D-dimer, Troponin, and Urea Level at Presentation With COVID-19 can Predict ICU Admission: A Single Centered Study

Outcomes of critically ill COVID-19 patients boarding in the emergency department of a tertiary care center in a developing country: a retrospective cohort study

BACKGROUND

Boarding of critically ill patients in the emergency department (ED) has long been known to compromise patient care and affect outcomes. During the COVID-19 pandemic, multiple hospitals worldwide experienced overcrowded emergency rooms. Large influx of patients outnumbered hospital beds and required prolonged length of stay (LOS) in the ED. Our aim was to assess the ED LOS effect on mortality and morbidity, in addition to the predictors of in-hospital mortality, intubation, and complications of critically ill COVID-19 ED boarder patients.

METHODS

This was a retrospective cohort study, investigating 145 COVID-19-positive adult patients who were critically ill, required intensive care unit (ICU), and boarded in the ED of a tertiary care center in Lebanon. Data on patients who boarded in the emergency from January 1, 2020, till January 31, 2021, was gathered and studied.

RESULTS

Overall, 66% of patients died, 60% required intubation, and 88% developed complications. Multiple risk factors were associated with mortality naming age above 65 years, vasopressor use, severe COVID pneumonia findings on CT chest, chemotherapy treatment in the previous year, cardiovascular diseases, chronic kidney diseases, prolonged ED LOS, and low SaO2 < 95% on triage. In addition, our study showed that staying long hours in the ED increased the risk of developing complications.

CONCLUSION

To conclude, all efforts need to be drawn to re-establish mitigation strategies and models of critical care delivery in the ED to alleviate the burden of critical boarders during pandemics, thus decreasing morbidity and mortality rates. Lessons from this pandemic should raise concern for complications seen in ED ICU boarders and allow the promotion of health measures optimizing resource allocation in future pandemic crises.

Remdesivir, dexamethasone and angiotensin-converting enzyme inhibitors use and mortality outcomes in COVID-19 patients with concomitant troponin elevation

BACKGROUND

There are indications that viral myocarditis, demand ischemia, and renin-angiotensin-aldosterone system pathway activation play essential roles in troponin elevation in coronavirus disease 2019 (COVID-19) patients. Antiviral medications and steroids are used to treat viral myocarditis, but their effect in patients with elevated troponin, possibly from myocarditis, has not been studied.

AIM

To evaluate the effect of dexamethasone, remdesivir, and angiotensin-converting enzyme (ACE) inhibitors (ACEI) on mortality in COVID-19 patients with elevated troponin.

METHODS

Our retrospective observational study involved 1788 COVID-19 patients at seven hospitals in Southern California, United States. We did a backward selection Cox multivariate regression analysis to determine predictors of mortality in our study population. Additionally, we did a Kaplan Meier survival analysis in the subset of patients with elevated troponin, comparing survival in patients that received dexamethasone, remdesivir, and ACEI with those that did not.

RESULTS

The mean age was 66 years (range 20-110), troponin elevation was noted in 11.5% of the patients, and 29.9% expired. The patients' age [hazard ratio (HR) = 1.02, P < 0.001], intensive care unit admission (HR = 5.07, P < 0.001), and ventilator use (HR = 0.68, P = 0.02) were significantly associated with mortality. In the subset of patients with elevated troponin, there was no statistically significant difference in survival in those that received remdesivir (0.07), dexamethasone (P = 0.63), or ACEI (P = 0.8) and those that did not.

CONCLUSION

Although elevated troponin in COVID-19 patients has been associated with viral myocarditis and ACE II receptors, conventional viral myocarditis treatment, including antiviral and steroids, and ACEI did not show any effect on mortality in these patients.

Development and Validation of a Prediction Model of the Risk of Pneumonia in Patients with SARS-CoV-2 Infection

Objective

To establish a prediction model of pneumonia risk in SARS-CoV-2-infected patients to reduce unnecessary chest CT scans.

Materials and Methods

The model was constructed based on a retrospective cohort study. We selected SARS-CoV-2 test-positive patients and collected their clinical data and chest CT images from the outpatient and emergency departments of Hunan Provincial People's Hospital, China. Univariate and multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression were utilized to identify predictors of pneumonia risk for patients infected with SARS-CoV-2. These predictors were then incorporated into a nomogram to establish the model. To ensure its performance, the model was evaluated from the aspects of discrimination, calibration, and clinical validity. In addition, a smoothed curve was fitted using a generalized additive model (GAM) to explore the association between the pneumonia grade and the model's predicted probability of pneumonia.

Results

We selected 299 SARS-CoV-2 test-positive patients, of whom 205 cases were in the training cohort and 94 cases were in the validation cohort. Age, CRP natural log-transformed value (InCRP), and monocyte percentage (%Mon) were found to be valid predictors of pneumonia risk. This predictive model achieved good discrimination of AUC in the training and validation cohorts which was 0.7820 (95% CI: 0.7254-0.8439) and 0.8432 (95% CI: 0.7588-0.9151), respectively. At the cut-off value of 0.5, it had a sensitivity and specificity of 70.75% and 66.33% in the training cohort and 76.09% and 73.91% in the validation cohort, respectively. With suitable calibration accuracy shown in calibration curves, decision curve analysis indicated high clinical value in predicting pneumonia probability in SARS-CoV-2-infected patients. The probability of pneumonia predicted by the model was positively correlated with the actual pneumonia classification.

Conclusion

This study has developed a pneumonia risk prediction model that can be utilized for diagnostic purposes in predicting the probability of pneumonia in patients infected with SARS-CoV-2.

Multi-omic longitudinal study reveals immune correlates of clinical course among hospitalized COVID-19 patients

The IMPACC cohort, composed of >1,000 hospitalized COVID-19 participants, contains five illness trajectory groups (TGs) during acute infection (first 28 days), ranging from milder (TG1-3) to more severe disease course (TG4) and death (TG5). Here, we report deep immunophenotyping, profiling of >15,000 longitudinal blood and nasal samples from 540 participants of the IMPACC cohort, using 14 distinct assays. These unbiased analyses identify cellular and molecular signatures present within 72 h of hospital admission that distinguish moderate from severe and fatal COVID-19 disease. Importantly, cellular and molecular states also distinguish participants with more severe disease that recover or stabilize within 28 days from those that progress to fatal outcomes (TG4 vs. TG5). Furthermore, our longitudinal design reveals that these biologic states display distinct temporal patterns associated with clinical outcomes. Characterizing host immune responses in relation to heterogeneity in disease course may inform clinical prognosis and opportunities for intervention.

Inflammatory Response in COVID-19 Depending on the Severity of the Disease and the Vaccination Status

The aim of this study was to analyze the serum concentration of interleukin-6 (IL-6), C-reactive protein (CRP), D-dimer, lactate dehydrogenase (LDH), ferritin, and procalcitonin in COVID-19 patients with different forms of the disease. We performed a prospective cohort study on 137 COVID-19 consecutive patients, divided into four groups according to the severity of the disease as follows: 30 patients in the mild form group, 49 in the moderate form group, 28 in the severe form group, and 30 in the critical form group. The tested parameters were correlated with COVID-19 severity. Significant differences were registered between the form of COVID-19 depending on the vaccination status, between LDH concentrations depending on the virus variant, and in IL-6, CRP, and ferritin concentrations and vaccination status depending on the gender. ROC analysis revealed that D-dimer best predicted COVID-19 severe forms and LDH predicted the virus variant. Our findings confirmed the interdependence relationships observed between inflammation markers in relation to the clinical severity of COVID-19, with all the tested biomarkers increasing in severe and critical COVID-19. IL-6, CRP, ferritin, LDH, and D-dimer were increased in all COVID-19 forms. These inflammatory markers were lower in Omicron-infected patients. The unvaccinated patients developed more severe forms compared to the vaccinated ones, and a higher proportion of them needed hospitalization. D-dimer could predict a severe form of COVID-19, while LDH could predict the virus variant.

Prognostic models in COVID-19 infection that predict severity: a systematic review

Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.

The Immune, Inflammatory and Hematological Response in COVID-19 Patients, According to the Severity of the Disease

INTRODUCTION

The aim of this study was to evaluate the immune and inflammatory responses in COVID-19 patients by dosing specific IgM and IgG total antibodies and interleukin 6, correlating them with the hematological and biochemical blood parameters and comparing them by the form of the disease.

MATERIALS AND METHODS

One hundred twenty-five patients with polymerase chain reaction-confirmed COVID-19, hospitalized between 15.03.2020 and 1.07.2020 in the Clinical Hospital of Infectious Diseases "Sf. Parascheva" Iaşi, were tested by chemiluminescence for the presence of anti-SARS-CoV-2 IgM and IgG and IL-6 in the serum. The results were correlated with the results of the CBC count and serum biochemical parameters detected on the admission day. The patients presented different forms of the disease (asymptomatic, mild, moderate, severe, and critical) according to World Health Organization (WHO) criteria for the clinical management of COVID-19.

RESULTS

The amplitude of the immune response was directly correlated with the form of the disease. In the asymptomatic/mild form patients, the IL-6 and CRP concentrations were significantly higher and eosinophil count was significantly lower compared with the reference interval. In the moderate form, the concentrations of IL-6, CRP, and IgG were significantly higher, compared with the reference interval, while eosinophil count and eGFR were significantly lower. In severe/critical COVID-19 patients, IL-6, CRP, NLR, PLR, glucose, AST, urea, creatinine, and eGFR were significantly higher compared with the reference interval, while eosinophil count was significantly lower. IL-6 boosted in all forms of COVID-19, with a major increase in severe and critical patients. IL-6, neutrophil count, % neutrophils, NLR, PLR, CRP, AST, and urea increased with the severity of the SARS-CoV-2 infection, and the lymphocyte count, % lymphocytes, eosinophil count, % eosinophils, and hemoglobin decreased with the increased severity of COVID-19.

CONCLUSIONS

The amplitude and the moment of appearance of the immune response depended on the form of the disease. IgM generally occurred in the first 14 days of illness, and IgG appeared beginning with the second week of disease. IgG titer increased rapidly until the fourth week of disease and decreased slowly after 4 weeks. The amplitudes of all the tested inflammatory and serological markers depended on the COVID-19 form, increasing somewhat in the moderate forms and even more in the critical ones. The lymphocyte and eosinophil count are able to predict the risk of severe COVID-19.

Clinical characteristics associated with mortality of COVID-19 patients admitted to an intensive care unit of a tertiary hospital in South Africa

BACKGROUND

Over 130 million people have been diagnosed with Coronavirus disease 2019 (COVID-19), and more than one million fatalities have been reported worldwide. South Africa is unique in having a quadruple disease burden of type 2 diabetes, hypertension, human immunodeficiency virus (HIV) and tuberculosis, making COVID-19-related mortality of particular interest in the country. The aim of this study was to investigate the clinical characteristics and associated mortality of COVID-19 patients admitted to an intensive care unit (ICU) in a South African setting.

METHODS AND FINDINGS

We performed a prospective observational study of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection admitted to the ICU of a South African tertiary hospital in Cape Town. The mortality and discharge rates were the primary outcomes. Demographic, clinical and laboratory data were analysed, and multivariable robust Poisson regression model was used to identify risk factors for mortality. Furthermore, Cox proportional hazards regression model was performed to assess the association between time to death and the predictor variables. Factors associated with death (time to death) at p-value < 0.05 were considered statistically significant. Of the 402 patients admitted to the ICU, 250 (62%) died, and another 12 (3%) died in the hospital after being discharged from the ICU. The median age of the study population was 54.1 years (IQR: 46.0-61.6). The mortality rate among those who were intubated was significantly higher at 201/221 (91%). After adjusting for confounding, multivariable robust Poisson regression analysis revealed that age more than 48 years, requiring invasive mechanical ventilation, HIV status, procalcitonin (PCT), Troponin T, Aspartate Aminotransferase (AST), and a low pH on admission all significantly predicted mortality. Three main risk factors predictive of mortality were identified in the analysis using Cox regression Cox proportional hazards regression model. HIV positive status, myalgia, and intubated in the ICU were identified as independent prognostic factors.

CONCLUSIONS

In this study, the mortality rate in COVID-19 patients admitted to the ICU was high. Older age, the need for invasive mechanical ventilation, HIV status, and metabolic acidosis were found to be significant predictors of mortality in patients admitted to the ICU.

The Predictive Score for Patients Hospitalized With COVID-19 in Resource-Limited Settings

Background and aims

The second wave of coronavirus disease 2019 (COVID-19) has been devastating in India and many developing countries. The mortality reported has been 40% higher than in the first wave, overwhelming the nation’s health infrastructure. Despite a better understanding of the disease and established treatment protocols including steroids and heparin, the second wave was disastrous. Subsequent waves have the potential to further cripple healthcare deliveries, also affecting non-COVID-19 care across many developing economies. It is then important to identify and triage high-risk patients to best use the limited resources. Routine tests such as neutrophil and monocyte counts have been identified but have not been successfully validated uniformly, and their utility is still being understood in COVID-19.

Various predictive models that are available require online resources and calculators and additionally await validation across all populations. These, although useful, might not be available or accessible across all institutions. It is then important to identify easy-to-use scores that utilize tests done routinely.

In identifying with this goal, we did a retrospective review of the institutional database to identify potential predictors of intensive care unit (ICU) admission and mortality in patients hospitalized during the second wave who accessed healthcare at our academic setup.

Results

Three predictors of mortality and four predictors of ICU admission were identified. Absolute neutrophil count was a common predictor of both ICU admission and mortality but with two separate cut points. An absolute neutrophil count of >4,200 predicted need for ICU admission (odds ratio (OR): 3.1 (95% confidence interval (CI): 2.0, 4.8)), and >7,200 predicted mortality (adjusted OR: 4.2 (95% CI: 1.9, 9.4)). We observed that a blood urea level greater than 45 was predictive of needing ICU care (adjusted OR: 8.0 (95% CI: 3.7, 17.6)). In our dataset, serum ferritin of >500 was predictive of ICU admission (adjusted OR: 2.7 (95% CI: 1.2, 5.9)). We noted a right shift of partial pressure (p50 is the oxygen tension at which hemoglobin is 50% saturated) (p50c) in SARS-CoV-2 as a predictor of ICU care (OR: 2.6 (95% CI: 1.7, 3.9)) when partial pressure is >26.5. In our analysis, a serum protein of less than 7 g/dL (OR: 2.8 (95% CI: 1.7, 4.4)) was a predictive variable for ICU admission. An LDH value of >675 was predictive of severity with a need for ICU admission (OR: 9.2 (95% CI: 5.4, 15.5)) in our series. We then assigned a score to each of the predictive variables based on the adjusted odds ratio.

Conclusion

We identified a set of easy-to-use predictive variables and scores to recognize the subset of patients hospitalized with COVID-19 with the highest risk of death or clinical worsening requiring ICU care.

Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population

Background

Since the outbreak of COVID-19 pandemic the interindividual variability in the course of the disease has been reported, indicating a wide range of factors influencing it. Factors which were the most often associated with increased COVID-19 severity include higher age, obesity and diabetes. The influence of cytokine storm is complex, reflecting the complexity of the immunological processes triggered by SARS-CoV-2 infection. A modern challenge such as a worldwide pandemic requires modern solutions, which in this case is harnessing the machine learning for the purpose of analysing the differences in the clinical properties of the populations affected by the disease, followed by grading its significance, consequently leading to creation of tool applicable for assessing the individual risk of SARS-CoV-2 infection.

Methods

Biochemical and morphological parameters values of 5,000 patients (Curisin Healthcare (India) were gathered and used for calculation of eGFR, SII index and N/L ratio. Spearman's rank correlation coefficient formula was used for assessment of correlations between each of the features in the population and the presence of the SARS-CoV-2 infection. Feature importance was evaluated by fitting a Random Forest machine learning model to the data and examining their predictive value. Its accuracy was measured as the F1 Score.

Results

The parameters which showed the highest correlation coefficient were age, random serum glucose, serum urea, gender and serum cholesterol, whereas the highest inverse correlation coefficient was assessed for alanine transaminase, red blood cells count and serum creatinine. The accuracy of created model for differentiating positive from negative SARS-CoV-2 cases was 97%. Features of highest importance were age, alanine transaminase, random serum glucose and red blood cells count.

Conclusion

The current analysis indicates a number of parameters available for a routine screening in clinical setting. It also presents a tool created on the basis of these parameters, useful for assessing the individual risk of developing COVID-19 in patients. The limitation of the study is the demographic specificity of the studied population, which might restrict its general applicability.

Development and Validation of a Predictive Nomogram with Age and Laboratory Findings for Severe COVID-19 in Hunan Province, China

Purpose

To identify more objectively predictive factors of severe outcome among patients hospitalized for coronavirus disease 2019 (COVID-19).

Patients and Methods

A retrospective cohort of 479 hospitalized patients diagnosed with COVID-19 in Hunan Province was selected. The prognostic effects of factors such as age and laboratory indicators were analyzed using the Kaplan–Meier method and Cox proportional hazards model. A prognostic nomogram model was established to predict the progression of patients with COVID-19.

Results

A total of 524 patients in Hunan province with COVID-19 from December 2019 to October 2020 were retrospectively recruited. Among them, 479 eligible patients were randomly assigned into the training cohort (n = 383) and validation cohort (n = 96), at a ratio of 8:2. Sixty-eight (17.8%) and 15 (15.6%) patients developed severe COVID-19 after admission in the training cohort and validation cohort, respectively. The differences in baseline characteristics were not statistically significant between the two cohorts with regard to age, sex, and comorbidities (P > 0.05). Multivariable analyses included age, C-reactive protein, fibrinogen, lactic dehydrogenase, neutrophil-to-lymphocyte ratio, urea, albumin-to-globulin ratio, and eosinophil count as predictive factors for patients with progression to severe COVID-19. A nomogram was constructed with sufficient discriminatory power (C index = 0.81), and proper consistency between the prediction and observation, with an area under the ROC curve of 0.81 and 0.86 in the training and validation cohort, respectively.

Conclusion

We proposed a simple nomogram for early detection of patients with non-severe COVID-19 but at high risk of progression to severe COVID-19, which could help optimize clinical care and personalized decision-making therapies.

Clinical Characteristics, Risk Factors for Severity and Pharmacotherapy in Hospitalized COVID-19 Patients in the United Arab Emirates

Data on the clinical characteristics, severity and management of COVID-19 from the Middle East region, especially the United Arab Emirates (UAE), is very limited. We studied the clinical characteristics, laboratory biomarkers, risk factors for severity and pharmacotherapy of hospitalized COVID-19 patients in this single-center, analytical cross-sectional study conducted in a secondary care hospital of the UAE. A total of 585 patients were included in the study (median age, 49 years (IQR, 39−59); 66% male). Age > 45 years (OR = 2.07, 95% CI: 1.04−4.14, p = 0.040), male gender (OR = 3.15, 95% CI: 1.52−6.51, p = 0.002), presentation symptoms such as fever (OR = 3.68, 95% CI:1.34−10.11, p = 0.011) and shortness of breath/dyspnea (OR = 5.36, 95% CI: 2.69−10.67, p < 0.001), Hb < 13 g/dL (OR = 3.17, 95% CI: 1.51−6.65, p = 0.002), neutrophils > 7 × 103/mcL (OR = 4.89, 95% CI: 1.66−14.37, p=0.004), lymphocytes < 1 × 103/mcL (OR = 7.78, 95% CI: 1.01−60.19, p = 0.049), sodium < 135 mmol/L (OR = 5.42, 95% CI: 1.05−27.95, p = 0.044), potassium < 3.6 mmol/L (OR = 3.36, 95% CI: 1.03−11.01, p = 0.045), urea > 6.5 mmol/L (OR = 3.37, 95% CI: 1.69−6.73, p = 0.001) and LDH > 227 IU/L (OR = 6.26, 95% CI: 1.61−24.32, p = 0.008) were independent predictors of the severity of COVID-19. Antivirals (524, 89.6%) and corticosteroids (358, 61.2%) were prescribed for the management of COVID-19. In conclusion, older age, male gender, presentation symptoms such as fever and dyspnea, low hemoglobin, neutrophilia, lymphopenia, hyponatremia, hypokalemia, elevated levels of urea and lactate dehydrogenase were found to be independent risk factors for severe COVID-19. The pharmacotherapy of COVID-19 patients in our study was diverse, and the medications were prescribed based on the clinical condition of the patients.

Organ function biomarker abnormalities, associated factors and disease outcome among hospitalized patients with COVID-19

Background: The aim of this study was to determine the magnitude of abnormal organ function tests and biomarkers in hospitalized patients with confirmed COVID-19 and to define the association among markers of organ failure, disease severity and its outcome in hospitalized COVID-19 patients in Ethiopia. Methods: A prospective cohort study was conducted among COVID-19 patients admitted to Millennium COVID-19 Treatment Center from December 2020 to June 2021. Results: The median age of the 440 study participants was 60.3 ± 1.3 years, and from these 71.3% of patients were male. Disease severity: p-value: 0.032; adjusted odds ratio (AOR) (95% CI): 4.4 (0.022-0.085); and the presence of any co-morbidity; p-value: 0.012; AOR (95% CI): 0.80 (0.47-0.83) was significantly associated with mortality. Aspartate transaminase, alanine transaminase and alkaline phosphatase parameter values of patients overall, were elevated - mainly among critical patients (56.9 ± 57.7, 58.5 ± 63 and 114.6 ± 60, respectively).

Monocyte distribution width as a novel sepsis indicator in COVID-19 patients

BACKGROUND

The severe acute respiratory syndrome coronavirus (SARS-CoV-2) is a highly transmittable virus which causes the novel coronavirus disease (COVID-19). Monocyte distribution width (MDW) is an in-vitro hematological parameter which describes the changes in monocyte size distribution and can indicate progression from localized infection to systemic infection. In this study we evaluated the correlation between the laboratory parameters and available clinical data in different quartiles of MDW to predict the progression and severity of COVID-19 infection.

METHODS

A retrospective analysis of clinical data collected in the Emergency Department of Rashid Hospital Trauma Center-DHA from adult individuals tested for SARS-CoV-2 between January and June 2020. The patients (n = 2454) were assigned into quartiles based on their MDW value on admission. The four groups were analyzed to determine if MDW was an indicator to identify patients who are at increased risk for progression to sepsis.

RESULTS

Our data showed a significant positive correlation between MDW and various laboratory parameters associated with SARS-CoV-2 infection. The study also revealed that MDW ≥ 24.685 has a strong correlation with poor prognosis of COVID-19.

CONCLUSIONS

Monitoring of monocytes provides a window into the systemic inflammation caused by infection and can aid in evaluating the progression and severity of COVID-19 infection.

Clinical features, laboratory characteristics and risk factors for mortality of COVID-19 patients in a secondary hospital in Oman during the first wave of the SARS-CoV-2 pandemic

BACKGROUND

The changing epidemiological profile of the COVID-19 pandemic and the uncertain clinical picture of patients characterise this ongoing and most challenging health event.

OBJECTIVES

To report clinical features, laboratory characteristics, and mortality risk factors among COVID-19 patients admitted to a secondary hospital in Oman.

METHODS

A retrospective study for the first 455 patients admitted with COVID-19 to Rustaq hospital from 12th April, 2020 to 27th September, 2020. A predesigned questionnaire collected data from the hospital medical electronic system.

RESULTS

The mean age was 42.84 (SD = 19.86) years, and the majority of patients were aged 30 to 59 and 60 or above; 207 (45.5%) and 189 (41.5%), respectively. Male patients constituted approximately two-thirds of the subjects. Fever, dyspnea and cough were the most common presenting symptoms (69%, 66%, and 62%, respectively), while comorbidities with diabetes mellitus and hypertension were 47% and 44%, respectively. Bacterial growth was identified at approximately 10%. Bivariate analysis turned out to be significant with a number of factors. However, multivariate analysis showed significance with patients aged over 60 (OR = 7.15, 95% CI 1.99-25.63), dyspnea (OR = 2.83, 95% CI 1.5-5.33), dyslipidemia (OR = 1.93, 95% CI 1.02-3.66) and being bed-ridden (OR = 5.01, 95% CI 1.73-14.44). Durations from onset of symptoms to admission and respiratory distress were lower among patients who died; p = 0.024 and p = 0.001, respectively. Urea, Troponin and LDH may act as potential diagnostic biomarkers for severity or mortality.

CONCLUSIONS

This study identified groups of patients with a higher risk of mortality, with severe disturbance in the laboratory markers while some could act as potential diagnostic biomarkers.

Using decision tree algorithms for estimating ICU admission of COVID-19 patients

Introduction

Materials and methods

Results

Conclusions

The inflammatory biomarkers profile of hospitalized patients with COVID-19 and its association with patient's outcome: A single centered study

Several reports highlighted the central role of inflammation in the pathogenesis of corona virus disease-19 (COVID-19) disease. Also, the hyper-inflammatory response that is triggered by severe acute respiratory syndrom-Covid-2 (SARS-CoV-2) infection was believed to play an essential role in disease severity and adverse clinical course. For that reason, the classical inflammatory markers were proposed as a possible indicator for COVID-19 severity. However, an extensive analysis of the predictive value of inflammatory biomarkers in large patients' cohorts is still limited and critically needed. In this study we investigated the predictive value of the classical inflammatory biomarkers in a patient cohort consists of 541 COVID-19 patients admitted to Al Kuwait Hospital, Dubai, UAE. A detailed analysis of the association between the essential inflammatory markers and clinical characteristics as well as clinical outcome of the patients were made. In addition, the correlation between those markers and a wide range of laboratory biomarkers and incidence of acute organs injury were investigated. Our results showed a significant elevation of many inflammatory markers including white cell count (WBC) count, neutrophils count, C-reactive protein (CRP), D-Dimer, ferritin, procalcitonin (PCT), and lactate dehydrogenase (LDH) levels in patients with more severe illness. Also, our results highlighted that higher levels of those markers can predict worse patient outcome including the need of ventilation, intensive care unit (ICU) admission, multiple organs dysfunction as well as death. In addition, Our results showed that the presence of lymphopenia and lower absolute lymphocyte count (ALC) at the time of admission were associated with severe to critical COVID-19 illness (P<0.0001), presence of acute respiratory distress syndrome (ARDS) (P<0.0001) and the need for ventilation and ICU admission., Moreover, our results showed a strong association between lower ALC count and multiple organs dysfunction and patient's death (P<0.0001). In conclusion, our results highlighted the possible use of classical inflammatory biomarkers at time of admission as a potential predictive marker for more severe clinical course in COVID-19 patients that might need more aggressive therapeutic approach including the need of ventilators and ICU admission. The presence of such predictive markers might improve patient's stratification and help in the direction of the available resources to patients in need, which in turn help in improving our response to the disease pandemic.

Smaller reaction volume of triplex taqman real-time reverse transcription-PCR assays for diagnosing coronavirus disease 2019

Background

Coronavirus disease 2019 (COVID‐19) has had a devastating impact on public health services worldwide. Currently, there are no standard remedies or therapies for COVID‐19. it is important to identify and diagnose COVID‐19 to control the spread. But clinical symptoms of COVID‐19 are very similar to those of other respiratory viruses.

Results

As a result, the diagnosis of COVID‐19 relies heavily on detecting pathogens. We established a bunch of triplex new TaqMan real‐time PCR assays. Three sets of primers and probes (targeting the ORF1ab, N, and E genes, respectively) are poorly consistent with other human coronaviruses and the human influenza virus. The sensitivity of established PCR assays notices as few as 100 copies per PCR of the ORF1ab, N, and E genes. Meanwhile, standard curves concluded from constant PCR reaction all showed glorious linear correlations between Ct values and the polymer loading copy variety (correlation coefficient (R²) of ORF1ab, N, and E genes is 0.996, 0.991, and 0.998, respectively). Surveillance of RNA‐based pseudovirus demonstrated that they were identified to be positive with respect to SARS‐CoV‐2 and that established PCR assays are achievable.

Conclusion

The assays established provide a smaller reaction volume for diagnosing COVID‐19.

Urea-to-Albumin Ratio and In-Hospital Mortality in Severe Pneumonia Patients

Objective

The urea-to-albumin ratio (UAR), as a new marker of the systemic inflammatory response, is associated with the mortality in pneumonia patients. However, the association between the UAR and in-hospital mortality in severe pneumonia (SP) has received little attention.

Methods

In this single-center retrospective cohort study, 212 SP patients in intensive care unit (ICU) from June 1, 2016, to June 1st, 2020, with baseline UAR were enrolled. The primary outcome was in-hospital mortality. The association of UAR with in-hospital mortality was assessed using a multivariable-adjusted Cox model.

Results

Of 212 patients, the median age was 73.0 (61.0, 82.8) years, 70.8% of patients were male, and the APACHE II score was 20.0 (16.0, 26.0). During the hospital period, 101 (47.6%) patients died. In-hospital mortality rates for the lower and higher UAR were 16 (27.6%) and 85 (55.2%), respectively (P < 0.001). Kaplan–Meier analysis revealed that survival rates were significantly different between the two groups (log rank = 13.71, P < 0.001). After adjusted for confounding factors, the higher UAR group was significantly associated with a hazard ratio (HR) for in-hospital mortality of 2.234 (95% confidence interval: 1.146–4.356, P=0.018). Besides, this pattern persisted in subgroup analyses considering sex (HR = 9.380; 95% CI: 2.248–39.138; P=0.002).

Conclusions

Higher UAR levels at the commencement of admission to ICU may be independently associated with increased in-hospital mortality in SP patients.

External validation of prognostic scores for COVID-19: a multicenter cohort study of patients hospitalized in Greater Paris University Hospitals

Purpose

The Coronavirus disease 2019 (COVID-19) has led to an unparalleled influx of patients. Prognostic scores could help optimizing healthcare delivery, but most of them have not been comprehensively validated. We aim to externally validate existing prognostic scores for COVID-19.

Methods

We used “COVID-19 Evidence Alerts” (McMaster University) to retrieve high-quality prognostic scores predicting death or intensive care unit (ICU) transfer from routinely collected data. We studied their accuracy in a retrospective multicenter cohort of adult patients hospitalized for COVID-19 from January 2020 to April 2021 in the Greater Paris University Hospitals. Areas under the receiver operating characteristic curves (AUC) were computed for the prediction of the original outcome, 30-day in-hospital mortality and the composite of 30-day in-hospital mortality or ICU transfer.

Results

We included 14,343 consecutive patients, 2583 (18%) died and 5067 (35%) died or were transferred to the ICU. We examined 274 studies and found 32 scores meeting the inclusion criteria: 19 had a significantly lower AUC in our cohort than in previously published validation studies for the original outcome; 25 performed better to predict in-hospital mortality than the composite of in-hospital mortality or ICU transfer; 7 had an AUC > 0.75 to predict in-hospital mortality; 2 had an AUC > 0.70 to predict the composite outcome.

Conclusion

Seven prognostic scores were fairly accurate to predict death in hospitalized COVID-19 patients. The 4C Mortality Score and the ABCS stand out because they performed as well in our cohort and their initial validation cohort, during the first epidemic wave and subsequent waves, and in younger and older patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00134-021-06524-w.

Temporal Associations of the SARS-CoV-2 NP Antigen and Anti-Spike Total Ig Levels with Laboratory Parameters in a Greek Cohort of Hospitalized COVID-19 Patients

Background

The direct effect of SARS-CoV-2 on the lungs results in increased hospitalization rates of patients with pneumonia. Severe COVID-19 patients often develop ARDS which is associated with poor prognosis. Assessing risk factors for COVID-19 severity is indispensable for implementing and evaluating therapeutic interventions. We investigated the temporal associations between the SARS-CoV-2 antigen (Ag), total Immunoglobulin (Ig) levels, and several laboratory parameters in hospitalized patients with varying degrees of COVID-19 severity.

Methods

The SARS-CoV-2 nucleocapsid protein (NP) and total Ig Spike (S) protein-specific antibodies were determined for each patient with lateral flow assays through repeated sampling every two days. Hematological and biochemical parameters were evaluated at the same time points.

Results

40 Greek COVID-19 patients (31 males, 9 females) with a median age of 59.50 ± 16.21 years were enrolled in the study. The median time from symptom onset to hospitalization was 8.0 ± 4.19 days. A significant negative correlation was observed between the SARS-CoV-2 Ag and total Ig levels. The temporal correlation patterns of the SARS-CoV-2 NP Ag and anti-S total Ig levels with laboratory markers varied among patients with differing degrees of COVID-19 severity. Severe-critical cases had lower SARS-CoV-2 Ag and increased total Ig levels as compared to mild-moderate cases.

Conclusions

Distinct temporal profiles of the SARS-CoV-2 NP Ag and anti-S total Ig levels may distinguish different groups of COVID-19 severity.

The Prognostic Value of ADAMTS-13 and von Willebrand Factor in COVID-19 Patients: Prospective Evaluation by Care Setting

Background: Endothelial dysfunction, coupled with inflammation, induces thrombo-inflammation. In COVID-19, this process is believed to be associated with clinical severity. Von Willebrand factor (VWF), and a disintegrin and metalloproteinase with thrombospondin motifs 13 (ADAMTS-13), are strong markers of endothelial dysfunction. We evaluated the impact of the VWF/ADAMTS-13 fraction on COVID-19 severity and prognosis. Materials and methods: A cohort study including 74 COVID-19 patients, with 22 admitted to the intensive care unit (ICU) and 52 to the medical ward (MW), was carried out. We also evaluated, in a group of 54 patients who were prospectively observed, whether variations in VWF/ADAMTS-13 correlated with the degree of severity and routine blood parameters. Results: A VWF:RCo/ADAMTS-13 fraction above 6.5 predicted in-hospital mortality in the entire cohort. At admission, a VWF:RCo/ADAMTS-13 fraction above 5.7 predicted admission to the ICU. Furthermore, the VWF:RCo/ADAMTS-13 fraction directly correlated with C-reactive protein (CRP) (Spearman r: 0.51, p < 0.0001) and D-dimer (Spearman r: 0.26, p = 0.03). In the prospective cohort, dynamic changes in VWF:RCo/ADAMTS-13 and the CRP concentration were directly correlated (Spearman r, p = 0.0014). This relationship was significant in both groups (ICU: p = 0.006; MW: p = 0.02).Conclusions: The present findings show that in COVID-19, the VWF/ADAMTS-13 fraction predicts in-hospital mortality. The VWF/ADAMTS-13 fraction may be a helpful tool to monitor COVID-19 patients throughout hospitalization.

Red Cell Distribution Width Upon Hospital Admission Predicts Short-Term Mortality in Hospitalized Patients With COVID-19: A Single-Center Experience

Background: Coronavirus disease (COVID-19) was first described at the end of 2019 in China and has since spread across the globe. Red cell distribution width (RDW) is a potent prognostic marker in several medical conditions and has recently been suggested to be of prognostic value in COVID-19.

Methods: This retrospective, observational study of consecutive patients with COVID-19 was conducted from March 12, 2020 to December 4, 2020 in the Wilhelminenhospital, Vienna, Austria. RDWlevels on admission were collected and tested for their predictive value of 28-day mortality.

Results: A total of 423 eligible patients with COVID-19 were included in the final analyses and 15.4% died within 28 days (n = 65). Median levels of RDWwere significantly higher in non-survivors compared to survivors [14.6% (IQR, 13.7–16.3) vs. 13.4% (IQR, 12.7– 14.4), P < 0.001]. Increased RDW was a significant predictor of 28-day mortality [crude odds ratio (OR) 1.717, 95% confidence interval (CI) 1.462–2.017; P = < 0.001], independent of clinical confounders, comorbidities and established prognostic markers of COVID-19 (adjusted OR of the final model 1.368, 95% CI 1.126–1.662; P = 0.002). This association remained consistent upon sub-group analysis. Our study data also demonstrate that RDW levels upon admission for COVID-19 were similar to previously recorded, non-COVID-19 associated RDW levels [14.2% (IQR, 13.3–15.7) vs. 14.0% [IQR, 13.2–15.1]; P = 0.187].

Conclusions: In this population, RDWwas a significant, independent prognostic marker of short-term mortality in patients with COVID-19.

Clinical and Laboratory Profile of Hospitalized Symptomatic COVID-19 Patients: Case Series Study From the First COVID-19 Center in the UAE

INTRODUCTION

COVID-19 is raising with a second wave threatening many countries. Therefore, it is important to understand COVID-19 characteristics across different countries.

METHODS

This is a cross-sectional descriptive study of 525 hospitalized symptomatic COVID-19 patients, from the central federal hospital in Dubai-UAE during period of March to August 2020.

RESULTS

UAE's COVID-19 patients were relatively young; mean (SD) of the age 49(15) years, 130 (25%) were older than 60 and 4 (<1%) were younger than 18 years old. Majority were male(47; 78%). The mean (SD) BMI was 29 (6) kg/m². While the source of contracting COVID-19 was not known in 369 (70%) of patients, 29 (6%) reported travel to overseas-country and 127 (24%) reported contact with another COVID-19 case/s. At least one comorbidity was present in 284 (54%) of patients and 241 (46%) had none. The most common comorbidities were diabetes (177; 34%) and hypertension (166; 32%). The mean (SD) of symptoms duration was 6 (3) days. The most common symptoms at hospitalization were fever (340; 65%), cough (296; 56%), and shortness of breath (SOB) (243; 46%). Most of the laboratory values were within normal range, but (184; 35%) of patients had lymphopenia, 43 (8%) had neutrophilia, and 116 (22%) had prolong international normalized ratio (INR), and 317 (60%) had high D-dimer. Chest x ray findings of consolidation was present in 334 (64%) of patients and CT scan ground glass appearance was present in 354 (68%). Acute cardiac injury occurred in 124 (24%), acute kidney injury in 111 (21%), liver injury in 101 (19%), ARDS in 155 (30%), acidosis in 118 (22%), and septic shock in 93 (18%). Consequently, 150 (29%) required ICU admission with 103 (20%) needed mechanical ventilation.

CONCLUSIONS

The study demonstrated the special profile of COVID-19 in UAE. Patients were young with diabetes and/or hypertension and associated with severe infection as shown by various clinical and laboratory data necessitating ICU admission.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE

To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease.

DESIGN

Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.

DATA SOURCES

PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.

STUDY SELECTION

Studies that developed or validated a multivariable covid-19 related prediction model.

DATA EXTRACTION

At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).

RESULTS

37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models.

CONCLUSION

Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.

SYSTEMATIC REVIEW REGISTRATION

Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.

READERS' NOTE

This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.