Patient characteristics and admitting vital signs associated with coronavirus disease 2019 (COVID-19)-related mortality among patients admitted with noncritical illness

Active Smokers Are at Higher Risk of COVID-19 Death: A Systematic Review and Meta-analysis

INTRODUCTION

Current evidence indicates that smoking worsens COVID-19 outcomes. However, when studies restricted their analyses to current smokers, the risks for COVID-19 severity and death are inconsistent.

AIMS AND METHODS

This meta-analysis explored the association between current smoking and the risk for mortality based on the studies that reported all three categories of smoking (current, former, and never smokers) to overcome the limitation of the previous meta-analyses which former smokers might have been classified as nonsmokers. We searched PubMed and Embase up to January 1, 2021. We included studies reporting all three categories of smoking behaviors of COVID-19 patients and mortality outcomes. A random-effects meta-analysis and meta-regression were used to examine relationships in the data.

RESULTS

A total of 34 articles with 35 193 COVID-19 patients was included. The meta-analysis confirmed the association between current smoking (odds ratio [OR] 1.26, 95% confidence interval [CI]: 1.01-1.58) and former smoking (OR 1.76, 95% CI: 1.53-2.03) with COVID-19 mortality. We also found that the risk for COVID-19 death in current smokers does not vary by age, but significantly drops by age in former smokers. Moreover, current smokers in non-high-income countries have higher risks of COVID-19 death compared with high-income countries (OR 3.11, 95% CI: 2.04-4.72 vs. OR 1.14, 95% CI: 0.91-1.43; p = .015).

CONCLUSIONS

Current and former smokers are at higher risk of dying from COVID-19. Tobacco control should be strengthened to encourage current smokers to quit and prevent the initiation of smoking. Public health professionals should take the COVID-19 pandemic as an opportunity to promote smoking prevention and cession.

IMPLICATIONS

This study makes an important contribution to the existing literature by distinguishing between current and former smoking and their separate effects on COVID-19 mortality. We also explore the effects by age of patients and country income level. Findings from this study provide empirical evidence against misinformation about the relationship between smoking and COVID-19 mortality.

Assessment of Clinical Profile and Treatment Outcome in Vaccinated and Unvaccinated SARS-CoV-2 Infected Patients

Vaccines against severe acute respiratory syndrome-corona virus-2 (SARS-CoV-2) infection, which causes coronavirus disease–19 (COVID-19) in humans, have been developed and are being tested for safety and efficacy. We conducted the cross-sectional prospective cohort study on 820 patients who were positive for SARS-CoV-2 and were admitted to Princess Krishnajammanni trauma care centre (PKTCC), Mysore, which was converted to a designated COVID hospital between April 2021 to July 2021. After obtaining the informed consent, RT-PCR report, vaccination certificate and patient history, patients were classified according to their vaccination status. Results from the study showed decreases in serum ferritin levels, clinical symptoms, improvement in oxygen saturation, early recovery in patients having diabetes and hypertension, and a substantial reduction in the overall duration of hospital stay in vaccinated patients compared to unvaccinated patients. Further, fully vaccinated patients showed better outcomes compared to single dose vaccinated and nonvaccinated patients. Taken together, our findings reaffirm the vaccine’s effectiveness in reducing case fatality and promoting faster recovery compared to nonvaccinated patients. Efforts to increase the number of immunized subjects in the community help to achieve herd immunity and offer protection against the severity of COVID-19 and associated complications while minimizing the public health and economic burden.

Heterogeneity and Risk of Bias in Studies Examining Risk Factors for Severe Illness and Death in COVID-19: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis synthesized the evidence on the impacts of demographics and comorbidities on the clinical outcomes of COVID-19, as well as the sources of the heterogeneity and publication bias of the relevant studies. Two authors independently searched the literature from PubMed, Embase, Cochrane library, and CINAHL on 18 May 2021; removed duplicates; screened the titles, abstracts, and full texts by using criteria; and extracted data from the eligible articles. The variations among the studies were examined by using Cochrane, Q.; I2, and meta-regression. Out of 11,975 articles that were obtained from the databases and screened, 559 studies were abstracted, and then, where appropriate, were analyzed by meta-analysis (n = 542). COVID-19-related severe illness, admission to the ICU, and death were significantly correlated with comorbidities, male sex, and an age older than 60 or 65 years, although high heterogeneity was present in the pooled estimates. The study design, the study country, the sample size, and the year of publication contributed to this. There was publication bias among the studies that compared the odds of COVID-19-related deaths, severe illness, and admission to the ICU on the basis of the comorbidity status. While an older age and chronic diseases were shown to increase the risk of developing severe illness, admission to the ICU, and death among the COVID-19 patients in our analysis, a marked heterogeneity was present when linking the specific risks with the outcomes.

Admission vital signs as predictors of COVID-19 mortality: a retrospective cross-sectional study

Background

COVID-19 remains a major healthcare concern. Vital signs are routinely measured on admission and may provide an early, cost-effective indicator of outcome – more so in developing countries where such data is scarce. We sought to describe the association between six routinely measured admission vital signs and COVID-19 mortality, and secondarily to derive potential applications for resource-limited settings.

Methods

Retrospective analysis of consecutive patients admitted to King Edward VIII Hospital, South Africa, with COVID-19 during June–September 2020 was undertaken. The sample was subdivided into survivors and non-survivors and comparisons made in terms of demographics and admission vital signs. Univariate and multivariate analysis of predictor variables identified associations with in-hospital mortality, with the resulting multivariate regression model evaluated for its predictive ability with receiver operating characteristic (ROC) curve analysis.

Results

The 236 participants enrolled comprised 153(77.54%) survivors and 53(22.46%) non-survivors. Most participants were Black African(87.71%) and female(59.75%) with a mean age of 53.08(16.96) years. The non-survivor group demonstrated a significantly lower median/mean for admission oxygen saturation (%) [87(78–95) vs. 96(90–98)] and diastolic BP (mmHg) [70.79(14.66) vs. 76.3(12.07)], and higher median for admission respiratory rate (breaths/minute) [24(20–28) vs. 20(20–23)] and glucose (mmol/l) [10.2(6.95–16.25) vs. 7.4(5.5–9.8)]. Age, oxygen saturation, respiratory rate, glucose and diastolic BP were found to be significantly associated with mortality on univariate analysis. A log rank test revealed significantly lower survival rates in patients with an admission oxygen saturation < 90% compared with ≥90% (p = 0.001). Multivariate logistic regression revealed a significant relationship between age and oxygen saturation with in-hospital mortality (OR 1.047; 95% CI 1.016–1.080; p = 0.003 and OR 0.922; 95% CI 0.880–0.965; p = 0.001 respectively). A ROC curve analysis generated an area under the curve (AUC) of 0.778 (p < 0.001) when evaluating the predictive ability of oxygen saturation, respiratory rate, glucose and diastolic BP for in-hospital death. This improved to an AUC of 0.832 (p < 0.001) with the inclusion of age.

Conclusion

A multivariate regression model comprising admission oxygen saturation, respiratory rate, glucose and diastolic BP (with/without age) demonstrated promising predictive capacity, and may provide a cost-effective means for early prognostication of patients admitted with COVID-19 in resource-limited settings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12873-022-00631-7.

Respiratory Support and Clinical Outcomes in Critically Ill Patients with COVID-19 in Intensive Care Unit: A Retrospective Study

Background

Appropriate respiratory support is crucial for improving the clinical outcomes of critically ill patients infected with the SARS-CoV-2 virus. This study aimed to investigate the different modalities of respiratory support and clinical outcomes in patients with COVID-19 in intensive care units (ICUs).

Materials and Methods

In a retrospective study, we enrolled 290 critically ill COVID-19 patients who were admitted to the ICUs of four hospitals in Mazandaran, northern Iran. Data were extracted from the medical records of all included patients, from December 2019 to July 2021. Patients' demographic data, symptoms, laboratory findings, comorbidities, treatment, and clinical outcomes were collected.

Results

46.55% of patients died. Patients with ≥2 comorbidities had significantly increased odds of death (OR=5.88, 95%CI: 1.97-17.52, P=0.001) as compared with patients with no comorbidities. Respiratory support methods such as face mask (survived=37, deceased=18, P=0.022), a non-rebreather mask (survived=39, deceased=12, P<0.001), and synchronized intermittent mandatory ventilation (SIMV) (survived=103, deceased=110, P=0.004) were associated with in-hospital mortality. Duration of respiratory support in nasal cannula (survived=3, deceased=2, P<0.001), face mask (survived=3, deceased=2, P<0.001), a non-rebreather mask (survived=3, deceased=2, P=0.033), mechanical ventilation (survived=5, deceased=6, P<0.019), continuous positive airway pressure (CPAP) (survived=3, deceased=2, P<0.017), and SIMV (survived=4, deceased=5, P=0.001) methods were associated with higher in-hospital mortality.

Conclusion

Special attention should be paid to COVID-19 patients with more than two comorbidities. As a specific point of interest, SIMV may increase the in-hospital mortality rate of critically ill patients with COVID-19 connected to mechanical ventilation and be associated with adverse outcomes.

Predictors of mortality in patients less than 50 years old with coronavirus disease 2019: a multicenter experience in Istanbul

OBJECTIVE

The objectives of this study were to identify predictors of mortality in young adult patients with coronavirus disease 2019 and to assess the link between blood type and mortality in those patients.

METHODS

This multicenter retrospective study, which was conducted in seven training and research hospitals in Istanbul, involved young adults who aged ≥18 and <50 years and hospitalized with coronavirus disease 2019.

RESULTS

Among 1,120 patients, confusion at admission (p<0.001) and oxygen saturation (p<0.001) were significantly predictive factors of mortality. Blood type O was significantly associated with mortality compared to those discharged from the hospital (p<0.001). Among co-morbidities, the most reliable predictive factors were cerebral vascular disease (p<0.001) and chronic renal failure (p=0.010). Among laboratory parameters, high C-reactive protein (p<0.001) and low albumin (p<0.001) levels were predictors of mortality in young adult patients with coronavirus disease 2019.

CONCLUSIONS

SpO2 at admission was the best predictor of mortality in young adult patients with coronavirus disease 2019. The mortality rate was increased by cerebral vascular disease and chronic renal failure. Also, high C-reactive protein and low albumin levels were predictive factors of mortality. Moreover, blood type O was associated with a higher mortality rate than the other types.

Early Determinants of Length of Hospital Stay: A Case Control Survival Analysis among COVID-19 Patients admitted in a Tertiary Healthcare Facility of East India

BACKGROUND

Length of hospital stay (LOS) for a disease is a vital estimate for healthcare logistics planning. The study aimed to illustrate the effect of factors elicited on arrival on LOS of the COVID-19 patients.

MATERIALS AND METHODS

It was a retrospective, record based, unmatched, case control study using hospital records of 334 COVID-19 patients admitted in an East Indian tertiary healthcare facility during May to October 2020. Discharge from the hospital (cases/survivors) was considered as an event while death (control/non-survivors) as right censoring in the case-control survival analysis using cox proportional hazard model.

RESULTS

Overall, we found the median LOS for the survivors to be 8 days [interquartile range (IQR): 7-10 days] while the same for the non-survivors was 6 days [IQR: 2-11 days]. In the multivariable cox-proportional hazard model; travel distance (>16 km) [adjusted hazard ratio (aHR): 0.69, 95% CI: (0.50-0.95)], mode of transport to the hospital (ambulance) [aHR: 0.62, 95% CI: (0.45-0.85)], breathlessness (yes) [aHR: 0.56, 95% CI: (0.40-0.77)], number of co-morbidities (1-2) [aHR: 0.66, 95% CI: (0.47-0.93)] (≥3) [aHR: 0.16, 95% CI: (0.04-0.65)], COPD/asthma (yes) [ [aHR: 0.11, 95% CI: (0.01-0.79)], DBP (<60/≥90) [aHR: 0.55, 95% CI: (0.35-0.86)] and qSOFA score (≥2) [aHR: 0.33, 95% CI: (0.12-0.92)] were the significant attributes affecting LOS of the COVID-19 patients.

CONCLUSION

Factors elicited on arrival were found to be significantly associated with LOS. A scoring system inculcating these factors may be developed to predict LOS of the COVID-19 patients.

Diabetes, hypertension, body mass index, smoking and COVID-19-related mortality: a systematic review and meta-analysis of observational studies

OBJECTIVES

We conducted a systematic literature review and meta-analysis of observational studies to investigate the association between diabetes, hypertension, body mass index (BMI) or smoking with the risk of death in patients with COVID-19 and to estimate the proportion of deaths attributable to these conditions.

METHODS

Relevant observational studies were identified by searches in the PubMed, Cochrane library and Embase databases through 14 November 2020. Random-effects models were used to estimate summary relative risks (SRRs) and 95% CIs. Certainty of evidence was assessed using the Cochrane methods and the Grading of Recommendations, Assessment, Development and Evaluations framework.

RESULTS

A total of 186 studies representing 210 447 deaths among 1 304 587 patients with COVID-19 were included in this analysis. The SRR for death in patients with COVID-19 was 1.54 (95% CI 1.44 to 1.64, I2=92%, n=145, low certainty) for diabetes and 1.42 (95% CI 1.30 to 1.54, I2=90%, n=127, low certainty) for hypertension compared with patients without each of these comorbidities. Regarding obesity, the SSR was 1.45 (95% CI 1.31 to 1.61, I2=91%, n=54, high certainty) for patients with BMI ≥30 kg/m2 compared with those with BMI <30 kg/m2 and 1.12 (95% CI 1.07 to 1.17, I2=68%, n=25) per 5 kg/m2 increase in BMI. There was evidence of a J-shaped non-linear dose-response relationship between BMI and mortality from COVID-19, with the nadir of the curve at a BMI of around 22-24, and a 1.5-2-fold increase in COVID-19 mortality with extreme obesity (BMI of 40-45). The SRR was 1.28 (95% CI 1.17 to 1.40, I2=74%, n=28, low certainty) for ever, 1.29 (95% CI 1.03 to 1.62, I2=84%, n=19) for current and 1.25 (95% CI 1.11 to 1.42, I2=75%, n=14) for former smokers compared with never smokers. The absolute risk of COVID-19 death was increased by 14%, 11%, 12% and 7% for diabetes, hypertension, obesity and smoking, respectively. The proportion of deaths attributable to diabetes, hypertension, obesity and smoking was 8%, 7%, 11% and 2%, respectively.

CONCLUSION

Our findings suggest that diabetes, hypertension, obesity and smoking were associated with higher COVID-19 mortality, contributing to nearly 30% of COVID-19 deaths.

TRIAL REGISTRATION NUMBER

CRD42020218115.

Federated learning for predicting clinical outcomes in patients with COVID-19

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.

Reactions and COVID-19 disease progression following SARS-CoV-2 monoclonal antibody infusion

SARS-CoV-2 monoclonal antibodies (mAbs) have been proposed as a treatment for mild to moderate COVID-19, with favorable outcomes reported in clinical trials and an emergency use authorization granted by the Food and Drug Administration. Real-world data remain limited, however, and thus this analysis presents findings from over 6,500 outpatient administrations of mAb at facilities affiliated with a large healthcare organization in the United States. Within 48 hours of mAb infusion, 15.6% (1,043) of patients received a drug that was indicative of a possible reaction to the infusion; the majority of these were mild (e.g., acetaminophen). Approximately 5.2% of patients who received mAb (n=347) had a post-infusion emergency department visit or admission for COVID-19 disease progression. The results of this analysis indicate that patients who receive mAb have a low likelihood of both an immediate negative reaction to the treatment as well as future inpatient admission related to COVID-19 disease progression.

Epidemiological, Clinical, and Laboratory Predictors of In-Hospital Mortality Among COVID-19 Patients Admitted in a Tertiary COVID Dedicated Hospital, Northern India: A Retrospective Observational Study

Introduction

COVID-19 pandemic still poses a serious challenge to health system worldwide. This study was planned to determine exposure characteristics, in-hospital mortality, and predictors of in hospital mortality among COVID-19 patients.

Material and methods

We retrospectively investigated epidemiological, clinical, and laboratory profile of confirmed COVID-19 patients admitted from 25th March to 31st August 2020. COVID-19 patient profiles were collected from Medical Record Section of the hospital.

Results

In hospital mortality occurred in 159 (11%) cases. Increasing respiratory rate, higher temperature, higher total leukocyte count, and high blood urea levels were found to be independent risk factors for in hospital mortality whereas higher hemoglobin and higher oxygen saturation at the time of hospital admission were found to be protective against in hospital mortality.

Conclusion

In hospital mortality among COVID-19 patients is almost 1 in 10 in tertiary care hospital. Patients with advancing age (AOR: 1.048; 95% CI: 1.021-1.076), higher respiratory rate (AOR: 1.248; 95% CI: 1.047-1.489), higher temperature (AOR: 1.758; 95% CI: 1.025-3.016), higher leukocyte count (AOR: 1.147; 95% CI: 1.035-1.270), and higher urea levels (AOR: 1.034; 95% CI: 1.005-1.064) at the time of admission are important predictors of COVID-19 in-hospital mortality.

Clinical characteristics and clinical predictors of mortality in hospitalised patients of COVID 19 : An Indian study

BACKGROUND

The rapid spread of the coronavirus disease 2019 (COVID-19) with high mortality rate necessitates disease characterization and accurate prognostication for prompt clinical decision-making. The aim of this study is to study clinical characteristics and predictors of mortality in hospitalized patients with COVID-19 in India.

METHODS

Retrospective cohort study was conducted in a tertiary care hospital in northern India. All consecutive confirmed hospitalized COVID-19 cases aged 15 years and older from 13 Apr till 31 Aug 2020 are included. Primary end point was 30-day mortality.

RESULTS

Of 1622 patients ,1536 cases were valid. Median age was 36 years, 88.3% were men and 58.1% were symptomatic. Fever (37.6%) was commonest presenting symptom. Dyspnea was reported by 15.4%. Primary hypertension (8.5%) was commonest comorbidity, followed by diabetes mellitus (6.7%). Mild, moderate, and severe hypoxemia were seen in 3.4%, 4.3%, and 0.8% respectively. Logistic regression showed greater odds of moderate/severe disease in patients with dyspnea, hypertension, Chronic Kidney Disease (CKD), and malignancy. Seventy six patients died (4.9%). In adjusted Cox proportional hazards model for mortality, patients with dyspnea (hazard ratio [HR]: 14.449 [5.043-41.402]), altered sensorium (HR: 2.762 [1.142-6.683]), Diabetes Mellitus (HR: 1.734 [1.001-3.009]), malignancy (HR:10.443 [4.396-24.805]) and Chronic Liver Disease (CLD) (HR: 14.432 [2.321-89.715]) had higher risk. Rising respiratory rate (HR: 1.098 [1.048-1.150]), falling oxygen saturation (HR: 1.057 per unit change 95% CI: 1.028-1.085) were significant predictors.

CONCLUSION

Analysis suggests that age, dyspnea, and malignancy were associated with both severe disease and mortality. Diabetes Mellitus and Chronic Liver Disease were associated with increased the risk of fatal outcome. Simple clinical parameters such as respiratory rate and oxygen saturation are strong predictors and with other risk factors at admission can be effectively used to triage patients.

Racial disparities in COVID-19 pandemic cases, hospitalisations, and deaths: A systematic review and meta-analysis

Background

People from racial minority groups in western countries experience disproportionate socioeconomic and structural determinants of health disadvantages. These disadvantages have led to inequalities and inequities in health care access and poorer health outcomes. We report disproportionate disparities in prevalence, hospitalisation, and deaths from COVID-19 by racial minority populations.

Methods

We conducted a systematic literature search of relevant databases to identify studies reporting on prevalence, hospitalisations, and deaths from COVID-19 by race groups between 01 January 2020 – 15 April 2021. We grouped race categories into Blacks, Hispanics, Whites and Others. Random effects model using the method of DerSimonian and Laird were fitted, and forest plot with respective ratio estimates and 95% confidence interval (CI) for each race category, and subgroup meta-regression analyses and the overall pooled ratio estimates for prevalence, hospitalisation and mortality rate were presented.

Results

Blacks experienced significantly higher burden of COVID-19: prevalence ratio 1.79 (95% confidence interval (CI) = 1.59-1.99), hospitalisation ratio 1.87 (95% CI = 1.69-2.04), mortality ratio 1.68 (95% CI = 1.52-1.83), compared to Whites: prevalence ratio 0.70 (95% CI = 0.0.64-0.77), hospitalisation ratio 0.74 (95% CI = 0.65-0.82), mortality ratio 0.82 (95% CI = 0.78-0.87). Also, Hispanics experienced a higher burden: prevalence ratio 1.78 (95% CI = 1.63-1.94), hospitalisation ratio 1.32 (95% CI = 1.08-1.55), mortality ratio 0.94 (95% CI = 0.84-1.04) compared to Whites. A higher burden was also observed for Other race groups: prevalence ratio 1.43 (95% CI = 1.19-1.67), hospitalisation ratio 1.12 (95% CI = 0.89-1.35), mortality ratio 1.06 (95% CI = 0.89-1.23) compared to Whites. The disproportionate burden among Blacks and Hispanics remained following correction for publication bias.

Conclusions

Blacks and Hispanics have been disproportionately affected by COVID-19. This is deeply concerning and highlights the systemically entrenched disadvantages (social, economic, and political) experienced by racial minorities in western countries; and this study underscores the need to address inequities in these communities to improve overall health outcomes.

A COVID-19-ready public health surveillance system: The Food and Drug Administration's Sentinel System

The US Food and Drug Administration's Sentinel System was established in 2009 to use routinely collected electronic health data for improving the national capability to assess post‐market medical product safety. Over more than a decade, Sentinel has become an integral part of FDA's surveillance capabilities and has been used to conduct analyses that have contributed to regulatory decisions. FDA's role in the COVID‐19 pandemic response has necessitated an expansion and enhancement of Sentinel. Here we describe how the Sentinel System has supported FDA's response to the COVID‐19 pandemic. We highlight new capabilities developed, key data generated to date, and lessons learned, particularly with respect to working with inpatient electronic health record data.

Early in the pandemic, Sentinel developed a multi‐pronged approach to support FDA's anticipated data and analytic needs. It incorporated new data sources, created a rapidly refreshed database, developed protocols to assess the natural history of COVID‐19, validated a diagnosis‐code based algorithm for identifying patients with COVID‐19 in administrative claims data, and coordinated with other national and international initiatives.

Sentinel is poised to answer important questions about the natural history of COVID‐19 and is positioned to use this information to study the use, safety, and potentially the effectiveness of medical products used for COVID‐19 prevention and treatment.

Predicting Severity and Intrahospital Mortality in COVID-19: The Place and Role of Oxidative Stress

SARS-CoV-2 virus causes infection which led to a global pandemic in 2020 with the development of severe acute respiratory syndrome. Therefore, this study was aimed at examining its possible role in predicting severity and intrahospital mortality of COVID-19, alongside with other laboratory and biochemical procedures, clinical signs, symptoms, and comorbidity. This study, approved by the Ethical Committee of Clinical Center Kragujevac, was designed as an observational prospective cross-sectional clinical study which was conducted on 127 patients with diagnosed respiratory COVID-19 viral infection from April to August 2020. The primary goals were to determine the predictors of COVID-19 severity and to determine the predictors of the negative outcome of COVID-19 infection. All patients were divided into three categories: patients with a mild form, moderate form, and severe form of COVID-19 infection. All biochemical and laboratory procedures were done on the first day of the hospital admission. Respiratory (p < 0.001) and heart (p = 0.002) rates at admission were significantly higher in patients with a severe form of COVID-19. From all observed hematological and inflammatory markers, only white blood cell count (9.43 ± 4.62, p = 0.001) and LDH (643.13 ± 313.3, p = 0.002) were significantly higher in the severe COVID-19 group. We have observed that in the severe form of SARS-CoV-2, the levels of superoxide anion radicals were substantially higher than those in two other groups (11.3 ± 5.66, p < 0.001) and the nitric oxide level was significantly lower in patients with the severe disease (2.66 ± 0.45, p < 0.001). Using a linear regression model, TA, anosmia, ageusia, O₂ ^-, and the duration at the ICU are estimated as predictors of severity of SARS-CoV-2 disease. The presence of dyspnea and a higher heart rate were confirmed as predictors of a negative, fatal outcome. Results from our study show that presence of hypertension, anosmia, and ageusia, as well as the duration of ICU stay, and serum levels of O₂ ^- are predictors of COVID-19 severity, while the presence of dyspnea and an increased heart rate on admission were predictors of COVID-19 mortality.

Controversial Roles of the Renin Angiotensin System and Its Modulators During the COVID-19 Pandemic

Since December 2019, the coronavirus 2019 (COVID-19) pandemic has rapidly spread and overwhelmed healthcare systems worldwide, urging physicians to understand how to manage this novel infection. Early in the pandemic, more severe forms of COVID-19 have been observed in patients with cardiovascular comorbidities, who are often treated with renin-angiotensin aldosterone system (RAAS)-blockers, such as angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs), but whether these are indeed independent risk factors is unknown. The cellular receptor for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the membrane-bound angiotensin converting enzyme 2 (ACE2), as for SARS-CoV(-1). Experimental data suggest that expression of ACE2 may be increased by RAAS-blockers, raising concerns that these drugs may facilitate viral cell entry. On the other hand, ACE2 is a key counter-regulator of the RAAS, by degrading angiotensin II into angiotensin (1-7), and may thereby mediate beneficial effects in COVID-19. These considerations have raised concerns about the management of these drugs, and early comments shed vivid controversy among physicians. This review will describe the homeostatic balance between ACE-angiotensin II and ACE2-angiotensin (1-7) and summarize the pathophysiological rationale underlying the debated role of the RAAS and its modulators in the context of the pandemic. In addition, we will review available evidence investigating the impact of RAAS blockers on the course and prognosis of COVID-19 and discuss why retrospective observational studies should be interpreted with caution. These considerations highlight the importance of solid evidence-based data in order to guide physicians in the management of RAAS-interfering drugs in the general population as well as in patients with more or less severe forms of SARS-CoV-2 infection.

Role of IgG against N-protein of SARS-CoV2 in COVID19 clinical outcomes

The Nucleocapsid Protein (N Protein) of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV2) is located in the viral core. Immunoglobulin G (IgG) targeting N protein is detectable in the serum of infected patients. The effect of high titers of IgG against N-protein on clinical outcomes of SARS-CoV2 disease has not been described. We studied 400 RT-PCR confirmed SARS-CoV2 patients to determine independent factors associated with poor outcomes, including Medical Intensive Care Unit (MICU) admission, prolonged MICU stay and hospital admissions, and in-hospital mortality. We also measured serum IgG against the N protein and correlated its concentrations with clinical outcomes. We found that several factors, including Charlson comorbidity Index (CCI), high levels of IL6, and presentation with dyspnea were associated with poor clinical outcomes. It was shown that higher CCI and higher IL6 levels were independently associated with in-hospital mortality. Anti-N protein IgG was detected in the serum of 55 (55%) patients at the time of admission. A high concentration of antibodies, defined as signal to cut off ratio (S/Co) > 1.5 (75 percentile of all measurements), was found in 25 (25%) patients. The multivariable logistic regression models showed that between being an African American, higher CCI, lymphocyte counts, and S/Co ratio > 1.5, only S/Co ratio were independently associated with MICU admission and longer length of stay in hospital. This study recommends that titers of IgG targeting N-protein of SARS-CoV2 at admission is a prognostic factor for the clinical course of disease and should be measured in all patients with SARS-CoV2 infection.

Federated Learning used for predicting outcomes in SARS-COV-2 patients

'Federated Learning' (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the "EXAM" (EMR CXR AI Model) model. EXAM achieved an average Area Under the Curve (AUC) of over 0.92, an average improvement of 16%, and a 38% increase in generalisability over local models. The FL paradigm was successfully applied to facilitate a rapid data science collaboration without data exchange, resulting in a model that generalised across heterogeneous, unharmonized datasets. This provided the broader healthcare community with a validated model to respond to COVID-19 challenges, as well as set the stage for broader use of FL in healthcare.