospitalized with COVID-19. Methods: We conducted a retrospective analysis of "5,371" patients hospitalized for COVID-19-related symptoms from the South Florida Memorial Health Care System between March 14th, 2020, and January 16th, 2021. A data set comprising patients' sociodemographic characteristics, pre-existing health information, and medication was analyzed. We trained Random Forest classifier to predict "mortality" for patients hospitalized with COVID-19. Results: Based on the interpretability of the model, age emerged as the primary predictor of "mortality", followed by diarrhea, diabetes, hypertension, BMI, early stages of kidney disease, smoking status, sex, pneumonia, and race in descending order of importance. Notably, individuals aged over 65 years (referred to as "older adults"), males, Whites, Hispanics, and current smokers were identified as being at higher risk of death. Additionally, BMI, specifically in the overweight and obese categories, significantly predicted "mortality". These findings indicated that the model effectively learned from various categories, such as patients' sociodemographic characteristics, pre-hospital comorbidities, and medications, with a predominant focus on characterizing pre-hospital comorbidities. Consequently, the model demonstrated the ability to predict "mortality" with transparency and reliability. Conclusion: AI can potentially provide healthcare workers with the ability to stratify patients and streamline optimal care solutions when time is of the essence and resources are limited. This work sets the platform for future work that forecasts patient responses to treatments at various levels of disease severity and assesses health disparities and patient conditions that promote improved health care in a broader context. This study contributed to one of the first predictive analyses applying AI/ML techniques to COVID-19 data using a vast sample from South Florida.

Distinct Clinical Presentations and Outcomes of Hospitalized Adults with the SARS-CoV-2 Infection Occurring during the Omicron Variant Surge.

The COVID-19 Omicron variant has imposed a tremendous burden on healthcare services. We characterized the types of the Omicron variant-associated hospitalizations and their associations with clinical outcomes. Consecutive adults hospitalized with COVID-19 during the Omicron variant surge period of 1-14 January 2022, were classified into one of three groups based on their clinical presentations on admission: Group 1-primary COVID-19; Group 2-extrapulmonary manifestations of COVID-19; and Group 3-incidental COVID-19. Of the 500 patients who were hospitalized, 51.4% fell into Group 1, 16.4% into Group 2, and 32.2% into Group 3. The patients in Groups 1 and 2 were older, with higher proportions of comorbidities than patients in Group 3. The Group 1 patients had the highest mortality rate (15.6%), followed by Group 2 (8.5%), and Group 3 (0.6%), with adjusted odds ratios (OR) of 22.65 (95% confidence interval [CI], 2.75-239.46; p = 0.004) and 10.95 (95% CI, 1.02-117.28; p = 0.048), respectively, compared to Group 3. Those in Group 1 showed a greater utilization of intensive care services (15.9%), followed by Group 2 (10.9%), and Group 3 (2.5%), with adjusted ORs of 7.95 (95% CI, 2.52-25.08; p < 0.001) and 5.07 (95% CI, 1.34-19.15; p = 0.017), respectively, compared to Group 3. The patients in Groups 1 and 2 had longer hospitalization stays than the patients in Group 3 (p < 0.001 and p = 0.002, respectively). Older age (≥65 years) was an independent factor associated with longer hospital stays (OR = 1.72, 95% CI, 1.07-2.77). These findings can help hospitals prioritize patient care and service planning for future SARS-CoV-2 variants.

Mortality and prognostic factors in hospitalized COVID-19 patients with cancer: an analysis from a large healthcare system in the United States.

Background: To evaluate clinical outcomes in patients with malignancy who are SARS-CoV-2 (COVID-19) positive and investigate if factors such as age, gender, and race contribute to COVID-19 mortality in patients with malignancy. Methods: Retrospective data was gathered from Memorial Healthcare System of COVID-19 patients hospitalized from March 1, 2020 to January 18, 2021. Active malignancy was defined as either receiving antineoplastic therapy or being under surveillance. The primary endpoint was in-hospital mortality. Descriptive statistics were used to summarize the characteristics and outcomes. Univariate and multivariate logistic analysis were performed to define baseline clinical characteristics potentially associated with mortality in cancer patients with COVID-19. Results: A total of 4,870 COVID-19 patients were enrolled in the study, and 265 of those patients had a diagnosis of active malignancy. The study population was diverse which included non-Hispanic whites (NHW) 816 (16.8%), Hispanics 2,271 (46.6%) and Blacks 1,534 (31.5%). Of the cancer patients, 24.1% were NHW, 43% were Hispanic and 28.7% were Black. Amongst the races, 37.5% of in-hospital mortalities were NHW, while 18.4% were Hispanics and 19.7% were Black. The in-hospital mortalities amongst the two malignancy types, solid and hematological, accounted for 24.6% and 23.5% of deaths and they were not found to be statistically significant (P=0.845). After adjustments for age, gender and race were made, cancer was independently associated with an increased in-hospital mortality, with an adjusted odds ratio of 1.48 [95% confidence interval (CI): 1.08-2.01]. Increased age and elevated serum levels of creatinine and C-reactive protein (CRP) were associated with an increased risk of death in cancer patients with COVID-19. Conclusions: COVID-19 in patients with cancer had poorer outcomes in comparison to those who were cancer-free. Both hematological and solid malignancies had similar in-hospital mortality rates. The highest in-hospital mortalities of cancer patients with COVID-19 were non-Hispanic whites in-comparison to Hispanics with the least. Age, elevated levels of creatinine and CRP were independently associated with increased risk of death in cancer patients hospitalized with COVID-19. The findings indicate the need for close surveillance and monitoring of these patients as they are more likely to have higher risk of death from COVID-19.

Impact of the COVID-19 pandemic on an emergency department-based opt-out HIV screening program in a South Florida hospital: An interrupted time series analysis, July 2018-March 2021.

BACKGROUND: The COVID-19 pandemic has posed a tremendous burden on healthcare services. We evaluated its impact on an emergency department (ED)-based opt-out Human immunodeficiency virus (HIV) testing in a public healthcare system. METHODS: The programmatic data of ED-based HIV testing from July 2018 to March 2021 at the Memorial Regional Hospital, Hollywood, Florida was analyzed by interrupted time series analysis to evaluate the immediate and gradual effects of the COVID-19 pandemic on the number of monthly HIV tests, with an interruption point at March 2020. RESULTS: The average number of monthly HIV tests were significantly lower during the pandemic than the pre-pandemic (791 ± 187 vs 1745 ± 266, P < .001). There was a slight decline trend in the number of monthly HIV tests before the pandemic (estimate -10.29, P = .541). HIV testing dramatically decreased during the initial 7 months of the pandemic, compared to the pre-pandemic period, with the largest decline in the number of HIV tests on March 2020 (estimate -678.48, P = .007). HIV testing slightly increased every month (estimate 4.84, P = .891) during the pandemic period, and the number of HIV tests per month rebounded to the pre-pandemic levels by October 2020. CONCLUSIONS: ED-based HIV testing significantly decreased during the initial 7 months of the pandemic in south Florida. Multiple strategies are necessary to maintain HIV testing during this pandemic era.

Lymphopenia as a Predictor for Adverse Clinical Outcomes in Hospitalized Patients with COVID-19: A Single Center Retrospective Study of 4485 Cases.

Lymphopenia is commonly present in patients with COVID-19. We sought to determine if lymphopenia on admission predicts COVID-19 clinical outcomes. A retrospective chart review was performed on 4485 patients with laboratory-confirmed COVID-19, who were admitted to the hospital. Of those, 2409 (57.3%) patients presented with lymphopenia (absolute lymphocyte count < 1.1 × 109/L) on admission, and had higher incidences of ICU admission (17.9% versus 9.5%, p < 0.001), invasive mechanical ventilation (14.4% versus 6.5%, p < 0.001), dialysis (3.4% versus 1.8%, p < 0.001) and in-hospital mortality (16.6% versus 6.6%, p < 0.001), with multivariable-adjusted odds ratios of 1.86 (95% confidence interval [CI], 1.55-2.25), 2.09 (95% CI, 1.69-2.59), 1.77 (95% CI, 1.19-2.68), and 2.19 (95% CI 1.76-2.72) for the corresponding outcomes, respectively, compared to those without lymphopenia. The restricted cubic spline models showed a non-linear relationship between lymphocyte count and adverse outcomes, with an increase in the risk of adverse outcomes for lower lymphocyte counts in patients with lymphopenia. The predictive powers of lymphopenia, expressed as areas under the receiver operating characteristic curves, were 0.68, 0.69, 0.78, and 0.79 for the corresponding adverse outcomes, respectively, after incorporating age, gender, race, and comorbidities. In conclusion, lymphopenia is a useful metric in prognosticating outcomes in hospitalized COVID-19 patients.

Characteristics, Vaccination Status and Outcomes Among Healthy Younger Adults in a Large Public Healthcare System in the South Florida Region.

Nationally, the 18-49 years old age group are less likely to be vaccinated compared to those 50 years and older. Data describing the risk of COVID-19 severe illness that requires hospitalization among younger healthy adults is limited. In an effort to underscore the importance of vaccination and provide data that may influence COVID-19 risk perception, COVID-19 data of a sample of hospitalized non-elderly age group who clinically may not be considered as high risk for severe COVID-19 illness are presented. Specifically, this retrospective chart review (spanning the period of March 2020 to September 2021) provides a descriptive analysis examining the characteristics, vaccination status and outcomes of adults who were hospitalized at Memorial Healthcare System with laboratory-confirmed COVID-19. The study's data focuses on non-pregnant adults, aged 18-49 years old, without underlying conditions and with no reported history of smoking. As a sub-analysis, data on young and otherwise healthy pregnant females who were hospitalized with COVID-19, as well as data stratified by the pre-Delta and Delta variant dominant period are also presented. There was a total of 482 young and otherwise healthy non-pregnant adults who were hospitalized with COVID-19. Overall, more than 13% of our study population had severe COVID-19 disease. Further, a higher proportion of unvaccinated patients had severe COVID-19 compared to those who received at least one dose of the vaccine. All ventilator or ECMO placements, 30-day readmissions and deaths occurred among unvaccinated patients.

Monocyte Chemotactic Protein-Induced Protein 1 (MCPIP-1): A Key Player of Host Defense and Immune Regulation.

Inflammatory response is a host-protective mechanism against tissue injury or infections, but also has the potential to cause extensive immunopathology and tissue damage, as seen in many diseases, such as cardiovascular diseases, neurodegenerative diseases, metabolic syndrome and many other infectious diseases with public health concerns, such as Coronavirus Disease 2019 (COVID-19), if failure to resolve in a timely manner. Recent studies have uncovered a superfamily of endogenous chemical molecules that tend to resolve inflammatory responses and re-establish homeostasis without causing excessive damage to healthy cells and tissues. Among these, the monocyte chemoattractant protein-induced protein (MCPIP) family consisting of four members (MCPIP-1, -2, -3, and -4) has emerged as a group of evolutionarily conserved molecules participating in the resolution of inflammation. The focus of this review highlights the biological functions of MCPIP-1 (also known as Regnase-1), the best-studied member of this family, in the resolution of inflammatory response. As outlined in this review, MCPIP-1 acts on specific signaling pathways, in particular NFκB, to blunt production of inflammatory mediators, while also acts as an endonuclease controlling the stability of mRNA and microRNA (miRNA), leading to the resolution of inflammation, clearance of virus and dead cells, and promotion of tissue regeneration via its pleiotropic effects. Evidence from transgenic and knock-out mouse models revealed an involvement of MCPIP-1 expression in immune functions and in the physiology of the cardiovascular system, indicating that MCPIP-1 is a key endogenous molecule that governs normal resolution of acute inflammation and infection. In this review, we also discuss the current evidence underlying the roles of other members of the MCPIP family in the regulation of inflammatory processes. Further understanding of the proteins from this family will provide new insights into the identification of novel targets for both host effectors and microbial factors and will lead to new therapeutic treatments for infections and other inflammatory diseases.

The Epidemiology and Predictors of Outcomes Among Confirmed COVID-19 Cases in a Large Community Healthcare System in South Florida.

The novel coronavirus disease 2019 (COVID-19) continues to be a major public health concern. The aim of this study was to describe the presenting characteristics, epidemiology and predictors of outcomes among confirmed COVID-19 cases seen at a large community healthcare system which serves the epicenter and diverse region of Florida. We conducted a retrospective analysis of individuals with lab-confirmed SARS-CoV-2 infection who were seen, from March 2, 2020 to May 31, 2020, at Memorial Healthcare System in South Florida. Data was extracted from a COVID-19 registry of patients with lab-confirmed SARS-CoV-2 infection. Univariate and backward stepwise multivariate logistic regression models were used to determine predictors of key study outcomes. There were a total of 1692 confirmed COVID-19 patients included in this study. Increasing age was found to be a significant predictor of hospitalization, 30-day readmission and death. Having a temperature of 38 °C or more and increasing comorbidity score were also associated with an increased risk of hospitalization. Significant predictors of ICU admission included having a saturated oxygen level less than 90%, hypertension, dementia, rheumatologic disease, having a respiratory rate greater than 24 breaths per minute. Being of Hispanic ethnicity and immunosuppressant utilization greatly increased the risk of 30-day readmission. Having an oxygen saturation less than 90% and an underlying neurological disorder were associated with an increased likelihood of death. Results show that a patient's demographic, underlying condition and vitals at triage may increase or reduce their risk of hospitalization, ICU admission, 30-day readmission or death.

COVID-19 infection among first responders in Broward County, Florida, March-April 2020.

BACKGROUND: First responders (FRs) may have a significant risk of coronavirus 19 (COVID-19) infection than the general population due to job-related exposures. We aimed to determine the prevalence and exposure patterns of COVID-19 among FRs. METHODS: Between March and April 2020, FRs in Broward County, Florida, were screened for COVID-19 infection by real-time reverse transcription polymerase chain reaction assay using nasopharyngeal swabs. Demographics and COVID-19 positive rate of the FRs were summarized. RESULTS: A total of 3375 FRs were screened for COVID-19 infection. The median age of FRs tested was 42 years (IQR 33-52 years), and 1464 (43.4%) were men. A total of 2902 (85.9%) were asymptomatic, and 473 (14.1%) reported symptoms associated with COVID-19. Overall, 289 (8.6%) were positive, with the highest rates among the age between 25 and 49 years. Of those testing positive, 235 (81.3%) were asymptomatic. Fourteen days after their first positive test, 81 (69.8%) of the 116 asymptomatically infected FRs were negative, and 35 (30.2%) remained positive and asymptomatic. CONCLUSIONS: The FRs in Broward County, FL, had an overall infection rate of 8.6% at the time of COVID-19 testing, and asymptomatic FRs accounted for 81.3% of infection. Active surveillance should be focused on the asymptomatic FRs with COVID-19.

A pilot study of the feasibility of confocal endomicroscopy for examination of the human airway.

BACKGROUND: Traditional methods of evaluating human airway histology, such as surgical biopsy or endobronchial biopsy, are limited by the risks associated with these tissue-sampling procedures. OBJECTIVE: The purpose of this study was to develop and evaluate the first confocal endomicroscope for real-time, in vivo imaging of human respiratory mucosa in a clinical setting. METHODS: A confocal endomicroscope prototype was designed using Pentax bronchoscope parts (EB1970K). Airways of adult patients (N=5) undergoing rigid bronchoscopy for various clinical indications were imaged with the confocal endomicroscope after intravenous administration of fluorescein sodium. The device was introduced into the airways through the rigid bronchoscope. Images were collected from the trachea, primary and secondary carinae, and any endobronchial mass. The images were compared with those obtained from histologic sections from conventional endobronchial biopsies. RESULTS: Confocal endomicroscopy provided real-time images of the cellular and subcellular structures of the respiratory mucosa and submucosa in vivo. The pseudostratified columnar epithelium (including columnar cells and goblet cells) could be visualized. Images obtained at increasing depth showed the lamina propria and microvasculature. Longitudinal folds in the mucosa enabled imaging in cross-section, showing alignment of epithelial cells along the basement membrane and cilia on the surface of the cells. Below the epithelium, the smooth muscle could be identified. In images from a patient with an endobronchial adenocarcinoma, confocal imaging could distinguish between a normal airway epithelium and malignant tissue. CONCLUSIONS: Confocal endomicroscopy is a feasible method for analyzing human airway wall architecture and endobronchial abnormalities in histologic detail in vivo.