ECG and Biomarker Profile in Patients with Acute Heart Failure: A Pilot Study

Background: Biomarkers, electrocardiogram (ECG) and Holter ECG are basic, accessible and feasible cardiac investigations. The combination of their results may lead to a more complex predictive model that may improve the clinical approach in acute heart failure (AHF). The main objective was to investigate which ECG parameters are correlated with the usual cardiac biomarkers (prohormone N-terminal proBNP, high-sensitive cardiac troponin I) in patients with acute heart failure, in a population from Romania. The relationship between certain ECG parameters and cardiac biomarkers may support future research on their combined prognostic value. Methods: In this prospective case-control study were included 49 patients with acute heart failure and 31 participants in the control group. For all patients we measured levels of prohormone N-terminal proBNP (NT-proBNP), high-sensitive cardiac troponin I (hs-cTnI) and MB isoenzyme of creatine phosphokinase (CK-MB) and evaluated the 12-lead ECG and 24 h Holter monitoring. Complete clinical and paraclinical evaluation was performed. Results: NT-proBNP level was significantly higher in patients with AHF (p < 0.001). In patients with AHF, NT-proBNP correlated with cQTi (p = 0.027), pathological Q wave (p = 0.029), complex premature ventricular contractions (PVCs) (p = 0.034) and ventricular tachycardia (p = 0.048). Hs-cTnI and CK-MB were correlated with ST-segment modification (p = 0.038; p = 0.018) and hs-cTnI alone with complex PVCs (p = 0.031). Conclusions: The statistical relationships found between cardiac biomarkers and ECG patterns support the added value of ECG in the diagnosis of AHF. We emphasize the importance of proper ECG analysis of more subtle parameters that can easily be missed. As a non-invasive technique, ECG can be used in the outpatient setting as a warning signal, announcing the acute decompensation of HF. In addition, the information provided by the ECG complements the biomarker results, supporting the diagnosis of AHF in cases of dyspnea of uncertain etiology. Further studies are needed to confirm long-term prognosis in a multi-marker approach.

The electrocardiographic manifestations and derangements of 2019 novel coronavirus disease (COVID-19)

Electrocardiographic (ECG) findings in patients admitted with COVID-19 and a decision tree to predict their survival were assessed. 145 consecutive patients with severe COVID-19 infection were selected. Patient demographics, ECG variables, peak troponins, use of standard medications, and clinical outcomes were analyzed using descriptive and inferential statistics, and a predictive model of survival was developed using classification tree analysis. Of the 145 admitted patients, 38 (26%) died. Deceased patients were more likely to have a significantly higher incidence of poor R-Wave progression [6 of 37 (16.2%) Vs. 0 of 104 (0%), p < 0.001] as well as prolonged QTc values [24 of 37 (64.9%) Vs. 38 of 99 (38.4%), p 0.006]. Significant ST segment depressions were found in 5 of 37 (13.5%) of the deceased category compared to 0% in the non-deceased (p < 0.01). Right and/or left atrial enlargement was more prevalent in the deceased cohort [7 of 37 (18.9%) Vs. 4 of 104 (3.8%), p = 0.03]. Bundle branch blocks were more prevalent in the deceased group [9 of 35 (25.8%) Vs. 7 of 104 (6.7%), p 0.002]. Peak troponins were significantly higher in the deceased group (1.0 Vs 0.07 ng/ml, p < 0.001) A prediction tree built utilizing age, PACs, troponins and QTc had an accuracy of 85.5%. 65 of 74 patients (87.8%) were correctly predicted to survive, while 23 of 29 (79.3%) were correctly predicted to become deceased. Among patients hospitalized with Covid-19, the parameters of age, QT interval, troponin and PACs are useful for prognostication and help predict survival with reasonable accuracy.