Corticosteroids and Outcomes in Solid Organ Transplant Recipients Infected With Severe Acute Respiratory Syndrome Coronavirus 2.

Objective: To examine outcomes in organ transplant and nontransplant patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection during the initial 22 months of the pandemic. Patients and Methods: We used Optum electronic health records to compare outcomes between an adult transplant group and a propensity-matched nontransplant group that tested positive for SARS-CoV-2 from February 1, 2020, to December 15, 2021. Baseline characteristics, hospitalization, intensive care unit admission, mechanical ventilation, renal replacement therapy, inpatient, and 90-day mortality were compared between the transplant and nontransplant groups and among specific transplant recipients. Cox proportional analysis was used to examine hospitalization and mortality by organ transplant, medical therapy, sex, and the period of the pandemic. Results: We identified 876,959 patients with SARS-CoV-2 infection, of whom 3548 were organ transplant recipients. The transplant recipients had a higher risk of hospitalization (30.6% vs 25%, respectively; P<.001), greater use of mechanical ventilation (7.8% vs 5.6%, respectively; P<.001), and increased inpatient mortality (6.7% vs 4.7%, respectively; P<.001) compared with the nontransplant patients. The initiation of mechanical ventilation was significantly more frequent in the transplant group. After adjustment for baseline characteristics and comorbidities, the transplant group had a higher risk of hospitalization (odds ratio, 1.38; 95% confidence interval, 1.19-1.59), without a difference in mortality. In the transplant group, lung transplant recipients had the highest inpatient mortality (11.6%). Conclusion: Among patients with SARS-CoV-2 infection, the transplant recipients were at a higher risk of hospitalization and inpatient mortality; however, mortality was mainly driven by advanced age and comorbidities rather than by transplant status or immunosuppressive medications. Lung transplant recipients had the greatest inpatient and 90-day mortality.

Corticosteroids and Outcomes in Solid Organ Transplant Recipients Infected with SARS-COV-2

Objective To examine outcomes of organ transplant and non-transplant patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection during the initial 22 months of the pandemic. Patients and Methods We used Optum electronic health records to compare outcomes between adult transplant and a propensity matched non-transplant group that tested positive for SARS-CoV-2 from February 1, 2020 to December 15, 2021. Baseline characteristics, hospitalization, intensive care unit admission, mechanical ventilation, renal replacement therapy, in-patient and 90-day mortality were compared for transplant and non-transplant groups and among specific transplant recipients. Cox-proportional analysis was used to examine hospitalization and mortality by organ transplant, medical therapy, sex and period of the pandemic. Results We identified 876,959 patients with SARS-CoV-2 infection, of whom 3,548 were organ transplant recipients. Transplant recipients had higher risk of hospitalization (30.6% and 25%, P<0.001), greater mechanical ventilation use (7.8% and 5.6%, P<0.001), and increased inpatient mortality (6.7% and 4.7%, P<0.001) compared to non-transplant group. Initiation of mechanical ventilation was significantly greater in the transplant group. After adjustment for baseline characteristics and comorbidities, the transplant group had higher risk of hospitalization (OR 1.38, 95% CI 1.19 – 1.59) without a mortality difference. Among transplant groups, lung transplant recipients had the highest inpatient mortality (11.6%). Conclusion In patients with SARS-CoV-2 infection, transplant recipients were at higher risk of hospitalization and inpatient mortality, however mortality was mainly driven by advanced age and comorbidities, rather than by transplant status or immunosuppressive medications. Lung transplant recipients had the greatest inpatient and 90-day mortality.

SARS-CoV-2 infection-induced immunity reduces rates of reinfection and hospitalization caused by the Delta or Omicron variants.

During a pandemic, effective vaccines are typically in short supply, particularly at onset intervals when the wave is accelerating. We conducted an observational, retrospective analysis of aggregated data from all patients who tested positive for SARS-CoV-2 during the waves caused by the Delta and Omicron variants, stratified based on their known previous infection and vaccination status, throughout the University of Texas Medical Branch (UTMB) network. Next, the immunity statuses within each medical parameter were compared to naïve individuals for the effective decrease of occurrence. Lastly, we conducted studies using mice and pre-pandemic human samples for IgG responses to viral nucleocapsid compared to spike protein toward showing a functional component supportive of the medical data results in relation to the immunity types. During the Delta and Omicron waves, both infection-induced and hybrid immunities were associated with a trend of equal or greater decrease of occurrence than vaccine-induced immunity in hospitalizations, intensive care unit admissions, and deaths in comparison to those without pre-existing immunity, with hybrid immunity often trending with the greatest decrease. Compared to individuals without pre-existing immunity, those vaccinated against SARS-CoV-2 had a significantly reduced incidence of COVID-19, as well as all subsequent medical parameters. Though vaccination best reduces health risks associated with initial infection toward acquiring immunity, our findings suggest infection-induced immunity is as or more effective than vaccination in reducing the severity of reinfection from the Delta or Omicron variants, which should inform public health response at pandemic onset, particularly when triaging towards the allotment of in-demand vaccinations.

ABV-CoViD: An Ensemble Forecasting Model to Predict Availability of Beds and Ventilators for COVID-19 Like Pandemics.

Recently, healthcare stakeholders have orchestrated steps to strengthen and curb the COVID-19 wave. There has been a surge in vaccinations to curb the virus wave, but it is crucial to strengthen our healthcare resources to fight COVID-19 and like pandemics. Recent researchers have suggested effective forecasting models for COVID-19 transmission rate, spread, and the number of positive cases, but the focus on healthcare resources to meet the current spread is not discussed. Motivated from the gap, in this paper, we propose a scheme, ABV-CoViD (Availibility of Beds and Ventilators for COVID-19 patients), that forms an ensemble forecasting model to predict the availability of beds and ventilators (ABV) for the COVID-19 patients. The scheme considers a region-wise demarcation for the allotment of beds and ventilators (BV), termed resources, based on region-wise ABV and COVID-19 positive patients (inside the hospitals occupying the BV resource). We consider an integration of artificial neural network (ANN) and auto-regressive integrated neural network (ARIMA) model to address both the linear and non-linear dependencies. We also consider the effective wave spread of COVID-19 on external patients (not occupying the BV resources) through a [Formula: see text]- ARNN model, which gives us long-term complex dependencies of BV resources in the future time window. We have considered the COVID-19 healthcare dataset on 3 USA regions (Illinois, Michigan, and Indiana) for testing our ensemble forecasting scheme from January 2021 to May 2022. We evaluated our scheme in terms of statistical performance metrics and validated that ensemble methods have higher accuracy. In simulation, for linear modelling, we considered the [Formula: see text] model, and [Formula: see text] model for ANN modelling. We considered the [Formula: see text](12,6) forecasting. On a population of 2,93,90,897, the obtained mean absolute error (MAE) on average for 3 regions is 170.5514. The average root means square error (RMSE) of [Formula: see text]-ARNN is 333.18, with an accuracy of 98.876%, which shows the scheme's efficacy in ABV measurement over conventional and manual resource allocation schemes.

A Taxonomy on Smart Healthcare Technologies: Security Framework, Case Study, and Future Directions

There is a massive transformation in the traditional healthcare system from the specialist-centric approach to the patient-centric approach by adopting modern and intelligent healthcare solutions to build a smart healthcare system. It permits patients to directly share their medical data with the specialist for remote diagnosis without any human intervention. Furthermore, the remote monitoring of patients utilizing wearable sensors, Internet of Things (IoT) technologies, and artificial intelligence (AI) has made the treatment readily accessible and affordable. However, the advancement also brings several security and privacy concerns that poorly maneuvered the effective performance of the smart healthcare system. An attacker can exploit the IoT infrastructure, perform an adversarial attack on AI models, and proliferate resource starvation attacks in smart healthcare system. To overcome the aforementioned issues, in this survey, we extensively reviewed and created a comprehensive taxonomy of various smart healthcare technologies such as wearable devices, digital healthcare, and body area networks (BANs), along with their security aspects and solutions for the smart healthcare system. Moreover, we propose an AI-based architecture with the 6G network interface to secure the data exchange between patients and medical practitioners. We have examined our proposed architecture with the case study based on the COVID-19 pandemic by adopting unmanned aerial vehicles (UAVs) for data exchange. The performance of the proposed architecture is evaluated using various machine learning (ML) classification algorithms such as random forest (RF), naive Bayes (NB), logistic regression (LR), linear discriminant analysis (LDA), and perceptron. The RF classification algorithm outperforms the conventional algorithms in terms of accuracy, i.e., 98%. Finally, we present open issues and research challenges associated with smart healthcare technologies.

Characteristics and Factors Associated With Mortality in Patients With Coronavirus Disease 2019 and Pneumothorax.

Objective: To describe the incidence, clinical characteristics, and factors associated with mortality in patients hospitalized for coronavirus disease 2019 (COVID-19) in whom pneumothorax developed. Patients and Methods: This study was a retrospective analysis conducted using a large administrative database of adult patients hospitalized for COVID-19 in the United States from February 1, 2020, to June 10, 2021. We characterized the clinical features of patients in whom pneumothorax developed and the factors associated with mortality and stratified pneumothorax by the timing of the initiation of invasive mechanical ventilation (IMV) and by the time of hospital admission (early versus late). Results: A total of 811,065 adult patients had a positive test result for severe acute respiratory syndrome coronavirus 2, of whom 103,858 (12.8%) were hospitalized. Pneumothorax occurred in 1915 patients (0.24% overall and 1.84% among hospitalized patients). Over time, the use of steroids and remdesivir increased, whereas the use of IMV, pneumothorax rates, and mortality decreased. The clinical characteristics associated with pneumothorax were male sex; the receipt of IMV; and treatment with steroids, remdesivir, or convalescent plasma. Most patients with pneumothorax received IMV, but pneumothorax developed before the initiation of IMV and/or early during hospitalization in majority. Multivariable analysis revealed that pneumothorax increased the risk of death (adjusted hazard ratio [aHR], 1.15; 95% CI, 1.06-1.24). In patients who did not receive IMV, pneumothorax led to nearly twice the mortality (aHR, 1.99; 95% CI, 1.56-2.54). Increased mortality was also noted when pneumothorax occurred before IMV (aHR, 1.37; 95% CI, 1.11-1.69) and within 7 days of hospital admission (aHR, 1.60; 95% CI, 1.29-1.98). Conclusion: The overall incidence of pneumothorax in patients hospitalized for COVID-19 was low. Pneumothorax is an independent risk factor for death.

Outcomes of Patients with COPD Hospitalized for Coronavirus Disease 2019.

RATIONALE: There is controversy concerning the association of chronic obstructive pulmonary disease (COPD) as an independent risk factor for mortality in patients hospitalized with Coronavirus Disease 2019 (COVID-19). We hypothesize that patients with COPD hospitalized for COVID-19 have increased mortality risk. OBJECTIVE: To assess whether COPD increased the risk of mortality among patients hospitalized for COVID-19. METHODS: We conducted a retrospective cohort analysis of patients with COVID-19 between February 10, 2020, and November 10, 2020, and hospitalized within 14 days of diagnosis. Electronic health records from U.S. facilities (Optum COVID-19 data) were used. RESULTS: In our cohort of 31,526 patients, 3030 (9.6%) died during hospitalization. Mortality in patients with COPD was higher than that of patients without COPD, 14.02% and 8.8%, respectively. Univariate (odds ratio [OR] 1.68; 95% confidence interval [CI] 1.54 to 1.84) and multivariate (OR 1.33; 95% CI 1.18 to 1.50) analysis showed that patients with COPD had greater odds of death due to COVID-19 than patients without COPD. We found significant interactions between COPD and sex and COPD and age. Specifically, the increased mortality risk associated with COPD was observed among female (OR 1.62; 95% CI 1.36 to 1.95) but not male patients (OR 1.14; 95% CI 0.97 to 1.34); and in patients aged 40 to 64 (OR 1.42; 95% CI 1.07 to 1.90) and 65 to 79 (OR 1.48; 95% CI 1.23 to 1.78) years. CONCLUSIONS: COPD is an independent risk factor for death in adults aged 40 to 79 years hospitalized with COVID-19 infection.

Determining Cause of Death During Coronavirus Disease 2019 Pandemic.

Controversy exists whether the cause of death due to severe acute respiratory syndrome coronavirus 2 is directly related to the infection or to underlying conditions. The purpose of this study is to assess the relationship of severe acute respiratory syndrome coronavirus 2 infection with the cause of death in hospitalized patients. DESIGN: Retrospective observational study; deidentified discharge summaries of deceased patients were reviewed by two intensivists and classified as coronavirus disease 2019-related (caused by severe acute respiratory syndrome coronavirus 2) or coronavirus disease 2019-unrelated (not caused by severe acute respiratory syndrome coronavirus 2 or indeterminate) deaths. For classification disagreement, a separate group of three intensivists reviewed the discharge summaries and arbitrated to determine the cause of death. SETTING: Single-center study performed at the University of Texas Medical Branch. PATIENTS: All adult patients (> 18 yr) admitted from March 10, 2020, to October 22, 2020, with positive severe acute respiratory syndrome coronavirus 2 test results who expired during their hospitalization were identified. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Patient demographics, comorbidities, prescribed medications, and ventilatory support data were collected. Comparison between groups was performed using t test and chi-square test. During the study period, 1,052 patients were admitted within 14 days of severe acute respiratory syndrome coronavirus 2-positive test results, of whom 100 expired during the hospitalization. Deceased patients were predominantly male and older than 65 years. Obesity (body mass index ≥ 30 kg/m2) was present in 41%, and common comorbidities included hypertension (47%), diabetes (30%), and heart failure (20%). Death was classified as directly caused by severe acute respiratory syndrome coronavirus 2 in 85% and not caused by severe acute respiratory syndrome coronavirus 2 in 5%. An indeterminate cause of death in 10% was due to insufficient information or an atypical presentation. The observed interrater agreement on the cause of death classification was 81%. CONCLUSIONS: In this single-center study, the majority of deaths in severe acute respiratory syndrome coronavirus 2-positive hospitalized patients were related to a typical or atypical presentation of coronavirus disease 2019 disease.