A review of pathophysiological mechanism, diagnosis, and treatment of thrombosis risk associated with COVID-19 infection

Coagulopathy and adverse outcomes in hospitalized patients with COVID-19: results from the NOR-Solidarity trial

Background

Several studies have examined parameters of increased thrombogenicity in COVID-19, but studies examining their association with long-term outcome and potential effects of antiviral agents in hospitalized patients with COVID-19 are scarce.

Objectives

To evaluate plasma levels of hemostatic proteins during hospitalization in relation to disease severity, treatment modalities, and persistent pulmonary pathology after 3 months.

Methods

In 165 patients with COVID-19 recruited into the NOR-Solidarity trial (NCT04321616) and randomized to treatment with hydroxychloroquine, remdesivir, or standard of care, we analyzed plasma levels of hemostatic proteins during the first 10 days of hospitalization (n = 160) and at 3 months of follow-up (n = 100) by enzyme immunoassay.

Results

Our main findings were as follows: (i) tissue plasminogen activator (tPA) and tissue factor pathway inhibitor (TFPI) were increased in patients with severe disease (ie, the combined endpoint of respiratory failure [Po2-to-FiO2 ratio, <26.6 kPa] or need for treatment at an intensive care unit) during hospitalization. Compared to patients without severe disease, tPA levels were a median of 42% (P < .001), 29% (P = .002), and 36% (P = .015) higher at baseline, 3 to 5 days, and 7 to 10 days, respectively. For TFPI, median levels were 37% (P = .003), 25% (P < .001), and 10% (P = .13) higher in patients with severe disease at these time points, respectively. No changes in thrombin-antithrombin complex; alpha 2-antiplasmin; a disintegrin and metalloproteinase with a thrombospondin type 1 motif, member 13; or antithrombin were observed in relation to severe disease. (ii) Patients treated with remdesivir had lower levels of TFPI than those in patients treated with standard of care alone. (iii) TFPI levels during hospitalization, but not at 3 months of follow-up, were higher in those with persistent pathology on chest computed tomography imaging 3 months after hospital admission than in those without such pathology. No consistent changes in thrombin-antithrombin complex, alpha 2-antiplasmin, ADAMTS-13, tPA, or antithrombin were observed in relation to pulmonary pathology at 3 months of follow-up.

Conclusion

TFPI and tPA are associated with severe disease in hospitalized patients with COVID-19. For TFPI, high levels measured during the first 10 days of hospitalization were also associated with persistent pulmonary pathology even 3 months after hospital admittance.

A U-shaped protection of altitude against mortality and infection of COVID-19 in Peru: an ecological study

BACKGROUND

The COVID-19 pandemic has affected the world in multiple ways and has been a challenge for the health systems of each country. From the beginning, risk factors for the severity and mortality of the disease were considered, as the spread of the virus was related to the living conditions of each population.

METHODS

In this ecological study we have evaluated the role of geography, precisely the altitude above sea level in the incidence and mortality of COVID-19 in Peru. Incidence and mortality data were taken from the open-access database of the government of Peru until March 2021. COVID-19 cases and COVID-19 mortality were treated as cases/density population and 1000 x cases/inhabitants while altitude was treated as continuous and as a categorical variable divided in 7 categories. The relationship between COVID-19 cases or deaths for COVID-19 and altitude as continuous variable was determined using Spearman correlation test. Meanwhile when altitude was considered as a categorical variable, Poisson regression or negative binomial analyses were applied.

RESULTS

A significant inverse correlation was found between COVID-19 cases by population density and altitude (r=-0.37 p < 0.001). By altitude categories, the lowest risk for infection was observed between 3,000 and 3,500 m (IRR 0.08; 95% CI 0.05,0.12). Moreover, we found an inverse correlation between altitude and COVID-19 mortality (r=-0.39 p < 0.001). Also, the lowest risk for mortality was observed between 3,000 and 3,500 m (IRR 0.12; 95%CI 0.08; 0.18). Similar results were found when analyses were adjusted for inhabitants and stratified by sex.

CONCLUSION

This study reports an inverse relationship between COVID-19 incidence and mortality with respect to the altitude of residence, particularly, a u-shaped protection is shown, with a highest benefit between 3000 and 3500 m. The possibility of using hypoxia as an alternative treatment requires more complex studies that should allow knowing the physiological and environmental mechanisms of the protective role.

A comprehensive and systematic literature review on the big data management techniques in the internet of things

The Internet of Things (IoT) is a communication paradigm and a collection of heterogeneous interconnected devices. It produces large-scale distributed, and diverse data called big data. Big Data Management (BDM) in IoT is used for knowledge discovery and intelligent decision-making and is one of the most significant research challenges today. There are several mechanisms and technologies for BDM in IoT. This paper aims to study the important mechanisms in this area systematically. This paper studies articles published between 2016 and August 2022. Initially, 751 articles were identified, but a paper selection process reduced the number of articles to 110 significant studies. Four categories to study BDM mechanisms in IoT include BDM processes, BDM architectures/frameworks, quality attributes, and big data analytics types. Also, this paper represents a detailed comparison of the mechanisms in each category. Finally, the development challenges and open issues of BDM in IoT are discussed. As a result, predictive analysis and classification methods are used in many articles. On the other hand, some quality attributes such as confidentiality, accessibility, and sustainability are less considered. Also, none of the articles use key-value databases for data storage. This study can help researchers develop more effective BDM in IoT methods in a complex environment.

Biochemical Parameters as Prognostic Markers in Severely Ill COVID-19 Patients

Background

Prognostication plays a pivotal role in critical care medicine. Its importance is indisputable in the management of coronavirus disease 2019 (COVID-19), as the presentation of this disease may vary from docile, self-limiting symptoms to lethal conditions. Amid the COVID-19 pandemic, much emphasis was initially placed on molecular and serological testing. However, it was realized later that routine laboratory tests also provide key information in terms of the severity of the disease and thus could be used to predict the outcome of these patients.

Methodology

The aim of our study was to evaluate the biochemical parameters as prognostic markers in severely ill COVID-19 patients. We carried out a retrospective, case-control study. The study population was comprised of all severely ill COVID-19 patients admitted between October 2020 and January 2021 at our level 3 COVID hospital. Cases were defined as the patients who expired despite treatment and all resuscitative measures as per the standard operating procedures (SOPs) of our COVID intensive care unit (ICU) while controls were defined as the patients that were transferred out of the COVID ICU for further recovery. The detailed history, findings of physical examination, vitals recorded by point of care testing (POCT) devices at our ICU, clinical diagnosis, and the results of the biochemical analysis were recorded in a specially designed pro forma. The biochemical parameters recorded at the time of admission were compared between the groups of controls and cases in order to evaluate their role as predictors of mortality using appropriate statistical methods. P-values less than 0.05 were considered statistically significant. For all the parameters that showed a statistically significant difference, receiver operating characteristics (ROC) analysis was done to assess the utility of biochemical parameters as predictors of mortality or survival. Areas under the curve (AUCs) of 0.6 to 0.7, 0.7 to 0.8, 0.8 to 0.9, and >0.9 were considered acceptable, fair, good, and excellent for discrimination, respectively.

Results

Of the 178 severely ill COVID-19 patients enrolled in the study, 86 were controls and 92 were cases (52% mortality). Serum urea (p<0.0001), creatinine (p=0.0019), aspartate transaminase (AST) (p=0.0104), lactate dehydrogenase (LDH) (p=0.0001), procalcitonin (PCT) (p=0.0344), and interleukin 6 (IL-6) (p=0.0311) levels were significantly higher (p<0.05), while total protein (p=0.0086), albumin (p<0.0001), and indirect bilirubin (p=0.0147) levels were significantly lower (p<0.05) in cases as compared to controls. The difference was statistically insignificant (p>0.05) for serum sodium, potassium, total and direct bilirubin, globulin, alanine transaminase (ALT), alkaline phosphatase (ALP), D-dimer, and ferritin. On ROC analysis, urea was fair (AUC=0.721), creatinine (AUC=0.698) and IL-6 (AUC=0.698) were acceptable predictors of mortality, while albumin (AUC=0.698) was an acceptable predictor of survival in severely ill COVID-19 patients during their intensive care stay.

Conclusion

Understanding the pathophysiological changes associated with the severity of COVID-19 in terms of an alteration of biochemical parameters is a pressing priority. Our study highlights the importance of routine laboratory tests in predicting outcomes in severely ill COVID-19 patients.