ISE/ISHNE expert consensus statement on the ECG diagnosis of left ventricular hypertrophy: The change of the paradigm.

The ECG diagnosis of LVH is predominantly based on the QRS voltage criteria. The classical paradigm postulates that the increased left ventricular mass generates a stronger electrical field, increasing the leftward and posterior QRS forces, reflected in the augmented QRS amplitude. However, the low sensitivity of voltage criteria has been repeatedly documented. We discuss possible reasons for this shortcoming and proposal of a new paradigm. The theoretical background for voltage measured at the body surface is defined by the solid angle theorem, which relates the measured voltage to spatial and non-spatial determinants. The spatial determinants are represented by the extent of the activation front and the distance of the recording electrodes. The non-spatial determinants comprise electrical characteristics of the myocardium, which are comparatively neglected in the interpretation of the QRS patterns. Various clinical conditions are associated with LVH. These conditions produce considerable diversity of electrical properties alterations thereby modifying the resultant QRS patterns. The spectrum of QRS patterns observed in LVH patients is quite broad, including also left axis deviation, left anterior fascicular block, incomplete and complete left bundle branch blocks, Q waves, and fragmented QRS. Importantly, the QRS complex can be within normal limits. The new paradigm stresses the electrophysiological background in interpreting QRS changes, i.e., the effect of the non-spatial determinants. This postulates that the role of ECG is not to estimate LV size in LVH, but to understand and decode the underlying electrical processes, which are crucial in relation to cardiovascular risk assessment.

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

Aims: Deep 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 results: Both 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. Conclusion: A-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.

Saliency maps provide insights into artificial intelligence-based electrocardiography models for detecting hypertrophic cardiomyopathy.

INTRODUCTION: A 12­lead electrocardiography (ECG)-based convolutional neural network (CNN) model can detect hypertrophic cardiomyopathy (HCM). However, since these models do not rely on discrete measurements as inputs, it is not apparent what drives their performance. We hypothesized that saliency maps could be used to visually identify ECG segments that contribute to a CNN's robust classification of HCM. METHODS: We derived a new one­lead (lead I) CNN model based on median beats using the same methodology and cohort used for the original 12­lead CNN model (3047 patients with HCM, and 63,926 sex- and age-matched non-HCM controls). One­lead, median-beat saliency maps were generated and visually evaluated in an independent cohort of 100 patients with a diagnosis of HCM and a high artificial intelligence (AI)-ECG-HCM probability score to determine which ECG segments contributed to the model's detection of HCM. RESULTS: The one­lead, median-beat CNN had an AUC of 0.90 (95% CI 0.89-0.92) for HCM detection, similar to the original 12­lead ECG model. In the independent HCM cohort (n = 100), saliency maps highlighted the ST-T segment in 92 ECGs, the atrial depolarization segment in 12 ECGs, and the QRS complex in 5 ECGs. CONCLUSIONS: Saliency maps of a one­lead, median-beat-based CNN model identified perturbations in ventricular repolarization as the main region of interest in detecting HCM.

ISE/ISHNE Expert Consensus Statement on ECG Diagnosis of Left Ventricular Hypertrophy: The Change of the Paradigm. The joint paper of the International Society of Electrocardiology and the International Society for Holter Monitoring and Noninvasive Electrocardiology.

The ECG diagnosis of LVH is predominantly based on the QRS voltage criteria, i.e. the increased QRS complex amplitude in defined leads. The classical ECG diagnostic paradigm postulates that the increased left ventricular mass generates a stronger electrical field, increasing the leftward and posterior QRS forces. These increased forces are reflected in the augmented QRS amplitude in the corresponding leads. However, the clinical observations document increased QRS amplitude only in the minority of patients with LVH. The low sensitivity of voltage criteria has been repeatedly documented. We discuss possible reasons for this shortcoming and proposal of a new paradigm.

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.

Diffuse myocardial fibrosis associates with incident ventricular arrhythmia in implantable cardioverter defibrillator recipients.

Background: Diffuse myocardial fibrosis (DMF) quantified by extracellular volume (ECV) may represent a vulnerable phenotype and associate with life threatening ventricular arrhythmias more than focal myocardial fibrosis. This principle remains important because 1) risk stratification for implantable cardioverter defibrillators (ICD) remains challenging, and 2) DMF may respond to current or emerging medical therapies (reversible substrate). Objectives: To evaluate the association between quantified by ECV in myocardium without focal fibrosis by late gadolinium enhancement (LGE) with time from ICD implantation to 1) appropriate shock, or 2) shock or anti-tachycardia pacing. Methods: Among patients referred for cardiovascular magnetic resonance (CMR) without congenital disease, hypertrophic cardiomyopathy, or amyloidosis who received ICDs (n=215), we used Cox regression to associate ECV with incident ICD therapy. Results: After a median of 2.9 (IQR 1.5-4.2) years, 25 surviving patients experienced ICD shock and 44 experienced shock or anti-tachycardia pacing. ECV ranged from 20.2% to 39.4%. No patient with ECV<25% experienced an ICD shock. ECV associated with both endpoints, e.g., hazard ratio 2.17 (95%CI 1.17-4.00) for every 5% increase in ECV, p=0.014 in a stepwise model for ICD shock adjusting for ICD indication, age, smoking, atrial fibrillation, and myocardial infarction, whereas focal fibrosis by LGE and global longitudinal strain (GLS) did not. Conclusions: DMF measured by ECV associates with ventricular arrhythmias requiring ICD therapy in a dose-response fashion, even adjusting for potential confounding variables, focal fibrosis by LGE, and GLS. ECV-based risk stratification and DMF representing a therapeutic target to prevent ventricular arrhythmia warrant further investigation.

An electrocardiography score predicts heart failure hospitalization or death beyond that of cardiovascular magnetic resonance imaging.

The electrocardiogram (ECG) and cardiovascular magnetic resonance imaging (CMR) provide powerful prognostic information. The aim was to determine their relative prognostic value. Patients (n = 783) undergoing CMR and 12-lead ECG with a QRS duration < 120 ms were included. Prognosis scores for one-year event-free survival from hospitalization for heart failure or death were derived using continuous ECG or CMR measures, and multivariable logistic regression, and compared. Patients (median [interquartile range] age 55 [43-64] years, 44% female) had 155 events during 5.7 [4.4-6.6] years. The ECG prognosis score included (1) frontal plane QRS-T angle, and (2) heart rate corrected QT duration (QTc) (log-rank 55). The CMR prognosis score included (1) global longitudinal strain, and (2) extracellular volume fraction (log-rank 85). The combination of positive scores for both ECG and CMR yielded the highest prognostic value (log-rank 105). Multivariable analysis showed an association with outcomes for both the ECG prognosis score (log-rank 8.4, hazard ratio [95% confidence interval] 1.29 [1.09-1.54]) and the CMR prognosis score (log-rank 47, hazard ratio 1.90 [1.58-2.28]). An ECG prognosis score predicted outcomes independently of CMR. Combining the results of ECG and CMR using both prognosis scores improved the overall prognostic performance.

Improved evaluation of left ventricular hypertrophy using the spatial QRS-T angle by electrocardiography.

Electrocardiographic (ECG) signs of left ventricular hypertrophy (LVH) lack sensitivity. The aim was to identify LVH based on an abnormal spatial peaks QRS-T angle, evaluate its diagnostic performance compared to conventional ECG criteria for LVH, and its prognostic performance. This was an observational study with four cohorts with a QRS duration < 120 ms. Based on healthy volunteers (n = 921), an abnormal spatial peaks QRS-T angle was defined as ≥ 40° for females and ≥ 55° for males. In other healthy volunteers (n = 461), the specificity of the QRS-T angle to detect LVH was 96% (females) and 98% (males). In patients with at least moderate LVH by cardiac imaging (n = 225), the QRS-T angle had a higher sensitivity than conventional ECG criteria (93-97% vs 13-56%, p < 0.001 for all). In clinical consecutive patients (n = 783), of those who did not have any LVH, 238/556 (43%) had an abnormal QRS-T angle. There was an association with hospitalization for heart failure or all-cause death in univariable and multivariable analysis. An abnormal QRS-T angle rarely occurred in healthy volunteers, was a mainstay of moderate or greater LVH, was common in clinical patients without LVH but with cardiac co-morbidities, and associated with outcomes.

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.

Predicting peri-operative troponin elevation by advanced electrocardiography.

BACKGROUND: Peri-operative mortality remains a global problem and an improved pre-operative risk assessment identifying those at highest risk for peri-operative myocardial injury might improve postsurgical outcomes. AIMS: To determine whether pre-operative measures of advanced electrocardiography (A-ECG) could predict elevated serum troponin T (TnT) in patients undergoing elective, major non-cardiac surgery. MATERIAL AND METHODS: This observational cohort study included 257 surgical patients who underwent elective major non-cardiac surgery between the years 2012-2013 and 2015-2016 at Karolinska University Hospital. All selected patients were ≥ 18 years of age [median age 70 (63-75) years], had a pre-operative digital 12­lead ECG < 6 months prior to the procedure and a postoperative high-sensitivity cardiac TnT (hs-cTnT) sample. A-ECG confounders including atrial fibrillation or flutter, abundant premature atrial or ventricular contractions, bundle branch blocks, QRS duration >110 ms, heart rate > 100 beats/min and paced rhythms were excluded. Previously validated A-ECG diagnostic scores that detect cardiovascular pathologies were calculated and compared in patients with and without peri-operative myocardial injury, defined as hs-cTnT >14 ng l-1. RESULTS: Pre-operative left ventricular systolic dysfunction by A-ECG was more probable in patients with than without peri-operative myocardial injury (p = 0.03). CONCLUSIONS: While a pre-operative A-ECG score for LVSD was able to differentiate between patients with versus without elevated peri-operative TnT levels, it did not add any further utility to standard clinical parameters for predicting troponin-related events in the studied population.

Extracellular Volume and Global Longitudinal Strain Both Associate With Outcomes But Correlate Minimally.

OBJECTIVES: This study examined how extracellular volume (ECV) and global longitudinal strain (GLS) relate to each other and to outcomes. BACKGROUND: Among myriad changes occurring in diseased myocardium, left ventricular imaging metrics of either the interstitium (e.g., ECV) or contractile function (e.g., GLS) may consistently associate with adverse outcomes yet correlate minimally with each other. This scenario suggests that ECV and GLS potentially represent distinct domains of cardiac vulnerability. METHODS: The study included 1,578 patients referred for cardiovascular magnetic resonance (CMR) without amyloidosis, and it quantified how ECV associated with GLS in linear regression models. ECV and GLS were then compared in their associations with incident outcomes (death and hospitalization for heart failure). RESULTS: ECV and GLS correlated minimally (R2 = 0.04). Over a median follow-up of 5.6 years, 339 patients experienced adverse events (149 hospitalizations for heart failure, 253 deaths, and 63 with both). GLS (univariable hazard ratio: 2.07 per 5% increment; 95% CI: 1.86 to 2.29) and ECV (univariable hazard ratio: 1.66 per 4% increment; 95% CI: 1.51 to 1.82) were principal variables associating with outcomes in univariable and multivariable Cox regression models. Similar results were observed in several clinically important subgroups. In the whole cohort, ECV added prognostic value beyond GLS in univariable and multivariable Cox regression models. CONCLUSIONS: GLS and ECV may represent principal but distinct domains of cardiac vulnerability, perhaps reflecting their distinct cellular origins. Whether combining ECV and GLS may advance pathophysiological understanding for a given patient, optimize risk stratification, and foster personalized medicine by targeted therapeutics requires further investigation.

Substantial prevalence of subclinical cardiovascular diseases in patients with hemophilia A evaluated by advanced electrocardiography.

BACKGROUND: Patients with hemophilia A (PWHA) have reportedly lower mortality due to cardiovascular disease (CVD) compared to the general population. AIM: To evaluate signs of CVD in asymptomatic PWHA using advanced electrocardiography (A-ECG). METHODS: PWHA (n = 29, median [interquartile range] age 57 [47-70] years) and age-matched male controls (n = 29, 59 [48-68] years) were evaluated. Digital resting 12­lead ECGs were retrospectively analysed using both conventional and A-ECG techniques including derived vectorcardiography and waveform complexity. Previously validated multivariate A-ECG scores designed to detect: 1) cardiac disease in general, 2) left ventricular systolic dysfunction (LVSD), 3) coronary artery disease or coronary microvascular disease (CAD/CMVD), or 4) left ventricular hypertrophy defined as left ventricular electrical remodelling (LVH/LVER), were quantified and compared between PWHA and controls. RESULTS: Compared to controls, PWHA had a higher probability of having cardiac disease (median [interquartile range] 84.6 [32.5-99.5] vs. 0.6 [0.2-8.2]%), LVSD (4.1 [1.3-12.9] vs. 0.9 [0.5-3.2]%), CAD/CMVD (84.3 [35.6-96.6] vs. 6.7 [0.8-24.4]%), and LVH/LVER (17 [5/29] vs. 0 [0/29]%). Compared to patients with non-severe HA (n = 20), patients with severe HA (n = 9) showed a non-significant trend towards lower probability of cardiac disease, CAD/CMVD, LVSD and LVH/LVER. CONCLUSION: In PWHA, A-ECG exhibits changes more indicative of overt or subclinical CVD compared to controls, and there is a tendency for lower scores for CVD in patients with severe compared to non-severe HA. These results suggest that PWHA ≥ 40 years could be at higher risk for CVD than age-matched controls and that A-ECG could potentially be used for early detection.

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.

The electrical determinants of increased wall thickness and mass in left ventricular hypertrophy.

INTRODUCTION: Left ventricular hypertrophy (LVH), defined as an increased left ventricular mass (LVM), can manifest as increased wall thickness, ventricular dilatation, or both. Existing LVH criteria from the electrocardiogram (ECG) have poor sensitivity. However, it is unknown whether changes in wall thickness and mass, respectively, can be separately detected by the ECG. METHODS: Patients undergoing cardiovascular magnetic resonance and resting 12-lead ECG were included. Exclusion criteria were clinical confounders that might influence the ECG, including myocardial scar. Advanced ECG (A-ECG) analysis included conventional ECG measures and amplitudes, derived vectorcardiographic and polarcardiographic measures, and singular value decomposition of waveform complexity. A-ECG scores for 1) increased LVM index (LVMI), and 2) increased global wall thickness index (GTI) beyond the upper limit of normal in healthy volunteers, respectively, were derived using multivariable logistic regression. The area under the curve (AUC) and its bootstrapped confidence interval (CI) for each score were compared to those of conventional ECG-LVH criteria including Cornell voltage, Cornell product, and Sokolow-Lyon voltage criteria. RESULTS: Out of 485 patients (median [interquartile range] age 51 [38-61] years, 54% female), 51 (11%) had increased LVMI and 65 (13%) had increased GTI. The A-ECG scores for increased LVMI (AUC [95% CI] 0.84 [0.78-0.90]), and increased GTI (0.80 [0.74-0.85]) differed, and had a higher AUC than the conventional ECG-LVH criteria (p < 0.001 for all). CONCLUSIONS: Increased LVMI differed from increased GTI in its electrocardiographic manifestation by A-ECG. New A-ECG scores outperform conventional ECG criteria for LVH in determining increased LVMI and GTI, respectively.

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

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.

Temporal Relation Between Myocardial Fibrosis and Heart Failure With Preserved Ejection Fraction: Association With Baseline Disease Severity and Subsequent Outcome.

Importance: Among myriad changes occurring during the evolution of heart failure with preserved ejection fraction (HFpEF), cardiomyocyte-extracellular matrix interactions from excess collagen may affect microvascular, mechanical, and electrical function. Objective: To investigate whether myocardial fibrosis (MF) is similarly prevalent both in those with HFpEF and those at risk for HFpEF, similarly associating with disease severity and outcomes. Design, Setting, and Participants: Observational cohort study from June 1, 2010, to September 17, 2015, with follow-up until December 14, 2015, at a cardiovascular magnetic resonance (CMR) center serving an integrated health system. Consecutive patients with preserved systolic function referred for CMR were eligible. Cardiovascular magnetic resonance was used to exclude patients with cardiac amyloidosis (n = 19). Exposures: Myocardial fibrosis quantified by extracellular volume (ECV) CMR measures. Main Outcome and Measures: Baseline BNP; subsequent hospitalization for heart failure or death. Results: Of 1174 patients identified (537 [46%] female; median [interquartile range {IQR}] age, 56 [44-66] years), 250 were "at risk" for HFpEF given elevated brain-type natriuretic peptide (BNP) level; 160 had HFpEF by documented clinical diagnosis, and 745 did not have HFpEF. Patients either at risk for HFpEF or with HFpEF demonstrated similarly higher prevalence/extent of MF and worse prognosis compared with patients with no HFpEF. Among those at risk for HFpEF or with HFpEF, the actual diagnosis of HFpEF was not associated with significant differences in MF (median ECV, 28.2%; IQR, 26.2%-30.7% vs 28.3%; IQR, 25.5%-31.4%; P = .60) or prognosis (log-rank 0.8; P = .38). Over a median of 1.9 years, 61 patients at risk for HFpEF or with HFpEF experienced adverse events (19 hospitalization for heart failure, 48 deaths, 6 with both). In those with HFpEF, ECV was associated with baseline log BNP (disease severity surrogate) in multivariable linear regression models, and was associated with outcomes in multivariable Cox regression models (eg, hazard ratio 1.75 per 5% increase in ECV, 95% CI, 1.25-2.45; P = .001 in stepwise model) whether grouped with patients at risk for HFpEF or not. Conclusions and Relevance: Among myriad changes occurring during the apparent evolution of HFpEF where elevated BNP is prevalent, MF was similarly prevalent in those with or at risk for HFpEF. Conceivably, MF might precede clinical HFpEF diagnosis. Regardless, MF was associated with disease severity (ie, BNP) and outcomes. Whether cells and secretomes mediating MF represent therapeutic targets in HFpEF warrants further evaluation.

Diffuse Myocardial Fibrosis Reduces Electrocardiographic Voltage Measures of Left Ventricular Hypertrophy Independent of Left Ventricular Mass.

BACKGROUND: Myocardial fibrosis quantified by myocardial extracellular volume fraction (ECV) and left ventricular mass (LVM) index (LVMI) measured by cardiovascular magnetic resonance might represent independent and opposing contributors to ECG voltage measures of left ventricular hypertrophy (LVH). Diffuse myocardial fibrosis can occur in LVH and interfere with ECG voltage measures. This phenomenon could explain the decreased sensitivity of LVH detectable by ECG, a fundamental diagnostic tool in cardiology. METHODS AND RESULTS: We identified 77 patients (median age, 53 [interquartile range, 26-60] years; 49% female) referred for contrast-enhanced cardiovascular magnetic resonance with ECV measures and 12-lead ECG. Exclusion criteria included clinical confounders that might influence ECG measures of LVH. We evaluated ECG voltage-based LVH measures, including Sokolow-Lyon index, Cornell voltage, 12-lead voltage, and the vectorcardiogram spatial QRS voltage, with respect to LVMI and ECV. ECV and LVMI were not correlated (R2=0.02; P=0.25). For all voltage-related parameters, higher LVMI resulted in greater voltage (r=0.33-0.49; P<0.05 for all), whereas increased ECV resulted in lower voltage (r=-0.32 to -0.57; P<0.05 for all). When accounting for body fat, LV end-diastolic volume, and mass-to-volume ratio, both LVMI (ß=0.58, P=0.03) and ECV (ß=-0.46, P<0.001) were independent predictors of QRS voltage (multivariate adjusted R2=0.39; P<0.001). CONCLUSIONS: Myocardial mass and diffuse myocardial fibrosis have independent and opposing effects upon ECG voltage measures of LVH. Diffuse myocardial fibrosis quantified by ECV can obscure the ECG manifestations of increased LVM. This provides mechanistic insight, which can explain the limited sensitivity of the ECG for detecting increased LVM.