Heart Rate in Patients with SARS-CoV-2 Infection: Prevalence of High Values at Discharge and Relationship with Disease Severity

A pilot study: Exploring the influence of COVID-19 on cardiovascular physiology and retinal microcirculation

BACKGROUND

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) affects the cardiovascular system. The current study investigated changes in heart rate (HR), blood pressure (BP), pulse wave velocity (PWV), and microcirculation in patients recovering from Coronavirus disease 2019 (COVID-19) infection.

METHODOLOGY

Out of 43 initially contacted COVID-19 patients, 35 (30 males, 5 females; age: 60 ± 10 years; and body mass index (BMI): 31.8 ± 4.9) participated in this study. Participants were seen on two occasions after hospital discharge; the baseline measurements were collected, either on the day of hospital discharge if a negative PCR test was obtained, or on the 10th day after hospitalization if the PCR test was positive. The second measurements were done 60 days after hospitalization. The vascular measurements were performed using the VICORDER® device and a retinal blood vessel image analysis.

RESULTS

A significant increase in systolic BP (SBP) (from 142 mmHg, SD: 15, to 150 mmHg, SD: 19, p = 0.041), reduction in HR (from 76 bpm, SD: 15, to 69 bpm, SD: 11, p = 0.001), and narrower central retinal vein equivalent (CRVE) (from 240.94 μm, SD: 16.05, to 198.05 μm, SD: 17.36, p = 0.013) were found. Furthermore, the trends of increasing PWV (from 11 m/s, SD: 3, to 12 m/s, SD: 3, p = 0.095) and decreasing CRAE (from 138.87 μm, SD: 12.19, to 136.77 μm, SD: 13.19, p = 0.068) were recorded.

CONCLUSION

The present study investigated cardiovascular changes following COVID-19 infection at two-time points after hospital discharge (baseline measurements and 60 days post-hospitalization). Significant changes were found in systolic blood pressure, heart rate, and microvasculature indicating that vascular adaptations may be ongoing even weeks after hospitalization from COVID-19 infection. Future studies could involve conducting additional interim assessments during the active infection and post-infection periods.

Elevated High Sensitivity Cardiac Troponin T is Nonlinearly Associated with Poor Prognosis in Aging COVID-19 Patients: A Retrospective Study

Objective

To evaluate the relationship between high-sensitivity cardiac troponin T (hs-cTnT) and prognosis in elderly patients with coronavirus disease 2019 (COVID-19).

Methods

This study recruited 1399 COVID-19 patients aged 65 years or older admitted to Taizhou Hospital or Enze Hospital in Zhejiang Province from December 15, 2022, to January 4, 2023. The Cox regression model was used to evaluate the relationship between hs-cTnT level and in-hospital death. The logistic regression model was used to evaluate the relationship between hs-cTnT level and major adverse events. The restricted cubic spline (RCS) model is used for nonlinear analysis.

Results

The increase of peak hs-cTnT was associated with the increased risk of death (HR 9.49; 95% CI 1.19-75.48; trend p = 0.012) and major adverse events (OR 20.59; 95% CI 10.41-40.71; trend p < 0.001). RCS model showed the association between peak hs-cTnT and death and major adverse events were nonlinear (P for non-line < 0.001). Starting from the limit of quantification of hs-cTnT (3ng/L), the risk of death and major adverse events had a steep S-shaped increase until hs-cTnT reached the 90th percentile point (hs-cTnT = 131 ng/L), followed by a plateau period. Peak hs-cTnT has good predictive value for death and major adverse events, with AUC of 0.834 and 0.804, respectively.

Conclusion

Peak hs-cTnT has high predictive value for the risk of in-hospital death and major adverse events in elderly patients with COVID-19. The association between peak hs-cTnT and death and major adverse events were nonlinear.

A multimodal AI-based non-invasive COVID-19 grading framework powered by deep learning, manta ray, and fuzzy inference system from multimedia vital signs

The COVID-19 pandemic has presented unprecedented challenges to healthcare systems worldwide. One of the key challenges in controlling and managing the pandemic is accurate and rapid diagnosis of COVID-19 cases. Traditional diagnostic methods such as RT-PCR tests are time-consuming and require specialized equipment and trained personnel. Computer-aided diagnosis systems and artificial intelligence (AI) have emerged as promising tools for developing cost-effective and accurate diagnostic approaches. Most studies in this area have focused on diagnosing COVID-19 based on a single modality, such as chest X-rays or cough sounds. However, relying on a single modality may not accurately detect the virus, especially in its early stages. In this research, we propose a non-invasive diagnostic framework consisting of four cascaded layers that work together to accurately detect COVID-19 in patients. The first layer of the framework performs basic diagnostics such as patient temperature, blood oxygen level, and breathing profile, providing initial insights into the patient's condition. The second layer analyzes the coughing profile, while the third layer evaluates chest imaging data such as X-ray and CT scans. Finally, the fourth layer utilizes a fuzzy logic inference system based on the previous three layers to generate a reliable and accurate diagnosis. To evaluate the effectiveness of the proposed framework, we used two datasets: the Cough Dataset and the COVID-19 Radiography Database. The experimental results demonstrate that the proposed framework is effective and trustworthy in terms of accuracy, precision, sensitivity, specificity, F1-score, and balanced accuracy. The audio-based classification achieved an accuracy of 96.55%, while the CXR-based classification achieved an accuracy of 98.55%. The proposed framework has the potential to significantly improve the accuracy and speed of COVID-19 diagnosis, allowing for more effective control and management of the pandemic. Furthermore, the framework's non-invasive nature makes it a more attractive option for patients, reducing the risk of infection and discomfort associated with traditional diagnostic methods.

The potential role of SARS‐CoV‐2 infection in acute coronary syndrome and type 2 myocardial infarction (T2MI): Intertwining spread

Coronavirus disease 2019 (COVID‐19) is a novel pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). It has been shown that SARS‐CoV‐2 infection‐induced inflammatory and oxidative stress and associated endothelial dysfunction may lead to the development of acute coronary syndrome (ACS). Therefore, this review aimed to ascertain the link between severe SARS‐CoV‐2 infection and ACS. ACS is a spectrum of acute myocardial ischemia due to a sudden decrease in coronary blood flow, ranging from unstable angina to myocardial infarction (MI). Primary or type 1 MI (T1MI) is mainly caused by coronary plaque rupture and/or erosion with subsequent occlusive thrombosis. Secondary or type 2 MI (T2MI) is due to cardiac and systemic disorders without acute coronary atherothrombotic disruption. Acute SARS‐CoV‐2 infection is linked with the development of nonobstructive coronary disorders such as coronary vasospasm, dilated cardiomyopathy, myocardial fibrosis, and myocarditis. Furthermore, SARS‐CoV‐2 infection is associated with systemic inflammation that might affect coronary atherosclerotic plaque stability through augmentation of cardiac preload and afterload. Nevertheless, major coronary vessels with atherosclerotic plaques develop minor inflammation during COVID‐19 since coronary arteries are not initially and primarily targeted by SARS‐CoV‐2 due to low expression of angiotensin‐converting enzyme 2 in coronary vessels. In conclusion, SARS‐CoV‐2 infection through hypercytokinemia, direct cardiomyocyte injury, and dysregulation of the renin‐angiotensin system may aggravate underlying ACS or cause new‐onset T2MI. As well, arrhythmias induced by anti‐COVID‐19 medications could worsen underlying ACS.

Autoimmunity in Long Covid and POTS

Orthostatic intolerance and other autonomic dysfunction syndromes are emerging as distinct symptom clusters in Long Covid. Often accompanying these are common, multi-system constitutional features such as fatigue, malaise and skin rashes which can signify generalized immune dysregulation. At the same time, multiple autoantibodies are identified in both Covid-related autonomic disorders and non-Covid autonomic disorders, implying a possible underlying autoimmune pathology. The lack of specificity of these findings precludes direct interpretations of cause and association, but their prevalence with its supporting evidence is compelling.

Assessment of hypertension and other factors associated with the severity of disease in COVID-19 pneumonia, Addis Ababa, Ethiopia: A case-control study

BACKGROUND

Various reports suggested that pre-existing medical illnesses, including hypertension and other demographic, clinical, and laboratory factors, could pose an increased risk of disease severity and mortality among COVID-19 patients. This study aimed to assess the relation of hypertension and other factors to the severity of COVID-19 pneumonia in patients discharged from Eka Kotebe Hospital in June-September, 2020.

METHODS

This is a single-center case-control study of 265 adult patients discharged alive or dead, 75 with a course of severe COVID-19 for the cases arm and 190 with the non-severe disease for the control arm. Three age and sex-matched controls were selected randomly for each patient on the case arm. Chi-square, multivariable binary logistic regression, and odds ratio (OR) with a 95% confidence interval was used to assess the association between the various factors and the severity of the disease. A p-value of <0.05 is considered statistically significant.

RESULTS

Of the 265 study participants, 80% were male. The median age was 43 IQR(36-60) years. Both arms had similar demographic characteristics. Hypertension was strongly associated with the severity of COVID-19 pneumonia based on effect outcome adjustment (AOR = 2.93, 95% CI 1.489, 5.783, p-value = 0.002), similarly, having diabetes mellitus (AOR = 3.17, 95% CI 1.374, 7.313, p-value<0.007), chronic cardiac disease (AOR = 4.803, 95% CI 1.238-18.636, p<0.023), and an increase in a pulse rate (AOR = 1.041, 95% CI 1.017, 1.066, p-value = 0.001) were found to have a significant association with the severity of COVID-19 pneumonia.

CONCLUSIONS

Hypertension was associated with the severity of COVID-19 pneumonia, and so were diabetes mellitus, chronic cardiac disease, and an increase in pulse rate.

Incident Atrial Fibrillation and In-Hospital Mortality in SARS-CoV-2 Patients

(1) Background: Among the different cardiovascular (CV) manifestations of the coronavirus disease 2019 (COVID-19), arrhythmia and atrial fibrillation (AF) in particular have recently received special attention. The aims of our study were to estimate the incidence of AF in patients hospitalized for COVID-19, and to evaluate its role as a possible predictor of in-hospital all-cause mortality. (2) Methods: We enrolled 3435 people with SARS-CoV2 infection admitted to three hospitals in Northern Italy from February 2020 to May 2021. We collected data on their clinical history, laboratory tests, pharmacological treatment and intensive care unit (ICU) admission. Incident AF and all-cause in-hospital mortality were considered as outcomes. (3) Results: 145 (4.2%) patients developed AF during hospitalization, with a median time since admission of 3 days (I-III quartile: 0, 12). Patients with incident AF were admitted more frequently to the ICU (39.3 vs. 12.4%, p < 0.001), and more frequently died (37.2 vs. 16.9%, p < 0.001). In the Cox regression model, the significant determinants of incident AF were age (HR: 1.041; 95% CI: 1.022, 1.060 per year), a history of AF (HR: 2.720; 95% CI: 1.508, 4.907), lymphocyte count (HR: 0.584; 95% CI: 0.384, 0.888 per 103/µL), estimated glomerular filtration rate (eGFR, HR: 0.988; 95% CI: 0.980, 0.996 per mL/min) and ICU admission (HR: 5.311; 95% CI: 3.397, 8.302). Incident AF was a predictor of all-cause mortality (HR: 1.405; 95% CI: 1.027, 1.992) along with age (HR: 1.057; 95% CI: 1.047, 1.067), male gender (HR: 1.315; 95% CI: 1.064; 1.626), dementia (HR: 1.373; 95% CI: 1.045, 1.803), lower platelet (HR: 0.997; 95% CI: 0.996, 0.998 per 103/µL) and lymphocyte counts (HR: 0.843; 95% CI: 0.725, 0.982 per 103/µL), C-Reactive protein values (HR: 1.004; 95% CI: 1.003, 1.005 per mg/L), eGFR (HR: 0.990; 95% CI: 0.986, 0.994 per mL/min), and ICU admission (HR: 1.759; 95% CI: 1.292, 2.395). (4) Conclusions: Incident AF is a common complication in COVID-19 patients during hospitalization, and its occurrence strongly predicts in-hospital mortality.

The Future Evolution of the Mortality Acceleration Due to the COVID-19: The Charlson Comorbidity Index in Stochastic Setting

The empirical evidence from different countries point out many of those who die from coronavirus would have died anyway in the relatively near future due to their existing frailties or co-morbidities. The acceleration of the mortality conceives the underlying insight according to deaths are “accelerated” ahead of schedule due to COVID-19. Starting from this idea, we forecast the future mortality acceleration, based on the deterioration due to the presence of the comorbidities at COVID-19 diagnosis. Accordingly, we explicitly determine the contribution of each comorbidity on the acceleration forecasting, showing the future trend of the excess of deaths due to the COVID-19. To this aim, our proposal consists in developing a revised Charlson Comorbidity Index in a stochastic environment. Based on a post-stratification scheme, we obtain an unbiased comorbidity index that varies by age, centered on the reference population.

Autonomic Dysfunction during Acute SARS-CoV-2 Infection: A Systematic Review

Although autonomic dysfunction (AD) after the recovery from Coronavirus disease 2019 (COVID-19) has been thoroughly described, few data are available regarding the involvement of the autonomic nervous system (ANS) during the acute phase of SARS-CoV-2 infection. The primary aim of this review was to summarize current knowledge regarding the AD occurring during acute COVID-19. Secondarily, we aimed to clarify the prognostic value of ANS involvement and the role of autonomic parameters in predicting SARS-CoV-2 infection. According to the PRISMA guidelines, we performed a systematic review across Scopus and PubMed databases, resulting in 1585 records. The records check and the analysis of included reports’ references allowed us to include 22 articles. The studies were widely heterogeneous for study population, dysautonomia assessment, and COVID-19 severity. Heart rate variability was the tool most frequently chosen to analyze autonomic parameters, followed by automated pupillometry. Most studies found ANS involvement during acute COVID-19, and AD was often related to a worse outcome. Further studies are needed to clarify the role of autonomic parameters in predicting SARS-CoV-2 infection. The evidence emerging from this review suggests that a complex autonomic nervous system imbalance is a prominent feature of acute COVID-19, often leading to a poor prognosis.

Analysis of Risk Factors for In-Hospital Death Due to COVID-19 in Patients Hospitalised at the Temporary Hospital Located at the National Stadium in Warsaw: A Retrospective Analysis

The outbreak of the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic has affected all aspects of social life and brought massive changes to the healthcare sector. The aim of this study was to identify the factors affecting the mortality of COVID-19 patients at a temporary hospital in Warsaw (Poland). The present study was conducted based on a retrospective analysis of the medical records of patients hospitalised at the temporary hospital located at the National Stadium in Warsaw between 1 March 2020 and 30 April 2021. The study included all cases of patients who were brought directly or transferred to the National Hospital from other hospitals for further treatment. With regard to comorbidities, the analysis found that five comorbidities—namely, diabetes (OR = 1.750, 95% CI: 1.009−2.444, p < 0.05), stroke history (OR = 2.408, 95% CI: 1.208−4.801, p < 0.05), renal failure (OR = 2.141, 95% CI: 1.052−4.356, p < 0.05), chronic obstructive pulmonary disease (OR = 2.044, 95% CI: 1.133−3.690, p < 0.05) and heart failure (OR = 1.930, 95% CI: 1.154−3.227, p < 0.05)—had a significant impact on the survival of COVID-19 patients. The analysis identified 14 factors that had a significant impact on the prognosis and mortality of the COVID-19 patients studied.