Risk factors for developing into critical COVID-19 patients in Wuhan, China: A multicenter, retrospective, cohort study

Dynamic heterogeneity in COVID-19: Insights from a mathematical model

Critical illness, such as severe COVID-19, is heterogenous in presentation and treatment response. However, it remains possible that clinical course may be influenced by dynamic and/or random events such that similar patients subject to similar injuries may yet follow different trajectories. We deployed a mechanistic mathematical model of COVID-19 to determine the range of possible clinical courses after SARS-CoV-2 infection, which may follow from specific changes in viral properties, immune properties, treatment modality and random external factors such as initial viral load. We find that treatment efficacy and baseline patient or viral features are not the sole determinant of outcome. We found patients with enhanced innate or adaptive immune responses can experience poor viral control, resolution of infection or non-infectious inflammatory injury depending on treatment efficacy and initial viral load. Hypoxemia may result from poor viral control or ongoing inflammation despite effective viral control. Adaptive immune responses may be inhibited by very early effective therapy, resulting in viral load rebound after cessation of therapy. Our model suggests individual disease course may be influenced by the interaction between external and patient-intrinsic factors. These data have implications for the reproducibility of clinical trial cohorts and timing of optimal treatment.

Early predictors of intensive care unit admission among COVID-19 patients in Qatar

Background

COVID-19 is associated with significant morbidity and mortality. This study aimed to explore the early predictors of intensive care unit (ICU) admission among patients with COVID-19.

Methods

This was a case-control study of adult patients with confirmed COVID-19. Cases were defined as patients admitted to ICU during the period February 29-May 29, 2020. For each case enrolled, one control was matched by age and gender.

Results

A total of 1,560 patients with confirmed COVID-19 were included. Each group included 780 patients with a predominant male gender (89.7%) and a median age of 49 years (interquartile range = 18). Predictors independently associated with ICU admission were cardiovascular disease (adjusted odds ratio (aOR) = 1.64, 95% confidence interval (CI): 1.16-2.32, p = 0.005), diabetes (aOR = 1.52, 95% CI: 1.08-2.13, p = 0.016), obesity (aOR = 1.46, 95% CI: 1.03-2.08, p = 0.034), lymphopenia (aOR = 2.69, 95% CI: 1.80-4.02, p < 0.001), high AST (aOR = 2.59, 95% CI: 1.53-4.36, p < 0.001), high ferritin (aOR = 1.96, 95% CI: 1.40-2.74, p < 0.001), high CRP (aOR = 4.09, 95% CI: 2.81-5.96, p < 0.001), and dyspnea (aOR = 2.50, 95% CI: 1.77-3.54, p < 0.001).

Conclusion

Having cardiovascular disease, diabetes, obesity, lymphopenia, dyspnea, and increased AST, ferritin, and CRP were independent predictors for ICU admission in patients with COVID-19.

Exploring the Relationship of Comorbidities, Smoking Status, HRCT Findings With COVID-19 Disease Severity and Outcomes

INTRODUCTION

Coronavirus disease 2019 (COVID-19) is a serious illness that can affect multiple organs including the lungs. The COVID-mortality risk is attributed to the quick transmission of the virus, the severity of disease, and preclinical risk factors, such as the presence of comorbidities. High-resolution computed tomography (HRCT) can predict disease severity in COVID-19 patients.

METHODOLOGY

This was a retrospective cohort study in which data were obtained from COVID centers at tertiary care hospitals in Azad Jammu and Kashmir. Details of clinical characteristics and HRCT findings along with details of smoking and comorbid history were obtained.

RESULTS

Fever at hospital admission, HRCT findings, and having a partner predicted disease severity showed a significant p-value of <0.05. Old age and living in a combined household were associated with severe outcomes (p<0.05). Symptoms of shortness of breath (SOB) on hospital admission could predict the need for ICU admission in COVID-19 patients.

CONCLUSION

HRCT has a good predictive value for disease severity in patients with COVID-19, and old age is a risk factor. Although, limited associations were established in the analysis, in this study hyperlipidemia and hypertension significantly affected the course of disease. Further studies should be done to explore the relationship.

Asthma and COPD as co-morbidities in patients hospitalised with Covid-19 disease: a global systematic review and meta-analysis

BACKGROUND

Factors predisposing to increased mortality with COVID-19 infection have been identified as male sex, hypertension, obesity, and increasing age. Early studies looking at airway diseases gave some contradictory results. The purpose of our study was to determine global variation in studies in patients hospitalized with COVID-19 in the prevalence of COPD and asthma; and to determine whether the presence of asthma or COPD affected mortality in the same hospital population.

METHODS

A systematic review and meta-analysis of the published literature of COPD and asthma as co-morbidities in patients hospitalized with COVID-19 was performed, looking firstly at the prevalence of these diseases in patients hospitalized with COVID-19, and secondly at the relative risk of death from any cause for patients with asthma or COPD.

RESULTS

Prevalence of both airway diseases varied markedly by region, making meaningful pooled global estimates of prevalence invalid and not of clinical utility. For individual studies, the interquartile range for asthma prevalence was 4.21 to 12.39%, and for COPD, 3.82 to 11.85%. The relative risk of death with COPD for patients hospitalized with COVID-19 was 1.863 (95% CI 1.640-2.115), while the risk with asthma was 0.918 (95% CI 0.767 to 1.098) with no evidence of increased mortality.

CONCLUSIONS

For asthma and COPD, prevalence in patients hospitalized with COVID-19 varies markedly by region. We found no evidence that asthma predisposed to increased mortality in COVID-19 disease. For COPD, there was clear evidence of an association with increased mortality.

TRIAL REGISTRATION

The trial was registered with PROSPERO: registration number CRD42021289886.

Predicting COVID-19 prognosis in hospitalized patients based on early status

IMPORTANCE

COVID-19 remains the fourth leading cause of death in the United States. Predicting COVID-19 patient prognosis is essential to help efficiently allocate resources, including ventilators and intensive care unit beds, particularly when hospital systems are strained. Our PLABAC and PRABLE models are unique because they accurately assess a COVID-19 patient's risk of death from only age and five commonly ordered laboratory tests. This simple design is important because it allows these models to be used by clinicians to rapidly assess a patient's risk of decompensation and serve as a real-time aid when discussing difficult, life-altering decisions for patients. Our models have also shown generalizability to external populations across the United States. In short, these models are practical, efficient tools to assess and communicate COVID-19 prognosis.

Viral and immunologic evaluation of smokers with severe COVID-19

Smoking negatively affects B cell function and immunoglobulin levels, but it is unclear if this immune dysfunction contributes to the risk of severe COVID-19 in smokers. We evaluated binding IgM, IgA and IgG antibodies to spike and receptor binding domain antigens, and used a pseudovirus assay to quantify neutralization titers in a set of 27 patients with severe COVID-19. We found no significant differences between binding and neutralization antibody responses for people with a smoking history and people who never smoked. High plasma viral load, but not antibody titers, was linked to an increased risk of death. Humoral immune dysfunction was not a major driver of severe COVID-19 in smokers.

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.

Modelling the economic burden of SARS-CoV-2 infection in health care workers in four countries

Health care workers (HCWs) experienced greater risk of SARS-CoV-2 infection during the COVID-19 pandemic. This study applies a cost-of-illness (COI) approach to model the economic burden associated with SARS-CoV-2 infections among HCWs in five low- and middle-income sites (Kenya, Eswatini, Colombia, KwaZulu-Natal province, and Western Cape province of South Africa) during the first year of the pandemic. We find that not only did HCWs have a higher incidence of COVID-19 than the general population, but in all sites except Colombia, viral transmission from infected HCWs to close contacts resulted in substantial secondary SARS-CoV-2 infection and death. Disruption in health services as a result of HCW illness affected maternal and child deaths dramatically. Total economic losses attributable to SARS-CoV-2 infection among HCWs as a share of total health expenditure ranged from 1.51% in Colombia to 8.38% in Western Cape province, South Africa. This economic burden to society highlights the importance of adequate infection prevention and control measures to minimize the risk of SARS-CoV-2 infection in HCWs.

Crosstalk between COVID-19 Infection and Kidney Diseases: A Review on the Metabolomic Approaches

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19, a respiratory disorder. Various organ injuries have been reported in response to this virus, including kidney injury and, in particular, kidney tubular injury. It has been discovered that infection with the virus does not only cause new kidney disease but also increases treatment difficulty and mortality rates in people with kidney diseases. In individuals hospitalized with COVID-19, urinary metabolites from several metabolic pathways are used to distinguish between patients with acute kidney injury (AKI) and those without. This review summarizes the pathogenesis, pathophysiology, treatment strategies, and role of metabolomics in relation to AKI in COVID-19 patients. Metabolomics is likely to play a greater role in predicting outcomes for patients with kidney disease and COVID-19 with varying levels of severity in the near future as data on metabolic profiles expand rapidly. Here, we also discuss the correlation between COVID-19 and kidney diseases and the available metabolomics approaches.

Limited permissibility of ENL-R and Mv-1-Lu mink cell lines to SARS-CoV-2

The SARS-CoV-2 pandemic started in the end of 2019 in Wuhan, China, which highlighted the scenario of frequent cross-species transmission events. From the outbreak possibly initiated by viral spill-over into humans from an animal reservoir, now we face the human host moving globally while interacting with domesticated and peridomestic animals. The emergence of a new virus into the ecosystem leads to selecting forces and species-specific adaptations. The adaptation of SARS-CoV-2 to other animals represents a risk to controlling the dissemination of this coronavirus and the emergence of new variants. Since 2020, several mink farms in Europe and the United States have had SARS-CoV-2 outbreaks with human–mink and mink–human transmission, where the mink-selected variants possibly hold evolutionary concerning advantages. Here we investigated the permissibility of mink lung-derived cells using two cell lines, Mv-1-Lu and ENL-R, against several lineages of SARS-CoV-2, including some classified as variants of concern. The viral release rate and the infectious titers indicate that these cells support infections by different SARS-CoV-2 lineages. The viral production occurs in the first few days after infection with the low viral release by these mink cells, which is often absent for the omicron variant for lung cells. The electron microscopy reveals that during the viral replication cycle, the endomembrane system of the mink-host cell undergoes typical changes while the viral particles are produced, especially in the first days of infection. Therefore, even if limited, mink lung cells may represent a selecting source for SARS-CoV-2 variants, impacting their transmissibility and pathogenicity and making it difficult to control this new coronavirus.

Time from symptom onset to severe COVID-19 and risk factors among patients in Southern Ethiopia: a survival analysis

Objective

This study aimed to assess the time to severe coronavirus disease 2019 (COVID-19) and risk factors among confirmed COVID-19 cases in Southern Ethiopia.

Method

This two-center retrospective cohort study involved patients with confirmed COVID-19 from 1 October 2020 to 30 September 2021. Kaplan–Meier graphs and log-rank tests were used to determine the pattern of COVID-19 severity among categories of variables. Bivariable and multivariable Cox proportional regression models were used to identify the risk factors of severe COVID-19.

Results

Four hundred thirteen patients with COVID-19 with a mean age of 41.9 ± 15.3 years were involved in the study. There were 194 severe cases (46.9.1%), including 77 (39.6%) deaths. The median time from symptom onset to severe COVID-19 was 8 days (interquartile range: 7–12 days). The risk factors for severe COVID-19 were age >65 (adjusted hazard ratio [AHR] = 2.65, 95% confidence interval [95%CI]: 1.02, 3.72), cough (AHR = 1.59, 95%CI: 1.39, 2.84), chest pain (AHR = 1.47, 95%CI: 1.34, 2.66), headache (AHR = 2.04, 95%CI: 1.43, 2.88), comorbidity (AHR = 1.3, 95%CI: 1.01, 2.04), asthma (AHR = 1.6. 95%CI: 1.04, 2.24), and symptom onset to admission more than 5 days (AHR = 0.48, 95%CI: 0.34, 0.68).

Conclusion

Patients with symptoms and comorbidities should be closely monitored.

Elevated Levels of Pleiotropic Interleukin-6 (IL-6) and Interleukin-10 (IL-10) are Critically Involved With the Severity and Mortality of COVID-19: An Updated Longitudinal Meta-Analysis and Systematic Review on 147 Studies

Objectives

Disruption in the natural immune reaction due to SARS-CoV-2 infection can initiate a potent cytokine storm among COVID-19 patients. An elevated level of IL-6 and IL-10 during a hyperinflammatory state plays a vital role in increasing the risk of severity and mortality. In this study, we aimed to evaluate the potential of circulating IL-6 and IL-10 levels as biomarkers for detecting the severity and mortality of COVID-19.

Methods

This study was conducted according to the Cochrane Handbook and PRISMA guidelines. Authorized databases were searched to extract suitable studies using specific search terms. RevMan 5.4 was applied for performing the meta-analysis. Mean differences in IL-6 and IL-10 levels were calculated among COVID-19 patients via a random-effects model. NOS scoring, publication bias and sensitivity analyses were checked to ensure study quality.

Results

A total of 147 studies were selected, with 31 909 COVID-19 patients under investigation. In the severity analysis, the mean concentration of IL-6 was significantly higher in the severe COVID-19 cases than in the non-severe cases (MD: 19.98; P < .001; 95% CI: 17.56, 22.40). Similar result was observed for IL-10 mean concentration in severe COVID-19 cases (MD: 1.35; P < .001; 95% CI: 0.90, 1.80). In terms of mortality analysis, circulating IL-6 showed sharp elevation in the deceased patients (MD: 42.11; P < .001; 95% CI: 36.86, 47.36). IL-10 mean concentration was higher in the dead patients than in the survived patients (MD: 4.79; P < .001; 95% CI: 2.83, 6.75). Publication bias was not found except for comparing IL-6 levels with disease severity. Sensitivity analysis also reported no significant deviation from the pooled outcomes.

Conclusions

Elevated levels of circulating IL-6 and IL-10 signifies worsening of COVID-19. To monitor the progression of SARS-CoV-2 infection, IL-6 and IL-10 should be considered as potential biomarkers for severity and mortality detection in COVID-19.

Systematic review registration

INPLASY registration number: INPLASY202240046.

SARS-CoV-2 RNA copy number is a factor associated with the mortality of COVID-19 and improves the predictive performance of mortality in severe cases

Factors associated with mortality are important for the treatment of coronavirus disease 2019 (COVID-19). The polymerase chain reaction (PCR) test is the gold standard for diagnosing COVID-19 and reflects the viral load in the upper respiratory tract. This study enrolled 523 patients. Four hundred forty-one and 75 patients were performed PCR testing in nasopharyngeal swabs and sputum specimens, respectively, within 20 days from the onset. We investigated the association between RNA copy number and the severity and mortality of COVID-19, and its effect on the predictive performance for the severity and mortality. RNA copy numbers in nasopharyngeal swabs were higher in the non-survivor group than in the survivor group. Multivariate logistic regression analysis identified that the high RNA copy number (≥ 9 log₁₀ /swab) in nasopharyngeal swabs was a factor associated with mortality (odds ratio, 4.50; 95% confidence interval, 1.510 - 13.100; p = 0.008). Furthermore, in severe cases, adding RNA copy number (≥ 9 log₁₀ /swab), which was adjusted by duration from onset to PCR, improved mortality predictive performance based on known factors. The RNA copy number is a factor associated with the mortality of COVID-19 and can improve the predictive performance of mortality in severe cases.

Comparison of Pneumonia Severity Indices, qCSI, 4C-Mortality Score and qSOFA in Predicting Mortality in Hospitalized Patients with COVID-19 Pneumonia

This is a retrospective and observational study on 1511 patients with SARS-CoV-2, who were diagnosed with COVID-19 by real-time PCR testing and hospitalized due to COVID-19 pneumonia. 1511 patients, 879 male (58.17%) and 632 female (41.83%) with a mean age of 60.1 ± 14.7 were included in the study. Survivors and non-survivors groups were statistically compared with respect to survival, discharge, ICU admission and in-hospital death. Although gender was not statistically significant different between two groups, 80 (60.15%) of the patients who died were male. Mean age was 72.8 ± 11.8 in non-survivors vs. 59.9 ± 14.7 in survivors (p < 0.001). Overall in-hospital mortality was found to be 8.8% (133/1511 cases), and overall ICU admission was 10.85% (164/1511 cases). The PSI/PORT score of the non-survivors group was higher than that of the survivors group (144.38 ± 28.64 versus 67.17 ± 25.63, p < 0.001). The PSI/PORT yielding the highest performance was the best predictor for in-hospital mortality, since it incorporates the factors as advanced age and comorbidity (AUROC 0.971; % 95 CI 0.961−0.981). The use of A-DROP may also be preferred as an easier alternative to PSI/PORT, which is a time-consuming evaluation although it is more comprehensive.

Survival Time to COVID-19 Severity and Its Predictors in South Gondar Zone, North-West Ethiopia: A Prospective Cohort Study

Background

Methods

Results

Conclusion and Recommendation

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.

A look back at the first wave of COVID-19 in China: A systematic review and meta-analysis of mortality and health care resource use among severe or critical patients

Background

To investigate the mortality and health care resource use among patients with severe or critical coronavirus disease of 2019 (COVID-19) in the first wave of pandemic in China.

Methods

We performed a systematic review and meta-analysis to investigate the mortality, discharge rate, length of hospital stay, and use of invasive ventilation in severe or critical COVID-19 cases in China. We searched electronic databases for studies from China with no restrictions on language or interventions patients received. We screened records, extracted data and assessed the quality of included studies in duplicate. We performed the meta-analysis using random-effect models through a Bayesian framework. Subgroup analyses were conducted to examine studies by disease severity, study location and patient enrolment start date. We also performed sensitivity analysis using various priors, and assessed between-study heterogeneity and publication bias for the primary outcomes.

Results

Out of 6,205 titles and abstracts screened, 500 were reviewed in full text. A total of 42 studies were included in the review, of which 95% were observational studies (n = 40). The pooled 28-day and 14-day mortalities among severe or critical patients were 20.48% (7,136 patients, 95% credible interval (CrI), 13.11 to 30.70) and 10.83% (95% CrI, 6.78 to 16.75), respectively. The mortality declined over time and was higher in patients with critical disease than severe cases (1,235 patients, 45.73%, 95% CrI, 22.79 to 73.52 vs. 3,969 patients, 14.90%, 95% CrI, 4.70 to 39.57) and patients in Hubei compared to those outside Hubei (6,719 patients, 26.62%, 95% CrI, 13.11 to 30.70 vs. 244 patients, 5.88%, 95% CrI 2.03 to 14.11). The length of hospital stay was estimated at 18.48 days (6,847 patients, 95% CrI, 17.59 to 21.21), the 28-day discharge rate was 50.48% (3,645 patients, 95% CrI, 26.47 to 79.53), and the use of invasive ventilation rate was 13.46% (4,108 patients, 95% CrI, 7.61 to 22.31).

Conclusions

Our systematic review and meta-analysis found high mortality among severe and critical COVID-19 cases. Severe or critical COVID-19 cases consumed a large amount of hospital resources during the outbreak.

Current utilization of interferon alpha for the treatment of coronavirus disease 2019: A comprehensive review

Recent studies have identified an association between perturbed type I interferon (IFN) responses and the severity of coronavirus disease 2019 (COVID-19). IFNα intervention may normalize the dysregulated innate immunity of COVID-19. However, details regarding its utilization and therapeutic evidence have yet to be systematically evaluated. The aim of this comprehensive review was to summarize the current utilization of IFNα for COVID-19 treatment and to explore the evidence on safety and efficacy. A comprehensive review of clinical studies in the literature prior to December 1st_, 2021, was performed to identify the current utilization of IFNα, which included details on the route of administration, the number of patients who received the treatment, the severity at the initiation of treatment, age range, the time from the onset of symptoms to treatment, dose, frequency, and duration as well as safety and efficacy. Encouragingly, no evidence was found against the safety of IFNα treatment for COVID-19. Early intervention, either within five days from the onset of symptoms or at hospital admission, confers better clinical outcomes, whereas late intervention may result in prolonged hospitalization.

Therapeutic implications of ongoing alveolar viral replication in COVID-19

In patients with moderate-to-severe COVID-19 pneumonia, an aberrant post-viral alveolitis with excessive inflammatory responses and immunothrombosis underpins use of immunomodulatory therapy (eg, corticosteroids and interleukin-6 receptor antagonism). By contrast, immunosuppression in individuals with mild COVID-19 who do not require oxygen therapy or in those with critical disease undergoing prolonged ventilation is of no proven benefit. Furthermore, a window of opportunity is thought to exist for timely immunosuppression in patients with moderate-to-severe COVID-19 pneumonia shortly after clinical presentation. In this Viewpoint, we explore the shortcomings of a universal immunosuppression approach in patients with moderate-to-severe COVID-19 due to disease heterogeneity related to ongoing SARS-CoV-2 replication, which can manifest as RNAaemia in some patients treated with immunotherapy. By contrast, immunomodulatory therapy has overall benefits in patients with rapid SARS-CoV-2 clearance, via blunting of multifaceted, excessive innate immune responses in the lungs, potentially uncontrolled T-cell responses, possible autoimmune responses, and immunothrombosis. We highlight this therapeutic dichotomy to better understand the immunopathology of moderate-to-severe COVID-19, particularly the role of RNAaemia, and to refine therapy choices.

Behavioural response to heterogeneous severity of COVID-19 explains temporal variation of cases among different age groups

Together with seasonal effects inducing outdoor or indoor activities, the gradual easing of prophylaxis caused second and third waves of SARS-CoV-2 to emerge in various countries. Interestingly, data indicate that the proportion of infections belonging to the elderly is particularly small during periods of low prevalence and continuously increases as case numbers increase. This effect leads to additional stress on the health care system during periods of high prevalence. Furthermore, infections peak with a slight delay of about a week among the elderly compared to the younger age groups. Here, we provide a mechanistic explanation for this phenomenology attributable to a heterogeneous prophylaxis induced by the age-specific severity of the disease. We model the dynamical adoption of prophylaxis through a two-strategy game and couple it with an SIR spreading model. Our results also indicate that the mixing of contacts among the age groups strongly determines the delay between their peaks in prevalence and the temporal variation in the distribution of cases. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.

COVID-19 and chronic kidney disease: an updated overview of reviews

BACKGROUND

Coronavirus disease (COVID-19) has resulted in the death of more than 3.5 million people worldwide. While COVID-19 mostly affects the lungs, different comorbidities can have an impact on its outcomes. We performed an overview of reviews to assess the effect of Chronic Kidney Disease (CKD) on contracting COVID-19, hospitalization, mortality, and disease severity.

METHODS

We searched published and preprint databases. We updated the reviews by searching for primary studies published after August 2020, and prioritized reviews that are most updated and of higher quality using the AMSTAR tool.

RESULTS

We included 69 systematic reviews and 66 primary studies. Twenty-eight reviews reported on the prevalence of CKD among patients with COVID-19, which ranged from 0.4 to 49.0%. One systematic review showed an increased risk of hospitalization in patients with CKD and COVID-19 (RR = 1.63, 95% CI 1.03-2.58) (Moderate certainty). Primary studies also showed a statistically significant increase of hospitalization in such patients. Thirty-seven systematic reviews assessed mortality risk in patients with CKD and COVID-19. The pooled estimates from primary studies for mortality in patients with CKD and COVID-19 showed a HR of 1.48 (95% CI 1.33-1.65) (Moderate certainty), an OR of 1.77 (95% CI 1.54-2.02) (Moderate certainty) and a RR of 1.6 (95% CI 0.88-2.92) (Low certainty).

CONCLUSIONS

Our review highlights the impact of CKD on the poor outcomes of COVID-19, underscoring the importance of identifying strategies to prevent COVID-19 infection among patients with CKD.

Diagnostic and prognostic value of Sepsis-Induced coagulopathy and International Society on Thrombosis and Hemostasis scoring systems in COVID-19-associated disseminated intravascular coagulopathy

BACKGROUND

The coronavirus disease 2019 (COVID-19) presents various phenotypes from asymptomatic involvement to death. Disseminated intravascular coagulopathy (DIC) is among the poor prognostic complications frequently observed in critical illness. To improve mortality, a timely diagnosis of DIC is essential. The International Society on Thrombosis and Hemostasis (ISTH) introduced a scoring system to detect overt DIC (score ≥5) and another category called sepsis-induced coagulopathy (SIC) to identify the initial stages of DIC (score ≥4). This study aimed to determine whether clinicians used these scoring systems while assessing COVID-19 patients and the role of relevant biomarkers in disease severity and outcome.

MATERIALS AND METHODS

An exhaustive search was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses, using Medline, Embase, Cochrane, CINAHL, and PubMed until August 2020. Studies considering disease severity or outcome with at least two relevant biomarkers were included. For all studies, the definite, maximum, and minimum ISTH/SIC scores were calculated.

RESULTS

A total of 37 papers and 12,463 cases were reviewed. Studies considering ISTH/SIC criteria to detect DIC suggested a higher rate of ISTH ≥5 and SIC ≥4 in severe cases and nonsurvivors compared with nonsevere cases and survivors. The calculated ISTH scores were dominantly higher in severe infections and nonsurvivors. Elevated D-dimer was the most consistent abnormality on admission.

CONCLUSION

Higher ISTH and SIC scores positively correlate with disease severity and death. In addition, more patients with severe disease and nonsurvivors met the ISTH and SIC scores for DIC. Given the high prevalence of coagulopathy in COVID-19 infection, dynamic monitoring of relevant biomarkers in the form of ISTH and SIC scoring systems is of great importance to timely detect DIC in suspicious patients.

Modelling the cost-effectiveness of essential and advanced critical care for COVID-19 patients in Kenya

BACKGROUND

Case management of symptomatic COVID-19 patients is a key health system intervention. The Kenyan government embarked to fill capacity gaps in essential and advanced critical care (ACC) needed for the management of severe and critical COVID-19. However, given scarce resources, gaps in both essential and ACC persist. This study assessed the cost-effectiveness of investments in essential and ACC to inform the prioritisation of investment decisions.

METHODS

We employed a decision tree model to assess the incremental cost-effectiveness of investment in essential care (EC) and investment in both essential and ACC (EC +ACC) compared with current healthcare provision capacity (status quo) for COVID-19 patients in Kenya. We used a health system perspective, and an inpatient care episode time horizon. Cost data were obtained from primary empirical analysis while outcomes data were obtained from epidemiological model estimates. We used univariate and probabilistic sensitivity analysis to assess the robustness of the results.

RESULTS

The status quo option is more costly and less effective compared with investment in EC and is thus dominated by the later. The incremental cost-effectiveness ratio of investment in essential and ACC (EC+ACC) was US$1378.21 per disability-adjusted life-year averted and hence not a cost-effective strategy when compared with Kenya's cost-effectiveness threshold (US$908).

CONCLUSION

When the criterion of cost-effectiveness is considered, and within the context of resource scarcity, Kenya will achieve better value for money if it prioritises investments in EC before investments in ACC. This information on cost-effectiveness will however need to be considered as part of a multicriteria decision-making framework that uses a range of criteria that reflect societal values of the Kenyan society.

Development and validation of a prognostic model for early triage of patients diagnosed with COVID-19

We developed a tool to guide decision-making for early triage of COVID-19 patients based on a predicted prognosis, using a Korean national cohort of 5,596 patients, and validated the developed tool with an external cohort of 445 patients treated in a single institution. Predictors chosen for our model were older age, male sex, subjective fever, dyspnea, altered consciousness, temperature ≥ 37.5 °C, heart rate ≥ 100 bpm, systolic blood pressure ≥ 160 mmHg, diabetes mellitus, heart disease, chronic kidney disease, cancer, dementia, anemia, leukocytosis, lymphocytopenia, and thrombocytopenia. In the external validation, when age, sex, symptoms, and underlying disease were used as predictors, the AUC used as an evaluation metric for our model's performance was 0.850 in predicting whether a patient will require at least oxygen therapy and 0.833 in predicting whether a patient will need critical care or die from COVID-19. The AUCs improved to 0.871 and 0.864, respectively, when additional information on vital signs and blood test results were also used. In contrast, the protocols currently recommended in Korea showed AUCs less than 0.75. An application for calculating the prognostic score in COVID-19 patients based on the results of this study is presented on our website ( https://nhimc.shinyapps.io/ih-psc/ ), where the results of the validation ongoing in our institution are periodically updated.

Impact of metabolic and cardiovascular disease on COVID-19 mortality: A systematic review and meta-analysis

BACKGROUND AND AIMS

This meta-analysis aims to highlight the impact of cardio-metabolic comorbidities on COVID-19 severity and mortality.

METHODS

A thorough search on major online databases was done for studies describing the clinical outcomes of COVID-19 patients. We used random-effects model to compute pooled estimates for critical or fatal disease.

RESULTS

A total of 20,475 patients from 33 eligible studies were included. Maximum risk of development of critical or fatal COVID-19 disease was seen in patients with underlying cardiovascular disease [OR: 3.44, 95% CI: 2.65-4.48] followed by chronic lung disease, hypertension and diabetes mellitus. Of the total cases, 64% had one of the four comorbidities with the most prevalent being hypertension with a pooled prevalence of 27%.

CONCLUSIONS

Presence of comorbidities like cardiovascular disease, chronic lung disease, hypertension and diabetes mellitus led to a higher risk of development of critical or fatal COVID-19 disease, with maximum risk seen with underlying cardiovascular disease.

Chronic Diseases as a Predictor for Severity and Mortality of COVID-19: A Systematic Review With Cumulative Meta-Analysis

Introduction: Given the ongoing coronavirus disease 2019 (COVID-19) pandemic and the consequent global healthcare crisis, there is an urgent need to better understand risk factors for symptom deterioration and mortality among patients with COVID-19. This systematic review aimed to meet the need by determining the predictive value of chronic diseases for COVID-19 severity and mortality.

Methods: We searched PubMed, Embase, Web of Science, and Cumulative Index to Nursing and Allied Health Complete to identify studies published between December 1, 2019, and December 31, 2020. Two hundred and seventeen observational studies from 26 countries involving 624,986 patients were included. We assessed the risk of bias of the included studies and performed a cumulative meta-analysis.

Results: We found that among COVID-19 patients, hypertension was a very common condition and was associated with higher severity, intensive care unit (ICU) admission, acute respiratory distress syndrome, and mortality. Chronic obstructive pulmonary disease was the strongest predictor for COVID-19 severity, admission to ICU, and mortality, while asthma was associated with a reduced risk of COVID-19 mortality. Patients with obesity were at a higher risk of experiencing severe symptoms of COVID-19 rather than mortality. Patients with cerebrovascular disease, chronic liver disease, chronic renal disease, or cancer were more likely to become severe COVID-19 cases and had a greater probability of mortality.

Conclusions: COVID-19 patients with chronic diseases were more likely to experience severe symptoms and ICU admission and faced a higher risk of mortality. Aggressive strategies to combat the COVID-19 pandemic should target patients with chronic diseases as a priority.

COVID-19 deaths and associated demographic factors in Central Java, Indonesia

Introduction

To date, the total number of COVID-19 deaths is still increasing, including in Central Java, with the third-highest total number of deaths in Indonesia. There are still limited studies related to the cases of COVID-19. Thus, this study's objective was to provide an overview of the characteristics of 4359 COVID-19 death cases in Central Java.

Methods

This research used a cross-sectional descriptive design with univariate, bivariate, and multivariate analysis involving secondary data acquired from a report by the Provincial Health Office of Central Java, recorded up to 13 December 2020.

Results

The results showed that the highest frequencies of death cases were contributed from ≥60 years group (n=1897 patients; 43.52%) and the male (n=2497 patients; 57.28%) group. The case fatality rate (CFR) rose with age, and the highest CFR was recorded in the elderly (17.95%), males (7.60%), in Pati District (17.45%), while entrepreneur (14.64%) was the highest reported job. Furthermore, the eldest group (≥60 years) and males were more susceptible to die, with ORs 5.49 (95%CI: 5.15-5.86) and 1.61 (95%CI: 1.51-1.71), sequentially. The majority of death cases had comorbidities (65.79%), while the most prevalent reported comorbidities were diabetes (n=1387, 31.82%) and hypertension (n=817, 18.74%). Meanwhile, patients of old age were more likely to associate comorbidity, p<0.001, OR 1.664 (95%CI: 1.425-1.944).

Conclusions

This study concludes that patients of older age and males may become more vulnerable than younger and females to experience death. Further study is required to measure the relationship between other characteristics of demographics, underlying medical conditions, and fatality.

A meta-analysis on the risk factors adjusted association between cardiovascular disease and COVID-19 severity

BACKGROUND

Cardiovascular disease (CVD), one of the most common comorbidities of coronavirus disease 2019 (COVID-19), has been suspected to be associated with adverse outcomes in COVID-19 patients, but their correlation remains controversial.

METHOD

This is a quantitative meta-analysis on the basis of adjusted effect estimates. PubMed, Web of Science, MedRxiv, Scopus, Elsevier ScienceDirect, Cochrane Library and EMBASE were searched comprehensively to obtain a complete data source up to January 7, 2021. Pooled effects (hazard ratio (HR), odds ratio (OR)) and the 95% confidence intervals (CIs) were estimated to evaluate the risk of the adverse outcomes in COVID-19 patients with CVD. Heterogeneity was assessed by Cochran's Q-statistic, I2test, and meta-regression. In addition, we also provided the prediction interval, which was helpful for assessing whether the variation across studies was clinically significant. The robustness of the results was evaluated by sensitivity analysis. Publication bias was assessed by Begg's test, Egger's test, and trim-and-fill method.

RESULT

Our results revealed that COVID-19 patients with pre-existing CVD tended more to adverse outcomes on the basis of 203 eligible studies with 24,032,712 cases (pooled ORs = 1.41, 95% CIs: 1.32-1.51, prediction interval: 0.84-2.39; pooled HRs = 1.34, 95% CIs: 1.23-1.46, prediction interval: 0.82-2.21). Further subgroup analyses stratified by age, the proportion of males, study design, disease types, sample size, region and disease outcomes also showed that pre-existing CVD was significantly associated with adverse outcomes among COVID-19 patients.

CONCLUSION

Our findings demonstrated that pre-existing CVD was an independent risk factor associated with adverse outcomes among COVID-19 patients.

Meta-analysis and comprehensive study of coronavirus outbreaks: SARS, MERS and COVID-19

BACKGROUND

Zoonotic coronaviruses have caused several endemic and pandemic situations around the world. SARS caused the first epidemic alert at the beginning of this century, followed by MERS. COVID-19 appeared to be highly contagious, with human-to-human transmission by aerosol droplets, and reached nearly all countries around the world. A plethora of studies were performed, with reports being published within a short period of time by scientists and medical physicians. It has been difficult to find the relevant data to create an overview of the situation according to studies from accumulated findings and reports. In the present study we aimed to perform a comprehensive study in the context of the case fatality ratios (CFRs) of three major human Coronavirus outbreaks which occurred during the first twenty years of 21st century.

METHODS

In this study, we performed meta-analyses on SARS, MERS and COVID-19 outbreak events from publicly available records. Study analyses were performed with the help of highly reputable scientific databases such as PubMed, WOS and Scopus to evaluate and present current knowledge on zoonotic coronavirus outbreaks, starting from 2000 to the end of 2020.

RESULTS

A total of 250,194 research studies and records were identified with specific keywords and synonyms for the three viruses in order to cover all publications. In the end, 41 records were selected and included after applying several exclusion and inclusion criteria on identified datasets. SARS was found to have a nearly 11% case fatality ratio (CFR), which means the estimated number of deaths as a proportion of confirmed positive cases; Taiwan was the country most affected by the SARS outbreak based on the CFR analysis. MERS had CFRs of 35.8 and 26 in Saudi Arabia during the 2012 and 2015 outbreaks, respectively. COVID-19 resulted in a 2.2 CFR globally, and the USA reported the highest mortality ratio in the world in the end of first year of COVID-19 pandemic.

CONCLUSION

Some members of the Coronaviridae family can cause highly contagious and devastating infections among humans. Within the last two decades, the whole world has witnessed several deadly emerging infectious diseases, which are most commonly zoonotic in nature. We conclude that pre-existing immunity during the early stages of a pandemic might be important, but case control and management strategies should be improved to decrease CFRs. Finally, we have addressed several concerns in relation to outbreak events in this study.

Risk Factors for SARS-CoV-2 Infection, Pneumonia, Intubation, and Death in Northeast Mexico

Despite the social distancing and mobility restriction measures implemented for susceptible people around the world, infections and deaths due to COVID-19 continued to increase, even more so in the first months of 2021 in Mexico. Thus, it is necessary to find risk groups that can benefit from more aggressive preventive measures in a high-density population. This is a case-control study of suspected COVID-19 patients from Nuevo León, Mexico. Cases were: (1) COVID-19-positive patients and COVID-19-positive patients who (2) developed pneumonia, (3) were intubated and (4) died. Controls were: (1) COVID-19-negative patients, (2) COVID-19-positive patients without pneumonia, (3) non-intubated COVID-19-positive patients and (4) surviving COVID-19-positive patients. ≥ 18 years of age, not pregnant, were included. The pre-existing conditions analysed as risk factors were age (years), sex (male), diabetes mellitus, hypertension, chronic obstructive pulmonary disease, asthma, immunosuppression, obesity, cardiovascular disease, chronic kidney disease and smoking. The Mann-Whitney U tests, Chi square and binary logistic regression were used. A total of 56,715 suspected patients were analysed in Nuevo León, México, with 62.6% being positive for COVID-19 and, of those infected, 14% developed pneumonia, 2.9% were intubated and 8.1% died. The mean age of those infected was 44.7 years, while of those complicated it was around 60 years. Older age, male sex, diabetes, hypertension, and obesity were risk factors for infection, complications, and death from COVID-19. This study highlights the importance of timely recognition of the population exposed to pre-existing conditions to prioritise preventive measures against the virus.

Clinical Characterization and Prediction of Clinical Severity of SARS-CoV-2 Infection Among US Adults Using Data From the US National COVID Cohort Collaborative

Importance

The National COVID Cohort Collaborative (N3C) is a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy.

Objectives

To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity.

Design, Setting, and Participants

In a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation).

Main Outcomes and Measures

Patient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression.

Results

The cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, age (odds ratio [OR], 1.03 per year; 95% CI, 1.03-1.04), male sex (OR, 1.60; 95% CI, 1.51-1.69), liver disease (OR, 1.20; 95% CI, 1.08-1.34), dementia (OR, 1.26; 95% CI, 1.13-1.41), African American (OR, 1.12; 95% CI, 1.05-1.20) and Asian (OR, 1.33; 95% CI, 1.12-1.57) race, and obesity (OR, 1.36; 95% CI, 1.27-1.46) were independently associated with higher clinical severity.

Conclusions and Relevance

This cohort study found that COVID-19 mortality decreased over time during 2020 and that patient demographic characteristics and comorbidities were associated with higher clinical severity. The machine learning models accurately predicted ultimate clinical severity using commonly collected clinical data from the first 24 hours of a hospital admission.

Biomarkers of cytokine storm as red flags for severe and fatal COVID-19 cases: A living systematic review and meta-analysis

OBJECTIVE

To describe the laboratory parameters and biomarkers of the cytokine storm syndrome associated with severe and fatal COVID-19 cases.

METHODS

A search with standardized descriptors and synonyms was performed on November 28th, 2020 of the MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, LILACS, and IBECS to identify studies of interest. Grey literature searches and snowballing techniques were additionally utilized to identify yet-unpublished works and related citations. Two review authors independently screened the retrieved titles and abstracts, selected eligible studies for inclusion, extracted data from the included studies, and then assessed the risk of bias using the Newcastle-Ottawa Scale. Eligible studies were those including laboratory parameters-including serum interleukin-6 levels-from mild, moderate, or severe COVID-19 cases. Laboratory parameters, such as interleukin-6, ferritin, hematology, C-Reactive Protein, procalcitonin, lactate dehydrogenase, aspartate aminotransferase, creatinine, and D-dimer, were extracted from the studies. Meta-analyses were conducted using the laboratory data to estimate mean differences with associated 95% confidence intervals.

DATA SYNTHESIS

The database search yielded 9,620 records; 40 studies (containing a total of 9,542 patients) were included in the final analysis. Twenty-one studies (n = 4,313) assessed laboratory data related to severe COVID-19 cases, eighteen studies (n = 4,681) assessed predictors for fatal COVID-19 cases and one study (n = 548) assessed laboratory biomarkers related to severe and fatal COVID-19 cases. Lymphopenia, thrombocytopenia, and elevated levels of interleukin-6, ferritin, D-dimer, aspartate aminotransferase, C-Reactive-Protein, procalcitonin, creatinine, neutrophils and leucocytes were associated with severe and fatal COVID-19 cases.

CONCLUSIONS

This review points to interleukin-6, ferritin, leukocytes, neutrophils, lymphocytes, platelets, C-Reactive Protein, procalcitonin, lactate dehydrogenase, aspartate aminotransferase, creatinine, and D-dimer as important biomarkers of cytokine storm syndrome. Elevated levels of interleukin-6 and hyperferritinemia should be considered as red flags of systemic inflammation and poor prognosis in COVID-19.

Separated serum was used for the analysisCombined Analysis of Anti SARS-CoV-2 IgG and IgM Responses in COVID19 Patients in India

Coronavirus disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a global health problem, India being the second most affected country. The kinetics of antibody response to SARS-CoV-2 in Indian population is not studied yet. To understand serological response in relation to age, gender, time period and severity of disease, Roche Elecsys anti-SARS-CoV-2 test was used which analysed both IgM and IgG. One hundred and three COVID-19 patients were enrolled. Seropositivity was seen in 64% of patients, with 33% at ≤ 7 days, 62% between 8 and 15 days and 81% at ≥ 16 days from the time of admission. Men (65%) showed higher antibody response than women (59%), whereas no difference was observed in seropositivity with respect to age of the patients. Dynamics of antibody responses revealed individual variations. Patients in ICU had higher antibody reactivity with 67% positivity as compared to 60% positivity in non-ICU patients. Kinetics of antibody response during COVID-19 disease varied in relation to gender, age, time period and severity and these factors might play an important role in treatment and control of COVID-19.

Angiotensin II Receptor Blockers (ARBs Antihypertensive Agents) Increase Replication of SARS-CoV-2 in Vero E6 Cells

Several comorbidities, including hypertension, have been associated with an increased risk of developing severe disease during SARS-CoV-2 infection. Angiotensin II receptor blockers (ARBs) are currently some of the most widely-used drugs to control blood pressure by acting on the angiotensin II type 1 receptor (AT1R). ARBs have been reported to trigger the modulation of the angiotensin I converting enzyme 2 (ACE2), the receptor used by the virus to penetrate susceptible cells, raising concern that such treatments may promote virus capture and increase their viral load in patients receiving ARBs therapy. In this in vitro study, we reviewed the effect of ARBs on ACE2 and AT1R expression and investigated whether treatment of permissive ACE2+/AT1R+ Vero E6 cells with ARBs alters SARS-CoV-2 replication in vitro in an angiotensin II-free system. After treating the cells with the ARBs, we observed an approximate 50% relative increase in SARS-CoV-2 production in infected Vero E6 cells that correlates with the ARBs-induced up-regulation of ACE2 expression. From this data, we believe that the use of ARBs in hypertensive patients infected by SARS-CoV-2 should be carefully evaluated.

Coronary heart disease and COVID-19: A meta-analysis

Objective

Since the World Health Organization (WHO) announced coronavirus disease 2019 (COVID-19) had become a global pandemic on March 11, 2020, the number of infections has been increasing. The purpose of this meta-analysis was to investigate the prognosis of COVID-19 in patients with coronary heart disease.

Method

Pubmed, Embase, and Cochrane Library databases were searched to collect the literature concerning coronary heart disease and COVID-19. The retrieval time was from inception to Nov 20, 2020, using Stata version 14.0 for meta-analysis.

Results

A total of 22,148 patients from 40 studies were included. The meta-analysis revealed that coronary heart disease was associated with poor prognosis of COVID-19 (OR=3.42, 95%CI [2.83, 4.13], P < 0.001). After subgroup analysis, coronary heart disease was found to be related to mortality (OR = 3.75, 95%CI [2.91, 4.82], P < 0.001), severe/critical COVID-19 (OR = 3.23, 95%CI [2.19, 4.77], P < 0.001), ICU admission (OR = 2.25, 95%CI [1.34, 3.79], P = 0.002), disease progression (OR = 3.01, 95%CI [1.46, 6.22], P = 0.003); Meta-regression showed that the association between coronary heart disease and poor prognosis of COVID-19 was affected by hypertension (P = 0.004), and subgroup analysis showed that compared with the proportion of hypertension >30% (OR = 2.85, 95%CI [2.33, 3.49]), the proportion of hypertension <30% (OR = 4.78, 95%CI [3.50, 6.51]) had a higher risk of poor prognosis.

Conclusion

Coronary heart disease is a risk factor for poor prognosis in patients with COVID-19.

Epidemiology, clinical characteristics, and virologic features of COVID-19 patients in Kazakhstan: A nation-wide retrospective cohort study

BACKGROUND

The earliest coronavirus disease-2019 (COVID-19) cases in Central Asia were announced in March 2020 by Kazakhstan. Despite the implementation of aggressive measures to curb infection spread, gaps remain in the understanding of the clinical and epidemiologic features of the regional pandemic.

METHODS

We did a retrospective, observational cohort study of patients with laboratory-confirmed COVID-19 hospitalized in Kazakhstan between February and April 2020. We compared demographic, clinical, laboratory and radiological data of patients with different COVID-19 severities on admission. Logistic regression was used to assess factors associated with disease severity and in-hospital death. Whole-genome SARS-CoV-2 analysis was performed in 53 patients.

FINDINGS

Of the 1072 patients with laboratory-confirmed COVID-19 in March-April 2020, the median age was 36 years (IQR 24-50) and 484 (45%) were male. On admission, 683 (64%) participants had asymptomatic/mild, 341 (32%) moderate, and 47 (4%) severe-to-critical COVID-19 manifestation; 20 in-hospital deaths (1•87%) were reported by 5 May 2020. Multivariable regression indicated increasing odds of severe disease associated with older age (odds ratio 1•05, 95% CI 1•03-1•07, per year increase; p<0•001), the presence of comorbidities (2•34, 95% CI 1•18-4•85; p=0•017) and elevated white blood cell count (WBC, 1•13, 95% CI 1•00-1•27; p=0•044) on admission, while older age (1•09, 95% CI 1•06-1•13, per year increase; p<0•001) and male sex (5•63, 95% CI 2•06-17•57; p=0•001) were associated with increased odds of in-hospital death. The SARS-CoV-2 isolates grouped into seven phylogenetic lineages, O/B.4.1, S/A.2, S/B.1.1, G/B.1, GH/B.1.255, GH/B.1.3 and GR/B.1.1.10; 87% of the isolates were O and S sub-types descending from early Asian lineages, while the G, GH and GR isolates were related to lineages from Europe and the Americas.

INTERPRETATION

Older age, comorbidities, increased WBC count, and male sex were risk factors for COVID-19 disease severity and mortality in Kazakhstan. The broad SARS-CoV-2 diversity suggests multiple importations and community-level amplification predating travel restriction.

FUNDING

Ministry of Education and Science of the Republic of Kazakhstan.

Hypertension, diabetes mellitus, and cerebrovascular disease predispose to a more severe outcome of COVID-19

INTRODUCTION

The world is currently facing the pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The total number of cases of coronavirus disease 2019 (COVID-19) is rising daily and no vaccine has yet been approved. While the pathophysiology behind the virus is still being studied, many possible several risk factors using small sample sizes have been found.

MATERIAL AND METHODS

We conducted a pooled analysis using several databases such as Medline, Scopus, Wangfang, Web of Science, Research Square, medrxiv, and Google Scholar to identify studies reporting severe and non-severe groups of COVID-19 patients. The odds ratios as well as the 95% confidence intervals for hypertension, diabetes, and cerebrovascular disease leading to severe COVID-19 were calculated using R-software.

RESULTS

Fifty-three articles were used for our analysis and they involved 30,935 confirmed cases of COVID-19 from several countries across the world. The odds ratio for severe COVID-19 in hypertensive patients, diabetics, and patients with a history of cerebrovascular disease was 2.58 (95% confidence interval (CI): 2.16-3.08, from 53 studies), 2.17 (95% CI: 1.72-2.74, from 44 studies), and 2.63 (95% CI: 1.80-3.85, from 25 studies), respectively.

CONCLUSIONS

Our analysis confirms that patients with hypertension, diabetes, or cerebrovascular disease are at a higher risk of a severe outcome of COVID-19. It is thus vital for physicians to identify the main risk factors for a severe outcome of this disease.

A Study of a New Technique of the CT Scan View and Disease Classification Protocol Based on Level Challenges in Cases of Coronavirus Disease

The chest Computer Tomography (CT scan) is used in the diagnosis of coronavirus disease 2019 (COVID-19) and is an important complement to the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. The paper aims to improve the radiological diagnosis in the case of coronavirus disease COVID-19 pneumonia on forms of noninvasive approaches with conventional and high-resolution computer tomography (HRCT) scan images upon chest CT images of patients confirmed with mild to severe findings. The preliminary study is to compare the radiological findings of COVID-19 pneumonia in conventional chest CT images with images processed by a new tool and reviewed by expert radiologists. The researchers used a new filter called Golden Key Tool (GK-Tool) which has confirmed the improvement in the quality and diagnostic efficacy of images acquired using our modified images. Further, Convolution Neural Networks (CNNs) architecture called VGG face was used to classify chest CT images. The classification has been performed by using VGG face on various datasets which are considered as a protocol to diagnose COVID-19, Non-COVID-19 (other lung diseases), and normal cases (no findings on chest CT). Accordingly, the performance evaluation of the GK-Tool was fairly good as shown in the first set of results, where 80-95% of participants show a good to excellent assessment of the new images view in the case of COVID-19 patients. The results, in general, illustrate good recognition rates in the diagnosis and, therefore, would be significantly higher in normal cases with COVID-19. These results could reduce the radiologist's workload burden and play a major role in the decision-making process.

Risk factors for poor outcomes in hospitalised COVID-19 patients: A systematic review and meta-analysis

BACKGROUND

Understanding the risk factors for poor outcomes among COVID-19 patients could help identify vulnerable populations who would need prioritisation in prevention and treatment for COVID-19. We aimed to critically appraise and synthesise published evidence on the risk factors for poor outcomes in hospitalised COVID-19 patients.

METHODS

We searched PubMed, medRxiv and the WHO COVID-19 literature database for studies that reported characteristics of COVID-19 patients who required hospitalisation. We included studies published between January and May 2020 that reported adjusted effect size of any demographic and/or clinical factors for any of the three poor outcomes: mortality, intensive care unit (ICU) admission, and invasive mechanical ventilation. We appraised the quality of the included studies using Joanna Briggs Institute appraisal tools and quantitatively synthesised the evidence through a series of random-effect meta-analyses. To aid data interpretation, we further developed an interpretation framework that indicated strength of the evidence, informed by both quantity and quality of the evidence.

RESULTS

We included a total of 40 studies in our review. Most of the included studies (29/40, 73%) were assessed as "good quality", with assessment scores of 80 or more. We found that male sex (pooled odds ratio (OR) = 1.32 (95% confidence interval (CI) = 1.18-1.48; 20 studies), older age (OR = 1.05, 95% CI = 1.04-1.07, per one year of age increase; 10 studies), obesity (OR = 1.59, 95% CI = 1.02-2.48; 4 studies), diabetes (OR = 1.25, 95% CI = 1.11-1.40; 11 studies) and chronic kidney diseases (6 studies; OR = 1.57, 95% CI = 1.27-1.93) were associated with increased risks for mortality with the greatest strength of evidence based on our interpretation framework. We did not find increased risk of mortality for several factors including chronic obstructive pulmonary diseases (5 studies), cancer (4 studies), or current smoker (5 studies); however, this does not indicate absence of risk due to limited data on each of these factors.

CONCLUSION

Male sex, older age, obesity, diabetes and chronic kidney diseases are important risk factors of COVID-19 poor outcomes. Our review provides not only an appraisal and synthesis of evidence on the risk factors of COVID-19 poor outcomes, but also a data interpretation framework that could be adopted by relevant future research.

Prognosis Score System to Predict Survival for COVID-19 Cases: a Korean Nationwide Cohort Study

BACKGROUND

As the COVID-19 pandemic continues, an initial risk-adapted allocation is crucial for managing medical resources and providing intensive care.

OBJECTIVE

In this study, we aimed to identify factors that predict the overall survival rate for COVID-19 cases and develop a COVID-19 prognosis score (COPS) system based on these factors. In addition, disease severity and the length of hospital stay for patients with COVID-19 were analyzed.

METHODS

We retrospectively analyzed a nationwide cohort of laboratory-confirmed COVID-19 cases between January and April 2020 in Korea. The cohort was split randomly into a development cohort and a validation cohort with a 2:1 ratio. In the development cohort (n=3729), we tried to identify factors associated with overall survival and develop a scoring system to predict the overall survival rate by using parameters identified by the Cox proportional hazard regression model with bootstrapping methods. In the validation cohort (n=1865), we evaluated the prediction accuracy using the area under the receiver operating characteristic curve. The score of each variable in the COPS system was rounded off following the log-scaled conversion of the adjusted hazard ratio.

RESULTS

Among the 5594 patients included in this analysis, 234 (4.2%) died after receiving a COVID-19 diagnosis. In the development cohort, six parameters were significantly related to poor overall survival: older age, dementia, chronic renal failure, dyspnea, mental disturbance, and absolute lymphocyte count <1000/mm3. The following risk groups were formed: low-risk (score 0-2), intermediate-risk (score 3), high-risk (score 4), and very high-risk (score 5-7) groups. The COPS system yielded an area under the curve value of 0.918 for predicting the 14-day survival rate and 0.896 for predicting the 28-day survival rate in the validation cohort. Using the COPS system, 28-day survival rates were discriminatively estimated at 99.8%, 95.4%, 82.3%, and 55.1% in the low-risk, intermediate-risk, high-risk, and very high-risk groups, respectively, of the total cohort (P<.001). The length of hospital stay and disease severity were directly associated with overall survival (P<.001), and the hospital stay duration was significantly longer among survivors (mean 26.1, SD 10.7 days) than among nonsurvivors (mean 15.6, SD 13.3 days).

CONCLUSIONS

The newly developed predictive COPS system may assist in making risk-adapted decisions for the allocation of medical resources, including intensive care, during the COVID-19 pandemic.

Predictors associated with clinical improvement of SARS-CoV-2 pneumonia

Background

There are few agents that have been proven effective for COVID-19. Predicting clinical improvement as well as mortality or severity is very important.

Objectives

This study aimed to investigate the factors associated with the clinical improvement of COVID-19.

Methods

Overall, 74 patients receiving treatment for COVID-19 at Tokyo Medical and Dental University Hospital from April 6th to May 15th, 2020 were included in this study. Clinical improvement was evaluated, which defined as the decline of two levels on a six-point ordinal scale of clinical status or discharge alive from the hospital within 28 days after admission. The clinical courses were particularly investigated and the factors related to time to clinical improvement were analyzed with the log-rank test and the Cox proportional hazard model.

Results

Forty-nine patients required oxygen support during hospitalization, 22 patients required invasive mechanical ventilation, and 5 patients required extracorporeal membrane oxygenation. A total of 83% of cases reached clinical improvement. Longer period of time from onset to admission (≥10 days) (HR, 1.057; 95% CI, 1.002–1.114), no hypertension (HR, 2.077; 95% CI, 1.006–4.287), and low D-dimer levels (<1 μg/ml) (HR, 2.372; 95% CI, 1.229–4.576) were confirmed to be significant predictive factors for time to clinical improvement. Furthermore, a lower SARS-CoV-2 RNA copy number was also a predictive factor for clinical improvement.

Conclusions

Several predictors for the clinical improvement of COVID-19 pneumonia were identified. These results may be important for the management of COVID-19 pneumonia.

Adverse Outcomes Associated With Corticosteroid Use in Critical COVID-19: A Retrospective Multicenter Cohort Study

Corticosteroid is commonly used to reduce damage from inflammatory reactions in coronavirus disease 2019 (COVID-19). We aim to determine the outcomes of corticosteroid use in critically ill COVID-19 patients. Ninety six critically ill patients, hospitalized in 14 hospitals outside Wuhan from January 16 to March 30, 2020 were enrolled in this study. Among 96 critical patients, 68 were treated with corticosteroid (CS group), while 28 were not treated with corticosteroids (non-CS group). Multivariable logistic regression were performed to determine the possible correlation between corticosteroid use and the treatment outcomes. Forty-six (68%) patients in the CS group died compared to six (21%) of the non-CS group. Corticosteroid use was also associated with the development of ARDS, exacerbation of pulmonary fibrosis, longer hospital stay and virus clearance time. On admission, no difference in laboratory findings between the CS and the non-CS group was observed. After corticosteroid treatment, patients treated with corticosteroids were associated with higher counts of white blood cells, neutrophils, neutrophil-to-lymphocyte ratio, alanine aminotransferase level and Sequential Organ Failure Assessment score. In conclusion, corticosteroid use in critically ill COVID-19 patients was associated with a much higher case fatality rate. Frequent incidence of liver injury and multi-organ failure in corticosteroid treated patients may have contributed to the adverse outcomes. The multi-organ failure is likely caused by more persistent SARS-CoV-2 infection and higher viral load, due to the inhibition of immune surveillance by corticosteroid.

Prevalence of Comorbid Asthma and Related Outcomes in COVID-19: A Systematic Review and Meta-Analysis

BACKGROUND

The impact of asthma on coronavirus disease 2019 (COVID-19) remains largely unknown.

OBJECTIVE

To investigate the asthma prevalence among patients with COVID-19 and compare outcomes between patients with and without asthma.

METHODS

In this systematic review and meta-analysis, we searched PubMed, Embase, Web of Science, bioRxiv, and medRxiv for studies reporting asthma prevalence in general patients with COVID-19 or comparing outcomes between patients with and without asthma, and excluded duplicate publications, reviews, editorials, comments, single case reports, or small case series (<10 cases). We determined the pooled estimates of effect using random-effect model.

RESULTS

On the basis of 131 studies (410,382 patients), we found great variability in the prevalence of comorbid asthma among patients with COVID-19 in different countries or regions ranging from 1.1% to 16.9%. No significant difference in asthma prevalence was found between hospitalized and nonhospitalized (risk ratio [RR], 1.15; 95% CI, 0.92-1.43), severe and nonsevere (RR, 1.21; 95% CI, 0.92-1.57), intensive care unit and non-intensive care unit (RR, 1.19; 95% CI, 0.92-1.54), dead and survived (RR, 0.90; 95% CI, 0.73-1.11), intubated/mechanically ventilated and nonintubated/mechanically ventilated (RR, 0.91; 95% CI, 0.71-1.17) patients with COVID-19. Patients with asthma have a lower risk of death compared with patients without asthma (RR, 0.65; 95% CI, 0.43-0.98). Asthma is not associated with a higher risk of intubation or mechanical ventilation (RR, 1.03; 95% CI, 0.72-1.46).

CONCLUSIONS

There is great variability in asthma prevalence among patients with COVID-19 in different countries or regions. Asthma is not associated with higher COVID-19 severity or worse prognosis, and patients with asthma are found to have a lower risk of death compared with patients without asthma.

Evaluating the effects of cardiometabolic exposures on circulating proteins which may contribute to severe SARS-CoV-2

BACKGROUND

Developing insight into the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is of critical importance to overcome the global pandemic caused by coronavirus disease 2019 (covid-19). In this study, we have applied Mendelian randomization (MR) to systematically evaluate the effect of 10 cardiometabolic risk factors and genetic liability to lifetime smoking on 97 circulating host proteins postulated to either interact or contribute to the maladaptive host response of SARS-CoV-2.

METHODS

We applied the inverse variance weighted (IVW) approach and several robust MR methods in a two-sample setting to systemically estimate the genetically predicted effect of each risk factor in turn on levels of each circulating protein. Multivariable MR was conducted to simultaneously evaluate the effects of multiple risk factors on the same protein. We also applied MR using cis-regulatory variants at the genomic location responsible for encoding these proteins to estimate whether their circulating levels may influence severe SARS-CoV-2.

FINDINGS

In total, we identified evidence supporting 105 effects between risk factors and circulating proteins which were robust to multiple testing corrections and sensitivity analyzes. For example, body mass index provided evidence of an effect on 23 circulating proteins with a variety of functions, such as inflammatory markers c-reactive protein (IVW Beta=0.34 per standard deviation change, 95% CI=0.26 to 0.41, P = 2.19 × 10-16) and interleukin-1 receptor antagonist (IVW Beta=0.23, 95% CI=0.17 to 0.30, P = 9.04 × 10-12). Further analyzes using multivariable MR provided evidence that the effect of BMI on lowering immunoglobulin G, an antibody class involved in protection from infection, is substantially mediated by raised triglycerides levels (IVW Beta=-0.18, 95% CI=-0.25 to -0.12, P = 2.32 × 10-08, proportion mediated=44.1%). The strongest evidence that any of the circulating proteins highlighted by our initial analysis influence severe SARS-CoV-2 was identified for soluble glycoprotein 130 (odds ratio=1.81, 95% CI=1.25 to 2.62, P = 0.002), a signal transductor for interleukin-6 type cytokines which are involved in inflammatory response. However, based on current case samples for severe SARS-CoV-2 we were unable to replicate findings in independent samples.

INTERPRETATION

Our findings highlight several key proteins which are influenced by established exposures for disease. Future research to determine whether these circulating proteins mediate environmental effects onto risk of SARS-CoV-2 infection or covid-19 progression are warranted to help elucidate therapeutic strategies for severe covid-19 disease.

FUNDING

The Medical Research Council, the Wellcome Trust, the British Heart Foundation and UK Research and Innovation.

The National COVID Cohort Collaborative: Clinical Characterization and Early Severity Prediction

Background:

The majority of U.S. reports of COVID-19 clinical characteristics, disease course, and treatments are from single health systems or focused on one domain. Here we report the creation of the National COVID Cohort Collaborative (N3C), a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative U.S. cohort of COVID-19 cases and controls to date. This multi-center dataset supports robust evidence-based development of predictive and diagnostic tools and informs critical care and policy.

Methods and Findings:

In a retrospective cohort study of 1,926,526 patients from 34 medical centers nationwide, we stratified patients using a World Health Organization COVID-19 severity scale and demographics; we then evaluated differences between groups over time using multivariable logistic regression. We established vital signs and laboratory values among COVID-19 patients with different severities, providing the foundation for predictive analytics. The cohort included 174,568 adults with severe acute respiratory syndrome associated with SARS-CoV-2 (PCR >99% or antigen <1%) as well as 1,133,848 adult patients that served as lab-negative controls. Among 32,472 hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March/April 2020 to 8.6% in September/October 2020 (p = 0.002 monthly trend). In a multivariable logistic regression model, age, male sex, liver disease, dementia, African-American and Asian race, and obesity were independently associated with higher clinical severity. To demonstrate the utility of the N3C cohort for analytics, we used machine learning (ML) to predict clinical severity and risk factors over time. Using 64 inputs available on the first hospital day, we predicted a severe clinical course (death, discharge to hospice, invasive ventilation, or extracorporeal membrane oxygenation) using random forest and XGBoost models (AUROC 0.86 and 0.87 respectively) that were stable over time. The most powerful predictors in these models are patient age and widely available vital sign and laboratory values. The established expected trajectories for many vital signs and laboratory values among patients with different clinical severities validates observations from smaller studies, and provides comprehensive insight into COVID-19 characterization in U.S. patients.

Conclusions:

This is the first description of an ongoing longitudinal observational study of patients seen in diverse clinical settings and geographical regions and is the largest COVID-19 cohort in the United States. Such data are the foundation for ML models that can be the basis for generalizable clinical decision support tools. The N3C Data Enclave is unique in providing transparent, reproducible, easily shared, versioned, and fully auditable data and analytic provenance for national-scale patient-level EHR data. The N3C is built for intensive ML analyses by academic, industry, and citizen scientists internationally. Many observational correlations can inform trial designs and care guidelines for this new disease.

Nutritional Risk Screening and Body Composition in COVID-19 Patients Hospitalized in an Internal Medicine Ward

BACKGROUND

Malnutrition in patients hospitalized in internal medicine wards is highly prevalent and represents a prognostic factor of worse outcomes. Previous evidence suggested the prognostic role of the nutritional status in patients affected by the coronavirus disease 2019 (COVID-19). We aim to investigate the nutritional risk in patients with COVID-19 hospitalized in an internal medicine ward and their clinical outcomes using the Nutritional Risk Screening 2002 (NRS-2002) and parameters derived from bioelectrical impedance analysis (BIA).

METHODS

Retrospective analysis of patients with COVID-19 aimed at exploring: 1) the prevalence of nutritional risk with NRS-2002 and BIA; 2) the relationship between NRS-2002, BIA parameters and selected outcomes: length of hospital stay (LOS); death and need of intensive care unit (ICU); prolonged LOS; and loss of appetite.

RESULTS

Data of 90 patients were analyzed. Patients at nutritional risk were 92% with NRS-2002, with BIA-derived parameters: 88% by phase angle; 86% by body cell mass; 84% by fat-free mass and 84% by fat mass (p-value ≤0.001). In ROC analysis, NRS had the maximum sensitivity in predicting the risk of death and need of ICU and a prolonged hospitalization showing moderate-low specificity; phase angle showed a good predictive power in terms of AUC. NRS-2002 was significantly associated with LOS (β 12.62, SE 5.79). In a multivariate analysis, blood glucose level and the early warning score are independent predictors of death and need of ICU (OR 2.79, p ≤0.001; 1.59, p-0.029, respectively).

CONCLUSION

Present findings confirm the clinical utility of NRS-2002 to assess nutritional risk in patients with COVID-19 at hospital admission and in predicting LOS, and that bioimpedance does not seem to add further predictive value. An early detection of nutritional risk has to be systematically included in the management of COVID-19 patients hospitalized in internal medicine wards.

Aging- and gender-related modulation of RAAS: potential implications in COVID-19 disease

Coronavirus disease 2019 (COVID-19) is a new infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 is frequently characterized by a marked inflammatory response with severe pneumonia and respiratory failure associated with multiorgan involvement. Some risk factors predispose patients to develop a more severe infection and to an increased mortality; among them, advanced age and male gender have been identified as major and independent risk factors for COVID-19 poor outcome. The renin-angiotensin-aldosterone system (RAAS) is strictly involved in COVID-19 because angiotensin converting enzyme 2 (ACE2) is the host receptor for SARS-CoV-2 and also converts pro-inflammatory angiotensin (Ang) II into anti-inflammatory Ang(1–7). In this review, we have addressed the effect of aging and gender on RAAS with emphasis on ACE2, pro-inflammatory Ang II/Ang II receptor 1 axis and anti-inflammatory Ang(1–7)/Mas receptor axis.