key: cord-0768902-ypllfxhh authors: Yoganandamoorthy, Sarangini; Munasinghe, M.A.D.S.N.; Wanigasuriya, L.V.U.; Priyankara, M.K.K.; Jayasinghe, Saroj title: Non-Invasive Assessment of Endothelial Dysfunction: A novel method to predict severe COVID-19? date: 2020-09-02 journal: Med Hypotheses DOI: 10.1016/j.mehy.2020.110229 sha: 2132a00d0b71c2b9cc48f99d2e1d08d4163186d6 doc_id: 768902 cord_uid: ypllfxhh The COVID-19 pandemic caused by the SARS-CoV-2 virus has infected millions and overburdened the healthcare infrastructure globally. Recent studies show that the endothelial dysfunction caused by the virus contributes to its high morbidity and mortality. A parameter that can identify patients who will develop complications early will be valuable in patient management and reducing the burden on medical resources. An emerging technology is currently being tested to predict the cardiovascular risk via non-invasively measuring the endothelial dysfunction. This paper reviews how the assessment of endothelial dysfunction using this technology can be used as a potential parameter in the prognostication and management of COVID-19 patients. COVID-19 is an ongoing pandemic that has devasted several countries. As of the 12 th of August 2020, a total of 20,162,474 confirmed cases of COVID- 19 have been reported to the WHO, and this includes 737,417 deaths. The highest number of cases so far have been reported from the North and South American region, while the European region currently comes in second. The USA has reported the highest number of cases so far with 5,039,709 confirmed cases, while Brazil comes in second with a total of 3,057,470 confirmed cases and India comes in third with 2,329,638 cases [1] . The key pathogenesis proposed for COVID-19 is an antibody enhanced cytokine storm and endothelial damage [2, 3] . Several countries have been overburdened by the number of patients who require intensive care leading to deaths due to resource limitations. A prognostic predictor may help solve this by identifying patients who are likely to become severe and directing the limited resources to them [4, 5] . Currently, technology that can non-invasively detect endothelial dysfunction is being studied in many countries with regard to their place in assessing cardiovascular risk. This paper proposes how this technology could be used in the prognostication and management of COVID-19 patients. The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus, enters the lungs, and binds to the Angiotensin-Converting Enzyme 2 (ACE2) receptors in the alveolar cells of the pulmonary tissue [2, 3, [6] [7] [8] . Once the virus binds to the receptor, it is internalized by endocytosis [6] and produces viral particles that are released into the surrounding tissue by cell destruction [7] . They either directly enter into the Antigen Presenting Cells (APC) (e.g. s dendritic cells and macrophages) or are phagocytosed while within infected cells by macrophages [9] . These APCs activate the B lymphocytes to differentiate into plasma cells which produce specific antibodies against the virus via CD4 cells in the regional lymph nodes [7, 9] . Specific antibody production leads to a condition known as "antibody enhanced cytokine storm" where these antibodies facilitate the phagocytosis of the virus and it further activates macrophages to produce excess cytokines such as Interleukin (IL)-2, IL-6, Tumor Necrosis Factor (TNF). This uncontrolled inflammatory reaction worsens the patient's condition by causing local and systemic damage [2, 9] . The initial focus of the pathogenesis of COVID-19 was on this Cytokine Storm. However, recent studies are more focused on endothelial damage and dysfunction [3, [6] [7] [8] . Endothelial dysfunction is defined as "a systemic condition in which the endothelium loses its physiological properties, including the tendency to promote vasodilation, fibrinolysis, and anti-aggregation." [8] Findings such as arterial and venous thrombosis leading to microthrombi formation [3, 7] , high Von Willebrand factor levels, and Computed Tomography (CT) findings of extensive vascular damage demonstrate the involvement of endothelial injury in the pathogenesis of COVID-19 [3, 6, 7] . In COVID-19 endothelial damage can occur in several ways. It is theorized that this viral infection downregulates the ACE2 receptors in the pulmonary tissue [6] disturbing the Renin-Angiotensin System, enhancing inflammation and increasing vascular permeability [2] . As ACE2 is also seen in endothelial tissue across all organs, an aggressive immune response causes widespread injury to the endothelium. Elevated levels of cytokines act on the endothelium dampening its antithrombotic action which activates the coagulation system causing thrombosis [3, 6] . Accumulations of inflammatory cells in the endothelium lead to an endothelitis and apoptosis of endothelial cells ultimately resulting in endothelial dysfunction. [10, 11] Nitric Oxide(NO) plays a role in the pathogenesis of COVID-19. Suppression of Nitric Oxide Synthase and preexisting low levels of Nitric Oxide in the endothelium contribute to the endothelial dysfunction [12, 13] Therefore, emerging studies suggest that COVID-19 disease pathogenesis is not only related to the immune response but also associated with widespread endothelial dysfunction and endothelitis leading to complications [3, 6, 10, [13] [14] [15] [16] . Several non-invasive methods are available to assess endothelial dysfunction. The underlying principle of these methods is the analyses of the pulse wave forms of blood flow. The pulse wave produced when blood is pumped into the aorta, rises in systole and falls during diastole. This diastolic drop is interrupted by a bump called the diastolic wave due to the reflection of the pulse wave from "branching points and resistance vessels", creating the asymmetric shape of the wave with the dicrotic notch [17, 18] . Early return of this wave in endothelial dysfunction changes the shape of the pulse wave and leads to the disappearance of the dicrotic notch [19] [20] [21] . One method is to use Photoplethysmography (PPG) to non-invasively assess the shape or contour of the waveform of the peripheries. Another non-invasive method is to measure the change in temperature before, during and after occlusion of blood flow to the arm using a blood pressure cuff. The occlusion of blood flow results in temperature drop and release of the blood flow produces a response known as " Reactive Hyperaemia"-a quick vasodilatory response due to increased Nitric Oxide (NO) production in the endothelial cells [22] . Decreased NO production that is involved in endothelial dysfunction and cardiovascular disease, leads to a slower rise in temperature [23] . Above changes in parameters of the pulse pressure wave and the temperature graph are analysed via machine learning software that uses Artificial Intelligence-based technology to assess the level of endothelial dysfunction [21, 24] . Other than these, invasive methods such as "intracoronary infusion of acetylcholine and quantitative angiography"; semi-invasive methods such as brachial artery plethysmography after infusion of acetylcholine; and non-invasive methods such as brachial artery plethysmography, flow-mediated vasodilation detected by ultrasound and finger plethysmography during reactive hyperemia, have also been used to test endothelial function [18, 25] . Endothelial function is being tested by several research groups to predict cardiovascular risk [26] [27] [28] . This non-invasive test that measures endothelial dysfunction is a potential and practical option to prognosticate patients with COVID-19. We propose 3 potential uses of this artificial intelligence-based technology in COVID-19 patients: 1) to identify patients who develop endothelial dysfunction early and hence are likely to develop complications 2) to identify highrisk patients with preexisting endothelial dysfunction and 3) to monitor the response to therapy. Identifying patients likely to develop endothelial dysfunction due to COVID-19 infection, which is associated with severe disease, could be very important in the management of this disease. Up to now, different scores, methods and models have been proposed to identify patients who will develop severe complications and organ failure but haven't been used in widespread clinical practice [29] . Some of the proposed prognostic parameters include age, sex, comorbidities, C reactive protein, body temperature, creatinine, D-dimer levels, white blood cell count, neutrophil count, lymphocyte count, platelet count, IL-6 and serum ferritin [8, [29] [30] [31] [32] --even artificial intelligence-based systems that use machine learning to identify high-risk patients using CT imaging of the lung have been proposed [33] . Patients who are affected by severe disease and multiple organ failure in COVID-19 develop features of thromboembolic disease and clotting disorders [8, 15, 34] which are also conditions shown to be linked with endothelial dysfunction. Early prognostication will allow better monitoring and management of severely ill patients, in addition to efficient planning and allocation of resources at hospital-level [5, 33] and this may be achieved by detecting the endothelial dysfunction early using this non-invasive technology. Comorbidities such as cardiovascular disease, diabetes mellitus, hypertension, male sex, smoking, obesity and increased age above 45 years have been associated with more complications, worse outcomes and higher fatality in COVID-19 [6, 8, [35] [36] [37] . All these conditions are associated with preexisting endothelial dysfunction [38] [39] [40] [41] and lower endothelial Nitric Oxide levels which may predisposes to the worse outcomes in COVID-19 seen in this group of patients [10, 11, 13] . This technology can be used to identify these high-risk patients before they develop complications. Hence they can be managed in a more intensive setting. Currently, there is no specific therapy for COVID-19 [35, 42] . Various treatment including antivirals, anti-inflammatory drugs, antimalarials have been tested, but the research and trials on treatment are continuously evolving [43] . Targeting endothelial function has also been proposed as a potential treatment option [44] . Currently, the management of severe COVID-19 patients includes respiratory support and intensive care. As further drugs can and will be tested in the future, the measurement of endothelial dysfunction may provide a quick, cost-effective and non-invasive method to monitor the success of these proposed therapies. Early prognostication of COVID-19 patients may be vital to their management. As there is increasing evidence regarding the pathophysiology of COVID-19 linked to endothelial dysfunction and endothelitis, there is also a possibility that preexisting subclinical endothelial dysfunction may be associated with worse outcomes in patients who have not been previously diagnosed with cardiovascular disease, diabetes mellitus and hypertension. The application of this promising technology to non-invasively detect endothelial dysfunction needs to be studied further with regards to improving the management and prognostication of COVID-19 patients. 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Recent studies show that the endothelial dysfunction caused by the virus contributes to its high morbidity and mortality. A parameter that can identify patients who will develop complications early will be valuable in patient management and reducing the burden on medical resources. An emerging technology is currently being tested to predict the cardiovascular risk via non-invasively measuring the endothelial dysfunction. This paper reviews how the assessment of endothelial dysfunction using this technology can be used as a potential parameter in the prognostication and management of COVID-19 patients. Mr. M. K. K. Priyankara is the founder of Jendo Innovations and a patent holder for a software algorithm which is used in a non-invasive method of calculating cardiovascular disease risk using endothelial dysfunction. Prof. Saroj Jayasinghe is a senior advisor (non-financial) to JendoInnovations. Other authors have no conflicts of interest.The authors confirm that the article content has no conflicts of interest.