Clinical values of resting electrocardiography in patients with known or suspected chronic coronary artery disease: a stress perfusion cardiac MRI study

Correlations between Resting Electrocardiogram Findings and Disease Profiles: Insights from the Qatar Biobank Cohort

BACKGROUND

Resting electrocardiogram (ECG) is a valuable non-invasive diagnostic tool used in clinical medicine to assess the electrical activity of the heart while the patient is resting. Abnormalities in ECG may be associated with clinical biomarkers and can predict early stages of diseases. In this study, we evaluated the association between ECG traits, clinical biomarkers, and diseases and developed risk scores to predict the risk of developing coronary artery disease (CAD) in the Qatar Biobank.

METHODS

This study used 12-lead ECG data from 13,827 participants. The ECG traits used for association analysis were RR, PR, QRS, QTc, PW, and JT. Association analysis using regression models was conducted between ECG variables and serum electrolytes, sugars, lipids, blood pressure (BP), blood and inflammatory biomarkers, and diseases (e.g., type 2 diabetes, CAD, and stroke). ECG-based and clinical risk scores were developed, and their performance was assessed to predict CAD. Classical regression and machine-learning models were used for risk score development.

RESULTS

Significant associations were observed with ECG traits. RR showed the largest number of associations: e.g., positive associations with bicarbonate, chloride, HDL-C, and monocytes, and negative associations with glucose, insulin, neutrophil, calcium, and risk of T2D. QRS was positively associated with phosphorus, bicarbonate, and risk of CAD. Elevated QTc was observed in CAD patients, whereas decreased QTc was correlated with decreased levels of calcium and potassium. Risk scores developed using regression models were outperformed by machine-learning models. The area under the receiver operating curve reached 0.84 using a machine-learning model that contains ECG traits, sugars, lipids, serum electrolytes, and cardiovascular disease risk factors. The odds ratio for the top decile of CAD risk score compared to the remaining deciles was 13.99.

CONCLUSIONS

ECG abnormalities were associated with serum electrolytes, sugars, lipids, and blood and inflammatory biomarkers. These abnormalities were also observed in T2D and CAD patients. Risk scores showed great predictive performance in predicting CAD.

Integrative review of electrocardiographic characteristics in patients with reduced, mildly reduced, and preserved heart failure

INTRODUCTION

Electrocardiographic (ECG) changes in heart failure with reduced, mildly reduced, and preserved ejection fractions can be critical in clinical assessment while waiting to perform echocardiograms or when it is unavailable. This integrative review aimed to identify ECG characteristics among hospitalized patients demonstrating three types of heart failure during acute decompensation.

METHODS

We searched an electronic database of PubMed, Web of Science, EMBASE, Scopus, Google Scholar, and ClinicalTrials.gov using medical subject headings (MeSH) terms and keywords. Sixteen studies were synthesized and reported.

RESULTS

Heart failure with reduced ejection fraction (HFrEF) was more common in men, comorbid with coronary artery diseases and diabetes mellitus, higher BNP/Pro-BNP, wide QRS, and left bundle branch block on ECG. On average, clients with heart failure with preserved ejection fraction (HFpEF) were older and more likely to have a history of atrial fibrillation, valvular heart diseases, hypertension, chronic obstructive pulmonary, and atrial fibrillation (AF) on ECG. Patients with mildly reduced (HFmrEF) were more similar to HFpEF in older patients, comorbid with hypertension, AF and valvular diseases, and AF on ECG.

CONCLUSIONS

ECG characteristics might be related to left ventricular ejection fraction. Demographics, BNP/Pro-BNP, and ECG changes might help differentiate different heart failure types. Therefore, ECG might be a prognostic tool while caring for heart failure patients when highly skilled resources are unavailable. These identified ECG characteristics help generate research hypotheses and warrant validation in future research.

Safety and clinical impact of adenosine stress perfusion cardiac magnetic resonance in Asian patients with known or suspected coronary artery disease

BACKGROUND

Adenosine stress cardiac magnetic resonance (CMR) imaging is increasingly utilized for evaluating patients with known or suspected coronary artery disease (CAD). This study aims to assess the safety and clinical impact of adenosine stress CMR in a tertiary care setting in Thailand.

METHODS

A total of 3,768 consecutive patients aged 18 years and above who underwent adenosine stress CMR between 2017 and 2020 were included in the study. Patient records were reviewed to collect data on clinical characteristics, hemodynamic measurements, complications during or immediately after CMR, and the rates of clinical changes resulting from CMR.

RESULTS

Among the included patients, the primary indications for adenosine stress CMR were risk stratification in suspected CAD (70.8%) and the assessment of myocardial ischemia/viability in patients with known CAD (26.5%). There were no reported deaths or acute myocardial infarctions during the procedure. Major complications, specifically acute pulmonary edema requiring hospital observation or admission for further management, occurred in four patients (0.11%), all of whom were elderly (ranging from 75 to 91 years) with a history of heart failure. Non-major complications were observed in 13.7% of patients, with dyspnea (9.8%) and mild chest pain (5.6%) being the most common. CMR provided a completely new diagnosis in 26.2% of patients. Overall, stress CMR resulted in a change in diagnosis or management for 48% of patients.

CONCLUSION

Adenosine stress CMR was found to be safe and to have a significant impact on clinical management in Asian patients with known or suspected CAD. These findings support the use of adenosine stress CMR as a valuable tool for evaluating and guiding treatment decisions in this patient population.

Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease

BACKGROUND

Information on electrocardiogram (ECG) has not been quantified in obstructive coronary artery disease (ObCAD), despite the deep learning (DL) algorithm being proposed as an effective diagnostic tool for acute myocardial infarction (AMI). Therefore, this study adopted a DL algorithm to suggest the screening of ObCAD from ECG.

METHODS

ECG voltage-time traces within a week from coronary angiography (CAG) were extracted for the patients who received CAG for suspected CAD in a single tertiary hospital from 2008 to 2020. After separating the AMI group, those were classified into ObCAD and non-ObCAD groups based on the CAG results. A DL-based model adopting ResNet was built to extract information from ECG data in the patients with ObCAD relative to those with non-ObCAD, and compared the performance with AMI. Moreover, subgroup analysis was conducted using ECG patterns of computer-assisted ECG interpretation.

RESULTS

The DL model demonstrated modest performance in suggesting the probability of ObCAD but excellent performance in detecting AMI. The AUC of the ObCAD model adopting 1D ResNet was 0.693 and 0.923 in detecting AMI. The accuracy, sensitivity, specificity, and F1 score of the DL model for screening ObCAD were 0.638, 0.639, 0.636, and 0.634, respectively, while the figures were up to 0.885, 0.769, 0.921, and 0.758 for detecting AMI, respectively. Subgroup analysis showed that the difference between normal and abnormal/borderline ECG groups was not notable.

CONCLUSIONS

ECG-based DL model showed fair performance for assessing ObCAD and it may serve as an adjunct to the pre-test probability in patients with suspected ObCAD during the initial evaluation. With further refinement and evaluation, ECG coupled with the DL algorithm may provide potential front-line screening support in the resource-intensive diagnostic pathways.