SVEAT score outperforms HEART score in patients admitted to a chest pain observation unit.

BACKGROUND: Timely and accurate identification of subgroup at risk for major adverse cardiovascular events among patients presenting with acute chest pain remains a challenge. Currently available risk stratification scores are suboptimal. Recently, a new scoring system called the Symptoms, history of Vascular disease, Electrocardiography, Age, and Troponin (SVEAT) score has been shown to outperform the History, Electrocardiography, Age, Risk factors and Troponin (HEART) score, one of the most used risk scores in the United States. AIM: To assess the potential usefulness of the SVEAT score as a risk stratification tool by comparing its performance to HEART score in chest pain patients with low suspicion for acute coronary syndrome and admitted for overnight observation. METHODS: We retrospectively reviewed medical records of 330 consecutive patients admitted to our clinical decision unit for acute chest pain between January 1st to April 17th, 2019. To avoid potential biases, investigators assigned to calculate the SVEAT, and HEART scores were blinded to the results of 30-d combined endpoint of death, acute myocardial infarction or confirmed coronary artery disease requiring revascularization or medical therapy [30-d major adverse cardiovascular event (MACE)]. An area under receiving-operator characteristic curve (AUC) for each score was then calculated. C-statistic and logistic model were used to compare predictive performance of the two scores. RESULTS: A 30-d MACE was observed in 11 patients (3.33% of the subjects). The AUC of SVEAT score (0.8876, 95%CI: 0.82-0.96) was significantly higher than the AUC of HEART score (0.7962, 95%CI: 0.71-0.88), P = 0.03. Using logistic model, SVEAT score with cut-off of 4 or less significantly predicts 30-d MACE (odd ratio 1.52, 95%CI: 1.19-1.95, P = 0.001) but not the HEART score (odd ratio 1.29, 95%CI: 0.78-2.14, P = 0.32). CONCLUSION: The SVEAT score is superior to the HEART score as a risk stratification tool for acute chest pain in low to intermediate risk patients.

Association between electrocardiographic features and mortality in COVID-19 patients.

BACKGROUND: Cardiovascular events have been reported in the setting of coronavirus disease-19 (COVID-19). It has been hypothesized that systemic inflammation may aggravate arrhythmias or trigger new-onset conduction abnormalities. However, the specific type and distribution of electrocardiographic disturbances in COVID-19 as well as their influence on mortality remain to be fully characterized. METHODS: Electrocardiograms (ECGs) were obtained from 186 COVID-19-positive patients at a large tertiary care hospital in Northern Nevada. The following arrhythmias were identified by cardiologists: sinus bradycardia, sinus tachycardia, atrial fibrillation (A-Fib), atrial flutter, multifocal atrial tachycardia (MAT), premature atrial contraction (PAC), premature ventricular contraction (PVC), atrioventricular block (AVB), and right bundle branch block (RBBB). The mean PR interval, QRS duration, and corrected QT interval were documented. Fisher's exact test was used to compare the ECG features of patients who died during the hospitalization with those who survived. The influence of ECG features on mortality was assessed with multivariable logistic regression analysis. RESULTS: A-Fib, atrial flutter, and ST-segment depression were predictive of mortality. In addition, the mean ventricular rate was higher among patients who died as compared to those who survived. The use of therapeutic anticoagulation was associated with reduced odds of death; however, this association did not reach statistical significance. CONCLUSION: The underlying pathogenesis of COVID-19-associated arrhythmias remains to be established, but we postulate that systemic inflammation and/or hypoxia may induce potentially lethal conduction abnormalities in affected individuals. Longitudinal studies are warranted to evaluate the risk factors, pathogenesis, and management of COVID-19-associated cardiac arrhythmias.

Humoral Immunity Profiling of Subjects with Myalgic Encephalomyelitis Using a Random Peptide Microarray Differentiates Cases from Controls with High Specificity and Sensitivity.

Myalgic encephalomyelitis (ME) is a complex, heterogeneous illness of unknown etiology. The search for biomarkers that can delineate cases from controls is one of the most active areas of ME research; however, little progress has been made in achieving this goal. In contrast to identifying biomarkers that are directly involved in the pathological process, an immunosignature identifies antibodies raised to proteins expressed during, and potentially involved in, the pathological process. Although these proteins might be unknown, it is possible to detect antibodies that react to these proteins using random peptide arrays. In the present study, we probe a custom 125,000 random 12-mer peptide microarray with sera from 21 ME cases and 21 controls from the USA and Europe and used these data to develop a diagnostic signature. We further used these peptide sequences to potentially uncover the naturally occurring candidate antigens to which these antibodies may specifically react with in vivo. Our analysis revealed a subset of 25 peptides that distinguished cases and controls with high specificity and sensitivity. Additionally, Basic Local Alignment Search Tool (BLAST) searches suggest that these peptides primarily represent human self-antigens and endogenous retroviral sequences and, to a minor extent, viral and bacterial pathogens.