ABSTRACT
The review discusses current challenges associated with the novel coronavirus disease COVID-19 and cardiovascular diseases. The results of few clinical trials and individual case reports have shown the presence of certain problems in treating patients with comorbidity and viral infection. The new data on the drug interactions are reported. Common patterns of typical cardiovascular diseases and COVID-19 are presented. The risk groups with the need for timely diagnosis and intensive cardiac care are identified to prevent adverse outcomes in patients with this comorbidity.Copyright © 2020 The Author(s).
ABSTRACT
The assessment of functional abilities reflects the ability to perform everyday life activities that require specific endurance and physical fitness. The Fullerton functional fitness test (FFFT) seems to be the most appropriate for assessing physical fitness in heart failure (HF) patients. The study group consisted of 30 consecutive patients hospitalized for the routine assessment of HF with a reduced ejection fraction (HFrEF). They formed the study group, and 24 healthy subjects formed the control group. Each patient underwent a cardiopulmonary exercise test (CPET), transthoracic echocardiography and FFFT modified by adding the measurement of the handgrip force of the dominant limb with the digital dynamometer. The HF patients had significantly lower peak oxygen uptake (peakVO2), maximal minute ventilation, and higher ventilatory equivalent (VE/VCO2). The concentrations of B-type natriuretic peptide (BNP) and N-terminal proBNP (NT-proBNP) were significantly higher in the study group. The results of all the FFFT items were significantly worse in the study group. FFFT parameters, together with the assessment of the strength of the handgrip, strongly correlated with the results of standard tests in HF. FFFT is an effective and safe tool for the functional evaluation of patients with HFrEF. Simple muscle strength measurement with a hand-held dynamometer can become a convenient and practical indicator of muscle strength in HF patients.
Subject(s)
Heart Failure , Exercise Test/methods , Hand Strength , Heart Failure/diagnosis , Humans , Male , Oxygen Consumption/physiology , Stroke Volume/physiologyABSTRACT
BACKGROUND: Adverse events in COVID-19 are difficult to predict. Risk stratification is encumbered by the need to protect healthcare workers. We hypothesize that artificial intelligence (AI) can help identify subtle signs of myocardial involvement in the 12-lead electrocardiogram (ECG), which could help predict complications. OBJECTIVE: Use intake ECGs from COVID-19 patients to train AI models to predict risk of mortality or major adverse cardiovascular events (MACE). METHODS: We studied intake ECGs from 1448 COVID-19 patients (60.5% male, aged 63.4 ± 16.9 years). Records were labeled by mortality (death vs discharge) or MACE (no events vs arrhythmic, heart failure [HF], or thromboembolic [TE] events), then used to train AI models; these were compared to conventional regression models developed using demographic and comorbidity data. RESULTS: A total of 245 (17.7%) patients died (67.3% male, aged 74.5 ± 14.4 years); 352 (24.4%) experienced at least 1 MACE (119 arrhythmic, 107 HF, 130 TE). AI models predicted mortality and MACE with area under the curve (AUC) values of 0.60 ± 0.05 and 0.55 ± 0.07, respectively; these were comparable to AUC values for conventional models (0.73 ± 0.07 and 0.65 ± 0.10). There were no prominent temporal trends in mortality rate or MACE incidence in our cohort; holdout testing with data from after a cutoff date (June 9, 2020) did not degrade model performance. CONCLUSION: Using intake ECGs alone, our AI models had limited ability to predict hospitalized COVID-19 patients' risk of mortality or MACE. Our models' accuracy was comparable to that of conventional models built using more in-depth information, but translation to clinical use would require higher sensitivity and positive predictive value. In the future, we hope that mixed-input AI models utilizing both ECG and clinical data may be developed to enhance predictive accuracy.