Microvascular flow alterations in critically ill COVID-19 patients: A prospective study

The microcirculation in the first days of ICU admission in critically ill COVID-19 patients is influenced by severity of disease

The objective of this study was to investigate the relationship between sublingual microcirculatory parameters and the severity of the disease in critically ill coronavirus disease 2019 (COVID-19) patients in the initial period of Intensive Care Unit (ICU) admission in a phase of the COVID-19 pandemic where patients were being treated with anti-inflammatory medication. In total, 35 critically ill COVID-19 patients were included. Twenty-one critically ill COVID-19 patients with a Sequential Organ Failure Assessment (SOFA) score below or equal to 7 were compared to 14 critically ill COVID-19 patients with a SOFA score exceeding 7. All patients received dexamethasone and tocilizumab at ICU admission. Microcirculatory measurements were performed within the first five days of ICU admission, preferably as soon as possible after admission. An increase in diffusive capacity of the microcirculation (total vessel density, functional capillary density, capillary hematocrit) and increased perfusion of the tissues by red blood cells was found in the critically ill COVID-19 patients with a SOFA score of 7-9 compared to the critically ill COVID-19 patients with a SOFA score ≤ 7. No such effects were found in the convective component of the microcirculation. These effects occurred in the presence of administration of anti-inflammatory medication.

Compartment Syndrome of All Extremities in the Setting of COVID-19-Induced Systemic Capillary Leak Syndrome With Superimposed Myositis

Three years following the pandemic's emergence, COVID-19 has continued to affect much of the symptomatic population with widely varied respiratory complaints, fevers, numerous unexpected prodromal manifestations, and unknown long-term consequences. Scattered cases involving myopathies, rhabdomyolysis, and compartment syndrome have also been reported throughout the pandemic. Some similar cases have been attributed to systemic capillary leak syndrome (SCLS). Here, we report the development of compartment syndrome involving all extremities in a 57-year-old vaccinated female known to have COVID-19. In retrospect, we believe the clinical severity and the patient's sudden deterioration can also be attributed to the lesser-known SCLS. Treatment required fasciotomies of both forearms, arms, and legs. This is the most significantly involved case, leading to survival reported thus far. Lab abnormalities, misleading imaging, and symmetric involvement of all extremities posed a significant challenge to proper diagnosis and treatment. This case serves as a reminder for providers to remain cognizant of neurovascular emergencies during the workup of critically ill patients when the presentation is unrecognized and usual treatments are refractory. Its purpose is also to contribute to the global understanding of and response to COVID-19.

COVID-19 detection using a model of photoplethysmography (PPG) signals

Objective:Coronavirus disease 2019 (COVID-19) targets several tissues of the human body; among these, a serious impact has been observed in the microvascular system. The aim of this study was to verify the presence of photoplethysmographic (PPG) signal modifications in patients affected by COVID-19 at different levels of severity.

Approach: The photoplethysmographic signal was evaluated in 93 patients with COVID-19 of different severity (46: grade 1; 47: grade 2) and in 50 healthy control subjects. A pre-processing step removes the long-term trend and segments of each pulsation in the input signal. Each pulse is approximated with a model generated from a multi-exponential curve, and a Least Squares fitting algorithm determines the optimal model parameters. Using the parameters of the mathematical model, three different classifiers (Bayesian, SVM and KNN) were trained and tested to discriminate among healthy controls and patients with COVID, stratified according to the severity of the disease. Results are validated with the leave-one-subject-out validation method.

Main results: Results indicate that the fitting procedure obtains a very high determination coefficient (above 99% in both controls and pathological subjects). The proposed Bayesian classifier obtains promising results, given the size of the dataset, and variable depending on the classification strategy. The optimal classification strategy corresponds to 79% of accuracy, with 90% of specificity and 67% of sensibility.

Significance:The proposed approach opens the possibility of introducing a low cost and non-invasive screening procedure for the fast detection of COVID-19 disease, as well as a promising monitoring tool for hospitalized patients, with the purpose of stratifying the severity of the disease.

Microcirculatory alterations in critically ill COVID-19 patients analyzed using artificial intelligence

BACKGROUND

The sublingual microcirculation presumably exhibits disease-specific changes in function and morphology. Algorithm-based quantification of functional microcirculatory hemodynamic variables in handheld vital microscopy (HVM) has recently allowed identification of hemodynamic alterations in the microcirculation associated with COVID-19. In the present study we hypothesized that supervised deep machine learning could be used to identify previously unknown microcirculatory alterations, and combination with algorithmically quantified functional variables increases the model's performance to differentiate critically ill COVID-19 patients from healthy volunteers.

METHODS

Four international, multi-central cohorts of critically ill COVID-19 patients and healthy volunteers (n = 59/n = 40) were used for neuronal network training and internal validation, alongside quantification of functional microcirculatory hemodynamic variables. Independent verification of the models was performed in a second cohort (n = 25/n = 33).

RESULTS

Six thousand ninety-two image sequences in 157 individuals were included. Bootstrapped internal validation yielded AUROC(CI) for detection of COVID-19 status of 0.75 (0.69-0.79), 0.74 (0.69-0.79) and 0.84 (0.80-0.89) for the algorithm-based, deep learning-based and combined models. Individual model performance in external validation was 0.73 (0.71-0.76) and 0.61 (0.58-0.63). Combined neuronal network and algorithm-based identification yielded the highest externally validated AUROC of 0.75 (0.73-0.78) (P < 0.0001 versus internal validation and individual models).

CONCLUSIONS

We successfully trained a deep learning-based model to differentiate critically ill COVID-19 patients from heathy volunteers in sublingual HVM image sequences. Internally validated, deep learning was superior to the algorithmic approach. However, combining the deep learning method with an algorithm-based approach to quantify the functional state of the microcirculation markedly increased the sensitivity and specificity as compared to either approach alone, and enabled successful external validation of the identification of the presence of microcirculatory alterations associated with COVID-19 status.

Coronary Microvascular Dysfunction is Common in Patients Hospitalized with COVID-19 Infection

Background and Aims

Microvascular disease is considered as one of the main drivers of morbidity and mortality in severe COVID‐19, and microvascular dysfunction has been demonstrated in the subcutaneous and sublingual tissues in COVID‐19 patients. The presence of coronary microvascular dysfunction (CMD) has also been hypothesized, but direct evidence demonstrating CMD in COVID‐19 patients is missing. In the present study, we aimed to investigate CMD in patients hospitalized with COVID‐19, and to understand whether there is a relationship between biomarkers of myocardial injury, myocardial strain and inflammation and CMD.

Methods

39 patients that were hospitalized with COVID‐19 and 40 control subjects were included to the present study. Biomarkers for myocardial injury, myocardial strain, inflammation, and fibrin turnover were obtained at admission. A comprehensive echocardiographic examination, including measurement of coronary flow velocity reserve (CFVR), was done after the patient was stabilized.

Results

Patients with COVID‐19 infection had a significantly lower hyperemic coronary flow velocity, resulting in a significantly lower CFVR (2.0 ± 0.3 vs. 2.4 ± 0.5, p < .001). Patients with severe COVID‐19 had a lower CFVR compared to those with moderate COVID‐19 (1.8 ± 0.2 vs. 2.2 ± 0.2, p < .001) driven by a trend toward higher basal flow velocity. CFVR correlated with troponin (p = .003, r: −.470), B‐type natriuretic peptide (p < .001, r: −.580), C‐reactive protein (p < .001, r: −.369), interleukin‐6 (p < .001, r: −.597), and d‐dimer (p < .001, r: −.561), with the three latter biomarkers having the highest areas‐under‐curve for predicting CMD.

Conclusions

Coronary microvascular dysfunction is common in patients with COVID‐19 and is related to the severity of the infection. CMD may also explain the “cryptic” myocardial injury seen in patients with severe COVID‐19 infection.

Microcirculatory Alterations in Critically Ill Patients with COVID-19-Associated Acute Respiratory Distress Syndrome

Background: Presently, a number of specific observations have been performed on microcirculatory function in a coronavirus disease-19 (COVID-19) setting. We hypothesized that, in the critically ill, endothelial dysfunction secondary to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the subsequent inflammation and coagulopathy may lead to microcirculatory alterations, further exacerbated by the hypoxemic state. A dysfunctional microcirculation may represent the hidden motor underlying the development of COVID-19’s clinical manifestations. Methods: A single center, prospective, observational study. We analyzed bedside sublingual microcirculation in twenty-four consecutive COVID-19-associated acute respiratory distress syndrome (ARDS) patients mechanically ventilated in an Intensive Care Unit (ICU), together with macro-hemodynamics, clinical parameters, echocardiography, and laboratory data at a single time-point after ICU admission. All participants were recruited between March and May 2020. Results: The microcirculatory pattern was characterized by increased values of total vessel density and perfused vessel density, a reduced value of proportion of perfused vessels and microvascular flow index, and high values of heterogeneity index. The duration of mechanical ventilation before microcirculation assessment was inversely associated with the proportion of perfused vessels (p = 0.023). Within the macro-hemodynamic parameters, the right ventricle end-diastolic diameter was inversely associated with proportion of perfused vessels and microvascular flow index (p = 0.039 and 0.014, respectively) and directly associated with the heterogeneity index (p = 0.033). Conclusions: In COVID-19-associated ARDS patients, the microcirculation showed impaired quality of flow parameters coupled with a high vessel density.

Cardiovascular Outcomes in the Acute Phase of COVID-19

The cumulative number of cases in the current global coronavirus disease 19 (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has exceeded 100 million, with the number of deaths caused by the infection having exceeded 2.5 million. Recent reports from most frontline researchers have revealed that SARS-CoV-2 can also cause fatal non-respiratory conditions, such as fatal cardiovascular events. One of the important mechanisms underlying the multiple organ damage that is now known to occur during the acute phase of SARS-CoV-2 infection is impairment of vascular function associated with inhibition of angiotensin-converting enzyme 2. To manage the risk of vascular dysfunction-related complications in patients with COVID-19, it would be pivotal to clearly elucidate the precise mechanisms by which SARS-CoV-2 infects endothelial cells to cause vascular dysfunction. In this review, we summarize the current state of knowledge about the mechanisms involved in the development of vascular dysfunction in the acute phase of COVID-19.