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BackgroundLeft atrial (LA) dilation provides a substrate for mitral regurgitation (MR) and atrial arrhythmias. ECG can screen for LA dilation but standard approaches do not assess LA geometry as a continuum, as does non-invasive imaging. This study tested ECG-quantified P wave area as an index of LA geometry.Methods and Results342 patients with CAD underwent ECG and CMR within 7 (0.1±1.4) days. LA area on CMR correlated best with P wave area in ECG lead V1 (r = 0.42, p<0.001), with lesser correlations for P wave amplitude and duration. P wave area increased stepwise in relation to CMR-evidenced MR severity (p<0.001), with similar results for MR on echocardiography (performed in 86% of patients). Pulmonary arterial (PA) pressure on echo was increased by 50% among patients in the highest (45±14 mmHg) vs. the lowest (31±9 mmHg) P wave area quartile of the population. In multivariate regression, CMR and echo-specific models demonstrated P wave area to be independently associated with LA size after controlling for MR, as well as echo-evidenced PA pressure. Clinical follow-up (mean 2.4±1.9 years) demonstrated ECG and CMR to yield similar results for stratification of arrhythmic risk, with a 2.6-fold increase in risk for atrial fibrillation/flutter among patients in the top P wave area quartile of the population (CI 1.1–5.9, p = 0.02), and a 3.2-fold increase among patients in the top LA area quartile (CI 1.4–7.0, p = 0.005).ConclusionsECG-quantified P wave area provides an index of LA remodeling that parallels CMR-evidenced LA chamber geometry, and provides similar predictive value for stratification of atrial arrhythmic risk.

BackgroundDifferential blood oxygenation between left (LV) and right ventricles (RV; ΔSaO2) is a key index of cardiac performance; LV dysfunction yields increased RV blood pool deoxygenation. Deoxyhemoglobin increases blood magnetic susceptibility, which can be measured using an emerging cardiovascular magnetic resonance (CMR) technique, Quantitative Susceptibility Mapping (QSM) – a concept previously demonstrated in healthy subjects using a breath-hold 2D imaging approach (2DBHQSM). This study tested utility of a novel 3D free-breathing QSM approach (3DNAVQSM) in normative controls, and validated 3DNAVQSM for non-invasive ΔSaO2 quantification in patients undergoing invasive cardiac catheterization (cath).MethodsInitial control (n = 10) testing compared 2DBHQSM (ECG-triggered 2D gradient echo acquired at end-expiration) and 3DNAVQSM (ECG-triggered navigator gated gradient echo acquired in free breathing using a phase-ordered automatic window selection algorithm to partition data based on diaphragm position). Clinical testing was subsequently performed in patients being considered for cath, including 3DNAVQSM comparison to cine-CMR quantified LV function (n = 39), and invasive-cath quantified ΔSaO2 (n = 15). QSM was acquired using 3 T scanners; analysis was blinded to comparator tests (cine-CMR, cath).Results3DNAVQSM generated interpretable QSM in all controls; 2DBHQSM was successful in 6/10. Among controls in whom both pulse sequences were successful, RV/LV susceptibility difference (and ΔSaO2) were not significantly different between 3DNAVQSM and 2DBHQSM (252 ± 39 ppb [17.5 ± 3.1%] vs. 211 ± 29 ppb [14.7 ± 2.0%]; p = 0.39). Acquisition times were 30% lower with 3DNAVQSM (4.7 ± 0.9 vs. 6.7 ± 0.5 min, p = 0.002), paralleling a trend towards lower LV mis-registration on 3DNAVQSM (p = 0.14). Among cardiac patients (63 ± 10y, 56% CAD) 3DNAVQSM was successful in 87% (34/39) and yielded higher ΔSaO2 (24.9 ± 6.1%) than in controls (p < 0.001). QSM-calculated ΔSaO2 was higher among patients with LV dysfunction as measured on cine-CMR based on left ventricular ejection fraction (29.4 ± 5.9% vs. 20.9 ± 5.7%, p < 0.001) or stroke volume (27.9 ± 7.5% vs. 22.4 ± 5.5%, p = 0.013). Cath measurements (n = 15) obtained within a mean interval of 4 ± 3 days from CMR demonstrated 3DNAVQSM to yield high correlation (r = 0.87, p < 0.001), small bias (− 0.1%), and good limits of agreement (±8.6%) with invasively measured ΔSaO2.Conclusion3DNAVQSM provides a novel means of assessing cardiac performance. Differential susceptibility between the LV and RV is increased in patients with cine-CMR evidence of LV systolic dysfunction; QSM-quantified ΔSaO2 yields high correlation and good agreement with the reference of invasively-quantified ΔSaO2.

Left ventricular (LV) infarct size affects prognosis after acute myocardial infarction (AMI). Delayed enhancement cardiac magnetic resonance (DE-CMR) provides accurate infarct quantification but is unavailable or contraindicated in many patients. This study tested whether simple electrocardiography (ECG) parameters can stratify LV infarct size. One hundred fifty-two patients with AMI underwent DE-CMR and serial 12-lead ECG. Electrocardiograms were quantitatively analyzed for multiple aspects of Q-wave morphology, including duration, amplitude, and geometric area (QWAr) summed across all leads except aVR. Patients with pathologic Q waves had larger infarcts measured by DE-CMR or enzymes (both p <0.001), even after controlling for infarct distribution by CMR or x-ray angiography. Comparison between early (4 ± 0.4 days after AMI) and follow-up (29 ± 6 days) ECG demonstrated temporal reductions in Q-wave amplitude (1.8 ± 1.4 vs 1.6 ± 1.6 mV; p = 0.03) but not QWAr (41 ± 38 vs 39 ± 43 mV•ms; p = 0.29). At both times, QWAr augmented stepwise with DE-CMR quantified infarct size (p <0.001). QWAr increased markedly at 10% LV infarct threshold, with differences more than threefold on early ECG (59 ± 39 vs 18 ± 20 mV•ms; p <0.001) and nearly fivefold (59 ± 46 vs 13 ± 16 mV•ms; p <0.001) on follow-up. Diagnostic performance compared with a 10% infarction cutoff was good on early (area under the curve = 0.84) and follow-up (area under the curve = 0.87) ECG. Optimization of sensitivity (95% to 98%) enabled QWAr to exclude affected patients with 90% to 94% negative predictive value at each time point. In conclusion, LV infarct size is accompanied by stepwise increments in Q-wave morphology, with QWAr increased three- to fivefold at a threshold of 10% LV infarction. Stratification based on QWAr provides excellent negative predictive value for exclusion of large (≥10%) LV infarct burden.