Associations between Electrocardiographic Interval Durations and Coronary Artery Calcium Scores: The Diabetes Heart Study

The association between coronary artery calcification and vectorcardiography in mechanically ventilated COVID-19 patients: the Maastricht Intensive Care COVID cohort

BACKGROUND

Coronary artery calcification (CAC) is associated with poor outcome in critically ill patients. A deterioration in cardiac conduction and loss of myocardial tissue could be an underlying cause. Vectorcardiography (VCG) and cardiac biomarkers provide insight into these underlying causes. The aim of this study was to investigate whether a high degree of CAC is associated with VCG-derived variables and biomarkers, including high-sensitivity troponin-T (hs-cTnT) and N-terminal pro-B-type natriuretic peptide (NT-proBNP).

METHODS

Mechanically ventilated coronavirus-19 (COVID-19) patients with an available chest computed tomography (CT) and 12-lead electrocardiogram (ECG) were studied. CAC scores were determined using chest CT scans. Patients were categorized into 3 sex-specific tertiles: low, intermediate, and high CAC. Daily 12 leads-ECGs were converted to VCGs. Daily hs-cTnT and NT-proBNP levels were determined. Linear mixed-effects regression models examined the associations between CAC tertiles and VCG variables, and between CAC tertiles and hs-cTnT or NT-proBNP levels.

RESULTS

In this study, 205 patients (73.2% men, median age 65 years [IQR 57.0; 71.0]) were included. Compared to the lowest CAC tertile, the highest CAC tertile had a larger QRS area at baseline (6.65 µVs larger [1.50; 11.81], p = 0.012), which decreased during admission (- 0.27 µVs per day [- 0.43; - 0.11], p = 0.001). Patients with the highest CAC tertile also had a longer QRS duration (12.02 ms longer [4.74; 19.30], p = 0.001), higher levels of log hs-cTnT (0.79 ng/L higher [0.40; 1.19], p < 0.001) and log NT-proBNP (0.83 pmol/L higher [0.30; 1.37], p = 0.002).

CONCLUSION

Patients with a high degree of CAC had the largest QRS area and higher QRS amplitude, which decreased more over time when compared to patients with a low degree of CAC. These results suggest that CAC might contribute to loss of myocardial tissue during critical illness. These insights could improve risk stratification and prognostication of patients with critical illness.

Machine learning derived ECG risk score improves cardiovascular risk assessment in conjunction with coronary artery calcium scoring

Background

Precision estimation of cardiovascular risk remains the cornerstone of atherosclerotic cardiovascular disease (ASCVD) prevention. While coronary artery calcium (CAC) scoring is the best available non-invasive quantitative modality to evaluate risk of ASCVD, it excludes risk related to prior myocardial infarction, cardiomyopathy, and arrhythmia which are implicated in ASCVD. The high-dimensional and inter-correlated nature of ECG data makes it a good candidate for analysis using machine learning techniques and may provide additional prognostic information not captured by CAC. In this study, we aimed to develop a quantitative ECG risk score (eRiS) to predict major adverse cardiovascular events (MACE) alone, or when added to CAC. Further, we aimed to construct and validate a novel nomogram incorporating ECG, CAC and clinical factors for ASCVD.

Methods

We analyzed 5,864 patients with at least 1 cardiovascular risk factor who underwent CAC scoring and a standard ECG as part of the CLARIFY study (ClinicalTrials.gov Identifier: NCT04075162). Events were defined as myocardial infarction, coronary revascularization, stroke or death. A total of 649 ECG features, consisting of measurements such as amplitude and interval measurements from all deflections in the ECG waveform (53 per lead and 13 overall) were automatically extracted using a clinical software (GE Muse™ Cardiology Information System, GE Healthcare). The data was split into 4 training (S_tr) and internal validation (S_v) sets [S_tr (1): S_v (1): 50:50; S_tr (2): S_v (2): 60:40; S_tr (3): S_v (3): 70:30; S_tr (4): S_v (4): 80:20], and the results were compared across all the subsets. We used the ECG features derived from S_tr to develop eRiS. A least absolute shrinkage and selection operator-Cox (LASSO-Cox) regularization model was used for data dimension reduction, feature selection, and eRiS construction. A Cox-proportional hazards model was used to assess the benefit of using an eRiS alone (M_ecg), CAC alone (M_cac) and a combination of eRiS and CAC (M_ecg+cac) for MACE prediction. A nomogram (M_nom) was further constructed by integrating eRiS with CAC and demographics (age and sex). The primary endpoint of the study was the assessment of the performance of M_ecg, M_cac, M_ecg+cac and M_nom in predicting CV disease-free survival in ASCVD.

Findings

Over a median follow-up of 14 months, 494 patients had MACE. The feature selection strategy preserved only about 18% of the features that were consistent across the various strata (S_tr). The M_ecg model, comprising of eRiS alone was found to be significantly associated with MACE and had good discrimination of MACE (C-Index: 0.7, p = <2e-16). eRiS could predict time-to MACE (C-Index: 0.6, p = <2e-16 across all S_v). The M_ecg+cac model was associated with MACE (C-index: 0.71). Model comparison showed that M_ecg+cac was superior to M_ecg (p = 1.8e-10) or M_cac (p < 2.2e-16) alone. The M_nom, comprising of eRiS, CAC, age and sex was associated with MACE (C-index 0.71). eRiS had the most significant contribution, followed by CAC score and other clinical variables. Further, M_nom was able to identify unique patient risk-groups based on eRiS, CAC and clinical variables.

Conclusion

The use of ECG features in conjunction with CAC may allow for improved prognostication and identification of populations at risk. Future directions will involve prospective validation of the risk score and the nomogram across diverse populations with a heterogeneity of treatment effects.

The influence of subclinical cardiovascular disease and related risk factors on cognition in type 2 diabetes mellitus: The DHS-Mind study

We hypothesized that measures of coronary artery calcified plaque (CAC) collected at baseline from the Diabetes Heart Study (DHS) would explain associations between cognition and diabetes collected at follow-up approximately 7 years later. The DHS is a sibling study of cardiovascular disease (CVD) in a cohort with a high prevalence of type 2 diabetes (~80%). Associations between baseline CAC and cognitive performance were tested using generalized estimating equations and mixed effects models to adjust for familial relationships. Diabetes status was associated (p<0.05) with poorer performance on tests of verbal memory, processing speed, and semantic fluency adjusting for age, sex, education, and hypertension status. As hypothesized, including CAC in the statistical model attenuated this association. Additionally, CAC and fasting glucose predicted performance in tasks not associated with diabetes status in this study (Stroop Task, Phonemic Fluency). These results confirm work attributing the heterogeneity of cognitive outcomes in type 2 diabetes to subclinical risk factors that combine to affect different aspects of brain function. Importantly, these results imply that risk factor intervention should begin before comorbidities, particularly CVD, become clinically apparent.

Impact of vascular calcification on corrected QT interval at the time of renal transplantation

BACKGROUND/AIMS

Sudden death is the major cause of cardiac mortality in dialysis patients, accounting for approximately 60% of cardiovascular deaths. A prolonged QT interval and arterial calcification have been associated with increased cardiovascular morbidity and mortality in different patient populations including patients with chronic kidney disease (CKD). In the present study, we aimed to elucidate the association of vascular calcification with corrected QT interval duration in patients with end-stage renal disease.

METHODS

We performed a single-center cross-sectional study in patients referred for renal transplantation. Patients taking QT-prolonging agents or with conduction abnormalities were excluded. Aortic calcifications were scored by means of lumbar X-rays.

RESULTS

In the final analysis, 193 patients (118 men, 52 years old) were included. A prolonged QT interval was observed in 26% of the patients. Multivariate analysis showed an independent and direct association between corrected QT duration and the extent of aortic calcifications (p = 0.0004) independent of age, gender, cardiovascular history, electrolytes and parameters of mineral metabolism.

CONCLUSIONS

A prolonged QT interval is prevalent in patients with CKD stage 5D. Aortic calcification is associated with a prolonged QT duration, independent of traditional determinants.

The ECG vertigo in diabetes and cardiac autonomic neuropathy

The importance of diabetes in the epidemiology of cardiovascular diseases cannot be overemphasized. About one third of acute myocardial infarction patients have diabetes, and its prevalence is steadily increasing. The decrease in cardiac mortality in people with diabetes is lagging behind that of the general population. Cardiovascular disease is a broad term which includes any condition causing pathological changes in blood vessels, cardiac muscle or valves, and cardiac rhythm. The ECG offers a quick, noninvasive clinical and research screen for the early detection of cardiovascular disease in diabetes. In this paper, the clinical and research value of the ECG is readdressed in diabetes and in the presence of cardiac autonomic neuropathy.