COVID-19 and Acute Cardiac Injury: Clinical Manifestations, Biomarkers, Mechanisms, Diagnosis, and Treatment.

PURPOSE OF REVIEW: This review aims to comprehensively explore the clinical characteristics of COVID-19-related cardiac injury and examine the potential mechanisms underlying cardiac injury in patients affected by COVID-19. RECENT FINDINGS: The COVID-19 pandemic has primarily been associated with severe respiratory symptoms. However, emerging evidence has indicated that a significant number of COVID-19 patients also experience myocardial injury, leading to conditions such as acute myocarditis, heart failure, acute coronary syndrome, and arrhythmias. The incidence of myocardial injury is notably higher in patients with preexisting cardiovascular diseases. Myocardial injury often manifests with elevated levels of inflammation biomarkers, as well as abnormalities observed on electrocardiograms and echocardiograms. COVID-19 infection has been found to be associated with myocardial injury, which can be attributed to several pathophysiological mechanisms. These mechanisms include injury caused by hypoxia, resulting from respiratory compromise, a systemic inflammatory response triggered by the infection, and direct attack on the myocardium by the virus itself. Furthermore, the angiotensin-converting enzyme 2 (ACE2) receptor plays a crucial role in this process. Early recognition, prompt diagnosis, and a comprehensive understanding of the underlying mechanisms are essential for effectively managing and reducing the mortality associated with myocardial injury in COVID-19 patients.

Location Scheme of Routine Nucleic Acid Testing Sites Based on Location-Allocation Models: A Case Study of Shenzhen City

Since late 2019, the explosive outbreak of Coronavirus Disease 19 (COVID-19) has emerged as a global threat, necessitating a worldwide overhaul of public health systems. One critical strategy to prevent virus transmission and safeguard public health, involves deploying Nucleic Acid Testing (NAT) sites. Nevertheless, determining the optimal locations for public NAT sites presents a significant challenge, due to the varying number of sites required in different regions, and the substantial influences of population, the population heterogeneity, and daily dynamics, on the effectiveness of fixed location schemes. To address this issue, this study proposes a data-driven framework based on classical location-allocation models and bi-objective optimization models. The framework optimizes the number and location of NAT sites, while balancing various cost constraints and adapting to population dynamics during different periods of the day. The bi-objective optimization process utilizes the Knee point identification (KPI) algorithm, which is computationally efficient and does not require prior knowledge. A case study conducted in Shenzhen, China, demonstrates that the proposed framework provides a broader service coverage area and better accommodates residents' demands during different periods, compared to the actual layout of NAT sites in the city. The study's findings can facilitate the rapid planning of primary healthcare facilities, and promote the development of sustainable healthy cities.

Using the internet search data to investigate symptom characteristics of COVID-19: A big data study.

OBJECTIVE: Analyzing the symptom characteristics of Coronavirus Disease 2019(COVID-19) to improve control and prevention. METHODS: Using the Baidu Index Platform (http://index.baidu.com) and the website of Chinese Center for Disease Control and Prevention as data resources to obtain the search volume (SV) of keywords for symptoms associated with COVID-19 from January 1 to February 20 in each year from 2017 to 2020 and the epidemic data in Hubei province and the other top 9 impacted provinces in China. Data of 2020 were compared with those of the previous three years. Data of Hubei province were compared with those of the other 9 provinces. The differences and characteristics of the SV of COVID-19-related symptoms, and the correlations between the SV of COVID-19 and the number of newly confirmed/suspected cases were analyzed. The lag effects were discussed. RESULTS: Comparing the SV from January 1, 2020 to February 20, 2020 with those for the same period of the previous three years, Hubei's SV for cough, fever, diarrhea, chest tightness, dyspnea, and other symptoms were significantly increased. The total SV of lower respiratory symptoms was significantly higher than that of upper respiratory symptoms (P＜0.001). The SV of COVID-19 in Hubei province was significantly correlated with the number of newly confirmed/suspected cases (r confirmed = 0.723, r suspected = 0.863, both p < 0.001). The results of the distributed lag model suggested that the patients who searched relevant symptoms on the Internet may begin to see doctors in 2-3 days later and be confirmed in 3-4 days later. CONCLUSION: The total SV of lower respiratory symptoms was higher than that of upper respiratory symptoms, and the SV of diarrhea also increased significantly. It warned us to pay attention to not only the symptoms of the lower respiratory tract but also the gastrointestinal symptoms, especially diarrhea in patients with COVID-19. Internet search behavior had a positive correlation with the number of newly confirmed/suspected cases, suggesting that big data has an important role in the early warning of infectious diseases.

Clinical characteristics of pediatric cases infected with the SARS-CoV-2 Omicron variant in a tertiary children's medical center in Shanghai, China.

BACKGROUND: The number of pediatric cases of infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant has increased. Here, we describe the clinical characteristics of children in a tertiary children's medical center in Shanghai. METHODS: A total of 676 pediatric coronavirus disease 2019 (COVID-19) cases caused by the Omicron variant who were admitted to the Shanghai Children's Medical Center from March 28 to April 30, 2022 were enrolled in this single-center, prospective, observational real-world study. Patient demographics and clinical characteristics, especially COVID-19 vaccine status, were assessed. RESULTS: Children of all ages appeared susceptible to the SARS-CoV-2 Omicron variant, with no significant difference between sexes. A high SARS-CoV-2 viral load upon admission was associated with leukocytopenia, neutropenia, and thrombocytopenia (P = 0.003, P = 0.021, and P = 0.017, respectively) but not with physical symptoms or radiographic chest abnormalities. Univariable linear regression models indicated that comorbidities (P = 0.001) were associated with a longer time until viral clearance, and increasing age (P < 0.001) and two doses of COVID-19 vaccine (P = 0.001) were associated with a shorter time to viral clearance. Multivariable analysis revealed an independent effect of comorbidities (P < 0.001) and age (P = 0.003). The interaction effect between age and comorbidity showed that the negative association between age and time to virus clearance remained significant only in patients without underlying diseases (P < 0.001). CONCLUSION: This study describes the clinical characteristics of children infected with the Omicron variant of SARS-CoV-2 and calls for additional studies to evaluate the effectiveness and safety of vaccination against COVID-19 in children.

Development and validation of prognosis model of mortality risk in patients with COVID-19.

This study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. Machine-learning methods (Random Forest and XGboost) were used to rank clinical features for mortality risk. Multivariate logistic regression models were applied to identify clinical features with statistical significance. The predictors of mortality were lactate dehydrogenase (LDH), C-reactive protein (CRP) and age based on 500 bootstrapped samples. A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission.

Efficacy of Qingfei Paidu Decoction on Patients with COVID-19 Pneumonia in Wuhan, China: A Propensity Score Matching Study.

BACKGROUND: In view of the global efforts to develop effective treatments for the current worldwide coronavirus 2019 (COVID-19) pandemic, Qingfei Paidu decoction (QPD), a novel traditional Chinese medicine (TCM) prescription, was formulated as an optimized combination of constituents of classic prescriptions used to treat numerous febrile and respiratory-related diseases. This prescription has been used to treat patients with COVID-19 pneumonia in Wuhan, China. Hypothesis/Purpose. We hypothesized that QPD would have beneficial effects on patients with COVID-19. We aimed to prove this hypothesis by evaluating the efficacy of QPD in patients with COVID-19 pneumonia. METHODS: In this single-center, retrospective, observational study, we identified eligible participants who received a laboratory diagnosis of COVID-19 between January 15 and March 15, 2020, in the west campus of Union Hospital in Wuhan, China. QPD was supplied as an oral liquid packaged in 200-mL containers, and patients were orally administered one package twice daily 40 minutes after a meal. The primary outcome was death, which was compared between patients who did and did not receive QPD (QPD and NoQPD groups, respectively). Propensity score matching (PSM) was used to identify cohorts. RESULTS: In total, 239 and 522 participants were enrolled in the QPD and NoQPD groups, respectively. After PSM at a 1 : 1 ratio, 446 patients meeting the criteria were included in the analysis with 223 in each arm. In the QPD and NoQPD groups, 7 (3.2%) and 29 (13.0%) patients died, and those in the QPD group had a significantly lower risk of death (hazard ratio (HR) 0.29, 95% CI: 0.13-0.67) than those in the NoQPD group (p = 0.004). Furthermore, the survival time was significantly longer in the QPD group than in the NoQPD group (p < 0.001). CONCLUSION: The use of QPD may reduce the risk of death in patients with COVID-19 pneumonia.

Mobile isolation wards in a fever clinic: a novel operation model during the COVID-19 pandemic.

A fever clinic within a hospital plays a vital role in pandemic control because it serves as an outpost for pandemic discovery, monitoring and handling. As the outbreak of coronavirus disease 2019 (COVID-19) in Wuhan was gradually brought under control, the fever clinic in the West Campus of Wuhan Union Hospital introduced a new model for construction and management of temporary mobile isolation wards. A traditional battlefield hospital model was combined with pandemic control regulations, to build a complex of mobile isolation wards that used adaptive design and construction for medical operational, medical waste management and water drainage systems. The mobile isolation wards allowed for the sharing of medical resources with the fever clinic. This increased the capacity and efficiency of receiving, screening, triaging and isolation and observation of patients with fever. The innovative mobile isolation wards also controlled new sudden outbreaks of COVID-19. We document the adaptive design and construction model of the novel complex of mobile isolation wards and explain its characteristics, functions and use.