Predictors of Cardiovascular Risk in Covid-19 Patients During Acute Disease and at Short Term Follow-up

Background



COVID-19 has shown a lower case-fatality rate compared to other major viral outbreaks in

contemporary history, including severe acute respiratory syndrome (SARS) of 2002-2003.

However, the relative susceptibility to symptomatic infection and the case fatality risk

increase substantially after 60 years of age, in men, and in overweight patients, raising

questions about the underlying biology of host responses. This includes possible genetic

derterminants of sex bias. Cardiac involvement, as characterized, by elevation of cardiac

Troponin I and brain-type, natriuretic peptide, is frequent in COVID-19 and it is associated

with worse prognosis. Myocardial injury and heart failure accounted for 40% of deaths in a

Wuhan cohort, either exclusively or in conjunction with respiratory failure. Thus, it seems

that cardiac involvement is both prevalent and of prognostic significance in COVID-19.

However, both the actual incidence of myocardial injury (biomarkers elevation may simply

reflect systemic illness in critically-ill patients) and the pathophysiology of cardiac

involvement remain to be clarified. The SARS-CoV-2 virus interacts through the structural

glycopeptides of the "crown" spikes with its cellular target that, in humans, is the

angiotensin2 (ACE2) converting enzyme, expressed in particular in the heart and lungs. ACE2

is used by SARS-CoV-2 to be internalized by alveolar epithelial cells. Therefore, chronic

intake of ACE inhibitors, or sartans, may influence the course of the COVID-19 disease

because an increased expression of ACE2 (such as that induced by ACEi therapy) could

facilitate the internalization of the virus and the progression of infection. However, the

infection by the virus leads to the down-regulation of ACE2. The imbalance between ACE and

ACE2 leads to an increase in angiotensin II, which binds AT1R, which increases pulmonary

vascular permeability and lung damage. Thus, the role ACEi and ARBs on the susceptibility to

SARS-COv-2 infection remain to be clarified.



COVID-19 is characterized by changes in heart rate and cardiac autonomic modulation, systemic

activation of inflammatory processes, with endothelial damage and involvement of

cardiovascular (CV) and respiratory systems. Although most patients remain asymptomatic or

mildly symptomatic, in a subset of them the host inflammatory response continues to amplify

with progressive lymphocytopenia, high white blood cells and neutrophil counts, to end-up

with a systemic inflammation characterized by multiple organ failure and elevation of key

inflammation markers (e.g. interleukin, tumor necrosis factor, interferon-y inducible

protein, etc.). These biomarkers are not just indicators of inflammation, but are also

associated with prognosis. Patients who died of COVID-19 showed higher levels of IL-6,

ferritin and CRP. Moreover, biomarkers of myocardial injury and ECG abnormalities were

associated with elevated inflammatory markers suggesting an indirect mechanism of cardiac

injury. However, recent data have demonstrated the presence of the virus within the

myocardium of some COVID-19 pts, implicating also direct myocardial injury. Also low Vit.D,

with immunomodulating action, is associated with poor outcome. Another interesting aspect of

the complex pathophysiology of COVID-19 is the finding that 71.4% of nonsurvivors and 0.6% of

survivors in a Wuhan hospital showed overt disseminated intravascular coagulation (DIC). It

is well known that sepsis is a common cause of DIC and inflammatory cytokines can promote the

activation of blood coagulation in many ways. However, whether SARS-Cov-2 is more prone to

DIC development and the role of anticoagulation in determining the prognosis in COVID-19 need

to be established. Finally, no data is available on the short-term sequelae in COVID-19 pts

after discharge, in terms of residual structural and functional cardiorespiratory damage and

its determinants (viral, inflammatory, metabolic and pro-thrombotic factors).



Hyphotesis and Significance



We hypothesize that COVID-19 could represent a "new" CV risk factor inducing acute and

chronic CV changes able to affect clinical evolution and long term prognosis. Suggested

important mechanisms of COVID-19 severity related to injury of CV and respiratory systems

include: 1) a pro-inflammatory cytokine storm, with endothelial damage and DIC; 2) patients'

demographic and clinical features (age, sex, body mass index, genetic factors, autonomic

cardiac modulation, medical history in particular diabetes and CV diseases, sleep disordered

breathing, low vitamin D levels, thyroid dysfunction, and current drug treatment); 3)

evidence of cardiac damage during course of the disease. All these possible determinants of

COVID-19 severity need to be systematically evaluated according to an integrated approach in

a large number of patients developing COVID-19 of different severity, including inpatients

and outpatients. Given the complexity of the hypothesized multifold pathogenetic mechanisms,

also approaches to data analysis through artificial intelligence (machine learning

algorithms) may allow to develop multivariable models to 1) effectively risk stratify

patients to identify those at highest risk requiring more intensive support; 2) Promptly

recognize patients most vulnerable for adverse outcomes, to prioritize palliative care and

improve cost/effectiveness of healthcare resources deployed. Finally, no information is yet

available on the short term residual structural and functional consequences on the immune, CV

and respiratory systems following discharge of COVID-19 patients who have recovered from

acute disease.



Preliminary Data



In spite of the widespread use of mechanical ventilation in patients with severe COVID-19 and

hypoxemia, hypoventilation is uncommon in these patients. Conversely, hypoxemia is usually

accompanied by an increased alveolar-to arterial O2 gradient, signifying either

ventilation-perfusion mismatch or intra-pulmonary shunting. The presence of a significant

ventilation-perfusion mismatch is further supported in COVID-19 patients by the increase of

PaO2 with supplemental oxygen. Whereas, when PaO2 does not increase with supplemental oxygen,

presence of intra-pulmonary shunt is the most likely cause of hypoxemia. Moreover,

preliminary data from China indicate that 71.4% of nonsurvivors and 0.6% of survivors in a

Wuhan hospital showed evidence of DIC. One critical mediator of DIC is the release of tissue

factor (TF), a glycoprotein activator of blood coagulation cascade present on surface of many

activated cell types, and of circulating microvesicles (MV). COVID-19 appears characterized

by predominantly pro-thrombotic DIC with high venous thromboembolism rates, elevated D-dimer

and fibrinogen levels in concert with low anti-thrombin levels, and pulmonary congestion with

microvascular thrombosis and occlusion on pathology and evidence of ischemic limbs, stroke,

myocardial infarction in critically ill patients. D-dimer is a biomarker of coagulation

activation triggered by TF but it does not identify per se the molecular mechanisms (venous

or arterial) and/or the dysfunctional cell population involved. MVs have received increasing

attention as novel players in CV disease (CVD). A subgroup of procoagulant MVs express also

TF, predict CV events and identify patients at high recurrence risk. COVID-19 clinical

manifestations are also similar to those of other autoimmune/inflammatory disorders in which

a thrombophilic vasculopathy is sustained by systemic inflammation, with activation of the

complement cascade. Also low levels of Vit D and thyroid dysfunction seem to characterize

more severe disease. However, the cross-link between inflammation and coagulation, as well as

the role of host biology, previous treatments and clinical history in modulating the clinical

course of COVID-19 remain to be clarified.



Aims



1. To investigate the epidemiological link of patients' clinical characteristics (gender,

BMI, age, presence of CV risk factors, ongoing treatment, underlying CV diseases and

myocardial injury) with outcomes.



2. To evaluate the pathophysiological role of: 1) activation of immune system and host

inflammatory response; 2) activation of thrombotic and coagulation factors and

endothelial damage (with possible DIC; 3) metabolic and endocrinologic factors,

including thyroid dysfunction, low Vit.D (vs ARDS); 4) occurrence of sleep related

breathing disorders and alterations in autonomic cardiac modulation; 5) genetic X-linked

factors related to ACE2 expression and to gender bias 6) cardiac structural and

functional changes as assessed by cardiac ultrasounds and MRI. Artificial intelligence

methods will also be applied to risk-stratify patients affected by SARS-Cov-2



3. To identify the persistence of viral load, immunologic or coagulation alterations

(plasma, cell or MV-related), and respiratory and cardiovascular consequences of

COVID-19, by clinical/instrumental follow-up assessment at 3-6 months after discharge



Sample size



For Aim 1, in this epidemiologic survey we expect to include about 5500 patients: 4500

in-patients and 1000 out-patients. For Aim 2 and 3, because the context is underpinned by

relatively sparse knowledge, ours will be considered as pilot assessments with no formal

sample size calculation. For Aim 3 we will include roughly 3000 discharged patients Specific

aim 1. Patients will be divided in two groups to identify outcome predictors. a) controls:

individuals who did not develop severe COVID-19, b) cases: individuals who developed severe

disease. The lack of enough knowledge in COVID- 19 patients about predictors of outcome

limits the performance of standard regression models. Machine learning techniques can

facilitate the objective interpretation of medical observations in building risk score. In

particular, a combination of association rule mining with the Dempster-Shafer theory (DST)

can compute probabilistic associations between clinical features and outcomes.



Statistical analysis



Specific aim 2.To identify the relationship between each potential group of predictors and

in-patients prognosis, we will apply multivariate logistic regression models. All association

estimates will be reported as Odds Ratio (OR) and relative 95% confidence intervals. To

address the problem of variable selection in high dimensional data (numerous predictors and

confounders), we will use a new statistical approach based on random forest. To overcome

problems due to uncommon outcome we will consider alternative regression model as

log-binomial and Poisson regression with robust variance The development of a

machine-learning algorithm to identify a new score of prognosis will be based on the above

results and conducted on a subsample of in-patients with all potential predictors and

phenotype. The sample will be randomly divided into training (70%) and validation (30%) set.

The training set will be used to build the score applying several machine learning

algorithms. The score with the best predictive performance (C-index) on the validation set

will be chosen by means of the two-tailed adequate hypothesis testing of equal predictive

performance assuming I error type of 0.05 and power of 80%. When the null hypothesis will not

be refused, the parsimony criterion will be applied.



Specific aim 3. To characterize patients at follow-up in terms of viral load and alterations

of immune or coagulative systems and respiratory/cardiovascular consequences, we will apply

generalized linear mixed models which take into account the correlated response during time

of the same patient, modeling appropriately the variance-covariance matrix of repeated

measurements.