Advanced electrocardiography in myocardial electrical remodeling : insights from cardiovascular magnetic resonance imaging

The electrocardiogram (ECG) is a common diagnostic tool in cardiology thanks to its high accessibility and low cost. However, in several cardiovascular diagnoses, including left ventricular hypertrophy (LVH), current conventional ECG measures and criteria have a poor diagnostic performance. LVH is associated with hypertension and diabetes, but is often missed by the standard 12-lead ECG. LVH is typically diagnosed by non-invasive imaging methods. Cardiovascular magnetic resonance (CMR) is the gold standard for diagnosing LVH. Advanced-ECG (A-ECG) is a term used to describe a combination of advanced ECG analysis methods, and has been shown to be of diagnostic and prognostic utility. The aims of this thesis were to investigate the ability of A-ECG to diagnose LVH, using CMR as reference, as well as investigating the prognostic ability of A-ECG measures with regards to morbidity and mortality. We found that increased extracellular volume fraction by CMR reduces voltage measures of conventional ECG criteria for LVH, including the Sokolow-Lyon index and Cornell indices. This may explain the limited sensitivity of the ECG in detecting LVH. We further investigated different patterns of LVH based on the relation between increased mass and wall thickness, and found that the different patterns differ on their electrocardiographic manifestation by A- ECG. Furthermore, A-ECG had a higher diagnostic performance compared to conventional ECG LVH criteria. The ECG detects electrical changes, while LVH represents a structural change. Therefore, the electrical changes associated with LVH may be better referred to as left ventricular electrical remodeling (LVER). LVER, defined as the A-ECG measure spatial QRS-T angle exceeding the upper limit of normal, was found to have a higher accuracy in diagnosing LVH compared to conventional ECG LVH criteria. We also found that patients with LVER have a worse prognosis compared to patients without LVER. Lastly, we optimized a score based on ECG and CMR measures, respectively, to predict morbidity and mortality, and found that ECG and CMR are both strong and independent predictors of events. In conclusion, conventional ECG criteria lack sensitivity in detecting LVH, which may be explained by increased extracellular volume fraction or different structural patterns in LVH. A-ECG has a higher diagnostic accuracy than conventional ECG criteria for LVH and is prognostic beyond CMR measures. Lastly, we suggest that LVER should be used when electrical changes in LVH are addressed

Hypertrophic cardiomyopathy detection with artificial intelligence electrocardiography in international cohorts: an external validation study

Aims: Recently, deep learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts. Methods and results: A convolutional neural network-based AI-ECG algorithm was developed previously in a single-centre North American HCM cohort (Mayo Clinic). This algorithm was applied to the raw 12-lead ECG data of patients with HCM and non-HCM controls from three external cohorts (Bern, Switzerland; Oxford, UK; and Seoul, South Korea). The algorithm’s ability to distinguish HCM vs. non-HCM status from the ECG alone was examined. A total of 773 patients with HCM and 3867 non-HCM controls were included across three sites in the merged external validation cohort. The HCM study sample comprised 54.6% East Asian, 43.2% White, and 2.2% Black patients. Median AI-ECG probabilities of HCM were 85% for patients with HCM and 0.3% for controls (P < 0.001). Overall, the AI-ECG algorithm had an area under the receiver operating characteristic curve (AUC) of 0.922 [95% confidence interval (CI) 0.910–0.934], with diagnostic accuracy 86.9%, sensitivity 82.8%, and specificity 87.7% for HCM detection. In age- and sex-matched analysis (case–control ratio 1:2), the AUC was 0.921 (95% CI 0.909–0.934) with accuracy 88.5%, sensitivity 82.8%, and specificity 90.4%. Conclusion: The AI-ECG algorithm determined HCM status from the 12-lead ECG with high accuracy in diverse international cohorts, providing evidence for external validity. The value of this algorithm in improving HCM detection in clinical practice and screening settings requires prospective evaluation

The dynamics of extracellular gadolinium-based contrast agent excretion into pleural and pericardial effusions quantified by T1 mapping cardiovascular magnetic resonance

Abstract Introduction Excretion of cardiovascular magnetic resonance (CMR) extracellular gadolinium-based contrast agents (GBCA) into pleural and pericardial effusions, sometimes referred to as vicarious excretion, has been described as a rare occurrence using T1-weighted imaging. However, the T1 mapping characteristics as well as presence, magnitude and dynamics of contrast excretion into these effusions is not known. Aims To investigate and compare the differences in T1 mapping characteristics and extracellular GBCA excretion dynamics in pleural and pericardial effusions. Methods Clinically referred patients with a pericardial and/or pleural effusion underwent CMR T1 mapping at 1.5 T before, and at 3 (early) and at 27 (late) minutes after administration of an extracellular GBCA (0.2 mmol/kg, gadoteric acid). Analyzed effusion characteristics were native T1, ΔR1 early and late after contrast injection, and the effusion-volume-independent early-to-late contrast concentration ratio ΔR1early/ΔR1late, where ΔR1 = 1/T1post-contrast - 1/T1native. Results Native T1 was lower in pericardial effusions (n = 69) than in pleural effusions (n = 54) (median [interquartile range], 2912 [2567–3152] vs 3148 [2692–3494] ms, p = 0.005). Pericardial and pleural effusions did not differ with regards to ΔR1early (0.05 [0.03–0.10] vs 0.07 [0.03–0.12] s− 1, p = 0.38). Compared to pleural effusions, pericardial effusions had a higher ΔR1late (0.8 [0.6–1.2] vs 0.4 [0.2–0.6] s− 1, p < 0.001) and ΔR1early/ΔR1late (0.19 [0.08–0.30] vs 0.12 [0.04–0.19], p < 0.001). Conclusions T1 mapping shows that extracellular GBCA is excreted into pericardial and pleural effusions. Consequently, the previously used term vicarious excretion is misleading. Compared to pleural effusions, pericardial effusions had both a lower native T1, consistent with lesser relative fluid content in relation to other components such as proteins, and more prominent early excretion dynamics, which could be related to inflammation. The clinical diagnostic utility of T1 mapping to determine quantitative contrast dynamics in pericardial and pleural effusions merits further investigation

Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation

AF is the most common clinically relevant cardiac arrhythmia associated with multiple comorbidities, cardiovascular complications (e.g. stroke) and increased mortality. As artificial intelligence (AI) continues to transform the practice of medicine, this review article highlights specific applications of AI for the screening, diagnosis and treatment of AF. Routinely used digital devices and diagnostic technology have been significantly enhanced by these AI algorithms, increasing the potential for large-scale population-based screening and improved diagnostic assessments. These technologies have similarly impacted the treatment pathway of AF, identifying patients who may benefit from specific therapeutic interventions. While the application of AI to the diagnostic and therapeutic pathway of AF has been tremendously successful, the pitfalls and limitations of these algorithms must be thoroughly considered. Overall, the multifaceted applications of AI for AF are a hallmark of this emerging era of medicine

Heart age gap estimated by explainable advanced electrocardiography is associated with cardiovascular risk factors and survival

AimsDeep neural network artificial intelligence (DNN-AI)-based Heart Age estimations have been presented and used to show that the difference between an electrocardiogram (ECG)-estimated Heart Age and chronological age is associated with prognosis. An accurate ECG Heart Age, without DNNs, has been developed using explainable advanced ECG (A-ECG) methods. We aimed to evaluate the prognostic value of the explainable A-ECG Heart Age and compare its performance to a DNN-AI Heart Age.Methods and resultsBoth A-ECG and DNN-AI Heart Age were applied to patients who had undergone clinical cardiovascular magnetic resonance imaging. The association between A-ECG or DNN-AI Heart Age Gap and cardiovascular risk factors was evaluated using logistic regression. The association between Heart Age Gaps and death or heart failure (HF) hospitalization was evaluated using Cox regression adjusted for clinical covariates/comorbidities. Among patients [n = 731, 103 (14.1%) deaths, 52 (7.1%) HF hospitalizations, median (interquartile range) follow-up 5.7 (4.7-6.7) years], A-ECG Heart Age Gap was associated with risk factors and outcomes [unadjusted hazard ratio (HR) (95% confidence interval) (5 year increments): 1.23 (1.13-1.34) and adjusted HR 1.11 (1.01-1.22)]. DNN-AI Heart Age Gap was associated with risk factors and outcomes after adjustments [HR (5 year increments): 1.11 (1.01-1.21)], but not in unadjusted analyses [HR 1.00 (0.93-1.08)], making it less easily applicable in clinical practice.ConclusionA-ECG Heart Age Gap is associated with cardiovascular risk factors and HF hospitalization or death. Explainable A-ECG Heart Age Gap has the potential for improving clinical adoption and prognostic performance compared with existing DNN-AI-type methods. Graphical Abstrac

Extracellular volume associates with outcomes more strongly than native or post-contrast myocardial T1

OBJECTIVES: Because risk stratification data represents a key domain of biomarker validation, we compared associations between outcomes and various cardiovascular magnetic resonance (CMR) metrics quantifying myocardial fibrosis (MF) in noninfarcted myocardium: extracellular volume fraction (ECV), native T1, post-contrast T1, and partition coefficient. // BACKGROUND: MF associates with vulnerability to adverse events (e.g., mortality and hospitalization for heart failure [HHF]), but investigators still debate its optimal measurement; most histological validation data show strongest ECV correlations with MF. // METHODS: We enrolled 1,714 consecutive patients without amyloidosis or hypertrophic cardiomyopathy from a single CMR referral center serving an integrated healthcare network. We measured T1 (MOdified Look-Locker Inversion recovery [MOLLI]) in nonenhanced myocardium, averaged from 2 short-axis slices (basal and mid) before and 15 to 20 min after a gadolinium contrast bolus. We compared chi-square test values from CMR MF measures in univariable and multivariable Cox regression models. We assessed "dose-response" relationships in Kaplan-Meier curves using log-rank statistics for quartile strata. We also computed net reclassification improvement (NRI) and integrated discrimination improvement (IDI for Cox models with ECV vs. native T1). // RESULTS: Over a median of 5.6 years, 374 events occurred after CMR (162 HHF events and 279 deaths, 67 with both). ECV yielded the best separation of Kaplan-Meier curves and the highest log-rank statistics. In univariable and multivariable models, ECV associated most strongly with outcomes, demonstrating the highest chi-square test values. Native T1 or post-contrast T1 did not associate with outcomes in the multivariable model. ECV provided added prognostic value to models with native T1, for example, in multivariable models IDI = 0.0037 (95% confidence interval [CI]: 0.0009 to 0.0071), p = 0.02; NRI = 0.151 (95% CI: 0.022 to 0.292), p = 0.04. // CONCLUSIONS: Analogous to histological previously published validation data, ECV myocardial fibrosis measures exhibited more robust associations with outcomes than other surrogate CMR MF measures. Superior risk stratification by ECV supports claims that ECV optimally measures MF in noninfarcted myocardium

Tandem deep learning and logistic regression models to optimize hypertrophic cardiomyopathy detection in routine clinical practice

Background: An electrocardiogram (ECG)-based artificial intelligence (AI) algorithm has shown good performance in detecting hypertrophic cardiomyopathy (HCM). However, its application in routine clinical practice may be challenging owing to the low disease prevalence and potentially high false-positive rates. Objective: Identify clinical characteristics associated with true- and false-positive HCM AI-ECG results to improve its clinical application. Methods: We reviewed the records of the 200 patients with highest HCM AI-ECG scores in January 2021 at our institution. Logistic regression was used to create a clinical variable–based “Candidacy for HCM Detection (HCM-DETECT)” score, differentiating true-positive from false-positive AI-ECG results. We validated the HCM-DETECT score in an independent cohort of 200 patients with the highest AI-ECG scores from January 2022. Results: In the 2021 cohort (median age 71 [interquartile range 58–80] years, 48% female), the rates of true-positive, false-positive, and indeterminate AI-ECG results for HCM detection were 36%, 48%, and 16%, respectively. In the 2022 cohort, the rates were 26%, 47%, and 27%, respectively. The HCM-DETECT score included age, coronary artery disease, prior pacemaker, and prior cardiac valve surgery, and had an area under the receiver operating characteristic curve of 0.81 (95% confidence interval 0.73–0.87) for differentiating true- vs false-positive AI results. When the 2022 cohort was limited to HCM detection candidates identified with the HCM-DETECT score, the false-positive AI-ECG rate was reduced from 47% to 13.5%. Conclusion: Application of a clinical score (HCM-DETECT) in tandem with an AI-ECG model improved HCM detection yield, reducing the false-positive rate of AI-ECG more than 3-fold