Changes of the electrocardiographic strain pattern in patients with aortic stenosis and its underling mechanisms

Electrocardiographic left ventricular strain pattern, ST-segment depression and atrial fibrillation at the time of diagnosis of systemic light chain amyloidosis: Incidence and clinical significance

BACKGROUND

ST/T abnormalities recognized as electrocardiographic (ECG) left ventricular (LV) strain pattern are known as a marker of myocyte death and reduced survival. The purpose of this study was to determine whether ECG LV strain pattern, its components and atrial fibrillation (AF) predict lower survival at the time of diagnosis of systemic light chain (AL) amyloidosis.

METHODS

12‑lead surface electrocardiogram (ECG), standard two-dimensional echocardiography, laboratory analyses were retrospectively evaluated within 2 months of diagnosis in 87 patients with biopsy-proven systemic AL amyloidosis from 2009 to 2017 in a single center. ECG strain pattern was defined as coexistence of ST-segment horizontal or downward sloping depression ≥0.05 mV at its most horizontal section and negative asymmetrical T-wave deeper than 0.1 mV in at least 1 of leads I,aVL,V1-V6. Patients with QRS >120 ms (BBB or major IVCD) were excluded from the analysis.

RESULTS

Kaplan-Meier survival analysis revealed a 1.8-fold shorter overall survival (OS) at 2 years in the ECG strain (21% of participants) group (p = 0.0078), 2.0-fold shorter OS in the ST-segment depression (STd) (isolated and strain related as one group) (34% of participants) group (p < 0.0001), and 3.9-fold shorter OS in AF (23% of participants) group (p < 0.0001) compared with those without. Median survival of patients with STd and AF were and 13.0 (range 1-74) and 9.5 (range 1-74) months respectively. In univariate analysis STd and AF were stronger predictors of inferior OS than relative wall thickness, average E/e' ratio, and LV ejection fraction, but weaker predictors of OS than B-type natriuretic peptide. In multivariate analysis STd and AF lost significance after adjustment for age, gender, number of organs involved and BNP.

CONCLUSIONS

ST-segment depression and AF were not significantly associated with reduced survival in AL amyloidosis at diagnosis.

Extended Precordial T Wave Inversions Are Associated with Right Ventricular Enlargement and Poor Prognosis in Pulmonary Hypertension

In pulmonary hypertension (PH), T wave inversions (TWI) are typically observed in precordial leads V1–V3 but can also extend further to the left-sided leads. To date, the cause and prognostic significance of this extension have not yet been assessed. Therefore, we aimed to assess the relationship between heart morphology and precordial TWI range, and the role of TWI in monitoring treatment efficacy and predicting survival. We retrospectively analyzed patients with pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH) treated in a reference pulmonary hypertension center. Patients were enrolled if they had a cardiac magnetic resonance (cMR) and 12-lead surface ECG performed at the time of assessment. They were followed from October 2008 until March 2021. We enrolled 77 patients with PAH and 56 patients with inoperable CTEPH. They were followed for a mean of 51 ± 33.5 months, and during this time 47 patients died (35.3%). Precordial TWI in V1–V6 were present in 42 (31.6%) patients, while no precordial TWI were observed only in 9 (6.8%) patients. The precordial TWI range correlated with markers of PH severity, including right ventricle to left ventricle volume RVEDVLVEDV (R = 0.76, p < 0.0001). The presence of TWI in consecutive leads from V1 to at least V5 predicted severe RV dilatation (RVEDVLVEDV ≥ 2.3) with a sensitivity of 88.9% and specificity of 84.1% (AUC of 0.90, 95% CI = 0.83–0.94, p < 0.0001). Presence of TWI from V1 to at least V5 was also a predictor of mortality in Kaplan–Meier estimation (p = 0.02). Presence of TWI from V1 to at least V5 had a specificity of 64.3%, sensitivity of 58.1%, negative predictive value of 75%, and positive predictive value of 45.5% as a mortality predictor. In patients showing a reduction in TWI range of at least one lead after treatment compared with patients without this reduction, we observed a significant improvement in RV-EDV and RV−EDVLV−EDV. We concluded that the extension of TWI to left-sided precordial leads reflects significant pathological alterations in heart geometry represented by an increase in RV/LV volume and predicts poor survival in patients with PAH and CTEPH. Additionally, we found that analysis of precordial TWI range can be used to monitor the effectiveness of hemodynamic response to treatment of pulmonary hypertension.

Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography

Background

Severe, symptomatic aortic stenosis (AS) is associated with poor prognoses. However, early detection of AS is difficult because of the long asymptomatic period experienced by many patients, during which screening tools are ineffective. The aim of this study was to develop and validate a deep learning–based algorithm, combining a multilayer perceptron and convolutional neural network, for detecting significant AS using ECGs.

Methods and Results

This retrospective cohort study included adult patients who had undergone both ECG and echocardiography. A deep learning–based algorithm was developed using 39 371 ECGs. Internal validation of the algorithm was performed with 6453 ECGs from one hospital, and external validation was performed with 10 865 ECGs from another hospital. The end point was significant AS (beyond moderate). We used demographic information, features, and 500‐Hz, 12‐lead ECG raw data as predictive variables. In addition, we identified which region had the most significant effect on the decision‐making of the algorithm using a sensitivity map. During internal and external validation, the areas under the receiver operating characteristic curve of the deep learning–based algorithm using 12‐lead ECG for detecting significant AS were 0.884 (95% CI, 0.880–0.887) and 0.861 (95% CI, 0.858–0.863), respectively; those using a single‐lead ECG signal were 0.845 (95% CI, 0.841–0.848) and 0.821 (95% CI, 0.816–0.825), respectively. The sensitivity map showed the algorithm focused on the T wave of the precordial lead to determine the presence of significant AS.

Conclusions

The deep learning–based algorithm demonstrated high accuracy for significant AS detection using both 12‐lead and single‐lead ECGs.