Persistent Increase of Sympathetic Activity in Post-Acute COVID-19 of Paucisymptomatic Healthcare Workers.

Healthcare workers (HCWs) represent a population with a significant burden of paucisymptomatic COVID-19, as the general population. We evaluated autonomic nervous system activity by means of heart rate variability (HRV) in HCWs during health surveillance visits. Short-term electrocardiogram (ECG) recordings were obtained 30 days (IQR 5.25-55.75) after a negative naso-pharyngeal swab for SARS-CoV-2 in 44 cases and compared with ECGs of 44 controls with similar age and sex distribution. Time and frequency domain HRV were evaluated. HCWs who used drugs, had comorbidities that affected HRV, or were hospitalized with severe COVID-19 were excluded. Frequency domain HRV analysis showed a significantly higher low/high-frequency power ratio (LF/HF) in the case study compared with controls (t = 2.84, p = 0.006). In time domain HRV analysis, mean standard deviation of normal-to-normal intervals (SDNN) and root mean square of successive RR interval differences (RMSSD) were significantly lower for cases compared with controls (t = -2.64, p = 0.01 and t = -3.27, p = 0.002, respectively). In the post-acute phase of infection, SARS-CoV-2 produces an autonomic imbalance mirrored by a reduction in HRV. These results are consistent with epidemiological data that suggest a higher risk of acute cardiovascular complications in the first 30 days after COVID-19 infection.

Cardiovascular/Stroke Risk Stratification in Parkinson's Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review.

Parkinson's disease (PD) is a severe, incurable, and costly condition leading to heart failure. The link between PD and cardiovascular disease (CVD) is not available, leading to controversies and poor prognosis. Artificial Intelligence (AI) has already shown promise for CVD/stroke risk stratification. However, due to a lack of sample size, comorbidity, insufficient validation, clinical examination, and a lack of big data configuration, there have been no well-explained bias-free AI investigations to establish the CVD/Stroke risk stratification in the PD framework. The study has two objectives: (i) to establish a solid link between PD and CVD/stroke; and (ii) to use the AI paradigm to examine a well-defined CVD/stroke risk stratification in the PD framework. The PRISMA search strategy selected 223 studies for CVD/stroke risk, of which 54 and 44 studies were related to the link between PD-CVD, and PD-stroke, respectively, 59 studies for joint PD-CVD-Stroke framework, and 66 studies were only for the early PD diagnosis without CVD/stroke link. Sequential biological links were used for establishing the hypothesis. For AI design, PD risk factors as covariates along with CVD/stroke as the gold standard were used for predicting the CVD/stroke risk. The most fundamental cause of CVD/stroke damage due to PD is cardiac autonomic dysfunction due to neurodegeneration that leads to heart failure and its edema, and this validated our hypothesis. Finally, we present the novel AI solutions for CVD/stroke risk prediction in the PD framework. The study also recommends strategies for removing the bias in AI for CVD/stroke risk prediction using the PD framework.

Deceleration capacity is associated with acute respiratory distress syndrome in COVID-19.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is considered the main cause of COVID-19 associated morbidity and mortality. Early and reliable risk stratification is of crucial clinical importance in order to identify persons at risk for developing a severe course of disease. Deceleration capacity (DC) of heart rate as a marker of cardiac autonomic function predicts outcome in persons with myocardial infarction and heart failure. We hypothesized that reduced modulation of heart rate may be helpful in identifying persons with COVID-19 at risk for developing ARDS. METHODS: We prospectively enrolled 60 consecutive COVID-19 positive persons presenting at the University Hospital of Tuebingen. Arterial blood gas analysis and 24 h-Holter ECG recordings were performed and analyzed at admission. The primary end point was defined as development of ARDS with regards to the Berlin classification. RESULTS: 61.7% (37 of 60 persons) developed an ARDS. In persons with ARDS DC was significantly reduced when compared to persons with milder course of infection (3.2 ms vs. 6.6 ms, p < 0.001). DC achieved a good discrimination performance (AUC = 0.76) for ARDS in COVID-19 persons. In a multivariate analysis, decreased DC was associated with the development of ARDS. CONCLUSION: Our data suggest a promising role of DC to risk stratification in COVID-19.