Exposure to SARS-CoV-2 in a high transmission setting increases the risk of severe COVID-19 compared with exposure to a low transmission setting?

Path Analysis to Assess Socio-Economic and Mitigation Measure Determinants for Daily Coronavirus Infections

(1) Background: With the rapid global spread of the coronavirus disease 2019 (COVID-19) and the relatively high daily cases recorded in a short time compared to other types of seasonal flu, the world remains under continuous threat unless we identify the key factors that contribute to these unexpected records. This identification is important for developing effective criteria and plans to reduce the spread of the COVID-19 pandemic and can guide national authorities to tighten or reduce mitigation measures, in addition to spreading awareness of the important factors that contribute to the propagation of the disease. (2) Methods: The data represents the daily infections (210 days) in four different countries (China, Italy, Iran, and Lebanon) taken approximately in the same duration, between January and March 2020. Path analysis was implemented on the data to detect the significant factors that affect the daily COVID-19 infections. (3) Results: The path coefficients show that quarantine commitment (β = −0.823) and full lockdown measures (β = −0.775) have the largest direct effect on COVID-19 daily infections. The results also show that more experience (β = −0.35), density in society (β = −0.288), medical resources (β = 0.136), and economic resources (β = 0.142) have indirect effects on daily COVID-19 infections. (4) Conclusions: The COVID-19 daily infections directly decrease with complete lockdown measures, quarantine commitment, wearing masks, and social distancing. COVID-19 daily cases are indirectly associated with population density, special events, previous experience, technology used, economic resources, and medical resources.

New Scoring System for Predicting Mortality in Patients with COVID-19

PURPOSE

We aimed to develop a novel mortality scoring system for inpatients with COVID-19 based on simple demographic factors and laboratory findings.

MATERIALS AND METHODS

We reviewed and analyzed data from patients who were admitted and diagnosed with COVID-19 at 10 hospitals in Daegu, South Korea, between January and July 2020. We randomized and assigned patients to the development and validation groups at a 70% to 30% ratio. Each point scored for selected risk factors helped build a new mortality scoring system using Cox regression analysis. We evaluated the accuracy of the new scoring system in the development and validation groups using the area under the curve.

RESULTS

The development group included 1232 patients, whereas the validation group included 528 patients. In the development group, predictors for the new scoring system as selected by Cox proportional hazards model were age ≥70 years, diabetes, chronic kidney disease, dementia, C-reactive protein levels >4 mg/dL, infiltration on chest X-rays at the initial diagnosis, and the need for oxygen support on admission. The areas under the curve for the development and validation groups were 0.914 [95% confidence interval (CI) 0.891-0.937] and 0.898 (95% CI 0.854-0.941), respectively. According to our scoring system, COVID-19 mortality was 0.4% for the low-risk group (score 0-3) and 53.7% for the very high-risk group (score ≥11).

CONCLUSION

We developed a new scoring system for quickly and easily predicting COVID-19 mortality using simple predictors. This scoring system can help physicians provide the proper therapy and strategy for each patient.

Will SARS-CoV-2 Become Just Another Seasonal Coronavirus?

The future prevalence and virulence of SARS-CoV-2 is uncertain. Some emerging pathogens become avirulent as populations approach herd immunity. Although not all viruses follow this path, the fact that the seasonal coronaviruses are benign gives some hope. We develop a general mathematical model to predict when the interplay among three factors, correlation of severity in consecutive infections, population heterogeneity in susceptibility due to age, and reduced severity due to partial immunity, will promote avirulence as SARS-CoV-2 becomes endemic. Each of these components has the potential to limit severe, high-shedding cases over time under the right circumstances, but in combination they can rapidly reduce the frequency of more severe and infectious manifestation of disease over a wide range of conditions. As more reinfections are captured in data over the next several years, these models will help to test if COVID-19 severity is beginning to attenuate in the ways our model predicts, and to predict the disease.

Outcomes of COVID-19 Among Hospitalized Health Care Workers in North America

Importance

Although health care workers (HCWs) are at higher risk of acquiring coronavirus disease 2019 (COVID-19), it is unclear whether they are at risk of poorer outcomes.

Objective

To evaluate the association between HCW status and outcomes among patients hospitalized with COVID-19.

Design, Setting, and Participants

This retrospective, observational cohort study included consecutive adult patients hospitalized with a diagnosis of laboratory-confirmed COVID-19 across 36 North American centers from April 15 to June 5, 2020. Data were collected from 1992 patients. Data were analyzed from September 10 to October 1, 2020.

Exposures

Data on patient baseline characteristics, comorbidities, presenting symptoms, treatments, and outcomes were collected, including HCW status.

Main Outcomes and Measures

The primary outcome was a requirement for mechanical ventilation or death. Multivariable logistic regression was performed to yield adjusted odds ratios (AORs) and 95% CIs for the association between HCW status and COVID-19-related outcomes in a 3:1 propensity score-matched cohort, adjusting for residual confounding after matching.

Results

In total, 1790 patients were included, comprising 127 HCWs and 1663 non-HCWs. After 3:1 propensity score matching, 122 HCWs were matched to 366 non-HCWs. Women comprised 71 (58.2%) of matched HCWs and 214 (58.5%) of matched non-HCWs. Matched HCWs had a mean (SD) age of 52 (13) years, whereas matched non-HCWs had a mean (SD) age of 57 (17) years. In the matched cohort, the odds of the primary outcome, mechanical ventilation or death, were not significantly different for HCWs compared with non-HCWs (AOR, 0.60; 95% CI, 0.34-1.04). The HCWs were less likely to require admission to an intensive care unit (AOR, 0.56; 95% CI, 0.34-0.92) and were also less likely to require an admission of 7 days or longer (AOR, 0.53; 95% CI, 0.34-0.83). There were no differences between matched HCWs and non-HCWs in terms of mechanical ventilation (AOR, 0.66; 95% CI, 0.37-1.17), death (AOR, 0.47; 95% CI, 0.18-1.27), or vasopressor requirements (AOR, 0.68; 95% CI, 0.37-1.24).

Conclusions and Relevance

In this propensity score-matched multicenter cohort study, HCW status was not associated with poorer outcomes among hospitalized patients with COVID-19 and, in fact, was associated with a shorter length of hospitalization and decreased likelihood of intensive care unit admission. Further research is needed to elucidate the proportion of HCW infections acquired in the workplace and to assess whether HCW type is associated with outcomes.

Multiple Peaked Cytokine Storm: Is Multiple Exposures to the COVID-19 Virus a Possible Cause?

Severe acute respiratory syndrome coronavirus 2 is the pathogen that causes coronavirus disease-2019 (COVID-19). Recent studies have shown that the “cytokine storm” (high concentration of proinflammatory cytokines) may contribute to the mortality of COVID-19. Repeated exposure to the virus can lead to a dose-dependent immune response that may be associated with more disease severity and higher mortality. Sudden deterioration/increased oxygen consumption after initial improvement may be due to multiple surges of cytokines storm within a short period, the possible cause may be due to multiple exposures within the incubation period. We hypothesize that multiple surges in cytokines storm in some patients may be due to multiple exposures of the same patient within the incubation period, sepsis, or other inflammatory lesions inside the body. In our cases, sepsis as a cause of cytokine storm was ruled out by doing the procalcitonin test, which was within the normal limit.

Death Rate Due to COVID-19 in Alzheimer’s Disease and Frontotemporal Dementia

We aimed to evaluate the frequency and mortality of COVID-19 in patients with Alzheimer’s disease (AD) and frontotemporal dementia (FTD). We conducted an observational case series. We enrolled 204 patients, 15.2% of whom were diagnosed with COVID-19, and 41.9% of patients with the infection died. Patients with AD were older than patients with FTD (80.36±8.77 versus 72.00±8.35 years old) and had a higher prevalence of arterial hypertension (55.8% versus 26.3%). COVID-19 occurred in 7.3% of patients living at home, but 72.0% of those living at care homes. Living in care facilities and diagnosis of AD were independently associated with a higher probability of death. We found that living in care homes is the most relevant factor for an increased risk of COVID-19 infection and death, with AD patients exhibiting a higher risk than those with FTD.

Overall decrease in SARS-CoV-2 viral load and reduction in clinical burden: the experience of a hospital in northern Italy

Objectives

In Italy the burden of patients with coronavirus disease 2019 (COVID-19) gradually decreased from March to the end of May. In this work we aimed to evaluate a possible association between the severity of clinical manifestations and viral load over time during the epidemiological transition from high-to low-transmission settings.

Methods

We reviewed the cases of COVID-19 diagnosed at the emergency room of our hospital, retrieving the proportion of patients admitted to the intensive care unit. A raw estimation of the viral load was done evaluating the Ct (cycle threshold) trend obtained from our diagnostic reverse transcriptase real-time PCR test.

Results

The proportion of patients requiring intensive care significantly decreased from 6.7% (19/281) in March to 1.1% (1/86) in April, and to none in May (Fisher's test p 0.0067). As for viral load, we observed a trend of Ct increasing from a median value of 24 (IQR 19–29) to 34 (IQR 29–37) between March and May, with a statistically significant difference between March and April (pairwise Wilcoxon test with stepdown Bonferroni adjustment for multiple testing, p 0.0003).

Conclusions

We observed a reduction over time in the proportion of patients with COVID-19 requiring intensive care, along with decreasing median values of viral load. As the epidemiological context changes from high-to low-transmission settings, people are presumably exposed to a lower viral load which has been previously associated with less severe clinical manifestations.

Predictors of COVID-19 severity: a systematic review and meta-analysis

Background: The unpredictability of the progression of coronavirus disease 2019 (COVID-19) may be attributed to the low precision of the tools used to predict the prognosis of this disease. Objective: To identify the predictors associated with poor clinical outcomes in patients with COVID-19. Methods: Relevant articles from PubMed, Embase, Cochrane, and Web of Science were searched as of April 5, 2020. The quality of the included papers was appraised using the Newcastle-Ottawa scale (NOS). Data of interest were collected and evaluated for their compatibility for the meta-analysis. Cumulative calculations to determine the correlation and effect estimates were performed using the Z test. Results: In total, 19 papers recording 1,934 mild and 1,644 severe cases of COVID-19 were included. Based on the initial evaluation, 62 potential risk factors were identified for the meta-analysis. Several comorbidities, including chronic respiratory disease, cardiovascular disease, diabetes mellitus, and hypertension were observed more frequent among patients with severe COVID-19 than with the mild ones. Compared to the mild form, severe COVID-19 was associated with symptoms such as dyspnea, anorexia, fatigue, increased respiratory rate, and high systolic blood pressure. Lower levels of lymphocytes and hemoglobin; elevated levels of leukocytes, aspartate aminotransferase, alanine aminotransferase, blood creatinine, blood urea nitrogen, high-sensitivity troponin, creatine kinase, high-sensitivity C-reactive protein, interleukin 6, D-dimer, ferritin, lactate dehydrogenase, and procalcitonin; and a high erythrocyte sedimentation rate were also associated with severe COVID-19. Conclusion: More than 30 risk factors are associated with a higher risk of severe COVID-19. These may serve as useful baseline parameters in the development of prediction tools for COVID-19 prognosis.

Predictors of COVID-19 severity: a systematic review and meta-analysis

Background: The unpredictability of the progression of coronavirus disease 2019 (COVID-19) may be attributed to the low precision of the tools used to predict the prognosis of this disease. Objective: To identify the predictors associated with poor clinical outcomes in patients with COVID-19. Methods: Relevant articles from PubMed, Embase, Cochrane, and Web of Science were searched and extracted as of April 5, 2020. Data of interest were collected and evaluated for their compatibility for the meta-analysis. Cumulative calculations to determine the correlation and effect estimates were performed using the Z test. Results: In total, 19 papers recording 1,934 mild and 1,644 severe cases of COVID-19 were included. Based on the initial evaluation, 62 potential risk factors were identified for the meta-analysis. Several comorbidities, including chronic respiratory disease, cardiovascular disease, diabetes mellitus, and hypertension were observed more frequent among patients with severe COVID-19 than with the mild ones. Compared to the mild form, severe COVID-19 was associated with symptoms such as dyspnea, anorexia, fatigue, increased respiratory rate, and high systolic blood pressure. Lower levels of lymphocytes and hemoglobin; elevated levels of leukocytes, aspartate aminotransferase, alanine aminotransferase, blood creatinine, blood urea nitrogen, high-sensitivity troponin, creatine kinase, high-sensitivity C-reactive protein, interleukin 6, D-dimer, ferritin, lactate dehydrogenase, and procalcitonin; and a high erythrocyte sedimentation rate were also associated with severe COVID-19. Conclusion: More than 30 risk factors are associated with a higher risk of severe COVID-19. These may serve as useful baseline parameters in the development of prediction tools for COVID-19 prognosis.