Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City

Neuropsychiatric complications of coronavirus disease 2019: Mount Sinai Health System cohort study

OBJECTIVE

To describe the frequency of neuropsychiatric complications among hospitalized patients with coronavirus disease 2019 (COVID-19) and their association with pre-existing comorbidities and clinical outcomes.

METHODS

We retrospectively identified all patients hospitalized with COVID-19 within a large multicenter New York City health system between March 15, 2020 and May 17, 2021 and randomly selected a representative cohort for detailed chart review. Clinical data, including the occurrence of neuropsychiatric complications (categorized as either altered mental status [AMS] or other neuropsychiatric complications) and in-hospital mortality, were extracted using an electronic medical record database and individual chart review. Associations between neuropsychiatric complications, comorbidities, laboratory findings, and in-hospital mortality were assessed using multivariate logistic regression.

RESULTS

Our study cohort consisted of 974 patients, the majority were admitted during the first wave of the pandemic. Patients were treated with anticoagulation (88.4%), glucocorticoids (24.8%), and remdesivir (10.5%); 18.6% experienced severe COVID-19 pneumonia (evidenced by ventilator requirement). Neuropsychiatric complications occurred in 58.8% of patients; 39.8% experienced AMS; and 19.0% experienced at least one other complication (seizures in 1.4%, ischemic stroke in 1.6%, hemorrhagic stroke in 1.0%) or symptom (headache in 11.4%, anxiety in 6.8%, ataxia in 6.3%). Higher odds of mortality, which occurred in 22.0%, were associated with AMS, ventilator support, increasing age, and higher serum inflammatory marker levels. Anticoagulant therapy was associated with lower odds of mortality and AMS.

CONCLUSION

Neuropsychiatric complications of COVID-19, especially AMS, were common, varied, and associated with in-hospital mortality in a diverse multicenter cohort at an epicenter of the COVID-19 pandemic.

Impact of plasma Epstein-Barr virus DNA in posttreatment nasopharyngeal carcinoma patients after SARS-CoV-2 infection

BACKGROUND

Nasopharyngeal carcinoma (NPC) is prevalent in southern China. EBV DNA is the most useful biomarker in NPC. However, the value of EBV DNA in posttreatment NPC patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains unclear.

METHODS

Sixty-four eligible NPC patients were enrolled between December 2022 and February 2023. Patients who met the following criteria were included: had non-metastatic NPC, completed radical treatment, were first firstly infected with SARS-CoV-2 and their EBV DNA changed from undetectable to detectable.

RESULTS

At the end of follow-up, 81.25% (52/64) of patients were confirmed not to relapse with undetectable EBV DNA (no-relapse). In addition, 18.75% (12/64) of patients experienced relapse with consistent detection of EBV DNA (yes-relapse). For all 64 patients, the average time from diagnosis of coronavirus disease 2019 (COVID-19) to detection of detectable EBV DNA was 35.41 days (2 to 139 days). For 52 no-relapse patients, the average time from EBV DNA changing from detectable to undetectable was 63.12 days (6 to 147 days). The levels of EBV DNA were greater in yes-relapse patients than that in no-relapse patients, and the average of EBV DNA levels were 1216 copies/ml and 53.18 copies/ml, respectively. Using 62.3 copies/mL as the threshold, the area under the curve for EBV DNA was 0.88 for distinguishing yes-relapse patients from no-relapse patients. The sensitivity and specificity were 81.97% (95% CI 0.71-0.95) and 86.67% (95% CI 0.70-0.95), respectively.

CONCLUSION

For NPC patients infected with SARS-CoV-2, EBV DNA alone is insufficient for monitoring relapse after radical therapy. Long-term follow-up and underlying mechanistic investigations of EBV DNA changes are urgently needed.

Effects of the pre-existing coronary heart disease on the prognosis of COVID-19 patients: A systematic review and meta-analysis

Although studies have shown severe Coronavirus disease 2019 (COVID-19) outcomes in patients with pre-existing coronary heart disease (CHD), the prognosis of COVID-19 patients with pre-existing CHD remains uncertain primarily due to the limited number of patients in existing studies. This study aimed to investigate the impacts of pre-existing CHD on the prognosis of COVID-19 patients. Five electronic databases were searched for eligible studies. This article focused on cohort and case-control studies involving the prognosis of COVID-19 patients with pre-existing CHD. The meta-analysis was performed using a random effects model. The odds ratios (ORs) and 95% confidence intervals (CIs) were used as valid indicators. The study was registered in PROSPERO with the identifier: CRD42022352853. A total of 81 studies, involving 157,439 COVID-19 patients, were included. The results showed that COVID-19 patients with pre-existing CHD exhibited an elevated risk of mortality (OR = 2.45; 95%CI: [2.04, 2.94], P < 0.001), severe/critical COVID-19 (OR = 2.57; 95%CI: [1.98, 3.33], P < 0.001), Intensive Care Unit or Coronary Care Unit (ICU/CCU) admission: (OR = 2.75, 95%CI: [1.61, 4.72], P = 0.002), and reduced odds of discharge/recovery (OR = 0.43, 95%CI: [0.28, 0.66], P < 0.001) compared to COVID-19 patients without pre-existing CHD. Subgroup analyses indicated that the prognosis of COVID-19 patients with pre-existing CHD was influenced by publication year, follow-up duration, gender, and hypertension. In conclusion, pre-existing CHD significantly increases the risk of poor prognosis in patients with COVID-19, particularly in those male or hypertensive patients.

General Hospitalization and Intensive Care Unit-Related Factors of COVID-19 Patients in Northeastern Colombia: Baseline Characteristics of a Cohort Study

Objective This study aims to describe demographic and clinical characteristics and the factors associated with the risk of COVID-19 general hospitalization and intensive care unit (ICU) care of patients who consulted in a third-level hospital in Santander, Colombia. Methods We used baseline data from an ambidirectional cohort study. We included all patients with positive real-time polymerase chain reaction (PCR) tests for COVID-19 who came to the emergency room (ER) for respiratory symptoms related to COVID-19. Information regarding patients' baseline characteristics and symptoms was collected through telephone interviews and review of medical records. Vital signs were extracted from medical records as well. Results We enrolled 3,030 patients, predominantly men, with a median age of 60 (interquartile range (IQR): 44-73). Symptoms of the acute phase varied between men and women. Men presented with more respiratory symptoms, and women had general symptoms. Hypertension, obesity, and diabetes were common risk factors for hospital admission. Antibiotic consumption may also play a role in hospital admission.  Conclusions Male sex, older age, hypertension, obesity, prior thrombotic events, and self-medicated antibiotics were associated with general hospitalization. Hypertension, obesity, diabetes, and cancer were associated with ICU admission. The Charlson comorbidity index (CCI) is a powerful tool for evaluate the impact of pre-existing health conditions on COVID-19 hospital admission. We highlight the importance of these findings as possible predictors in our region.

Population-weighted exposure to green spaces tied to lower COVID-19 mortality rates: A nationwide dose-response study in the USA

The COVID-19 pandemic has caused a huge loss of human life globally. However, few studies investigated the link between exposure to green space and risk of COVID-19 mortality rate, while also distinguishing the effects of various types of green space, considering the spatial distribution of human population and green space, and identifying the optimal buffer distances of nearby green space. It is critical and pressing to fill these significant knowledge gaps to protect and promote billions of people's health and life across the world. This study adopted a negative binomial generalized linear mixed-effects model to examine the association between the ratios of various types of green space, population-weighted exposure to those various types of green space, and COVID-19 mortality rates across 3025 counties in the USA, adjusted for sociodemographic, pre-existing chronic disease, policy and regulation, behavioral, and environmental factors. The findings show that greater exposure to forest was associated with lower COVID-19 mortality rates, while developed open space had mixed associations with COVID-19 mortality rates. Forest outside park had the largest effect size across all buffer distances, followed by forest inside park. The optimal exposure buffer distance was 1 km for forest outside park, with per one-unit of increase in exposure associated with a 9.9 % decrease in COVID-19 mortality rates (95 % confidence interval (CI): 6.9 %-12.8 %). The optimal exposure buffer distance of forest inside park was 400 m, with per one-unit of increase in exposure associated with a 4.7 % decrease in mortality rates (95 % CI: 2.4 %-6.9 %). The results suggest that greater exposure to green spaces, especially to nearby forests, may mitigate the risk of COVID-19 mortality. Although findings of an ecological study cannot be directly used to guide medical interventions, this study may pave a critical new way for future research and practice across multiple disciplines.

Lycopene: a therapeutic strategy against coronavirus disease 19 (COVID- 19)

Lycopene is a group of phytochemicals found in nature, primarily in fruits and vegetables. Lycopene is thought to protect against a variety of diseases attributed to its antioxidant capabilities. Lycopene has anti-inflammatory, anti-cancer, and immunity-boosting qualities, among other biological and pharmacological benefits. COVID-19 (coronavirus disease 19) is an infectious disease caused by the SARS-CoV-2 virus, which has recently emerged as one of the world's leading causes of death. Patients may be asymptomatic or show signs of respiratory, cytokine release syndrome, gastrointestinal, or even multiple organ failure, all of which can lead to death. In COVID-19, inflammation, and cytokine storm are the key pathogenic mechanisms, according to SARS-CoV-2 infection symptoms. ARDS develops in some vulnerable hosts, which is accompanied by an inflammatory "cytokine syndrome" that causes lung damage. Immunological and inflammatory markers were linked to disease severity in mild and severe COVID-19 cases, implying that inflammatory markers, including IL-6, CRP, ESR, and PCT were significantly linked with COVID-19 severity. Patients with severe illness have reduced levels of several immune subsets, including CD4 + T, NK, and CD8 + cells. As a result, lycopene can be commended for bolstering physiological defenses against COVID-19 infections.

Using patient biomarker time series to determine mortality risk in hospitalised COVID-19 patients: A comparative analysis across two New York hospitals

A large range of prognostic models for determining the risk of COVID-19 patient mortality exist, but these typically restrict the set of biomarkers considered to measurements available at patient admission. Additionally, many of these models are trained and tested on patient cohorts from a single hospital, raising questions about the generalisability of results. We used a Bayesian Markov model to analyse time series data of biomarker measurements taken throughout the duration of a COVID-19 patient’s hospitalisation for n = 1540 patients from two hospitals in New York: State University of New York (SUNY) Downstate Health Sciences University and Maimonides Medical Center. Our main focus was to quantify the mortality risk associated with both static (e.g. demographic and patient history variables) and dynamic factors (e.g. changes in biomarkers) throughout hospitalisation, by so doing, to explain the observed patterns of mortality. By using our model to make predictions across the hospitals, we assessed how predictive factors generalised between the two cohorts. The individual dynamics of the measurements and their associated mortality risk were remarkably consistent across the hospitals. The model accuracy in predicting patient outcome (death or discharge) was 72.3% (predicting SUNY; posterior median accuracy) and 71.3% (predicting Maimonides) respectively. Model sensitivity was higher for detecting patients who would go on to be discharged (78.7%) versus those who died (61.8%). Our results indicate the utility of including dynamic clinical measurements when assessing patient mortality risk but also highlight the difficulty of identifying high risk patients.

Association of Patient Characteristics, Diabetes, BMI, and Obesity With Severe COVID-19 in Metropolitan Detroit, MI

Identification of specific risk factors for severe coronavirus disease 2019 (COVID-19) is crucial for prevention of poor outcomes and mortality. This retrospective cohort study of patients hospitalized with COVID-19 demonstrated that older age, male sex, Black race, diabetes, elevated BMI, and elevated inflammatory markers were correlated with critical illness in COVID-19. Older age, male sex, diabetes, and inflammatory markers, but not elevated BMI, were associated with mortality. Despite having greater odds of critical illness, Black patients had lower odds of death than White patients. Older age, male sex, diabetes, and elevated inflammatory markers were significantly associated with venous thromboembolism. These findings suggest a need to aggressively identify and manage modifiable risk factors (i.e., diabetes and elevated BMI) and encourage vaccination of at-risk individuals to prevent poor outcomes from COVID-19.

The impact of comorbid allergic airway disease on the severity and mortality of COVID-19: a systematic review and meta-analysis

PURPOSE

To analyze the impact of AAD on the severity and mortality of COVID-19 patients and compare clinical outcomes between patients with and without AAD.

METHODS

In the systematic review and meta-analysis, we searched PubMed, Embase, Web of Science for studies reporting allergic rhinitis, asthma prevalence in COVID-19 patients and compared clinical outcomes, and excluded duplicate publications, reviews, comments, single or few cases reports (< 100 cases). We determined the pooled effect estimates using random effect model.

RESULTS

Thirty-four studies (345,091) were finally included for the meta-analysis. On the basis of 32 studies (337,821) involving with the severity of COVID-19, we did not find significant association between AAD and the severity of COVID-19 (p = 0.35, OR 1.10, 95% CI 0.90-1.35). Subgroup analysis indicated there was no the variability in the prevalence of AAD among COVID-19 patients in different study designs, disease categories, countries, the definition of severity, and population size of AAD. Based on 21 studies (306,331) involving with the mortality of COVID-19, AAD was significantly associated with the decreased mortality of COVID-19 (p < 0.05, OR 0.83, 95% CI 0.70-0.99). The subgroup analysis showed AAD was not associated with the mortality of COVID-19 in different countries or regions. Based on the population size of AAD, we found AAD within 100 cases was not associated with the mortality of COVID-19 (p = 0.63, OR 1.15, 95% CI 0.65-2.03). Moreover, study design was possible heterogeneity source as the heterogeneity I2 was reduced to 0 in prospective studies.

CONCLUSION

The preexisting AAD was not inclined to deteriorate the course of COVID-19. The prevalence of AAD was not associated with the severity of COVD-19 patients and inclined to be significantly associated with the decreased mortality risk of COVID-19.

Factores asociados con una evolución desfavorable en pacientes diagnosticados de COVID-19 y seguidos en atención primaria en 2020

Objetivo

Conocer los factores asociados con una evolución desfavorable (ED) de pacientes diagnosticados de COVID-19 con seguimiento total o parcial en atención primaria.

Diseño

Serie de casos.

Emplazamiento

Centro de salud urbano.

Participantes

Pacientes de 18 o más años diagnosticados de COVID-19, con confirmación analítica, en 2020 y seguidos por sus médicos de familia, seleccionados por muestreo sistemático.

Mediciones principales

Variable dependiente: ingreso en hospital o fallecimiento por COVID-19. Variables independientes: edad, sexo, antecedentes personales, y datos clínicos y de tratamiento relacionados con la enfermedad. Análisis estadístico, con SPSS 25.0: estadística descriptiva, comparación de proporciones (X²) y medianas (U de Mann-Whitney). Se completó el análisis con regresión logística.

Resultados

Se incluyeron en el estudio 610 pacientes, cuya mediana de edad era de 49 años (rango intercuartiles: 35-61); 51,8% eran mujeres. Presentaron ED 14,9% (IC 95%: 12,0-17,8). Las variables sociodemográficas o relacionadas con antecedentes personales que mostraron una asociación independiente con una ED fueron edad (OR: 1,066; IC 95%:1,038-1,095), sexo (OR para hombre: 3,277; IC 95%: 1,304-8,235) y ser fumador o exfumador (OR: 2,565; IC 95%: 1,135-5,800). En cuanto a variables clínicas propias de la enfermedad, esa asociación se encontró para alteración de la conciencia (OR: 62,829; IC 95%: 9,177-430,149), disnea (OR: 14,339; IC 95%:6,046-34,009), expectoración (OR: 4,764; IC 95%: 1,858-12,213) y astenia (OR: 3,993; IC 95%: 1,705-9,351).

Conclusiones

Presentaron una ED 14,9% de los pacientes diagnosticados de COVID-19. Una mayor edad, el sexo masculino y ser fumador o exfumador aumentaban la probabilidad de ED. Los datos clínicos que mejor predecían la ED fueron alteración de la conciencia, disnea, expectoración y astenia.

Childhood obesity and risk of SARS-CoV-2 infection

Objective

To estimate the association between childhood obesity and the risk of SARS-CoV-2 infection in a cohort followed from 4 to 12 years of age.

Methods

The data were obtained from two independent sources: the Longitudinal Childhood Obesity Study (ELOIN) and the epidemiological surveillance system data from the Community of Madrid (Spain), which served to identify the population within the cohort with confirmed SARS-CoV-2 infection. The SARS-CoV-2 registry was cross-checked with the cohort population at 11–12 years of age. A total of 2018 eligible participants were identified in the cohort, who underwent physical examinations at 4, 6, and 9 years of age during which weight, height, and waist circumference were recorded. General obesity (GO) was determined according to the WHO-2007 criteria whereas abdominal obesity (AO) was defined based on the International Diabetes Federation (IDF) criteria. The relative risks (RRs) of infection were estimated using a Poisson regression model and adjusted by sociodemographic variables, physical activity, and perceived health reported by the parents.

Results

The accumulated incidence of SARS-CoV-2 infection was 8.6% (95% CI: 7.3–9.8). The estimated RR of SARS-CoV-2 infection was 2.53 (95% CI: 1.56–4.10) and 2.56 (95% CI: 1.55–4.21) for children 4–9 years old with stable GO and AO, respectively, compared with those who did not present GO.

Conclusions

Childhood obesity is an independent risk factor for SARS-CoV-2 infection. This study provides new evidence that indicates that obesity increases the vulnerability of the paediatric population to infectious diseases.

Clinical Presentations of the Survivor and Non-survivor Hospitalized Patients with COVID-19 in the Golestan Province of Iran during the First Peak of the Epidemics

Background

Considering the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic, which causes coronavirus disease 2019 (COVID-19), we aimed to report the clinical features of 427 patients with COVID-19 and the outcomes after one-month admission to major teaching hospitals in the northeast of Iran.

Materials and Methods

Data of patients hospitalized with COVID-19 from 20 February 2020 to 20 April 2020 was analyzed using the R software. The cases and their outcomes were monitored up to one month following their admission.

Results

Among 427 patients with a median age of 53 years (50.8% male), 81 (19%) were directly admitted to the ICU ward, and 68 (16%) died during the study. The mean (SD) lengths of hospital stay were significantly higher in the non-survivors (6 (9) days) than survivors (4 (5) days) (P = 0.018). Ventilation need was reported in 67.6% of the non-survivors and 0.8% of the survivors (P < 0.001). Cough (72.8%), fever (69.3%), and dyspnea (64.0%) were the most common symptoms. There were more comorbidities in the severe cases (73.5%) and non-survivor (77.5%). Liver and kidney damage were significantly more common in non-survivors. Ninety percent of the patients had at least one abnormal chest CT scan finding, including crazy paving and consolidation patterns (27.1%), followed by the ground-glass opacity (24.7%).

Conclusion

Results showed that the patients' age, underlying comorbidities, levels of SpO₂, and laboratory findings at the time of admission may predict the progress of the disease and can be considered mortality-related factors.

ACE2 is the critical in vivo receptor for SARS-CoV-2 in a novel COVID-19 mouse model with TNF- and IFNγ-driven immunopathology

Despite tremendous progress in the understanding of COVID-19, mechanistic insight into immunological, disease-driving factors remains limited. We generated maVie16, a mouse-adapted SARS-CoV-2, by serial passaging of a human isolate. In silico modeling revealed how only three Spike mutations of maVie16 enhanced interaction with murine ACE2. maVie16 induced profound pathology in BALB/c and C57BL/6 mice, and the resulting mouse COVID-19 (mCOVID-19) replicated critical aspects of human disease, including early lymphopenia, pulmonary immune cell infiltration, pneumonia, and specific adaptive immunity. Inhibition of the proinflammatory cytokines IFNγ and TNF substantially reduced immunopathology. Importantly, genetic ACE2-deficiency completely prevented mCOVID-19 development. Finally, inhalation therapy with recombinant ACE2 fully protected mice from mCOVID-19, revealing a novel and efficient treatment. Thus, we here present maVie16 as a new tool to model COVID-19 for the discovery of new therapies and show that disease severity is determined by cytokine-driven immunopathology and critically dependent on ACE2 in vivo.

Biomarkers of ageing and frailty may predict COVID-19 severity

Coronavirus Disease 2019 (COVID-19) is caused by the novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) - the culprit of an ongoing pandemic responsible for the loss of over 3 million lives worldwide within a year and a half. While the majority of SARS-CoV-2 infected people develop no or mild symptoms, some become severely ill and may die from COVID-19-related complications. In this review, we compile and comment on a number of biomarkers that have been identified and are expected to enhance the detection, protection and treatment of individuals at high risk of developing severe illnesses, as well as enable the monitoring of COVID-19 prognosis and responsiveness to therapeutic interventions. Consistent with the emerging notion that the majority of COVID-19 deaths occur in older and frail individuals, we researched the scientific literature and report the identification of a subset of COVID-19 biomarkers indicative of increased vulnerability to developing severe COVID-19 in older and frail patients. Mechanistically, increased frailty results from reduced disease tolerance, a phenomenon aggravated by ageing and comorbidities. While biomarkers of ageing and frailty may predict COVID-19 severity, biomarkers of disease tolerance may predict resistance to COVID-19 with socio-economic factors such as access to adequate health care remaining as major non-biomolecular influencers of COVID-19 outcomes.

CRP, SAA, LDH, and DD predict poor prognosis of coronavirus disease (COVID-19): a meta-analysis from 7739 patients

Understanding factors associated with disease severity and mortality from coronavirus disease (COVID-19) was critical for effective risk stratification. We aimed to investigate the association between biomarkers of clinical laboratory tests, including serum C-reactive protein (CRP), serum amyloid protein (SAA), lactate dehydrogenase (LDH), and D-dimer (DD) and poor prognosis of COVID-19. We have searched many studies on COVID-19 on PubMed (Medline), Web of Science and Cochrane until 1 March 2021. The interest of this study was original articles reporting on laboratory testing projects and outcome of patients with COVID-19 that comprises mortality, acute respiratory distress syndrome (ARDS), need for care in an intensive care unit (ICU), and severe COVID-19. After synthesizing all data, we performed meta-analysis of random effects, and determined mean difference (MD) and standard mean difference at the biomarker level for different disease severity. A total of 7,739 patients with COVID-19 were pooled from 32 studies. CRP was significantly associated with poor prognosis of COVID-19 (SMD = 0.98, 95% CI = (0.85, 1.11), p < .001). Elevated SAA was associated with an increased composite poor outcome in COVID-19 (SMD = 1.06, 95% CI = (0.39, 1.72), p = .002). An elevated LDH was associated with a composite poor outcome (SMD = 1.18, 95% CI = (1.00, 1.36), p < .001). Patients with a composite poor outcome had a higher DD level (SMD = 0.91, 95% CI = (0.79, 1.02), p < .001). This meta-analysis showed that elevated serum CRP, SAA, LDH, and DD were associated with a poor outcome in COVID-19.

Prevalence and characteristics of cancer patients with COVID-19: a meta-analysis study

Background: Cancer patients are considered susceptible to coronavirus disease (COVID-19) due to an immunosuppressive state. This study determined the prevalence of cancer in COVID-19 patients, severe events, case fatality rate, history of anticancer therapy associated with severe events, and type of cancer in cancer patients with COVID-19 in the world. Methods: This study used a meta-analysis study approach, sourcing studies from various countries related to cancer and COVID-19. Inclusion and exclusion criteria were established to select studies. A PRISMA flowchart was presented to assess the selection process. Data from inclusion studies were analyzed using Review Manager 5.4. Results: The prevalence of cancer in COVID-19 patients was 4.63% (95% CI, 3.78-5.49%) worldwide. The lowest prevalence was the Asian study group with 2.36% (95% CI, 1.86-2.87%) and the highest prevalence was the European study group with 10.93% (95% CI, 6.62-15.24%). About 43.26% (95% CI, 34.71-51.80%) of cancer patients with COVID-19 experienced severe events of COVID-19. In total, 58.13% (95% CI, 42.79-73.48%) of cancer patients with COVID-19 who in the last month had a history of anticancer therapy experienced severe events. The prevalence of lung cancer in cancer patients with COVID-19 was 20.23% (95% CI, 7.67-32.78%). Forest plots are also presented related to the results of meta-analysis research. Conclusions: High prevalence of cancer among COVID-19 patients indicates the susceptibility of cancer patients to SARS-CoV-2 infection. Cancer in COVID-19 patients and use of anticancer therapy increase severe events of COVID-19.

A distinct association of inflammatory molecules with outcomes of COVID-19 in younger versus older adults

Aging can alter immunity affecting host defense. COVID-19 has the most devastating clinical outcomes in older adults, raising the implication of immune aging in determining its severity and mortality. We investigated biological predictors for clinical outcomes in a dataset of 13,642 ambulatory and hospitalized adult COVID-19 patients, including younger (age < 65, n = 566) and older (age ≥ 65, n = 717) subjects, with in-depth analyses of inflammatory molecules, cytokines and comorbidities. Disease severity and mortality in younger and older adults were associated with discrete immune mechanisms, including predominant T cell activation in younger adults, as measured by increased soluble IL-2 receptor alpha, and increased IL-10 in older adults although both groups also had shared inflammatory processes, including acute phase reactants, contributing to clinical outcomes. These observations suggest that progression to severe disease and death in COVID-19 may proceed by different immunologic mechanisms in younger versus older subjects and introduce the possibility of age-based immune directed therapies.

Le dosage de la procalcitonine peut-il être utile à la prise en charge d’une infection à SARS-CoV-2 aux urgences et en réanimation ?

De nombreuses études d’impact ont validé l’intérêt du dosage de la procalcitonine pour rationaliser les antibiothérapies dans les infections respiratoires basses. En l’absence d’étude prospective interventionnelle spécifiquement chez les patients atteints de Covid-19, le dosage de la procalcitonine peut cependant être utilisé aux urgences comme dans les autres IRB pour l’aide à la décision d’antibiothérapie. La majorité des infections à SARS-CoV-2 non compliquées ont des valeurs de procalcitonine inférieures à 0,25 μg/l. Parallèlement, des concentrations supérieures à 0,5 μg/l peuvent traduire un risque d’évolution vers une forme grave et/ou une co-infection bactérienne. En soins critiques, la procalcitonine est prometteuse chez les patients avec un syndrome de détresse respiratoire aiguë lié à la Covid. Chez ces patients, la pertinence de l’antibiothérapie est à reconsidérer quotidiennement, compte tenu du risque d’infection bactérienne qui évolue tout au long de séjours souvent prolongés, afin de réduire la sélection d’espèces résistantes et de limiter les toxicités.

Risk Factors for Death Among Hospitalized Patients Aged 21-64 Years Diagnosed with COVID-19-New York City, March 13-April 9, 2020

Background

COVID-19 mortality studies have primarily focused on persons aged ≥ 65 years; less is known about decedents aged <65 years.

Methods

We conducted a case-control study among NYC residents aged 21–64 years hospitalized with COVID-19 diagnosed March 13–April 9, 2020, to determine risk factors for death. Case-patients (n=343) were hospitalized decedents with COVID-19 and control-patients (n=686) were discharged from hospitalization with COVID-19 and matched 2:1 to case-patients on age and residential neighborhood. Conditional logistic regression models were adjusted for patient sex, insurance status, and marital status. Matched adjusted odds ratios (aORs) were calculated for selected underlying conditions, combinations of conditions, and race/ethnic group.

Results

Median age of both case-patients and control-patients was 56 years (range: 23–64 years). Having ≥ 1 selected underlying condition increased odds of death 4.45-fold (95% CI: 2.33–8.49). Patients with diabetes; morbid obesity; heart, kidney, or lung disease; cancer; neurologic/neurodevelopmental conditions; mental health conditions; or HIV had significantly increased odds of death. Compared with having neither condition, having both diabetes and obesity or diabetes and heart disease was associated with approximately threefold odds of death. Five select underlying conditions were more prevalent among non-Hispanic Black control-patients than among control-patients of other races/ethnicities.

Conclusions and Relevance

Selected underlying conditions were risk factors for death, and most prevalent among racial/ethnic minorities. Social services; health care resources, including vaccination; and tailored public health messaging are important for COVID-19 prevention. Strengthening these strategies for racial/ethnic minority groups could minimize COVID-19 racial/ethnic disparities.

SARS-CoV-2 sensing by RIG-I and MDA5 links epithelial infection to macrophage inflammation

SARS-CoV-2 infection causes broad-spectrum immunopathological disease, exacerbated by inflammatory co-morbidities. A better understanding of mechanisms underpinning virus-associated inflammation is required to develop effective therapeutics. Here, we discover that SARS-CoV-2 replicates rapidly in lung epithelial cells despite triggering a robust innate immune response through the activation of cytoplasmic RNA sensors RIG-I and MDA5. The inflammatory mediators produced during epithelial cell infection can stimulate primary human macrophages to enhance cytokine production and drive cellular activation. Critically, this can be limited by abrogating RNA sensing or by inhibiting downstream signalling pathways. SARS-CoV-2 further exacerbates the local inflammatory environment when macrophages or epithelial cells are primed with exogenous inflammatory stimuli. We propose that RNA sensing of SARS-CoV-2 in lung epithelium is a key driver of inflammation, the extent of which is influenced by the inflammatory state of the local environment, and that specific inhibition of innate immune pathways may beneficially mitigate inflammation-associated COVID-19.

SARS-CoV-2 sensing by RIG-I and MDA5 links epithelial infection to macrophage inflammation

SARS-CoV-2 infection causes broad-spectrum immunopathological disease, exacerbated by inflammatory co-morbidities. A better understanding of mechanisms underpinning virus-associated inflammation is required to develop effective therapeutics. Here, we discover that SARS-CoV-2 replicates rapidly in lung epithelial cells despite triggering a robust innate immune response through the activation of cytoplasmic RNA sensors RIG-I and MDA5. The inflammatory mediators produced during epithelial cell infection can stimulate primary human macrophages to enhance cytokine production and drive cellular activation. Critically, this can be limited by abrogating RNA sensing or by inhibiting downstream signalling pathways. SARS-CoV-2 further exacerbates the local inflammatory environment when macrophages or epithelial cells are primed with exogenous inflammatory stimuli. We propose that RNA sensing of SARS-CoV-2 in lung epithelium is a key driver of inflammation, the extent of which is influenced by the inflammatory state of the local environment, and that specific inhibition of innate immune pathways may beneficially mitigate inflammation-associated COVID-19.

Effect of comorbid pulmonary disease on the severity of COVID-19: A systematic review and meta-analysis

Coronavirus disease 2019 (COVID‐19) caused by infection with severe acute respiratory syndrome coronavirus 2 was first detected in Wuhan, China, in late 2019 and continues to spread worldwide. Persistent questions remain about the relationship between the severity of COVID‐19 and comorbid diseases, as well as other chronic pulmonary conditions. In this systematic review and meta‐analysis, we aimed to examine in detail whether the underlying chronic obstructive pulmonary diseases (COPD), asthma and chronic respiratory diseases (CRDs) were associated with an increased risk of more severe COVID‐19. A comprehensive literature search was performed using five international search engines. In the initial search, 722 articles were identified. After eliminating duplicate records and further consideration of eligibility criteria, 53 studies with 658,073 patients were included in the final analysis. COPD was present in 5.2% (2191/42,373) of patients with severe COVID‐19 and in 1.4% (4203/306,151) of patients with non‐severe COVID‐19 (random‐effects model; OR = 2.58, 95% CI = 1.99–3.34, Z = 7.15, p < 0.001). CRD was present in 8.6% (3780/44,041) of patients with severe COVID‐19 and in 5.7% (16,057/280,447) of patients with non‐severe COVID‐19 (random‐effects model; OR = 2.14, 95% CI = 1.74–2.64, Z = 7.1, p < 0.001). Asthma was present in 2.3% (1873/81,319) of patients with severe COVID‐19 and in 2.2% (11,796/538,737) of patients with non‐severe COVID‐19 (random‐effects model; OR = 1.13, 95% CI = 0.79–1.60, Z = 0.66, p = 0.50). In conclusion, comorbid COPD and CRD were clearly associated with a higher severity of COVID‐19; however, no association between asthma and severe COVID‐19 was identified.

A Dynamic Bayesian Model for Identifying High-Mortality Risk in Hospitalized COVID-19 Patients

As Coronavirus Disease 2019 (COVID-19) hospitalization rates remain high, there is an urgent need to identify prognostic factors to improve patient outcomes. Existing prognostic models mostly consider the impact of biomarkers at presentation on the risk of a single patient outcome at a single follow up time. We collected data for 553 Polymerase Chain Reaction (PCR)-positive COVID-19 patients admitted to hospital whose eventual outcomes were known. The data collected for the patients included demographics, comorbidities and laboratory values taken at admission and throughout the course of hospitalization. We trained multivariate Markov prognostic models to identify high-risk patients at admission along with a dynamic measure of risk incorporating time-dependent changes in patients' laboratory values. From the set of factors available upon admission, the Markov model determined that age >80 years, history of coronary artery disease and chronic obstructive pulmonary disease increased mortality risk. The lab values upon admission most associated with mortality included neutrophil percentage, red blood cells (RBC), red cell distribution width (RDW), protein levels, platelets count, albumin levels and mean corpuscular hemoglobin concentration (MCHC). Incorporating dynamic changes in lab values throughout hospitalization lead to dramatic gains in the predictive accuracy of the model and indicated a catalogue of variables for determining high-risk patients including eosinophil percentage, white blood cells (WBC), platelets, pCO2, RDW, large unstained cells (LUC) count, alkaline phosphatase and albumin. Our prognostic model highlights the nuance of determining risk for COVID-19 patients and indicates that, rather than a single variable, a range of factors (at different points in hospitalization) are needed for effective risk stratification.

Sex differences in viral entry protein expression and host transcript responses to SARS-CoV-2

Epidemiological studies suggest that men exhibit a higher mortality rate to COVID-19 than women, yet the underlying biology is largely unknown. Here, we seek to delineate sex differences in the gene expression of viral entry proteins ACE2 and TMPRSS2, and host transcriptional responses to SARS-CoV-2 through large-scale analysis of genomic and clinical data. We first compiled 220,000 human gene expression profiles from three databases and completed the meta-information through machine learning and manual annotation. Large scale analysis of these profiles indicated that male samples show higher expression levels of ACE2 and TMPRSS2 than female samples, especially in the older group (>60 years) and in the kidney. Subsequent analysis of 6,031 COVID-19 patients at Mount Sinai Health System revealed that men have significantly higher creatinine levels, an indicator of impaired kidney function. Further analysis of 782 COVID-19 patient gene expression profiles taken from upper airway and blood suggested men and women present distinct expression changes. Computational deconvolution analysis of these profiles revealed male COVID-19 patients have enriched kidney-specific mesangial cells in blood compared to healthy patients. Together, this study suggests biological differences in the kidney between sexes may contribute to sex disparity in COVID-19.