T-wave axis deviation and left ventricular hypertrophy interaction in diabetes and hypertension

Association of T-wave electrocardiogram changes and type 2 diabetes: a cross-sectional sub-analysis of the MASHAD cohort population using the Minnesota coding system

BACKGROUND

Type 2 Diabetes Mellitus (T2DM) has become a major health concern with an increasing prevalence and is now one of the leading attributable causes of death globally. T2DM and cardiovascular disease are strongly associated and T2DM is an important independent risk factor for ischemic heart disease. T-wave abnormalities (TWA) on electrocardiogram (ECG) can indicate several pathologies including ischemia. In this study, we aimed to investigate the association between T2DM and T-wave changes using the Minnesota coding system.

METHODS

A cross-sectional study was conducted on the MASHAD cohort study population. All participants of the cohort population were enrolled in the study. 12-lead ECG and Minnesota coding system (codes 5-1 to 5-4) were utilized for T-wave observation and interpretation. Regression models were used for the final evaluation with a level of significance being considered at p < 0.05.

RESULTS

A total of 9035 participants aged 35-65 years old were included in the study, of whom 1273 were diabetic. The prevalence of code 5-2, 5-3, major and minor TWA were significantly higher in diabetics (p < 0.05). However, following adjustment for age, gender, and hypertension, the presence of TWAs was not significantly associated with T2DM (p > 0.05). Hypertension, age, and body mass index were significantly associated with T2DM (p < 0.05).

CONCLUSIONS

Although some T-wave abnormalities were more frequent in diabetics, they were not statistically associated with the presence of T2DM in our study.

Correlation analysis of deep learning methods in S-ICD screening

BACKGROUND

Machine learning methods are used in the classification of various cardiovascular diseases through ECG data analysis. The concept of varying subcutaneous implantable cardiac defibrillator (S-ICD) eligibility, owing to the dynamicity of ECG signals, has been introduced before. There are practical limitations to acquiring longer durations of ECG signals for S-ICD screening. This study explored the potential use of deep learning methods in S-ICD screening.

METHODS

This was a retrospective study. A deep learning tool was used to provide descriptive analysis of the T:R ratios over 24 h recordings of S-ICD vectors. Spearman's rank correlation test was used to compare the results statistically to those of a "gold standard" S-ICD simulator.

RESULTS

A total of 14 patients (mean age: 63.7 ± 5.2 years, 71.4% male) were recruited and 28 vectors were analyzed. Mean T:R, standard deviation of T:R, and favorable ratio time (FVR)-a new concept introduced in this study-for all vectors combined were 0.21 ± 0.11, 0.08 ± 0.04, and 79 ± 30%, respectively. There were statistically significant strong correlations between the outcomes of our novel tool and the S-ICD simulator (p < .001).

CONCLUSION

Deep learning methods could provide a practical software solution to analyze data acquired for longer durations than current S-ICD screening practices. This could help select patients better suited for S-ICD therapy as well as guide vector selection in S-ICD eligible patients. Further work is needed before this could be translated into clinical practice.

Role of deep learning methods in screening for subcutaneous implantable cardioverter defibrillator in heart failure

INTRODUCTION

S-ICD eligibility is assessed at pre-implant screening where surface ECG traces are used as surrogates for S-ICD vectors. In heart failure (HF) patients undergoing diuresis, electrolytes and fluid shifts can cause changes in R and T waves. Subsequently, T:R ratio, a major predictor of S-ICD eligibility, can be dynamic.

METHODS

This is a prospective study of patients with structurally normal hearts and HF patients undergoing diuresis. All patients were fitted with Holters® to record their S-ICD vectors. Our deep learning model was used to analyze the T:R ratios across the recordings. Welch two sample t-test and Mann-Whitney U were used to compare the data between the two groups.

RESULTS

Twenty-one patients (age 58.43 ± 18.92, 62% male, 14 HF, 7 normal hearts) were enrolled. There was a significant difference in the T:R ratios between both groups. Mean T: R was higher in the HF group (0.18 ± 0.08 vs 0.10 ± 0.05, p < .001). Standard deviation of T: R was also higher in the HF group (0.09 ± 0.05 vs 0.07 ± 0.04, p = .024). There was no difference between leads within the same group.

CONCLUSIONS

T:R ratio, a main determinant for S-ICD eligibility, is higher and has more tendency to fluctuate in HF patients undergoing diuresis. We hypothesize that our novel neural network model could be used to select HF patients eligible for S-ICD by better characterization of T:R ratio reducing the risk of T-wave over-sensing (TWO) and inappropriate shocks. Further work is required to consolidate our findings before applying to clinical practice.

Deep learning methods for screening patients' S-ICD implantation eligibility

Subcutaneous Implantable Cardioverter-Defibrillators (S-ICDs) are used for prevention of sudden cardiac death triggered by ventricular arrhythmias. T Wave Over Sensing (TWOS) is an inherent risk with S-ICDs which can lead to inappropriate shocks. A major predictor of TWOS is a high T:R ratio (the ratio between the amplitudes of the T and R waves). Currently, patients' Electrocardiograms (ECGs) are screened over 10 s to measure the T:R ratio to determine the patients' eligibility for S-ICD implantation. Due to temporal variations in the T:R ratio, 10 s is not a long enough window to reliably determine the normal values of a patient's T:R ratio. In this paper, we develop a convolutional neural network (CNN) based model utilising phase space reconstruction matrices to predict T:R ratios from 10-second ECG segments without explicitly locating the R or T waves, thus avoiding the issue of TWOS. This tool can be used to automatically screen patients over a much longer period and provide an in-depth description of the behavior of the T:R ratio over that period. The tool can also enable much more reliable and descriptive screenings to better assess patients' eligibility for S-ICD implantation.

Paediatric/young versus adult patients with long QT syndrome

Introduction

Long QT syndrome (LQTS) is a less prevalent cardiac ion channelopathy than Brugada syndrome in Asia. The present study compared the outcomes between paediatric/young and adult LQTS patients.

Methods

This was a population-based retrospective cohort study of consecutive patients diagnosed with LQTS attending public hospitals in Hong Kong. The primary outcome was spontaneous ventricular tachycardia/ventricular fibrillation (VT/VF).

Results

A total of 142 LQTS (mean onset age=27±23 years old) were included. Arrhythmias other than VT/VF (HR 4.67, 95% CI (1.53 to 14.3), p=0.007), initial VT/VF (HR=3.25 (95% CI 1.29 to 8.16), p=0.012) and Schwartz score (HR=1.90 (95% CI 1.11 to 3.26), p=0.020) were predictive of the primary outcome for the overall cohort, while arrhythmias other than VT/VF (HR=5.41 (95% CI 1.36 to 21.4), p=0.016) and Schwartz score (HR=4.67 (95% CI 1.48 to 14.7), p=0.009) were predictive for the adult subgroup (>25 years old; n=58). A random survival forest model identified initial VT/VF, Schwartz score, initial QTc interval, family history of LQTS, initially asymptomatic and arrhythmias other than VT/VF as the most important variables for risk prediction.

Conclusion

Clinical and ECG presentation varies between the paediatric/young and adult LQTS population. Machine learning models achieved more accurate VT/VF prediction.

Assessment of Hypertension Using Clinical Electrocardiogram Features: A First-Ever Review

Hypertension affects an estimated 1.4 billion people and is a major cause of morbidity and mortality worldwide. Early diagnosis and intervention can potentially decrease cardiovascular events later in life. However, blood pressure (BP) measurements take time and require training for health care professionals. The measurements are also inconvenient for patients to access, numerous daily variables affect BP values, and only a few BP readings can be collected per session. This leads to an unmet need for an accurate, 24-h continuous, and portable BP measurement system. Electrocardiograms (ECGs) have been considered as an alternative way to measure BP and may meet this need. This review summarizes the literature published from January 1, 2010, to January 1, 2020, on the use of only ECG wave morphology to monitor BP or identify hypertension. From 35 articles analyzed (9 of those with no listed comorbidities and confounders), the P wave, QTc intervals and TpTe intervals may be promising for this purpose. Unfortunately, with the limited number of articles and the variety of participant populations, we are unable to make conclusions about the effectiveness of ECG-only BP monitoring. We provide 13 recommendations for future ECG-only BP monitoring studies and highlight the limited findings in pregnant and pediatric populations. With the advent of convenient and portable ECG signal recording in smart devices and wearables such as watches, understanding how to apply ECG-only findings to identify hypertension early is crucial to improving health outcomes worldwide.

Nonalcoholic Fatty Liver Is Associated With Further Left Ventricular Abnormalities in Patients With Type 2 Diabetes Mellitus: A 3-Dimensional Speckle-Tracking Study

OBJECTIVES

The aim of this study was to detect left ventricular (LV) structure and function abnormalities in patients with type 2 diabetes mellitus with or without nonalcoholic fatty liver (NAFL) using 3-dimensional speckle-tracking echocardiography.

METHODS

Eighty patients with type 2 diabetes and a normal LV ejection fraction (≥55%), including 40 with coexistent NAFL, and 40 age- and sex-matched control participants were recruited. Conventional echocardiography and 3-dimensional speckle-tracking echocardiography were performed, and global longitudinal strain, global circumferential strain, global area strain, and global radial strain values were measured.

RESULTS

Significant differences in 2-dimensional LV functional patterns were found among the 3 groups (P = .031), and LV hypertrophy was the most prevalent in patients with diabetes and NAFL. The patients with diabetes only had significantly lower global longitudinal strain, global circumferential strain, and global radial strain than the controls (all P < .05). The patients with diabetes and NAFL had severely lower global longitudinal strain, global circumferential strain, global area strain, and global radial strain than the controls (all P < .001), and they also had severely lower global longitudinal strain, global area strain, and global radial strain than the patients with diabetes only (all P < 0.001). The hemoglobin A_1c level and NAFL were independently associated with strain values in all patients with diabetes. The strain values in multiple directions (≥2 of global longitudinal, global circumferential, global area, and global radial strain) decreased significantly in the patients with diabetes and moderate and severe NAFL compared to those with mild NAFL (all P < .05).

CONCLUSIONS

Nonalcoholic fatty liver could aggravate LV hypertrophy and dysfunction in patients with type 2 diabetes. The combined application of conventional and 3-dimensional speckle-tracking echocardiography could detect these asymptomatic preclinical abnormalities.

Frontal plane T-wave axis orientation predicts coronary events: Findings from the Moli-sani study

BACKGROUND AND AIMS

The orientation of the frontal plane T-wave axis (T axis) is a reliable measure of ventricular repolarisation. We investigated the association between T-axis and the risk of coronary heart disease (CHD), heart failure (HF), atrial fibrillation (AF), stroke and cardiovascular (CVD) mortality.

METHODS

A sample of 21,287 Moli-sani participants randomly recruited from the general adult (≥35 y) Italian population, free of CVD disease, were followed for a median of 4.4 years. T-axis was measured from a standard 12-lead resting ECG.

RESULTS

After adjusting for CVD risk factors, subjects with abnormal T-axis showed an increase in the risk of both CHD (Hazard Ratio (HR) = 2.65; 95% CI = 1.67-4.21), HF (HR = 2.56; 1.80-3.63), AF (HR = 2.48; 1.56-3.94) and CVD mortality (HR = 2.83; 1.50-5.32). The association with CHD and HF, but not with AF or CVD death, remained significant after further adjustment for ECG abnormalities. Subjects with abnormal T-axis showed higher levels of subclinical inflammation, hs-troponin I and hs-NT-proBNP (p < 0.001 for all). However, further adjustment for troponin I and/or NT-proBNP determined a reduction of HRs ranging from 12.1 to 24.0% for CHD, while additional adjustment for inflammation markers did not change any association.

CONCLUSIONS

An abnormal T-axis orientation is associated with an increased risk of both CHD and HF, independently of common CVD risk factors and other ECG abnormalities. This association was partially explained by increased hs-troponin I and hs-NT-proBNP levels.

ECG-ViEW II, a freely accessible electrocardiogram database

The Electrocardiogram Vigilance with Electronic data Warehouse II (ECG-ViEW II) is a large, single-center database comprising numeric parameter data of the surface electrocardiograms of all patients who underwent testing from 1 June 1994 to 31 July 2013. The electrocardiographic data include the test date, clinical department, RR interval, PR interval, QRS duration, QT interval, QTc interval, P axis, QRS axis, and T axis. These data are connected with patient age, sex, ethnicity, comorbidities, age-adjusted Charlson comorbidity index, prescribed drugs, and electrolyte levels. This longitudinal observational database contains 979,273 electrocardiograms from 461,178 patients over a 19-year study period. This database can provide an opportunity to study electrocardiographic changes caused by medications, disease, or other demographic variables. ECG-ViEW II is freely available at http://www.ecgview.org.

T wave axis deviation and QRS-T angle - Controversial indicators of incident coronary heart events

Abnormal orientation of the T-wave axis and increased angle between the QRS complex (depolarization) and the T-wave (repolarization) have long been assumed to provide a global measure of repolarization abnormality, and have been used to assess ventricular repolarization. The ability of the T wave axis deviation and the QRS-T angle to predict incident coronary heart events was examined in several studies. However, conflicting results have led to significant controversy in the literature concerning their purported ability. Potential explanations involve true variation between study populations, non-standardized cut-off values, different baseline cardiovascular risk levels or different patterns of confounding by other concomitant cardiovascular risk factors. In the present article we will attempt to briefly present the rationale and pathophysiology behind these indices, summarize existing knowledge regarding their prognostic significance and their correlation with established cardiovascular disease risk factors. Further prospective studies are necessary to confirm or refute whether T-wave axis deviation, QRS-T angle and ventricular gradient may in the future serve as indicators of incident coronary heart events and mortality, both in populations with higher prevalence of subclinical advanced atherosclerotic heart disease and in apparently healthy subjects.

T-wave axis deviation is associated with biomarkers of low-grade inflammation. Findings from the MOLI-SANI study

T-wave axis deviation (TDev) may help identifying subjects at risk for major cardiac events and mortality, but the pathogenesis of TDev is not well established; in particular, the possible association between TDev and inflammation is unexplored and unknown. We aimed at investigating the association between low-grade inflammation and TDev abnormalities by conducting a cross-sectional analysis on 17,507 subjects apparently free from coronary heart and haematological diseases enrolled in the MOLI-SANI study. TDev was measured from a standard 12-lead resting electrocardiogram. High sensitivity (Hs) C-reactive protein (CRP), leukocyte (WBC) and platelet counts, neutrophil or granulocyte to lymphocyte ratios were used as markers of inflammation. In multivariable model subjects reporting high CRP levels had higher odds of having borderline and abnormal TDev (OR=1.70; 95 %CI: 1.53-1.90 and OR=1.72; 95 %CI: 1.23-2.41, respectively); the association was still significant, although reduced, after controlling for body mass index (OR=1.17; 95 %CI: 1.05-1.32, for borderline and OR=1.46; 95 %CI: 1.03-2.08, for abnormal). Similarly, higher neutrophil or granulocyte to lymphocyte ratios were associated with increased odds of having abnormal TDev. Neither platelet nor leukocyte counts were associated with abnormal TDev. The relationship between CRP with TDev abnormalities was significantly stronger in men, in non- obese or normotensive individuals, and in those without metabolic syndrome. In conclusion, C-reactive protein and some cellular biomarkers of inflammation such as granulocyte or neutrophil to lymphocyte ratios were independently associated with abnormal TDev, especially in subjects at low CVD risk. These results suggest that a low-grade inflammation likely contributes to the pathogenesis of T- wave axis deviation.