Risk Factors of Fatal Outcome in Hospitalized Subjects With Coronavirus Disease 2019 From a Nationwide Analysis in China

Association of atherogenic indices with myocardial damage and mortality in COVID-19

BACKGROUND

Lipoproteins in cell membranes are related to membrane stability and play a role against microorganisms. Patients with COVID-19 often experience myocyte membrane damage.

OBJECTIVE

This study aimed to search the relationship of atherogenic indices with myocardial damage and mortality in COVID-19.

METHODS

This was an observational, single-center, retrospective study. The study population was grouped according to in-hospital mortality. C-reactive protein (CRP), CRP to albumin ratio (CAR), monocyte to high density lipoprotein cholesterol ratio (MHR), levels of total cholesterol (TC), triglycerides, high-density lipoprotein cholesterol (HDLc), and low-density lipoprotein cholesterol (LDLc) and cardiac troponin I (cTnI) were recorded. Atherogenic indices (plasma atherogenic index [AIP], atherogenic coefficient [AC], Castelli's risk indices I and II [CRI I and II], triglyceride to HDLc ratio (THR) were calculated.

RESULTS

A total of 783 patients were included. The mortality rate was 15.45% (n = 121). The median age of non-survivor group (NSG) was higher than survivor group (SG) [66.0 years (Q1 -Q3: 55.0-77.5) vs 54.0 years (Q1 -Q3: 43.0-63.0)] (p < 0.001). Study parameters which were measured significantly higher in the NSG were CRP, cTnI, triglyceride, CRI-I, CRI-II, AC, AIP, ferritin, CAR, MHR and THR. LDLc, HDLc, TC and albumin were significantly lower in NSG (p<0.001).

CONCLUSION

THR is positively correlated with myocardial damage and strongly predicts in-hospital mortality in COVID-19.

Genomic analysis of severe COVID-19 considering or not asthma comorbidity: GWAS insights from the BQC19 cohort

BACKGROUND

The severity of COVID-19 is influenced by various factors including the presence of respiratory diseases. Studies have indicated a potential relationship between asthma and COVID-19 severity.

OBJECTIVE

This study aimed to conduct a genome-wide association study (GWAS) to identify genetic and clinical variants associated with the severity of COVID-19, both among patients with and without asthma.

METHODS

We analyzed data from 2131 samples sourced from the Biobanque québécoise de la COVID-19 (BQC19), with 1499 samples from patients who tested positive for COVID-19. Among these, 1110 exhibited mild-to-moderate symptoms, 389 had severe symptoms, and 58 had asthma. We conducted a comparative analysis of clinical data from individuals in these three groups and GWAS using a logistic regression model. Phenotypic data analysis resulted in the refined covariates integrated into logistic models for genetic studies.

RESULTS

Considering a significance threshold of 1 × 10-6, seven genetic variants were associated with severe COVID-19. These variants were located proximal to five genes: sodium voltage-gated channel alpha subunit 1 (SCN10A), desmoplakin (DSP), RP1 axonemal microtubule associated (RP1), IGF like family member 1 (IGFL1), and docking protein 5 (DOK5). The GWAS comparing individuals with severe COVID-19 with asthma to those without asthma revealed four genetic variants in transmembrane protein with EGF like and two follistatin like domains 2 (TMEFF2) and huntingtin interacting protein-1 (HIP1) genes.

CONCLUSION

This study provides significant insights into the genetic profiles of patients with severe forms of the disease, whether accompanied by asthma or not. These findings enhance our comprehension of the genetic factors that affect COVID-19 severity.

KEY MESSAGES

Seven genetic variants were associated with the severe form of COVID-19; Four genetic variants were associated with the severe form of COVID-19 in individuals with comorbid asthma; These findings help define the genetic component of the severe form of COVID-19 in relation to asthma as a comorbidity.

The Impact of COVID-19 on Mortality and Clinical Characteristics in Hospitalized Patients With Peripheral Artery Disease in the Year 2020 in Germany

BACKGROUND

The COVID-19 pandemic developed its full destructive capacity in 2020. This retrospective study aimed to examine the effects of COVID-19 on the mortality and the clinical characteristics in PAD patients with COVID-19 compared to PAD patients without COVID-19.

METHODS AND RESULTS

Data derived from a German nationwide register of the year 2020 which encompassed all hospitalized patients with PAD (n = 173.075); N = 2553 also suffered from a COVID-19 infection and had significantly higher mortality rates of 11.2%. PAD + COVID-19 patients presented more clinical complications like major amputations (11.59%), myocardial infarction (2.08%), cardiogenic shock (2.98%), chronic kidney failure with GFR<= 15 mL/min (5.33%) and prolonged ventilation time >48 h (3.37%). Rates of pulmonary thromboembolism (0.24%), myocardial infarction (2.08%), and stroke (1.02%) were low in patients with PAD + COVID-19. Adjusted regression analyses for risk differences revealed possible causes of higher mortality rates, such as prolonged ventilation time, pneumonia, major amputations, multiple organ system failure, and length of hospital stay in patients with severe PAD (Rutherford 5-6) + COVID-19.

CONCLUSION

Pneumonia and major amputations were associated with high mortality rates in PAD + COVID-19 in 2020. However, we could not detect a relevant influence of pulmonary thromboembolism, myocardial infarction or stroke on higher death rates of PAD + COVID-19.

Effects of COVID-19 Status on Hip Fracture Surgical Outcomes: An ACS NSQIP Study

BACKGROUND

Studies have indicated that patients infected with the SARS-CoV-2 virus fare worse clinically after a traumatic injury, especially those who are older and have other comorbidities.

OBJECTIVE

This study aims to understand the effects of Corona Virus Disease 19 (COVID-19) diagnosis on patients undergoing surgery for hip fractures.

METHODS

This is a retrospective review of the 2021 American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) Targeted Hip Fracture database for patients who underwent surgery. Two cohorts were formed based on patients' preoperative COVID-19 status, as tested within 14 days prior to the operation. Several clinical factors were compared.

RESULTS

The COVID-positive cohort consisted of 184 patients, all of whom had a laboratory-confirmed or clinically suspected SARS-CoV-2 infection, while the COVID-negative cohort consisted of 12,211 patients with no infection. A lower proportion of COVID-positive patients had an emergent operation compared to the COVID-negative cohort (58.70% vs. 73.09%, p < .001). Preoperatively, the COVID-positive cohort showed higher rates of coagulopathy/bleeding disorders (22.83% vs. 14.12%), congestive heart failure (16.30% vs. 9.84%), diabetes mellitus (28.26% vs. 19.24%), and dementia (42.39% vs. 28.07%), with p ≤ .005 for all. Postoperatively, a higher proportion of COVID-positive patients died (9.78% vs. 5.40%) or had pneumonia (8.70% vs. 3.65%), hospital readmission within 30 days (10.87% vs. 6.76%), and pressure sores (8.15% vs. 4.55%), with p ≤ .033 for all.

CONCLUSION

The diagnosis of COVID-19 in hip fracture patients was associated with higher rates of postoperative complications, including mortality, when compared to COVID-negative patients, indicating the severity of the viral infection.

The Impact of Comorbidities among Ethnic Minorities on COVID-19 Severity and Mortality in Canada and the USA: A Scoping Review

(1) Current literature on ethnic minorities, comorbidities, and COVID-19 tends to investigate these factors separately, leaving gaps in our understanding about their interactions. Our review seeks to identify a relationship between ethnicity, comorbidities, and severe COVID-19 outcomes (ICU admission and mortality). We hope to enhance our understanding of the various factors that exacerbate COVID-19 severity and mortality in ethnic minorities in Canada and the USA. (2) All articles were received from PubMed, Scopus, CINAHL, and Ovid EMBASE from November 2020 to June 2022. Included articles contain information regarding comorbidities among ethnic minorities in relation to COVID-19 severity and mortality. (3) A total of 59 articles were included that examined various ethnic groups, including Black/African American, Asian, Hispanic, White/Caucasian, and Indigenous people. We found that the most examined comorbidities were diabetes, hypertension, obesity, and chronic kidney disease. A total of 76.9% of the articles (40 out of 52) found a significant association between different races and COVID-19 mortality, whereas 21.2% of the articles (11 out of 52) did not. (4) COVID-19 ICU admissions and mortality affect various ethnic groups differently, with Black patients generally having the most adverse outcomes. These outcomes may also interact with sex and age, though more research is needed assessing these variables together with ethnicity.

Development and validation of machine learning-based models for predicting healthcare-associated bacterial/fungal infections among COVID-19 inpatients: a retrospective cohort study

BACKGROUND

COVID-19 and bacterial/fungal coinfections have posed significant challenges to human health. However, there is a lack of good tools for predicting coinfection risk to aid clinical work.

OBJECTIVE

We aimed to investigate the risk factors for bacterial/fungal coinfection among COVID-19 patients and to develop machine learning models to estimate the risk of coinfection.

METHODS

In this retrospective cohort study, we enrolled adult inpatients confirmed with COVID-19 in a tertiary hospital between January 1 and July 31, 2023, in China and collected baseline information at admission. All the data were randomly divided into a training set and a testing set at a ratio of 7:3. We developed the generalized linear and random forest models for coinfections in the training set and assessed the performance of the models in the testing set. Decision curve analysis was performed to evaluate the clinical applicability.

RESULTS

A total of 1244 patients were included in the training cohort with 62 healthcare-associated bacterial/fungal infections, while 534 were included in the testing cohort with 22 infections. We found that patients with comorbidities (diabetes, neurological disease) were at greater risk for coinfections than were those without comorbidities (OR = 2.78, 95%CI = 1.61-4.86; OR = 1.93, 95%CI = 1.11-3.35). An indwelling central venous catheter or urinary catheter was also associated with an increased risk (OR = 2.53, 95%CI = 1.39-4.64; OR = 2.28, 95%CI = 1.24-4.27) of coinfections. Patients with PCT > 0.5 ng/ml were 2.03 times (95%CI = 1.41-3.82) more likely to be infected. Interestingly, the risk of coinfection was also greater in patients with an IL-6 concentration < 10 pg/ml (OR = 1.69, 95%CI = 0.97-2.94). Patients with low baseline creatinine levels had a decreased risk of bacterial/fungal coinfections(OR = 0.40, 95%CI = 0.22-0.71). The generalized linear and random forest models demonstrated favorable receiver operating characteristic curves (ROC = 0.87, 95%CI = 0.80-0.94; ROC = 0.88, 95%CI = 0.82-0.93) with high accuracy, sensitivity and specificity of 0.86vs0.75, 0.82vs0.86, 0.87vs0.74, respectively. The corresponding calibration evaluation P statistics were 0.883 and 0.769.

CONCLUSIONS

Our machine learning models achieved strong predictive ability and may be effective clinical decision-support tools for identifying COVID-19 patients at risk for bacterial/fungal coinfection and guiding antibiotic administration. The levels of cytokines, such as IL-6, may affect the status of bacterial/fungal coinfection.

The fatal contribution of serine protease-related genetic variants to COVID-19 outcomes

Introduction

Serine proteases play a critical role during SARS-CoV-2 infection. Therefore, polymorphisms of transmembrane protease serine 2 (TMPRSS2) and serpine family E member 1 (SERPINE1) could help to elucidate the contribution of variability to COVID-19 outcomes.

Methods

To evaluate the genetic variants of the genes previously associated with COVID-19 outcomes, we performed a cross-sectional study in which 1536 SARS-CoV-2-positive participants were enrolled. TMPRSS2 (rs2070788, rs75603675, rs12329760) and SERPINE1 (rs2227631, rs2227667, rs2070682, rs2227692) were genotyped using the Open Array Platform. The association of polymorphisms with disease outcomes was determined by logistic regression analysis adjusted for covariates (age, sex, hypertension, type 2 diabetes, and obesity).

Results

According to our codominant model, the GA genotype of rs2227667 (OR=0.55; 95% CI = 0.36-0.84; p=0.006) and the AG genotype of rs2227667 (OR=0.59; 95% CI = 0.38-0.91; p=0.02) of SERPINE1 played a protective role against disease. However, the rs2227692 T allele and TT genotype SERPINE1 (OR=1.45; 95% CI = 1.11-1.91; p=0.006; OR=2.08; 95% CI = 1.22-3.57; p=0.007; respectively) were associated with a decreased risk of death. Similarly, the rs75603675 AA genotype TMPRSS2 had an OR of 1.97 (95% CI = 1.07-3.6; p=0.03) for deceased patients. Finally, the rs2227692 T allele SERPINE1 was associated with increased D-dimer levels (OR=1.24; 95% CI = 1.03-1.48; p=0.02).

Discussion

Our data suggest that the rs75603675 TMPRSS2 and rs2227692 SERPINE1 polymorphisms are associated with a poor outcome. Additionally, rs2227692 SERPINE1 could participate in hypercoagulable conditions in critical COVID-19 patients, and this genetic variant could contribute to the identification of new pharmacological targets and treatment strategies to block the inhibition of TMPRSS2 entry into SARS-CoV-2.

HB-EGF Plasmatic Level Contributes to the Development of Early Risk Prediction Nomogram for Severe COVID-19 Cases

(1) Background: Heparin-Binding Epidermal Growth Factor-like Growth Factor (HB-EGF) is involved in wound healing, cardiac hypertrophy, and heart development processes. Recently, circulant HB-EGF was reported upregulated in severely hospitalized COVID-19 patients. However, the clinical correlations of HB-EGF plasma levels with COVID-19 patients' characteristics have not been defined yet. In this study, we assessed the plasma HB-EGF correlations with the clinical and paraclinical patients' data, evaluated its predictive clinical value, and built a risk prediction model for severe COVID-19 cases based on the resulting significant prognostic markers. (2) Methods: Our retrospective study enrolled 75 COVID-19 patients and 17 control cases from May 2020 to September 2020. We quantified plasma HB-EGF levels using the sandwich ELISA technique. Correlations between HB-EGF plasma levels with clinical and paraclinical patients' data were calculated using two-tailed Spearman and Point-Biserial tests. Significantly upregulated parameters for severe COVID-19 cases were identified and selected to build a multivariate logistic regression prediction model. The clinical significance of the prediction model was assessed by risk prediction nomogram and decision curve analyses. (3) Results: HB-EGF plasma levels were significantly higher in the severe COVID-19 subgroup compared to the controls (p = 0.004) and moderate cases (p = 0.037). In the severe COVID-19 group, HB-EGF correlated with age (p = 0.028), pulse (p = 0.016), dyspnea (p = 0.014) and prothrombin time (PT) (p = 0.04). The multivariate risk prediction model built on seven identified risk parameters (age p = 0.043, HB-EGF p = 0.0374, Fibrinogen p = 0.009, PT p = 0.008, Creatinine p = 0.026, D-Dimers p = 0.024 and delta miR-195 p < 0.0001) identifies severe COVID-19 with AUC = 0.9556 (p < 0.0001). The decision curve analysis revealed that the nomogram model is clinically relevant throughout a wide threshold probability range. (4) Conclusions: Upregulated HB-EGF plasma levels might serve as a prognostic factor for severe COVID-19 and help build a reliable risk prediction nomogram that improves the identification of high-risk patients at an early stage of COVID-19.

Fever as a Predictor of COVID-19 Outcomes in Hospitalized Patients

INTRODUCTION

With the advent of the COVID-19 pandemic, numerous questions have arisen regarding the screening, diagnosis, treatment, and prognosis of infected patients. Among these, screening infected patients through body temperature measurement has proven ineffective. However, doubts persist regarding the role of fever as a prognostic factor in the disease.

OBJECTIVE

To assess the prevalence of fever and its relevance as a marker of mortality in COVID-19.

METHODOLOGY

This prospective and longitudinal cohort study was conducted between April 2020 and December 2021 and analyzed 1400 COVID-19 patients systematically admitted to the emergency department of a reference hospital during the period from April 2020 to December 2021, in the city of Curitiba, Brazil. [LG1] The study evaluated [LG2] the presence of fever (body temperature above 37,7ºC) upon admission and/or during hospitalization, patient profiles, and outcomes (in-hospital death, discharge, admission at the intensive care unit, need of mechanical ventilation).

RESULTS

Fever was present in 128 participants (9.1%), with a higher prevalence in males (71%) and obese individuals (42.9%). Among the febrile patients, 39 required intubation (30.4%), with two intubated upon admission (1.5%), 104 were discharged (81.2%), and 24 deceased (18.7%). Fever was not associated with a higher mortality rate.

CONCLUSION

Fever showed low prevalence, is more common in males and obese individuals, and is not related to worse clinical outcomes.

A Retrospective Study on the Outcome of Coronavirus Disease 2019 (COVID-19) Patients Admitted to a District General Hospital and Predictors of High Mortality

BACKGROUND

The clinical features and severity of coronavirus disease 2019 (COVID-19) vary between patients and countries. Patients with certain conditions are predisposed to poor outcomes compared with those without medical conditions, such as diabetes, dementia, and hypertension (HTN).

METHODS

The aim of this retrospective study was to assess factors associated with higher mortality in patients with COVID-19 infections and to identify the reason for hospital admission in these patients. The study was performed on patients admitted between 1 and 31 March 2020. Data collection was done retrospectively from electronic medical records.

RESULTS

There were 269 patient admissions during this period, of which 147 were included in this audit. The mean age of COVID-19-positive patients was 62.8 years and 65.9 years for COVID-19-negative patients during this period. Forty-seven patients requiring hospital admission were COVID-19 positive and 93 were COVID-19 negative. There were no COVID-19 swabs in the seven patients included in the audit. Approximately 50% of the COVID-19-positive patients presented with fever and shortness of breath (sob), followed by dyspnea and cough (seven patients). The most common comorbidity was HTN, followed by type 2 diabetes mellitus (T2DM) and ischemic heart disease (IHD). The survival rate was 72.3% in COVID-19-positive patients and 80% in COVID-19-negative patients. The average length of stay was 14.4 days for COVID-19-positive survivors compared to 7.8 days for COVID-19-negative survivors. Most patients who tested positive for COVID-19 infection received oseltamivir vaccination and antibiotics. The presence of HTN, diabetes mellitus (DM), age, and organ failure was associated with a high mortality risk.

CONCLUSION

Our study supports the findings of previous studies that diabetes, HTN, coronary artery disease, old age, and organ failure were associated with high mortality in patients admitted to hospitals with COVID-19 infections.

Analysis of Age-Specific Predictors of Mortality in Patients with Coronavirus Disease 2019

AIM

We investigated predictors of mortality, including demographic, clinical, and laboratory parameters, in hospitalized patients with coronavirus disease 2019 (COVID-19) pneumonia.

PATIENTS AND METHODS

This retrospective, single-center study included 651 consecutive Turkish adults who had been admitted to the emergency department with a diagnosis of COVID-19. We recorded the demographic, clinical, and laboratory parameters of the patients. The patients were divided into two groups: patients aged ≥65 years and patients aged <65 years. The predictors of mortality for hospitalized COVID-19 patients were evaluated.

RESULTS

The study included 651 patients (354 [54.4%] men and 297 [45.6%] women; mean age, 56.40 ± 15.70 years). The most common comorbidities were hypertension (37.6%), diabetes mellitus (28.9%), and coronary artery disease (CAD) (16.1%). The overall mortality rate was 10.6% (n = 69); the mortality rate was higher in men than in women. Advanced age; chronic renal failure (CRF); prolonged activated partial thromboplastin time; high serum neutrophil and platelet counts; high C-reactive protein to albumin (CRP/albumin) ratio; and high levels of albumin, lactate dehydrogenase (LDH), and high-sensitivity troponin I (TnI-hs) were independent predictors of mortality in all age groups.

CONCLUSION

Multivariate logistic regression analysis showed that chronic obstructive pulmonary disease (COPD), high serum platelet count, high CRP/albumin ratio, and high levels of albumin, TnI-hs, and D-dimer were independent predictors of mortality in patients aged <65 years. Conversely, advanced age, CAD, CRF, and high levels of serum CRP and LDH were independent predictors of mortality in patients aged ≥65 years.

Safety and Efficacy of Inactivated SARS-CoV-2 Vaccine in Patients with Rheumatic Diseases and Serum Antibody Changes Post-Omicron Variant Infection

INTRODUCTION

The purpose of this study was to investigate whether the inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine has a similar effectiveness and safety profile in patients with rheumatic and musculoskeletal diseases (RMDs) and healthy controls (HCs).

METHODS

Between August 10, 2021 and September 30, 2021, 134 HCs and 269 patients with RMDs were recruited. All participants who tested negative for COVID-19 were vaccinated with SARS-CoV-2 inactivated vaccine. Next, 150 patients with RMDs and 30 HCs infected with the SARS-CoV-2 Omicron variant within the previous 12 weeks were recruited between February 20, 2023 and March 1, 2023. Serum samples were collected from each participant, and the serum immunoglobulin G (IgG) and immunoglobulin M (IgM) antibody titers against SARS-CoV-2 were determined using a chemiluminescence assay.

RESULTS

No statistically significant difference was found in the titer of anti-SARS-CoV-2 IgG and IgM antibodies, or in the incidence of vaccination-related adverse events between the RMD and HC groups (P = 0.183, P = 0.903, and P = 0.27, respectively). Serum IgG titers of SARS-CoV-2 neutralizing antibodies were significantly higher in patients who received two or more doses of inactivated vaccine than in patients who were unvaccinated or had received one dose of vaccine (244.36 ± 109.79 vs. 66.20 ± 82.50; P < 0.001).

CONCLUSIONS

SARS-CoV-2 inactivated vaccines have similar protective effects in HCs and patients with RMDs, with an appreciable safety profile. Fully vaccinated patients with RMDs infected with the Omicron variant were able to produce effective neutralizing antibody concentrations.

Cardiomyopathy Does Not Exacerbate the Severity of Pneumonia Caused by a SARS-CoV-2 Delta Variant in the J2N-k Hamster Model

Cardiovascular disease is one of many risk factors that have been linked to increased severity or mortality in coronavirus disease 2019 (COVID-19) patients; however, the exact role of SARS-CoV-2 in the pathogenesis of cardiac inflammatory injury has not been established. A previous study reported that SARS-CoV-2 causes more severe disease with cardiomyopathy in a J2N-k animal model. Here, we investigated the sensitivity of J2N-k hamsters, as a cardiomyopathy animal model, to a delta strain of SARS-CoV-2 compared to J2N-n control animals. We found that J2N-k hamsters were less susceptible to this delta strain than J2N-n animals, and we found no evidence that cardiomyopathy is a risk factor in this animal model. Since the previous study reported that SARS-CoV-2 causes more severe disease with cardiomyopathy in the same animal model, further analysis of the relationship between cardiomyopathy and SARS-CoV-2 infection is needed.

Predictors and Outcomes of Extracorporeal Membrane Oxygenation in COVID-19 Patients With ARDS: A Propensity-Matched Analysis of National Inpatient Sample

Extracorporeal membrane oxygenation (ECMO) is a significant treatment modality for COVID-19 patients on ventilators. The current data is limited for understanding the indicators and outcomes of ECMO in COVID-19 patients with acute respiratory distress syndrome (ARDS). The National Inpatient Sample (NIS) database from 2020 was queried in this study. Among 1,666,960 patients admitted with COVID-19, 99,785 (5.98%) patients developed ARDS, and 60,114 (60.2%) were placed on mechanical ventilation. Of these mechanically ventilated COVID-ARDS patients, 2580 (4.3%) were placed on ECMO. Patients with ECMO intervention had higher adjusted odds (aOR) of blood loss anemia (aOR 9.1, 95% CI: 6.16-13.5, propensity score-matched (PSM) 42% vs 5.4%, P < 0.001), major bleeding (aOR 3.79, 95% CI: 2.5-5.6, PSM 19.9% vs 5.9%, P < 0.001) and acute liver injury (aOR 1.7, 95% CI: 1.14-2.6 PSM 14% vs 6%, P = 0.009) compared to patients without ECMO intervention. However, in-hospital mortality, acute kidney injury, transfusions, acute MI, and cardiac arrest were insignificant. On subgroup analysis, patients placed on veno-arterial ECMO had higher odds of cardiogenic shock (aOR 13.4, CI 3.95-46, P < 0.0001), cardiac arrest (aOR 3.5, CI 1.45-8.47, P = 0.0057), acute congestive heart failure (aOR 4.18, CI 1.05-16.5, P = 0.042) and lower odds of major bleeding (aOR 0.26, CI 0.07-0.92). However, there was no significant difference in mortality, intracranial hemorrhage, and acute MI. Further studies are needed before considering COVID-19 ARDS patients for placement on ECMO.

Risk factors associated with fatal thrombosis in hospitalized coronavirus disease 2019 (COVID-19) patients on anticoagulant therapy

BACKGROUND

The purpose of this study was to determine the factors that increase the risk of fatal thrombotic events in hospitalized coronavirus disease 2019 (COVID-19) patients receiving standard therapy according to the National Clinical Practice Guidelines (National Guidelines).

MATERIALS AND METHODS

In this case-control study, cases included 83 adults with COVID-19 who had died from thrombosis and controls comprised 83 COVID-19 patients with comparable criteria who survived. Data was abstracted by reviewing the medical records of selected patients and analyzed using Statistica. Parametric and non-parametric tests, as appropriate, were used to compare continuos variables between cases and controls, whereas Chi-square test was employed to compare categorical variables. Odds ratio (OR) was also calculated to measure the strength of association of case status and various independent variables.

RESULTS

Fatal outcomes were higher in patients with chronic tubulointerstitial nephritis, (OR = 2.4, 95% CI 1.2-4.9); obesity, (OR = 2.1, 95% CI 0.5-8.6); and coronary heart disease (OR = 1.6, 95% CI 0.8-3.2). In the group with a D-dimer level from 250 to 1000 ng/ml, a statistically significant moderate positive correlation was found between the day of death and D-dimer level (P = 0.026). The lack of use of the PADUA Prediction Score for the risk of venous thromboembolism scale (PADUA Scale) and control of laboratory parameters (APTT and D-dimer) were associated with increased risk of fatal outcome. Overall, 19.2% cases and 8.4% of controls had no coagulation control; (OR = 2.6, 95% CI 1-6.7).

CONCLUSION

Chronic tubulointerstitial nephritis, obesity, and coronary heart disease were associatied with fatal thrombosis. A slight elevation of D-dimer level, lack of the PADUA Scale and laboratory monitoring in the management of hospitalized patients with COVID-19. was associated with an increased risk of thromboembolism.

COVID-19 fatality rates in hospitalized patients: A new systematic review and meta-analysis

BACKGROUND

SARS-COV2 or COVID-19 disease is an infectious illness that emerged for the first time at the end of 2019, in Wuhan, China and rapidly turned out to be an international pandemic with deleterious effects all over the world. In March 2021, A. Macedo et al., has published the first meta-analysis of hospital mortality, so the authors decided to update those data at a time of emergence of new therapies and increasing vaccination rates.

METHODS

As the outcome of interest was the mortality in hospitalized general patients, the authors looked for articles evaluating the clinical characteristics of those patients, consulting PUBMED (The US National Library of Medicine) and EMBASE (Medical database) in an independent selection using predefined terms of search. A meta-analysis random-effect model was estimated using Mantel-Haenszel method. Heterogeneity among studies was tested using Tau2 statistics and Chi2 statistics.

RESULTS

In a first instance 25 articles were included for final analysis with a total of 103,840 patients, but as the goal was to update the anterior data, these studies were analysed together with the 21 studies of the previous meta-analysis, with a total of 114609 patients. The mortality rate of COVID-19 general patients admitted to the hospital was 16% (95% CI 12; 21, I2 =100%).

CONCLUSION

Global hospital mortality of COVID-19 of general patients was 16%, with quite different rates according to the different geographic areas analysed.

Differences in the severity and mortality risk factors for patients hospitalized for COVID-19 pneumonia between the early wave and the very late stage of the pandemic

Background

Since China's dynamic zero-COVID policy is cancelled on December 7, 2022, the rapidly growing number of patients has brought a major public health challenge. This study aimed to assess whether there were differences in the severity and mortality risk factors for patients hospitalized for COVID-19 pneumonia between the early wave and the very late stage of the pandemic.

Methods

A retrospective cross-sectional study was carried out using data from 223 hospitalized patients diagnosed with COVID-19 pneumonia during the Omicron surge in Xi'an People's Hospital (Xi'an Fourth Hospital) from December 8, 2022, to January 31, 2023. Univariable and multivariable logistic regression analyses were used to identify potential risk factors associated with the severity and mortality of COVID-19 pneumonia during the first wave of the pandemic after the dynamic zero-COVID policy was retracted. Differences in the severity and mortality risk factors were assessed at different stages of the pandemic, mainly from demographic, clinical manifestation, laboratory tests and radiological findings of patients on admission.

Results

The mean age of the 223 participants was 71.2 ± 17.4. Compared with the patients in the initial stage of the pandemic, the most common manifestation among patients in this study was cough (90.6%), rather than fever (79.4%). Different from the initial stage of the pandemic, older age, chest tightness, elevated neutrophil-to-lymphocyte ratio (NLR), decreased albumin (ALB) level and ground glass opacification (GGO) in radiological finding were identified as severity risk factors, instead of mortality risk factors for COVID-19 patients in the very late stage of the pandemic. Arterial partial pressure of oxygen/fraction of inspired oxygen (PaO2/FiO2) ≤300 mmHg, cardiovascular disease and laboratory findings including elevated levels of D-dimer, α-hydroxybutyrate dehydrogenase (α-HBDH), total bilirubin (TBIL), alanine aminotransferase (ALT), urea nitrogen (BUN), creatinine (CR), fasting blood glucose (FBG) and decreased platelet count (PLT) were still associated with mortality in the very late stage of the pandemic.

Conclusion

Monitoring continuously differences in the severity and mortality risk factors for COVID-19 patients between different stages of the pandemic could provide evidence for exploring uncharted territory in the coming post-pandemic era.

A double-blind, randomised, placebo-controlled parallel study to investigate the effect of sex and dietary nitrate on COVID-19 vaccine-induced vascular dysfunction in healthy men and women: protocol of the DiNOVasc-COVID-19 study

BACKGROUND

Cardiovascular events, driven by endothelial dysfunction, are a recognised complication of COVID-19. SARS-CoV-2 infections remain a persistent concern globally, and an understanding of the mechanisms causing endothelial dysfunction, particularly the role of inflammation, nitric oxide, and whether sex differences exist in this response, is lacking. We have previously demonstrated important sex differences in the inflammatory response and its impact on endothelial function and separately that the ingestion of inorganic nitrate can protect the endothelium against this dysfunction. In this study, we will investigate whether sex or a dietary inorganic nitrate intervention modulates endothelial function and inflammatory responses after the COVID-19 vaccine.

METHODS

DiNOVasc-COVID-19 is a double-blind, randomised, single-centre, placebo-controlled clinical trial. A total of 98 healthy volunteers (49 males and 49 females) will be recruited. Participants will be randomised into 1 of 2 sub-studies: part A or part B. Part A will investigate the effects of sex on vascular and inflammatory responses to the COVID-19 vaccine. Part B will investigate the effects of sex and dietary inorganic nitrate on vascular and inflammatory responses to the COVID-19 vaccine. In part B, participants will be randomised to receive 3 days of either nitrate-containing beetroot juice (intervention) or nitrate-deplete beetroot juice (placebo). The primary outcome for both sub-studies is a comparison of the change in flow-mediated dilatation (FMD) from baseline after COVID-19 vaccination. The study has a power of > 80% to assess the primary endpoint. Secondary endpoints include change from baseline in inflammatory and leukocyte counts and in pulse wave analysis (PWA) and pulse wave velocity (PWV) following the COVID-19 vaccination.

DISCUSSION

This study aims to evaluate whether sex or dietary influences endothelial function and inflammatory responses in healthy volunteers after receiving the COVID-19 vaccine.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04889274. Registered on 5 May 2023. The study was approved by the South Central - Oxford C Research Ethics Committee (21/SC/0154).

Longitudinal anti-SARS-CoV-2 antibody immune response in acute and convalescent patients

Despite global efforts to assess the early response and persistence of SARS-CoV-2 antibodies in patients infected with or recovered from COVID-19, our understanding of the factors affecting its dynamics remains limited. This work aimed to evaluate the early and convalescent immunity of outpatients infected with SARS-CoV-2 and to determine the factors that affect the dynamics and persistence of the IgM and IgG antibody response. Seropositivity of volunteers from Mexico City and the State of Mexico, Mexico, was evaluated by ELISA using the recombinant receptor-binding domain (RBD) of the SARS-CoV-2 Spike protein for 90 days, at different time points (1, 15, 45, 60, and 90 days) after molecular diagnosis (RT-qPCR). Gender, age range, body mass index (BMI), comorbidities, and clinical spectrum of disease were analyzed to determine associations with the dynamics of anti-SARS-CoV-2 antibodies. On 90 days post-infection, individuals with moderate and asymptomatic disease presented the lowest levels of IgM, while for IgG, at the same time, the highest levels occurred with mild and moderate disease. The IgM and IgG levels were related to the clinical spectrum of disease, BMI, and the presence/absence of comorbidities through regression trees. The results suggest that the dynamics of anti-SARS-CoV-2 IgM and IgG antibodies in outpatients could be influenced by the clinical spectrum of the disease. In addition, the persistence of antibodies against SARS-CoV-2 could be related to the clinical spectrum of the disease, BMI, and the presence/absence of comorbidities.

Characteristics of COVID-19 Patients Admitted to Intensive Care Unit in Multispecialty Hospital of Riyadh, Saudi Arabia: A Retrospective Study

During the early stages of the COVID-19 pandemic, infection rates were high and symptoms were severe. Medical resources, including healthcare experts and hospital facilities, were put to the test to ensure their readiness to deal with this unique event. An intensive care unit (ICU) is expected to be required by many hospitalized patients. Many hospitals worldwide lacked resources during the pandemic's peak stages, particularly in critical care treatment. Because of this, there were issues with capacity, as well as an excessive influx of patients. Additionally, even though the research location provides medical care to a sizable population, there is a paucity of scientific data detailing the situation as it pertains to COVID-19 patients during the height of the outbreak. Therefore, this study aimed to identify and describe the features of COVID-19 patients hospitalized in the ICU of one of the multispecialty hospitals in Riyadh, Saudi Arabia. An observational retrospective study was conducted using a chart review of COVID-19 patients admitted to the ICU between March 2020 and December 2020. To characterize the patients, descriptive statistics were utilized. An exploratory multivariate regression analysis was carried out on the study cohort to investigate the factors that were shown to be predictors of death and intubation. Only 333 (29.33%) of the 1135 samples from the hospital's medical records were used for the final analysis and interpretation. More than 76% of the patients in the study were male, with a mean BMI of 22.07 and an average age of around 49 years. The most frequent chronic condition found among the patients who participated in the study was diabetes (39.34%), followed by hypertension (31.53%). At the time of admission, 63 of the total 333 patients needed to have intubation performed. In total, 22 of the 333 patients died while undergoing therapy. People with both diabetes and hypertension had a 7.85-fold higher risk of death, whereas those with only diabetes or hypertension had a 5.43-fold and 4.21-fold higher risk of death, respectively. At admission, intubation was necessary for many male patients (49 out of 63). Most intubated patients had hypertension, diabetes, or both conditions. Only 13 of the 63 patients who had been intubated died, with the vast majority being extubated. Diabetes and hypertension were significant contributors to the severity of illness experienced by COVID-19 participants. The presence of multiple comorbidities had the highest risk for intubation and mortality among ICU-admitted patients. Although more intubated patients died, the fatality rate was lower than in other countries due to enhanced healthcare management at the ICU of the study center. However, large-scale trials are needed to determine how effective various strategies were in preventing ICU admission, intubation, and death rates.

Development and validation of a predicted nomogram for mortality of COVID-19: a multicenter retrospective cohort study of 4,711 cases in multiethnic

Background

Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly worldwide. As it quickly spreads and can cause severe disease, early detection and treatment may reduce mortality. Therefore, the study aims to construct a risk model and a nomogram for predicting the mortality of COVID-19.

Methods

The original data of this study were from the article "Neurologic Syndromes Predict Higher In-Hospital Mortality in COVID-19." The database contained 4,711 multiethnic patients. In this secondary analysis, a statistical difference test was conducted for clinical demographics, clinical characteristics, and laboratory indexes. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis were applied to determine the independent predictors for the mortality of COVID-19. A nomogram was conducted and validated according to the independent predictors. The area under the curve (AUC), the calibration curve, and the decision curve analysis (DCA) were carried out to evaluate the nomogram.

Results

The mortality of COVID-19 is 24.4%. LASSO and multivariate logistic regression analysis suggested that risk factors for age, PCT, glucose, D-dimer, CRP, troponin, BUN, LOS, MAP, AST, temperature, O2Sats, platelets, Asian, and stroke were independent predictors of CTO. Using these independent predictors, a nomogram was constructed with good discrimination (0.860 in the C index) and internal validation (0.8479 in the C index), respectively. The calibration curves and the DCA showed a high degree of reliability and precision for this clinical prediction model.

Conclusion

An early warning model based on accessible variates from routine clinical tests to predict the mortality of COVID-19 were conducted. This nomogram can be conveniently used to facilitate identifying patients who might develop severe disease at an early stage of COVID-19. Further studies are warranted to validate the prognostic ability of the nomogram.

The JAK1/2 inhibitor ruxolitinib in patients with COVID-19 triggered hyperinflammation: the RuxCoFlam trial

Dysregulated hyperinflammatory response is key in the pathogenesis in patients with severe COVID-19 leading to acute respiratory distress syndrome and multiorgan failure. Whilst immunosuppression has been proven to be effective, potential biological targets and optimal timing of treatment are still conflicting. We sought to evaluate efficacy and safety of the Janus Kinase 1/2 inhibitor ruxolitinib, employing the previously developed COVID-19 Inflammation Score (CIS) in a prospective multicenter open label phase II trial (NCT04338958). Primary objective was reversal of hyperinflammation (CIS reduction of ≥25% at day 7 in ≥20% of patients). In 184 patients with a CIS of ≥10 (median 12) ruxolitinib was commenced at an initial dose of 10 mg twice daily and applied over a median of 14 days (range, 2-31). On day 7, median CIS declined to 6 (range, 1-13); 71% of patients (CI 64-77%) achieved a ≥25% CIS reduction accompanied by a reduction of markers of inflammation. Median cumulative dose was 272.5 mg/d. Treatment was well tolerated without any grade 3-5 adverse events related to ruxolitinib. Forty-four patients (23.9%) died, all without reported association to study drug. In conclusion, ruxolitinib proved to be safe and effective in a cohort of COVID-19 patients with defined hyperinflammation.

Kidney Injury in Critically Ill Patients with COVID-19 - From Pathophysiological Mechanisms to a Personalized Therapeutic Model

Acute kidney injury is a common complication of COVID-19, frequently fuelled by a complex interplay of factors. These include tubular injury and three primary drivers of cardiocirculatory instability: heart-lung interaction abnormalities, myocardial damage, and disturbances in fluid balance. Further complicating this dynamic, renal vulnerability to a "second-hit" injury, like a SARS-CoV-2 infection, is heightened by advanced age, chronic kidney disease, cardiovascular diseases, and diabetes mellitus. Moreover, the influence of chronic treatment protocols, which may constrain the compensatory intrarenal hemodynamic mechanisms, warrants equal consideration. COVID-19-associated acute kidney injury not only escalates mortality rates but also significantly affects long-term kidney function recovery, particularly in severe instances. Thus, the imperative lies in developing and applying therapeutic strategies capable of warding off acute kidney injury and decelerating the transition into chronic kidney disease after an acute event. This narrative review aims to proffer a flexible diagnostic and therapeutic strategy that recognizes the multi-faceted nature of COVID-19-associated acute kidney injury in critically ill patients and underlines the crucial role of a tailored, overarching hemodynamic and respiratory framework in managing this complex clinical condition.

Decreased level of serum NT-proCNP associates with disease severity in COVID-19

BACKGROUND

C-type natriuretic peptide (CNP) is an endothelium-derived paracrine molecule with an important role in vascular homeostasis. In septic patients, the serum level of the amino-terminal propeptide of CNP (NT-proCNP) shows a strong positive correlation with inflammatory biomarkers and, if elevated, correlates with disease severity and indicates a poor outcome. It is not yet known whether NT-proCNP also correlates with the clinical outcome of patients suffering from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In the current study, we aimed to determine possible changes in the NT-proCNP levels of patients with coronavirus disease 2019 (COVID-19), with special regard to disease severity and outcome.

METHODS

In this retrospective analysis, we determined the serum level of NT-proCNP in hospitalized patients with symptoms of upper respiratory tract infection, using their blood samples taken on admission, stored in a biobank. The NT-proCNP levels of 32 SARS-CoV-2 positive and 35 SARS-CoV-2 negative patients were measured to investigate possible correlation with disease outcome. SARS-CoV-2 positive patients were then divided into two groups based on their need for intensive care unit treatment (severe and mild COVID-19).

RESULTS

The NT-proCNP was significantly different in the study groups (e.g. severe and mild COVID-19 and non-COVID-19 patients), but showed inverse changes compared to previous observations in septic patients: lowest levels were detected in critically ill COVID-19 patients, while highest levels in the non-COVID-19 group. A low level of NT-proCNP on admission was significantly associated with severe disease outcome.

CONCLUSIONS

Low-level NT-proCNP on hospital admission is associated with a severe COVID-19 disease course. The pathomechanism underlying this observation remains to be elucidated, while future studies in larger patient cohorts are necessary to confirm these observations and reveal therapeutic importance. Trial registration DRKS00026655 Registered 26. November 2021.

Symptoms, Treatment, and Outcomes of COVID-19 Patients Coinfected with Clostridioides difficile: Single-Center Study from NE Romania during the COVID-19 Pandemic

The Coronavirus disease 2019 (COVID-19) pandemic has brought new challenges across medical disciplines, particularly in infectious disease medicine. In Romania, the incidence of SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) infection increased dramatically since March 2020 until March 2022. Antibiotic administration for pulmonary superinfections in COVID-19 intensified and, consequently, increased rates of Clostridioides difficile infection (CDI) were hypothesized. We conducted a single-center, retrospective, observational study on patients from North-Eastern Romania to assess clinical characteristics and outcomes of COVID-19 and Clostridioides difficile (CD) coinfection, and to identify risk factors for CDI in COVID-19 patients. The study enrolled eighty-six CDI and COVID-19 coinfected patients admitted during March 2020-February 2021 (mean age 59.14 years, 53.49% men, 67.44% urban residents) and a group of eighty-six COVID-19 patients. On admission, symptoms were more severe in mono-infected patients, while coinfected patients associated a more intense acute inflammatory syndrome. The main risk factors for severe COVID-19 were smoking, diabetes mellitus, and antibiotic administration. Third generation cephalosporins (55%) and carbapenems (24%) were the main antibiotics used, and carbapenems were significantly associated with severe COVID-19 in patients coinfected with CD during hospitalization. Coinfection resulted in longer hospitalization and poorer outcomes. The extensive use of antibiotics in COVID-19, particularly carbapenems, contributed substantially to CD coinfection.

Identification of Multisystem Inflammatory Syndrome in Children Classes and Development of Hyperinflammation Score in Pediatric COVID-19

The aim of this study is to describe characteristics and hospital course of children admitted with COVID-19 to a tertiary care pediatric center in Southeastern United States, and to present the frequency of three classes of multisystem inflammatory syndrome in children (MIS-C) and develop pediatric COVID-19 associated hyperinflammation score (PcHIS). A retrospective cohort study of 68 children was performed. Critical illness was defined as any child requiring respiratory or cardiovascular support or renal replacement therapy. PcHIS was developed by using six variables: fever, hematological dysfunction, coagulopathy, hepatic injury, macrophage activation, and cytokinemia. Centers for Disease Control and Prevention criteria were used to identify MIS-C, and three classes of MIS-C were identified based on the findings of recently published latent class analysis (Class 1: MIS-C without Kawasaki like disease, Class 2: MIS-C with respiratory disease, and Class 3: MIS-C with Kawasaki like disease). The median age was 6.4 years. Fever, respiratory, and gastrointestinal were common presenting symptoms. MIS-C was present in 32 (47%), critical COVID-19 illness in 11 (16%), and 17 (25%) were admitted to the PICU. Children with critical illness were adolescents with elevated body mass index and premorbid conditions. PcHIS score of 3 had a sensitivity of 100% and a specificity of 77% for predicting critical COVID-19 illness. Among MIS-C patients, 15 (47%) were in Class 1, 8 (25%) were in Class 2, and 9 (28%) were in Class 3. We conclude that most children with COVID-19 have mild-to-moderate illness. Critical COVID-19 is mainly seen in obese adolescents with premorbid conditions. Three Classes of MIS-C are identifiable based on clinical features. Validation and clinical implication of inflammation score in pediatric COVID-19 need further investigation.

COVID-19 and coronary artery disease; A systematic review and meta-analysis

Background and aim

Patients with underlying cardiovascular disorders such as coronary artery disease (CAD) are more prone to severe forms and multiple complications of COVID-19. The present systematic review and meta-analysis aimed to investigate the impact of CAD on patients with COVID-19.

Methods

Main electronic databases, including Medline (via PubMed), EMBASE, and Web of Science, were carefully searched and reviewed for original research articles published between 2019 and 2021. One hundred nine studies that address CAD in patients with COVID-19 were selected and analyzed.

Results

Following search and screening processes, 109 relevant publications were selected for analysis. The meta-analysis of prevalence studies indicated that the frequency of CAD among patients with COVID-19 was reported in 10 countries with an overall frequency of 12.4% [(95% CI) 11.1-13.8] among 20079 COVID-19 patients. According to case reports/case series studies, 50.9% of COVID-19 patients suffered from CAD. Fever was the most common symptom in these patients (47%); 36.5% also had hypertension.

Conclusion

The results obtained during the present study show that the simultaneous presence of COVID-19 and CAD, especially in men and elderly patients, can increase the risks and complications of both diseases. Therefore, careful examination of the condition of this group of patients for timely diagnosis and treatment is strongly recommended.

Circulating eosinophils associated with responsiveness to COVID-19 vaccine and the disease severity in patients with SARS-CoV-2 omicron variant infection

OBJECTIVE

This study aimed to investigate the longitudinal circulating eosinophil (EOS) data impacted by the COVID-19 vaccine, the predictive role of circulating EOS in the disease severity, and its association with T cell immunity in patients with SARS-CoV-2 Omicron BA.2 variant infection in Shanghai, China.

METHODS

We collected a cohort of 1,157 patients infected with SARS-CoV-2 Omicron/BA.2 variant in Shanghai, China. These patients were diagnosed or admitted between Feb 20, 2022, and May 10, 2022, and were classified as asymptomatic (n = 705), mild (n = 286) and severe (n = 166) groups. We compiled and analyzed data of patients' clinical demographic characteristics, laboratory findings, and clinical outcomes.

RESULTS

COVID-19 vaccine reduced the incidence of severe cases. Severe patients were shown to have declined peripheral blood EOS. Both the 2 doses and 3 doses of inactivated COVID-19 vaccines promoted the circulating EOS levels. In particular, the 3rd booster shot of inactivated COVID-19 vaccine was shown to have a sustained promoting effect on circulating EOS. Univariate analysis showed that there was a significant difference in age, underlying comorbidities, EOS, lymphocytes, CRP, CD4, and CD8 T cell counts between the mild and the severe patients. Multivariate logistic regression analysis and ROC curve analysis indicate that circulating EOS (AUC = 0.828, p = 0.025), the combination of EOS and CD4 T cell (AUC = 0.920, p = 0.017) can predict the risk of disease severity in patients with SARS-CoV-2 Omicron BA.2 variant infection.

CONCLUSIONS

COVID-19 vaccine promotes circulating EOS and reduces the risk of severe illness, and particularly the 3rd booster dose of COVID-19 vaccine sustainedly promotes EOS. Circulating EOS, along with T cell immunity, may have a predictive value for the disease severity in SARS-CoV-2 Omicron infected patients.

COVID-ConvNet: A Convolutional Neural Network Classifier for Diagnosing COVID-19 Infection

The novel coronavirus (COVID-19) pandemic still has a significant impact on the worldwide population's health and well-being. Effective patient screening, including radiological examination employing chest radiography as one of the main screening modalities, is an important step in the battle against the disease. Indeed, the earliest studies on COVID-19 found that patients infected with COVID-19 present with characteristic anomalies in chest radiography. In this paper, we introduce COVID-ConvNet, a deep convolutional neural network (DCNN) design suitable for detecting COVID-19 symptoms from chest X-ray (CXR) scans. The proposed deep learning (DL) model was trained and evaluated using 21,165 CXR images from the COVID-19 Database, a publicly available dataset. The experimental results demonstrate that our COVID-ConvNet model has a high prediction accuracy at 97.43% and outperforms recent related works by up to 5.9% in terms of prediction accuracy.

The Clinical Effects of Laser Acupuncture on Hospitalized Patients With Severe COVID-19: A Randomized Clinical Trial

Introduction: The coronavirus disease (COVID-19) was extended to the entire population in China and around the world, and its mortality rate was about 3.4%. The impact of laser therapy on chronic respiratory diseases has been shown in previous studies. This study was aimed at examining the effects of laser acupuncture (LA) on patients with severe COVID-19. Methods: In the present study, 60 patients with a positive reverse transcription-polymerase chain reaction (RT-PCR) test were assigned to the intervention and control groups (30 patients in each group). The intervention group was treated with LA, that is, laser light with low energy on acupuncture points, once a day for five consecutive days. Results: The participants' mean age in the intervention and control groups was 48.96±12.65 and 53.16±12.28 respectively; 70% of the patients were male and 30% of them were female. IL6 had a significant reduction in the intervention group (P value=0.038) in comparison with the control group (P value=0.535). Furthermore, the mean admission time in the control group was significantly higher than that in the intervention group (P value=0.047). However, the mortality rate in the intervention group was zero, but three patients in the control group died. Conclusion: Our study showed that LA can be used as supportive therapy for routine treatment in patients with severe COVID-19. Moreover, due to LA safety and it's low cost, it could be recommended as an adjuvant to conventional therapy in patients interested in treating their disease with such a method.

BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data

Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge from the datasets collected from 930 COVID-19 patients hospitalized in Italy during the first wave of COVID-19 (March-June 2020). The dataset consists of twenty-five biomarkers from electronic health record and Chest X-ray (CXR) images. It is found that the system can diagnose low- or high-risk patients with an accuracy, sensitivity, and F1-score of 89.03%, 90.44%, and 89.03%, respectively. The system exhibits 6% higher accuracy than the systems that employ either CXR images or biomarker data. In addition, the system can calculate the mortality risk of high-risk patients using multivariate logistic regression-based nomogram scoring technique. Interested physicians can use the presented system to predict the early mortality risks of COVID-19 patients using the web-link: Covid-severity-grading-AI. In this case, a physician needs to input the following information: CXR image file, Lactate Dehydrogenase (LDH), Oxygen Saturation (O2%), White Blood Cells Count, C-reactive protein, and Age. This way, this study contributes to the management of COVID-19 patients by predicting early mortality risk.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00521-023-08606-w.

Human Endogenous Retrovirus, SARS-CoV-2, and HIV Promote PAH via Inflammation and Growth Stimulation

Pulmonary arterial hypertension (PAH) is a pulmonary vascular disease characterized by the progressive elevation of pulmonary arterial pressures. It is becoming increasingly apparent that inflammation contributes to the pathogenesis and progression of PAH. Several viruses are known to cause PAH, such as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), human endogenous retrovirus K(HERV-K), and human immunodeficiency virus (HIV), in part due to acute and chronic inflammation. In this review, we discuss the connections between HERV-K, HIV, SARS-CoV-2, and PAH, to stimulate research regarding new therapeutic options and provide new targets for the treatment of the disease.

Long-Term Consequences of COVID-19: A 1-Year Analysis

Long-lasting symptoms after SARS-CoV-2 infection have been described many times in the literature and are referred to as Long COVID. In this prospective, longitudinal, monocentric, observational study, we collected the health complaints of 474 patients (252 ambulatory and 222 hospitalized) at Lausanne University Hospital 1 year after COVID-19 diagnosis. Using a self-reported health survey, we explored cardiopulmonary, vascular, neurological, and psychological complaints. Our results show that age, Charlson comorbidity index, and smoking habits were associated with hospital admission. Regarding the vascular system, we found that having had thromboembolism before SARS-CoV-2 infection was significantly associated with a higher risk of recurrence of thromboembolism at 1 year. In the neurologic evaluation, the most frequent symptom was fatigue, which was observed in 87.5% of patients, followed by “feeling slowed down”, headache, and smell disturbance in 71.5%, 68.5%, and 60.7% of cases, respectively. Finally, our cohort subjects scored higher overall in the STAI, CESD, Maastricht, and PSQI scores (which measure anxiety, depression, fatigue, and sleep, respectively) than the healthy population. Using cluster analysis, we identified two phenotypes of patients prone to developing Long COVID. At baseline, CCS score, prior chronic disease, stroke, and atrial fibrillation were associated with Long COVID. During COVID infection, mechanical ventilation and five neurological complaints were also associated with Long COVID. In conclusion, this study confirms the wide range of symptoms developed after COVID with the involvement of all the major systems. Early identification of risk factors associated with the development of Long COVID could improve patient follow-up; nevertheless, the low specificity of these factors remains a challenge to building a systematic approach.

COVID-19 in Adult Patients with Hematological Malignancies-Lessons Learned after Three Years of Pandemic

The COVID-19 pandemic is undoubtedly the most difficult health challenge of the 21st century with more than 600 million laboratory-confirmed SARS-CoV-2 infections and over 6.5 million deaths worldwide. The coronavirus pandemic contributed to rapid development of mRNA vaccines, which, along with new antiviral drugs, have been the subject of extensive research for many decades. Nevertheless, elderly, multi-morbid and immunocompromised patients continue to face a more severe clinical course and a higher risk of death from COVID-19, even now that the risk of COVID-19 in the general population is significantly reduced due to the introduction of global vaccination strategies. In this paper, we present the mechanisms of increased susceptibility to infectious complications and the evolution of the clinical course of COVID-19 in patients with hematological malignancies, taking into account the mutation of the virus and the introduction of vaccines and new antiviral drugs. We also present current recommendations for prophylactic and therapeutic management in patients with hematological malignancies.

Development of a Discharge Criteria Checklist for COVID-19 Patients From the Intensive Care Unit

This study aims to develop and validate a checklist of discharge readiness criteria for COVID-19 patients from the intensive care unit (ICU). We conducted a Delphi design study. The degree of agreement among 7 experts had been evaluated using the content validity index (CVI) through a 4-point Likert scale. The instrument was validated with 17 items. All the experts rated all items as very relevant which scored the item-CVI 1, which validates all checklist items. Using the mean of all items, the scale-CVI was calculated, and it was 1. This meant validation of the checklist as a whole. With regard to the overall checklist evaluation, the mean expert proportion of the instrument was 1, and the S-CVI/UA was 1. This discharge criteria checklist improves transition of care for COVID-19 patients and can help nurses, doctors, and academics to discharge COVID-19 patients from the ICU safely.

Identification of Clinical Features Associated with Mortality in COVID-19 Patients

Understanding clinical features and risk factors associated with COVID-19 mortality is needed to early identify critically ill patients, initiate treatments and prevent mortality. A retrospective study on COVID-19 patients referred to a tertiary hospital in Iran between March and November 2020 was conducted. COVID-19-related mortality and its association with clinical features including headache, chest pain, symptoms on computerized tomography (CT), hospitalization, time to infection, history of neurological disorders, having a single or multiple risk factors, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia were investigated. Based on the investigation outcome, decision tree and dimension reduction algorithms were used to identify the aforementioned risk factors. Of the 3008 patients (mean age 59.3 ± 18.7 years, 44% women) with COVID-19, 373 died. There was a significant association between COVID-19 mortality and old age, headache, chest pain, low respiratory rate, oxygen saturation < 93%, need for a mechanical ventilator, having symptoms on CT, hospitalization, time to infection, neurological disorders, cardiovascular diseases and having a risk factor or multiple risk factors. In contrast, there was no significant association between mortality and gender, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia. Our results might help identify early symptoms related to COVID-19 and better manage patients according to the extracted decision tree. The proposed ML models identified a number of clinical features and risk factors associated with mortality in COVID-19 patients. These models if implemented in a clinical setting might help to early identify patients needing medical attention and care. However, more studies are needed to confirm these findings.

Role of epigenetics in the clinical evolution of COVID-19 disease. Epigenome-wide association study identifies markers of severe outcome

BACKGROUND

COVID-19 has a wide spectrum of clinical manifestations and given its impact on morbidity and mortality, there is an unmet medical need to discover endogenous cellular and molecular biomarkers that predict the expected clinical course of the disease. Recently, epigenetics and especially DNA methylation have been pointed out as a promising tool for outcome prediction in several diseases.

METHODS AND RESULTS

Using the Illumina Infinium Methylation EPIC BeadChip850K, we investigated genome-wide differences in DNA methylation in an Italian Cohort of patients with comorbidities and compared severe (n = 64) and mild (123) prognosis. Results showed that the epigenetic signature, already present at the time of Hospital admission, can significantly predict risk of severe outcomes. Further analyses provided evidence of an association between age acceleration and a severe prognosis after COVID-19 infection. The burden of Stochastic Epigenetic Mutation (SEMs) has been significantly increased in patients with poor prognosis. Results have been replicated in silico considering COVID-19 negative subjects and available previously published datasets.

CONCLUSIONS

Using original methylation data and taking advantage of already published datasets, we confirmed in the blood that epigenetics is actively involved in immune response after COVID-19 infection, allowing the identification of a specific signature able to discriminate the disease evolution. Furthermore, the study showed that epigenetic drift and age acceleration are associated with severe prognosis. All these findings prove that host epigenetics undergoes notable and specific rearrangements to respond to COVID-19 infection which can be used for a personalized, timely, and targeted management of COVID-19 patients during the first stages of hospitalization.

Machine learning-based prediction of COVID-19 mortality using immunological and metabolic biomarkers

COVID-19 mortality prediction Background COVID-19 has become a major global public health problem, despite prevention and efforts. The daily number of COVID-19 cases rapidly increases, and the time and financial costs associated with testing procedure are burdensome. Method To overcome this, we aim to identify immunological and metabolic biomarkers to predict COVID-19 mortality using a machine learning model. We included inpatients from Hong Kong's public hospitals between January 1, and September 30, 2020, who were diagnosed with COVID-19 using RT-PCR. We developed three machine learning models to predict the mortality of COVID-19 patients based on data in their electronic medical records. We performed statistical analysis to compare the trained machine learning models which are Deep Neural Networks (DNN), Random Forest Classifier (RF) and Support Vector Machine (SVM) using data from a cohort of 5,059 patients (median age = 46 years; 49.3% male) who had tested positive for COVID-19 based on electronic health records and data from 532,427 patients as controls. Result We identified top 20 immunological and metabolic biomarkers that can accurately predict the risk of mortality from COVID-19 with ROC-AUC of 0.98 (95% CI 0.96-0.98). Of the three models used, our result demonstrate that the random forest (RF) model achieved the most accurate prediction of mortality among COVID-19 patients with age, glomerular filtration, albumin, urea, procalcitonin, c-reactive protein, oxygen, bicarbonate, carbon dioxide, ferritin, glucose, erythrocytes, creatinine, lymphocytes, PH of blood and leukocytes among the most important biomarkers identified. A cohort from Kwong Wah Hospital (131 patients) was used for model validation with ROC-AUC of 0.90 (95% CI 0.84-0.92). Conclusion We recommend physicians closely monitor hematological, coagulation, cardiac, hepatic, renal and inflammatory factors for potential progression to severe conditions among COVID-19 patients. To the best of our knowledge, no previous research has identified important immunological and metabolic biomarkers to the extent demonstrated in our study.

Supplementary Information

The online version contains supplementary material available at 10.1186/s44247-022-00001-0.

Validation of the RIM Score-COVID in the Spanish SEMI-COVID-19 Registry

The significant impact of COVID-19 worldwide has made it necessary to develop tools to identify patients at high risk of severe disease and death. This work aims to validate the RIM Score-COVID in the SEMI-COVID-19 Registry. The RIM Score-COVID is a simple nomogram with high predictive capacity for in-hospital death due to COVID-19 designed using clinical and analytical parameters of patients diagnosed in the first wave of the pandemic. The nomogram uses five variables measured on arrival to the emergency department (ED): age, sex, oxygen saturation, C-reactive protein level, and neutrophil-to-platelet ratio. Validation was performed in the Spanish SEMI-COVID-19 Registry, which included consecutive patients hospitalized with confirmed COVID-19 in Spain. The cohort was divided into three time periods: T1 from February 1 to June 10, 2020 (first wave), T2 from June 11 to December 31, 2020 (second wave, pre-vaccination period), and T3 from January 1 to December 5, 2021 (vaccination period). The model's accuracy in predicting in-hospital COVID-19 mortality was assessed using the area under the receiver operating characteristics curve (AUROC). Clinical and laboratory data from 22,566 patients were analyzed: 15,976 (70.7%) from T1, 4,233 (18.7%) from T2, and 2,357 from T3 (10.4%). AUROC of the RIM Score-COVID in the entire SEMI-COVID-19 Registry was 0.823 (95%CI 0.819-0.827) and was 0.834 (95%CI 0.830-0.839) in T1, 0.792 (95%CI 0.781-0.803) in T2, and 0.799 (95%CI 0.785-0.813) in T3. The RIM Score-COVID is a simple, easy-to-use method for predicting in-hospital COVID-19 mortality that uses parameters measured in most EDs. This tool showed good predictive ability in successive disease waves.

Impact of the seventh day nucleated red blood cell count on mortality in COVID-19 intensive care unit patients: A retrospective case-control study

Background

COVID-19 covers a broad clinical spectrum, threatening global health. Although several studies have investigated various prognostic biochemical and hematological parameters, they generally lack specificity and are insufficient for decision-making. Beyond the neonatal period, NRBCs (nucleated red blood cells) in peripheral blood is rare and often associated with malignant neoplasms, bone marrow diseases, and other severe disorders such as sepsis and hypoxia. Therefore, we investigated if NRBCs can predict mortality in hypoxic ICU (Intensive Care Unit) patients of COVID-19.

Methods

Seventy-one unvaccinated RT-PCR confirmed COVID-19 ICU patients was divided into those who survived (n=35, mean age=58) and died (n=36, mean age=75). Venous blood samples were collected in K3 EDTA tubes and analyzed on a Sysmex XN-1000 hematology analyzer with semiconductor laser flow cytometry and nucleic acid fluorescence staining method for NRBC analysis. NRBC numbers and percentages of the patients were compared on the first and seventh days of admission to the ICU. Results are reported as a proportion of NRBCs per 100 WBCs NRBCs/100 WBC (NRBC% and as absolute NRBC count (NRBC #, × 109/L).

Results

NRBC 7th-day count and % values were statistically higher in non-survival ones. The sensitivity for 7th day NRBC value <0.01 (negative) was 86.11%, the specificity was 48.57%, for <0.02; 75.00%, and 77.14%, for <0.03; 61.11%, and 94.60%.

Conclusions

In conclusion, our results indicate that NRBC elevation (>0.01) significantly predicts mortality in ICU hospitalized patients due to COVID-19. Worse, a high mortality rate is expected, especially with NRBC values of >0.03.

Time to death and its predictors among adult patients with COVID-19: A retrospective cohort study in Ethiopia

Background

Coronavirus (COVID-19) disease affected people throughout the globe and has become a severe threat to the health and wellbeing of the global community. Time to death and predictors of mortality vary across settings. So far, no or few related studies have been undertaken in Ethiopia. Studying the time to death from COVID-19 and its predictors is essential to understand the characteristics of the disease and thereby contribute to the identification of indicators for early detection and initiation of treatment. Therefore, this study aimed to estimate time to death and its predictors among adults with COVID-19 in Ethiopia.

Methods

A retrospective follow-up study was conducted among 602 adults with COVID-19 attending Eka Kotebe General Hospital, COVID-19 Treatment Center, between 13 March 2020 and 13 November 2020. The data were entered by Epi-data version 4.2 while the analysis was carried out using STATA version 16. A Kaplan-Meier survivor curve was computed to estimate the survival probabilities. A log-rank test was used to compare the difference in survival curves. Cox proportional hazard models were fitted to identify the predictors of time to death.

Results

The overall median time to death was 21 days. Older adults (aged ≥65 years) [adjusted hazard ratio (AHR) 2.22, 95% confidence interval (CI) 1.02-4.86], being men (AHR 3.04, 95% CI 1.61-5.74), shortness of breathing at admission (AHR 2.29, 95% CI 1.16-4.54), comorbidity (AHR 2.23, 95% CI 1.04-4.80), diabetes mellitus (AHR 2.31, 95% CI 1.30-4.08), altered cardiac function (AHR 2.07, 95% CI 1.21-3.43), and baseline white blood cell count of greater than 10 (103/µl) (AHR 2.62, 95% CI 1.55-4.44) were independent predictors of COVID-19 mortality.

Conclusion

Male sex, older adults, shortness of breathing at admission, patients with comorbidities, and higher blood cell count were significant predictors of time to death from COVID-19. Therefore, concerned stakeholders should focus on those predictors of mortality and design interventions accordingly to enhance the survival of patients with COVID-19.

Lethality risk markers by sex and age-group for COVID-19 in Mexico: a cross-sectional study based on machine learning approach

BACKGROUND

Mexico ranks fifth worldwide in the number of deaths due to COVID-19. Identifying risk markers through easily accessible clinical data could help in the initial triage of COVID-19 patients and anticipate a fatal outcome, especially in the most socioeconomically disadvantaged regions. This study aims to identify markers that increase lethality risk in patients diagnosed with COVID-19, based on machine learning (ML) methods. Markers were differentiated by sex and age-group.

METHODS

A total of 11,564 cases of COVID-19 in Mexico were extracted from the Epidemiological Surveillance System for Viral Respiratory Disease. Four ML classification methods were trained to predict lethality, and an interpretability approach was used to identify those markers.

RESULTS

Models based on Extreme Gradient Boosting (XGBoost) yielded the best performance in a test set. This model achieved a sensitivity of 0.91, a specificity of 0.69, a positive predictive value of 0.344, and a negative predictive value of 0.965. For female patients, the leading markers are diabetes and arthralgia. For males, the main markers are chronic kidney disease (CKD) and chest pain. Dyspnea, hypertension, and polypnea increased the risk of death in both sexes.

CONCLUSIONS

ML-based models using an interpretability approach successfully identified risk markers for lethality by sex and age. Our results indicate that age is the strongest demographic factor for a fatal outcome, while all other markers were consistent with previous clinical trials conducted in a Mexican population. The markers identified here could be used as an initial triage, especially in geographic areas with limited resources.

Correlates of Coronavirus Disease 2019 Inpatient Mortality at a Southern California Community Hospital With a Predominantly Hispanic/Latino Adult Population

Background

Studies of inpatient coronavirus disease 2019 (COVID-19) mortality risk factors have mainly used data from academic medical centers or large multihospital databases and have not examined populations with large proportions of Hispanic/Latino patients. In a retrospective cohort study of 4881 consecutive adult COVID-19 hospitalizations at a single community hospital in Los Angeles County with a majority Hispanic/Latino population, we evaluated factors associated with mortality.

Methods

Data on demographic characteristics, comorbidities, laboratory and clinical results, and COVID-19 therapeutics were abstracted from the electronic medical record. Cox proportional hazards regression modeled statistically significant, independently associated predictors of hospital mortality.

Results

Age ≥65 years (hazard ratio [HR] = 2.66; 95% confidence interval [CI] = 1.90-3.72), male sex (HR = 1.31; 95% CI = 1.07-1.60), renal disease (HR = 1.52; 95% CI = 1.18-1.95), cardiovascular disease (HR = 1.45; 95% CI = 1.18-1.78), neurological disease (HR = 1.84; 95% CI = 1.41-2.39), D-dimer ≥500 ng/mL (HR = 2.07; 95% CI = 1.43-3.0), and pulse oxygen level <88% (HR = 1.39; 95% CI = 1.13-1.71) were independently associated with increased mortality. Patient household with (1) multiple COVID-19 cases and (2) Asian, Black, or Hispanic compared with White non-Hispanic race/ethnicity were associated with reduced mortality. In hypoxic COVID-19 inpatients, remdesivir, tocilizumab, and convalescent plasma were associated with reduced mortality, and corticosteroid use was associated with increased mortality.

Conclusions

We corroborate several previously identified mortality risk factors and find evidence that the combination of factors associated with mortality differ between populations.

A Nomogram for Early Diagnosis of Community-Acquired Pneumonia Based on Bronchoalveolar Lavage Fluid Metabolomics

Purpose

There is a high disease burden associated with community-acquired pneumonia (CAP) around the world. A timely and correct diagnosis of CAP can facilitate early treatment and prevent illness progression. The present study aimed to find some novel biomarkers of CAP by metabolic analysis and construct a nomogram model for precise diagnosis and individualized treatment of CAP patients.

Patients and Methods

42 CAP patients and 20 controls were enrolled in this study. The metabolic profiles of bronchoalveolar lavage fluid (BALF) samples were identified by untargeted LC-MS/MS analysis. With a VIP score ≥ 1 in OPLS-DA analysis and P < 0.05, the significantly dysregulated metabolites were estimated as potential biomarkers of CAP, which were further included in the construction of the diagnostic prediction model along with laboratory inflammatory indexes via stepwise backward regression analysis. Discrimination, calibration, and clinical applicability of the nomogram were evaluated by the C-index, the calibration curve, and the decision curve analysis (DCA) estimated by bootstrap resampling.

Results

The metabolic profiles differed obviously between CAP patients and healthy controls, as shown by PCA and OPLS-DA plots. Seven metabolites significantly dysregulated in CAP were established: dimethyl disulfide, oleic acid (d5), N-acetyl-a-neuraminic acid, pyrimidine, choline, LPC (12:0/0:0) and PA (20:4/2:0). Multivariate logistic regression revealed that the expression levels of PA (20:4/2:0), N-acetyl-a-neuraminic acid, and CRP were associated with CAP. After being validated by bootstrap resampling, this model showed satisfactory diagnostic performance.

Conclusion

A novel nomogram prediction model containing metabolic potential biomarkers in BALF that was developed for the early diagnosis of CAP offers insights into the pathogenesis and host response in CAP.

Genetic predisposition to blood cell indices in relation to severe COVID-19

Despite considerable variation in disease manifestations observed among coronavirus disease 2019 (COVID-19) patients infected with severe acute respiratory syndrome coronavirus 2, the risk factors predicting disease severity remain elusive. Recent studies suggest that peripheral blood cells play a pivotal role in COVID-19 pathogenesis. Here, we applied two-sample Mendelian randomization (MR) analyses to evaluate the potential causal contributions of blood cell indices variation to COVID-19 severity, using single-nucleotide polymorphisms (SNPs) as instrumental variables for 17 indices from the UK Biobank and INTERVAL genome-wide association studies (N = 173 480). Data on the associations between the SNPs and very severe respiratory confirmed COVID-19 were obtained from the COVID-19 host genetics initiative (N = 8779/1 001 875). We observed significant negative association between hematocrit (HCT; odds ratio, OR = 0.775, 95% confidence interval, CI = 0.635-0.915, p = 3.48E-04) or red blood cell count (OR = 0.830, 95% CI = 0.728-0.932, p = 2.19E-03) and very severe respiratory confirmed COVID-19, as well as nominal negative association of hemoglobin concentration (OR = 0.808, 95% CI = 0.673-0.943, p = 3.95E-03) with very severe respiratory confirmed COVID-19 (no effect survived multiple correction). In conclusion, the MR study supports a protective effect of high HCT and red blood cell count from very severe respiratory confirmed COVID-19, suggesting potential strategies to ameliorate/treat clinical conditions in very severe respiratory confirmed COVID-19.

Ventilatory Parameters Measured After One Week of Mechanical Ventilation and Survival in COVID-19-Related ARDS

BACKGROUND

Ventilatory parameters measured soon after initiation of mechanical ventilation have limited ability to predict outcome of COVID-19-related ARDS. We hypothesized that ventilatory parameters measured after one week of mechanical ventilation might differ between survivors and non-survivors.

METHODS

One hundred twenty-seven subjects with COVID-related ARDS had gas exchange and lung mechanics assessed on the day of intubation and one week later. The main parameters of interest were PaO2 /FIO2 , ventilatory ratio (VR), respiratory system compliance (CRS), and a composite score that was calculated as (PaO2 /FIO2 /100) × CRS/VR. The primary outcome was death in the ICU.

RESULTS

Of the 127 subjects, 42 (33%) died in the ICU and 85 (67%) were successfully extubated. On the day of intubation, PaO2 /FIO2 , CRS, and composite score of survivors and non-survivors were similar, but survivors had a lower VR. At one week, as compared to survivors, non-survivors had a significantly higher VR (2.04 ± 0.76 vs 1.60 ± 0.43, P < .001), lower CRS (27.4 ± 6.4 mL/cm H2O vs 32.4 ± 9.3 mL/cm H2O, P = .002), and lower composite score (20.6 ± 11.9 vs 34.5 ± 18.6, P < .001), with no statistically significant difference in PaO2 /FIO2 (137 ± 49 vs 155 ± 48, P = .08).

CONCLUSIONS

In subjects with COVID ARDS, parameters that reflect dead space (VR), lung mechanics (CRS), and a combined score that included PaO2 /FIO2 , VR, and CRS differed between survivors and non-survivors after one week of mechanical ventilation but with considerable overlap of values between survivors and non-survivors.

Clinical Characteristics and Management of Patients with a Suspected COVID-19 Infection in Emergency Departments: A European Retrospective Multicenter Study

Background: Our aim is to describe and compare the profile and outcome of patients attending the ED with a confirmed COVID-19 infection with patients with a suspected COVID-19 infection. Methods: We conducted a multicentric retrospective study including adults who were seen in 21 European emergency departments (ED) with suspected COVID-19 between 9 March and 8 April 2020. Patients with either a clinical suspicion of COVID-19 or confirmed COVID-19, detected using either a RT-PCR or a chest CT scan, formed the C+ group. Patients with non-confirmed COVID-19 (C− group) were defined as patients with a clinical presentation in the ED suggestive of COVID-19, but if tests were performed, they showed a negative RT-PCR and/or a negative chest CT scan. Results: A total of 7432 patients were included in the analysis: 1764 (23.7%) in the C+ group and 5668 (76.3%) in the C− group. The population was older (63.8 y.o. ±17.5 vs. 51.8 y.o. +/− 21.1, p < 0.01), with more males (54.6% vs. 46.1%, p < 0.01) in the C+ group. Patients in the C+ group had more chronic diseases. Half of the patients (n = 998, 56.6%) in the C+ group needed oxygen, compared to only 15% in the C− group (n = 877). Two-thirds of patients from the C+ group were hospitalized in ward (n = 1128, 63.9%), whereas two-thirds of patients in the C− group were discharged after their ED visit (n = 3883, 68.5%). Conclusion: Our study was the first in Europe to examine the emergency department’s perspective on the management of patients with a suspected COVID-19 infection. We showed an overall more critical clinical situation group of patients with a confirmed COVID-19 infection.

Coronary artery aneurysm presenting as ST-elevation myocardial infarction in a man with coronavirus disease 2019: a case report

BACKGROUND

Patients with underlying cardiovascular risk factors have worse clinical outcomes when they have coronavirus disease. In addition, a reduced workload of cardiovascular emergencies has been reported during the coronavirus pandemic due to patients' reluctance to attend hospitals for fear of contracting the disease. Regional health service reorganization, separating hospitals into coronavirus and non-coronavirus can mitigate this effect. However, the effectiveness of this approach on outcomes and patient satisfaction is unknown.

CASE PRESENTATION

A 35-year-old Pakistani man with acute ST myocardial infarction was found to have thrombosis of the right coronary artery aneurysm and concomitant coronavirus disease. He had percutaneous coronary angiography and thrombus removal, and was transferred to a coronavirus hospital for the management of the infection. Due to the large size of the aneurysm, he was considered for surgical intervention. Following discharge from the coronavirus hospital and a period of stay at the isolation center, he failed to keep his cardiology follow-up appointment.

CONCLUSION

This case illustrates an unusual cause of myocardial infarction in a patient with coronavirus infection whose care may have been adversely affected by the healthcare system restructuring.

Characteristics, treatment outcomes and experiences of COVID-19 patients under home-based care in Kapelebyong district in Uganda: a mixed-methods study

Background

A rapid increase in community transmission of COVID-19 across the country overwhelmed Uganda’s health care system. In response, the Ministry of Health adopted the home-based care strategy for COVID-19 patients with mild-to-moderate disease. We determined the characteristics, treatment outcomes and experiences of COVID-19 patients under home-based care during the second wave in Kapelebyong district, in eastern Uganda.

Methods

We conducted a sequential explanatory mixed-methods study. We first collected quantitative data using an interviewer-administered questionnaire to determine characteristics and treatment outcomes of COVID-19 patients under home-based care. Cured at home was coded as 1 (considered a good outcome) while being admitted to a health facility and/or dying were coded as 0 (considered poor outcomes). Thereafter, we conducted 11 in-depth interviews to explore the experiences of COVID-19 patients under home-based care. Multivariable logistic regression was used to assess factors associated with poor treatment outcomes using Stata v.15.0. Thematic content analysis was used to explore lived experiences of COVID-19 patients under home-based care using NVivo 12.0.0

Results

A total of 303 study participants were included. The mean age ± standard deviation of participants was 32.2 years ± 19.9. Majority of the participants [96.0% (289/303)] cured at home, 3.3% (10/303) were admitted to a health facility and 0.7% (2/303) died. Patients above 60 years of age had 17.4 times the odds of having poor treatment outcomes compared to those below 60 years of age (adjusted odds ratio (AOR): 17.4; 95% CI: 2.2–137.6). Patients who spent more than one month under home-based care had 15.3 times the odds of having poor treatment outcomes compared to those that spent less than one month (AOR: 15.3; 95% CI: 1.6–145.7). From the qualitative interviews, participants identified stigma, fear, anxiety, rejection, not being followed up by health workers and economic loss as negative experiences encountered during home-based care. Positive lived experiences included closeness to friends and family, more freedom, and easy access to food.

Conclusion

Home-based care of COVID-19 was operational in eastern Uganda. Older age (> 60 years) and prolonged illness (> 1 months) were associated with poor treatment outcomes. Social support was an impetus for home-based care.

Integrative transcriptome analysis of SARS-CoV-2 human-infected cells combined with deep learning algorithms identifies two potential cellular targets for the treatment of coronavirus disease

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) quickly spread worldwide, leading coronavirus disease 2019 (COVID-19) to hit pandemic level less than 4 months after the first official cases. Hence, the search for drugs and vaccines that could prevent or treat infections by SARS-CoV-2 began, intending to reduce a possible collapse of health systems. After 2 years, efforts to find therapies to treat COVID-19 continue. However, there is still much to be understood about the virus' pathology. Tools such as transcriptomics have been used to understand the impact of SARS-CoV-2 on different cells isolated from various tissues, leaving datasets in the databases that integrate genes and differentially expressed pathways during SARS-CoV-2 infection. After retrieving transcriptome datasets from different human cells infected with SARS-CoV-2 available in the database, we performed an integrative analysis associated with deep learning algorithms to determine differentially expressed targets mainly after infection. The targets found represented a fructose transporter (GLUT5) and a component of proteasome 26s. These targets were then molecularly modeled, followed by molecular docking that identified potential inhibitors for both structures. Once the inhibition of structures that have the expression increased by the virus can represent a strategy for reducing the viral replication by selecting infected cells, associating these bioinformatics tools, therefore, can be helpful in the screening of molecules being tested for new uses, saving financial resources, time, and making a personalized screening for each infectious disease.