Association between atrial premature beat and left atrial function in hypertrophic cardiomyopathy on cardiovascular magnetic resonance myocardial tissue tracking: a reproducibility cohort study

Introduction

The left atrial (LA) size and function in patients is associated with adverse events such as arrhythmias, heart failure, and sudden cardiac death and thus has garnered heightened research attention (1-3). The changes in LA function may be synchronized with or occur earlier than atrial size change and may be of considerable value for the early identification of myocardial injury. The atrial function consists of three main components: (I) reservoir function (i.e., collection of blood from pulmonary venous reflux during ventricular systole); (II) conduit function [i.e., delivery of blood to the left ventricle (LV) during early ventricular diastole]; and (III) booster pump function (i.e., activation of LA contraction to increase ventricular filling during late ventricular diastole) (4). These three functions are interdependent. In different processes of disease development, adaptive redistribution can occur with hemodynamic changes to ensure left ventricular filling and maintain cardiac function.

Hypertrophic cardiomyopathy (HCM) is a genetic-related disorder of cardiac myocytes characterized by cardiac hypertrophy, myofiber disarray, and myocardial interstitial fibrosis (5). Due to decreased myocardial compliance and impaired relaxation, the LA and pulmonary pressure are increased in HCM. The thin LA wall is sensitive to volume and pressure changes. Increased LV load pressure leads to LA dilatation, and LA remodeling may increase sensitivity to electrical instability and cause atrial arrhythmia, in particular, atrial fibrillation (AF), which is an important determinant of clinical deterioration due to heart failure or embolic stroke (6). However, there are no effective screening or prevention programs for AF at present. Atrial premature beat (APB) is known to trigger AF, which is an emerging risk marker that can help identify patients who are likely to have or develop paroxysmal AF (7-9). Early detection of APB can provide superior AF risk discrimination, and appropriate intervention for patients with APB may improve upon the poor prognosis of patients with HCM caused by AF. The frequency of APBs has been reported to be independently associated with age, height, history of cardiovascular disease, natriuretic peptide levels, physical activity, and high-density lipoprotein antibodies (10). However, it remains unclear whether the occurrence of APB is related to the decrease of LA function.

Cardiovascular magnetic resonance myocardial tissue tracking (CMR-TT) can achieve visualization of cardiac regional wall motion to assess myocardial deformation by tracking pixels in a standard cine sequence without requiring additional sequences or any contrast agents. CMR-TT provides a novel approach for the quantitative measurement of LA function through strain, which merely reflects size and volume. CMR-TT has been used to quantify atrial deformation (11-13). In this study, we sought to quantitatively evaluate LA function with CMR-TT in different clinical phenotypes of HCM and to assess the association between impaired LA function and APB. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1279/rc).

Methods

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments and was approved by the Institutional Ethics Board of The Second Affiliated Hospital of Nanchang University. Informed consent was obtained from all patients.

Study population

According to current international diagnostic guidelines (5), the inclusion criteria for patients with HCM were as follows: end-diastolic wall thickness ≥15 or ≥13 mm, a familial history of HCM, and the absence of other systemic or cardiac diseases to cause cardiac hypertrophy. Meanwhile, the exclusion criteria were as follows: (I) pacemakers, defibrillator devices, or other metal implants and claustrophobia; (II) previous AF/flutter; (III) other types of cardiomyopathy, myocarditis, myocardial infarction, or severe valvular disease; (IV) prior septal myectomy or septal ablation; and (V) cardiovascular magnetic resonance (CMR) images with poor quality due to difficulty with breath-holding.

A total of 104 patients with basal HCM who underwent conventional CMR scanning and echocardiography from September 2009 to June 2017 were examined (Figure 1). Based on pressure differences of the LV outflow tract, 50 cases were placed in an obstructive HCM (HOCM) group (>30 mmHg) and 54 in a nonobstructive HCM (NOHCM) group (≤30 mmHg) (5). In addition, all participants underwent 24-hour Holter monitoring within 3 days before or after their CMR scans to ensure concurrent assessment of cardiac rhythm and structural/functional parameters. The frequency of APBs was quantified, and a clinically significant APB burden was defined according to two evidence-based thresholds.

Figure 1 Flowchart of participant enrollment. AF, atrial fibrillation; HCM, hypertrophic cardiomyopathy; HOCM, obstructive hypertrophic cardiomyopathy; NOHCM, nonobstructive hypertrophic cardiomyopathy.

One threshold was ≥32 APBs per hour, which was adopted based on previous studies demonstrating that the specificity for predicting AF is significantly increased for premature atrial contraction (PAC) counts more than 32 beats/h, representing a cutoff for frequent APBs and holding prognostic relevance (7,14). The other threshold was >100 PACs/24 hours. Among 25 studies examining Holter, a portion of them reported similar cutoffs for the dichotomization of frequent APBs (15-18). Additionally, 28 age-matched individuals were recruited as the control group. They had no history of cardiac diseases and normal findings for physical examination, echocardiography, and Holter monitoring.

Magnetic resonance imaging protocols

CMR was performed on a 1.5-Tesla scanner (Signa HDxt, GE HealthCare, Chicago, IL, USA) with an 8-channel phased-array cardiac receiver coil. All images were electrocardiogram-triggered and respiratory-gated and were acquired with breath-holding at the end of expiration, with the patient in the supine position. The protocol consisted of stacks of fast equilibrium steady-state precession sequence (FIESTA) cine images in the consecutive short-axis views covering the left entire ventricle and long-axis (two- and four-chamber) views. Scans were conducted under the following imaging parameters: repetition time/echo time, 3.8 ms/1.6 ms; field of view, 38 cm × 38 cm; matrix size, 256×256; slice thickness, 6–8 mm; slice gap, 0 mm; flip angle, 55°; and phases per cardiac cycle, 20.

Imaging analysis

CMR analysis was performed with commercially offline semiautomated tissue-tracking software (cvi42 version 5.6, Circle Cardiovascular Imaging Ltd., Calgary, Canada). The assessment of interobserver variability was conducted by two certified radiologists: reader 1 (S.Z., with 5 years of experience in cardiac imaging) and reader 2 (S.L., with 3 years of experience). Each reader independently performed blinded measurements of LA and LV function on the same set of short-axis and long-axis images. The readers were unaware of each other’s results.

For intraobserver variability, reader 1 (S.Z.) repeated the measurements on the entire cohort a second time. To minimize recall bias, a minimum cool-off period of 3 weeks was enforced between the first and second reading sessions. The readers were blinded to their previous results during the second analysis.

The variability was statistically quantified with the intraclass correlation coefficient (ICC) for absolute agreement for continuous variables (strain and strain rate). The ICC values were interpreted as follows: <0.50, poor; 0.50–0.75, moderate; 0.76–0.90, good; and >0.90, excellent reliability. Additionally, Bland-Altman analysis was performed to assess the limits of agreement between measurements. The results of these analyses are presented in the “Results” section.

Functional assessment

LV endocardial and epicardial contours were manually traced in the short-axis views at the end of the diastole and systole with commercially available software (Cardiac VX, GE HealthCare). LV end-diastolic volume (LVEDV), LV end-systolic volume (LVESV), LV stroke volume (LVSV), LV ejection fraction (LVEF), and LV cardiac output (LVCO) were analyzed.

LA strain and strain rate analysis

The endocardial and epicardial borders of LA in the two- and four-chamber views during LV end-diastole were manually traced, with the pulmonary veins and the LA appendage being excluded (Figure 2). Manual adjustments were made if the contoured borders diverged from real myocardial motion. The longitudinal strain and strain rate curves were calculated via the software using an automated tracking algorithm. From the strain curves, the LA total strain (εs), LA passive strain (εe), and LA active strain (εa) were identified. From the strain rate curves, the LA peak positive strain rate (SRs), LA peak early negative strain rate (SRe), and LA peak late negative strain rate (SRa) were identified. The global longitudinal strain and strain rate of LA were calculated by averaging the global values in the longitudinal direction in the long-axis and two- and four-chamber views. Stretching and shortening were defined by positive and negative strain values, respectively.

Figure 2 LA measurements by CMR-TT in a patient with HCM. (A,B) Manually tracing of the endocardium and epicardial borders of the LA in the two- and four-chamber views at the end of LV diastole. (C,D) The LA strain and strain rate curve. The LA εs, εa, and εe were identified from the strain curve. The SRs during LV systole, SRe, and SRa were analyzed from the strain rate curve. CMR-TT, cardiovascular magnetic resonance myocardial tissue tracking; εa, active strain; εe, passive strain; εs, total strain; HCM, hypertrophic cardiomyopathy; LA, left atrial; LV, left ventricle; SRa, peak late negative strain rate; SRe, peak early negative strain rate; SRs, peak positive strain rate.

LA volumetric analysis

The endocardial borders of LA in the two- and four-chamber views on each phase were manually traced throughout the heart cycle, with the pulmonary veins and LA appendage being excluded. LA volume was automatically calculated at each phase with an automated tracking algorithm (Figure 3). The maximum of LA volume (LAV_max) was assessed at LV end-systole just before mitral valve opening. The minimum of LA volume (LAV_min) was assessed at late LV end-diastole before mitral valve closure and after LA contraction. The volume before LA contraction (LAV_pre) was assessed at the last frame prior to the reopening of the mitral valve. The LAV_max, LAV_min, and LAV_pre were calculated by averaging the volume obtained in long-axis two- and four-chamber views. The LA emptying fractions (LAEFs) were calculated as follows (19): (I) total LAEF (LATEF) = (LAV_max − LAV_min) × 100%/LAV_max; (II) passive LAEF (LAPEF) = (LAV_max − LAV_pre) × 100%/LAV_max; and (III) active LAEF (LAAEF) = (LAV_pre − LAV_min) × 100%/LAV_pre.

Figure 3 LA volume measurements by CMR-TT in a patient with HCM. (A,B) LA endocardium border tracking in the two- and four-chamber views on each phase throughout the cardiac cycle, with pulmonary veins and LA appendage being excluded. CMR-TT, cardiovascular magnetic resonance myocardial tissue tracking; HCM, hypertrophic cardiomyopathy; LA, left atrial.

Statistical analysis

Statistical analysis was performed with SPSS 23.0 (IBM Corp., Armonk, NY, USA). The normality of distribution of continuous variables was validated with the one-sample Kolmogorov-Smirnov test. Comparisons of the LA parameters between groups were performed with one-way analysis of variance (ANOVA). The incidences of APB in the HCM subgroups were analyzed with the χ² test. The associations between LA variables and APB were determined via univariate and multivariate logistic regression analyses. Only variables found to be significant in the univariate model were included in the multivariate model. A box-plot graph was used to illustrate the εs parameter variation between patients with and without APB. Receiver operating characteristic (ROC) curves were used to derive the cutoff values and evaluate the efficacy of εs in the diagnosis of APB. The intra- and interobserver variability for strain and strain rate measurements were assessed via the ICC and by Bland-Altman analysis of 30 randomly selected participants. A P value of less than 0.05 was considered statistically significant.

Results

Patients characteristics and LV function

A total of 104 patients (age 51±12 years; 62% males; 48% HOCM) and 28 healthy controls (age 46±13 years; 32% male) were included in the current study. The patient characteristics and LV function are shown in Table 1. There was no significant difference in age, height, or weight between the two groups. Compared to the NOHCM group, the HOCM group had a higher body mass index (BMI) (P<0.05). The incidence of APB of HOCM was significantly higher than in the NOHCM group (P<0.05). LVESV was significantly lower in the HOCM and NOHOCM group than in the control group, while LVSV and LVEF were significantly higher in the control group. Notably, there was a statistically significant difference in LVEF between the HOCM group and NOHCM group (72.2±5.4 vs. 68.4±8.2, P<0.001).

LA function in patients and controls

Table 2 presents the significantly different values in LA strain, strain rate, volume, and ejection fraction between patients with HOCM or NOHCM and the control group. All measurements of LA strain and strain rate were significantly lower in both patient groups as compared to the control group. The HOCM group, as compared to the NOHCM group, had lower LA strain (εs, εe, and εa) and strain rate (SRs, SRe, and SRa) but higher LAV_min, LAV_max, and LAV_pre. LATEF and LAPEF were lower in the HOCM group than in the NOHCM group, but there was no significant difference in LAAEF between these groups.

Univariate and multivariate analyses

The association between CMR-measured LA parameters and APB is summarized in Table 3. In model 1, univariate analysis indicated that APB could be attributed to lower strain (εs, εe, and εa), lower strain rate (SRs and SRa), and larger LA volumes (LAV_max, LAV_pre, and LAV_min). It indicates the LA function being significant in the HOCM and NOHCM groups and associated with APB. The variables included in multivariable models were not selected arbitrarily but were based on established clinical knowledge and prior literature suggesting their potential significance in patients with HCM. In model 2, which was adjusted for age, sex, BMI, and LVEF, only SRs and LAV_min were included in the multivariate model. We used backward stepwise regression (iteratively removing variables with P>0.05) to refine model parsimony rather than forcing all candidates into the model. This approach prioritized only the most impactful predictors, reducing model complexity. The variables included in the multivariate model were εs and SRs from model 3 and εs and LAV_min from model 4. After a serial analysis of univariate and multivariate logistic regression, we found an independent association between εs and APB frequency (P<0.01), with εs being significantly lower in patients with APB than in those without APB and the control group (17.4±5.6 vs. 23.9±7.0 vs. 30.4±7.8, P<0.001; Figure 4).

Figure 4 Box-and-whisker plots showing significantly lower εs in the APB group compared with the non-APB group and control group (mean ± SD; all P values <0.001). APB, atrial premature beat; εs, total strain; SD, standard deviation.

Diagnostic accuracy

ROC curves were used to establish the optimum diagnostic threshold and evaluate the efficacy of εs in the diagnosis of patients with and without APB occurrence. The optimal threshold of εs was 20.05%, with a sensitivity of 80.3% and a specificity of 72.7% [area under the curve (AUC) =0.827; 95% confidence interval (CI): 0.758–0.897; Figure 5].

Figure 5 ROC curves (in blue) of εs for the efficacy of APB diagnosis. ROC curves for the performance of εs ≤20.05% in predicting APB, with a sensitivity of 80.3% and a specificity of 72.7% (AUC =0.827; 95% CI: 0.758–0.897). Diagonal reference line (in black) is also shown. APB, atrial premature beat; AUC, area under the curve; CI, confidence interval; εs, total strain; ROC, receiver operating characteristic.

Reproducibility

The analysis of LA strain and strain rate parameters showed good consistency and reproducibility on an intra- and interobserver level. The Bland-Altman plots for strain and strain rate measurements are shown in Figure 6. The ICC for repeated measurements of a single observer and between observers is shown in Table 4.

Figure 6 Bland-Altman plots for intra- and interobserver variability obtained for εs, εe, εa, SRs, SRe, and SRa. εa, active strain; εe, passive strain; εs, total strain; SD, standard deviation; SRa, peak late negative strain rate; SRe, peak early negative strain rate; SRs, peak positive strain rate.

Discussion

To our knowledge, this is the first study to evaluate LA function and its relationship with APB in patients with different HCM types using CMR-TT. The principal findings are as follows: (I) CMR-TT could be used to measure LA function, including strain, strain rate, volume, and ejection fraction with excellent intra- and interobserver reproducibility; (II) lower LA strain, strain rate, and emptying fraction, along with greater LA volume, were indicators of impaired reservoir, conduit, and pump function in patients with HCM as compared to controls, and patients with HOCM exhibited greater impairment than did those with NOHCM; and (III) decreased εs was independently associated with APB.

CMR-TT

LA size and function are most often assessed by echocardiography (20,21). However, using Doppler and speckle-tracking echocardiography to assess LA function can be challenging due to the inconsistent image quality. CMR is the gold reference standard for the measurement of LA dimensions and volumes due to its great accuracy and reproducibility (22,23). CMR-TT can be performed with a routine cine CMR examination, does not require additional imaging for the measurement or the use of contrast media, and can identify LA deformation and dysfunction at an early stage (24). In our study, we used CMR-TT to measure multiple LA parameters and analyzed LA function, with our results being consistent with previous reports (1,25). Moreover, the intra- and interobserver reproducibility of CMR-TT was validated.

LA parameters in patients with HCM

LA size and function are reflected by the measured parameters in CMR. εs and SRs corresponding to atrial reservoir function, reflecting the passive stretch of the LA during LV systole. There are two phases of LA filling, early and late, which are related to relaxation from the preceding LA contraction and LV longitudinal fiber systolic shortening and LA chamber stiffness, respectively (26). In our study, εs and SRs were lower in patients with HCM than in the controls; furthermore, εs and SRs were lower in the HOCM group than in the NOHCM group, indicating that LA reservoir function is impaired in patients with HCM and that patients with HOCM have greater impairment than do those with NOHCM. LA chamber stiffness was higher in patients with HCM and was affected by increased LV wall thickness. Moreover, evidence indicates that left ventricular systolic functions are reduced in patients with HCM, can be quantified by myocardial strain (27), and may decrease with the increase in myocardial hypertrophy (28) and the degree of LV outflow tract obstruction (29).

εe and SRe correspond to atrial conduit function, are closely related to atrial compliance, and are mainly regulated by LV diastolic function (4). In patients with HCM, the LV diastolic rate decreased due to the significant thickening of the LV myocardium and the reduction of compliance, which resulted in the decrease of LA conduit function. In accordance with previous findings, in our study, both patients with HOCM and NOHCM had decreased εe and SRe as compared to controls.

εa and SRa correspond to atrial booster pump function; they are affected by the extent of venous return (atrial preload), LV end-diastolic pressure (atrial afterload), and LV systolic reserve (4); and reflect the magnitude and duration of atrial contraction. A literature review on LA function in patients with HCM found that the findings on the effect of pump function are inconsistent, being reported as decreased (5), normal (30), and increased (31). These discrepancies may be due to the difference in LA compliance, hemodynamic change, and atrioventricular interaction. Our results suggest that decreased pump function in patients with HCM, even in those with HOCM, may be due to the patients of our study mainly having advanced disease or an intrinsic atrial abnormality.

LA enlargement is a time-dependent adaptive regulation of cardiomyocytes and reflects the burden of diastolic dysfunction in patients without AF or significant valvular disease (32). Consistent with the finding of LA volume increasing with severity of diastolic dysfunction (32,33), significantly greater LA volumes were observed in patients with HOCM as compared to those with NOHCM in our study. The possible reasons for the difference in capacity are that the LA wall is thin and has good compliance with volume and pressure, the long-term increase in LA load due to diastolic dysfunction causes LA to gradually expand, and the majority of patients with HOCM are accompanied by different degrees of systolic anterior motion. Several studies have shown that LA enlargement carries important clinical and prognostic implications, particularly in AF. LA volume is a superior indicator of LA size to LA diameter (34-36), therefore, this may be necessary to be incorporated into routine clinical evaluation for patients with HCM.

LAEF is the ratio of volume exerted by the LA into LV during atrial systole to atrial inflow and is a marker of atrial systolic function (37). Murata et al. demonstrated that the LAEF decreases as diastolic dysfunction deteriorates (38). In our study, the HOCM group had reduced LATEF and LAPET as compared to the NOHCM group, while no LAAEF difference was found between these two groups. One possible explanation for this finding is that a portion of the LA contraction function was compensated via the Frank-Starling mechanism, which is in line with the results reported by Kobayashi et al. (39).

Relationship between LA function and arrhythmia

Arrhythmia is commonly observed in patients with HCM. The high incidence and mortality rate of serious ventricular arrhythmia and AF are well established (40). However, APB has been regarded as a relatively benign phenomenon and nonsignificant in prognostic risk stratification. Several studies have demonstrated that APB is a strong independent predictor for individuals who are at elevated risk of developing or experiencing paroxysmal AF and is dose-dependent (7,41). APB has been recognized as an early marker of atrial electrical remodeling, which precedes the development of AF (42,43). It may thus serve as a surrogate marker or a prodromal stage of AF, whose presence has been associated with an increased risk of cardiovascular death. Moreover, targeted ablation of APB has been shown to reduce arrhythmia recurrence in patients with AF (44). Therefore, early identification of APB in patients with HCM can advance the clinical intervention time and reduce the probability of adverse events, and it thus has important clinical value.

Novel biomarkers quantified by CMR-TT for identifying patients with HCM at risk of APB may significantly aid in clinical care. Our results indicated that APB could be attributed to lower strain (εs, εe, and εa), lower strain rate (SRs and SRa), and larger LA volume (LAV_max, LAV_pre, and LAV_min), explaining why patients with HOCM are more predisposed to APB than are those with NOHCM. Multivariate analysis identified an independent association between εs and APB frequency. This indicates that APB likely results from a change in LA size and function, especially LA reservoir function. One possible reason for this is that the pulmonary veins are an important source of APBs (44). Our study identified an εs optimal cutoff of 20.05% as strongly predictive of new-onset APB, but this needs to be confirmed in future cohort studies.

Other studies reported LA size to be a risk factor of adverse events in patients with HCM (45,46). Our results suggest that LA reservoir function is more closely associated with APB than are LA volume and ejection fraction and can reflect APB progression in patients with HCM. In other words, early detection of dysfunction via strain may have greater prognostic value than LA size. Overall, our findings may have immediate clinical implications, and we suggest that the treatment of APB with drugs or targeted ablation in patients with HCM may provide a primary prevention strategy to help prevent cardiovascular events such as AF, stroke, and heart failure. It is necessary to further determine whether the maintenance or restoration of LA is conducive to reducing the incidence of APB.

Limitations

There are several limitations to our study which should be acknowledged. First, selection bias might have been introduced, as we employed a single-center and retrospective design, the total number of patients included was limited, and the cohort was restricted to patients with HCM who underwent CMR examination. In clinical practice, CMR is often prioritized for patients with HCM with more complex clinical presentations and is not a routine screening tool for all patients with HCM. This means our cohort may be overrepresented by patients with more severe disease or symptomatic phenotypes, which could limit the generalizability of our findings to the broader HCM population (e.g., asymptomatic patients or those managed solely with echocardiography), and the incidence of AF caused by APB in patients HCM remains unclear, the findings should be confirmed in future studies with expanded sample sizes (n>300). Moreover, subsequent research should include subgroup analysis for types of HCM, such as asymmetric hypertrophy of interventricular septum and apical hypertrophy, with additional parameters, such as displacement and movement speed, also being analyzed. Second, the thin LA wall, extreme curvature of the LA, and interference caused by surrounding structures, including the ostium of pulmonary veins and the LA appendage, rendered the assessment of LA deformation challenging, leading to underestimate strain and strain rate values. Finally, LA fibrosis may be an important intrinsic determinant of impaired LA function, which was not evaluated by CMR in our study.

Conclusions

CMR-TT is a noninvasive technology for quantifying myocardial deformation, which can assess the mechanical changes of LA. Our study identified a significant association between abnormal LA function, as quantified by εs, and the presence of APB in patients with HCM. Furthermore, patients with HOCM demonstrated more severe impairment than did those with NOHCM and had a higher prevalence of APB. Given that APB is a known precursor of AF and is associated with adverse cardiovascular events, the strong association with LA strain suggests that CMR-TT-derived εs could serve as a valuable imaging biomarker for identifying patients with an electrophysiological profile linked to higher AF risk. This identification may be beneficial for guiding more vigilant monitoring and prompting personalized management strategies for patients with HCM, potentially helping to prevent the occurrence of stroke and heart failure. However, as this was a cross-sectional study, the temporal relationship between impaired strain and APB development could not be definitively established. Further longitudinal studies are necessary to determine whether the impairment of LA function precedes the development of APBs and whether maintaining or restoring LA function is helpful to alleviating the clinical progression of HCM.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1279/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1279/dss

Funding: This research was supported by the National Natural Science Foundation of China (No. 82260342).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1279/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institutional Ethics Board of The Second Affiliated Hospital of Nanchang University and informed consent was taken from all the patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Kowallick JT, Kutty S, Edelmann F, Chiribiri A, Villa A, Steinmetz M, Sohns JM, Staab W, Bettencourt N, Unterberg-Buchwald C, Hasenfuß G, Lotz J, Schuster A. Quantification of left atrial strain and strain rate using Cardiovascular Magnetic Resonance myocardial feature tracking: a feasibility study. J Cardiovasc Magn Reson 2014;16:60. [Crossref] [PubMed]
2. Leng S, Tan RS, Zhao X, Allen JC, Koh AS, Zhong L. Validation of a rapid semi-automated method to assess left atrial longitudinal phasic strains on cine cardiovascular magnetic resonance imaging. J Cardiovasc Magn Reson 2018;20:71. [Crossref] [PubMed]
3. Hinojar R, Zamorano JL, Fernández-Méndez M, Esteban A, Plaza-Martin M, González-Gómez A, Carbonell A, Rincón LM, Nácher JJJ, Fernández-Golfín C. Prognostic value of left atrial function by cardiovascular magnetic resonance feature tracking in hypertrophic cardiomyopathy. Int J Cardiovasc Imaging 2019;35:1055-65. [Crossref] [PubMed]
4. Hoit BD. Left atrial size and function: role in prognosis. J Am Coll Cardiol 2014;63:493-505. [Crossref] [PubMed]
5. Elliott PM, Anastasakis A, Borger MA, Borggrefe M, Cecchi F, et al. 2014 ESC Guidelines on diagnosis and management of hypertrophic cardiomyopathy: the Task Force for the Diagnosis and Management of Hypertrophic Cardiomyopathy of the European Society of Cardiology (ESC). Eur Heart J 2014;35:2733-79. [Crossref] [PubMed]
6. Yang H, Woo A, Monakier D, Jamorski M, Fedwick K, Wigle ED, Rakowski H. Enlarged left atrial volume in hypertrophic cardiomyopathy: a marker for disease severity. J Am Soc Echocardiogr 2005;18:1074-82. [Crossref] [PubMed]
7. Dewland TA, Vittinghoff E, Mandyam MC, Heckbert SR, Siscovick DS, Stein PK, Psaty BM, Sotoodehnia N, Gottdiener JS, Marcus GM. Atrial ectopy as a predictor of incident atrial fibrillation: a cohort study. Ann Intern Med 2013;159:721-8. [Crossref] [PubMed]
8. Marcus GM, Dewland TA. Premature Atrial Contractions: A Wolf in Sheep's Clothing? J Am Coll Cardiol 2015;66:242-4. [Crossref] [PubMed]
9. Persson AP, Fedorowski A, Hedblad B, Persson M, Juul-Möller S, Engström G, Johnson LSB. Heart rate and premature atrial contractions at 24hECG independently predict atrial fibrillation in a population-based study. Heart 2020;106:287-91. [Crossref] [PubMed]
10. Conen D, Adam M, Roche F, Barthelemy JC, Felber Dietrich D, Imboden M, Künzli N, von Eckardstein A, Regenass S, Hornemann T, Rochat T, Gaspoz JM, Probst-Hensch N, Carballo D. Premature atrial contractions in the general population: frequency and risk factors. Circulation 2012;126:2302-8. [Crossref] [PubMed]
11. Evin M, Cluzel P, Lamy J, Rosenbaum D, Kusmia S, Defrance C, Soulat G, Mousseaux E, Roux C, Clement K, Hatem SN, Redheuil A, Kachenoura N. Assessment of left atrial function by MRI myocardial feature tracking. J Magn Reson Imaging 2015;42:379-89. [Crossref] [PubMed]
12. Truong VT, Palmer C, Wolking S, Sheets B, Young M, Ngo TNM, Taylor M, Nagueh SF, Zareba KM, Raman S, Mazur W. Normal left atrial strain and strain rate using cardiac magnetic resonance feature tracking in healthy volunteers. Eur Heart J Cardiovasc Imaging 2020;21:446-53. [Crossref] [PubMed]
13. Wen J, Zhang X, Li S, Tao X, Fang Q, Zhou S, Xia L, Gong L. Identification of heart failure with preserved ejection fraction in patients with hypertension: a left atrial myocardial strain cardiac magnetic resonance study. Quant Imaging Med Surg 2023;13:2881-94. [Crossref] [PubMed]
14. Binici Z, Intzilakis T, Nielsen OW, Køber L, Sajadieh A. Excessive supraventricular ectopic activity and increased risk of atrial fibrillation and stroke. Circulation 2010;121:1904-11. [Crossref] [PubMed]
15. Acharya T, Tringali S, Bhullar M, Nalbandyan M, Ilineni VK, Carbajal E, Deedwania P. Frequent Atrial Premature Complexes and Their Association With Risk of Atrial Fibrillation. Am J Cardiol 2015;116:1852-7. [Crossref] [PubMed]
16. Chong BH, Pong V, Lam KF, Liu S, Zuo ML, Lau YF, Lau CP, Tse HF, Siu CW. Frequent premature atrial complexes predict new occurrence of atrial fibrillation and adverse cardiovascular events. Europace 2012;14:942-7. [Crossref] [PubMed]
17. Suzuki S, Sagara K, Otsuka T, Kano H, Matsuno S, Takai H, Uejima T, Oikawa Y, Koike A, Nagashima K, Kirigaya H, Yajima J, Tanabe H, Sawada H, Aizawa T, Yamashita T. Usefulness of frequent supraventricular extrasystoles and a high CHADS2 score to predict first-time appearance of atrial fibrillation. Am J Cardiol 2013;111:1602-7. [Crossref] [PubMed]
18. Yodogawa K, Seino Y, Ohara T, Hayashi M, Miyauchi Y, Katoh T, Mizuno K. Prediction of atrial fibrillation after ischemic stroke using P-wave signal averaged electrocardiography. J Cardiol 2013;61:49-52. [Crossref] [PubMed]
19. Farzaneh-Far A, Ariyarajah V, Shenoy C, Dorval JF, Kaminski M, Curillova Z, Wu H, Brown KB, Kwong RY. Left atrial passive emptying function during dobutamine stress MR imaging is a predictor of cardiac events in patients with suspected myocardial ischemia. JACC Cardiovasc Imaging 2011;4:378-88. [Crossref] [PubMed]
20. Anwar AM, Geleijnse ML, Soliman OI, Nemes A, ten Cate FJ. Left atrial Frank-Starling law assessed by real-time, three-dimensional echocardiographic left atrial volume changes. Heart 2007;93:1393-7. [Crossref] [PubMed]
21. Jenkins C, Bricknell K, Marwick TH. Use of real-time three-dimensional echocardiography to measure left atrial volume: comparison with other echocardiographic techniques. J Am Soc Echocardiogr 2005;18:991-7. [Crossref] [PubMed]
22. Maceira AM, Cosín-Sales J, Roughton M, Prasad SK, Pennell DJ. Reference left atrial dimensions and volumes by steady state free precession cardiovascular magnetic resonance. J Cardiovasc Magn Reson 2010;12:65. [Crossref] [PubMed]
23. Hof IE, Velthuis BK, Van Driel VJ, Wittkampf FH, Hauer RN, Loh P. Left atrial volume and function assessment by magnetic resonance imaging. J Cardiovasc Electrophysiol 2010;21:1247-50. [Crossref] [PubMed]
24. Yang Y, Yin G, Jiang Y, Song L, Zhao S, Lu M. Quantification of left atrial function in patients with non-obstructive hypertrophic cardiomyopathy by cardiovascular magnetic resonance feature tracking imaging: a feasibility and reproducibility study. J Cardiovasc Magn Reson 2020;22:1. [Crossref] [PubMed]
25. Imai M, Ambale Venkatesh B, Samiei S, Donekal S, Habibi M, Armstrong AC, Heckbert SR, Wu CO, Bluemke DA, Lima JA. Multi-ethnic study of atherosclerosis: association between left atrial function using tissue tracking from cine MR imaging and myocardial fibrosis. Radiology 2014;273:703-13. [Crossref] [PubMed]
26. Barbier P, Solomon SB, Schiller NB, Glantz SA. Left atrial relaxation and left ventricular systolic function determine left atrial reservoir function. Circulation 1999;100:427-36. [Crossref] [PubMed]
27. Serri K, Reant P, Lafitte M, Berhouet M, Le Bouffos V, Roudaut R, Lafitte S. Global and regional myocardial function quantification by two-dimensional strain: application in hypertrophic cardiomyopathy. J Am Coll Cardiol 2006;47:1175-81. [Crossref] [PubMed]
28. Xu HY, Chen J, Yang ZG, Li R, Shi K, Zhang Q, Liu X, Xie LJ, Jiang L, Guo YK. Early marker of regional left ventricular deformation in patients with hypertrophic cardiomyopathy evaluated by MRI tissue tracking: The effects of myocardial hypertrophy and fibrosis. J Magn Reson Imaging 2017;46:1368-76. [Crossref] [PubMed]
29. Mazurkiewicz Ł, Ziółkowska L, Petryka J, Śpiewak M, Małek Ł, Kubik A, Marczak M, Misko J, Brzezińska-Rajszys G. Left-ventricular mechanics in children with hypertrophic cardiomyopathy. CMR study. Magn Reson Imaging 2017;43:56-65. [Crossref] [PubMed]
30. Kowallick JT, Silva Vieira M, Kutty S, Lotz J, Hasenfu G, Chiribiri A, Schuster A. Left Atrial Performance in the Course of Hypertrophic Cardiomyopathy: Relation to Left Ventricular Hypertrophy and Fibrosis. Invest Radiol 2017;52:177-85. [Crossref] [PubMed]
31. Anwar AM, Soliman OI, Geleijnse ML, Michels M, Vletter WB, Nemes A, ten Cate FJ. Assessment of left atrial ejection force in hypertrophic cardiomyopathy using real-time three-dimensional echocardiography. J Am Soc Echocardiogr 2007;20:744-8. [Crossref] [PubMed]
32. Tsang TS, Barnes ME, Gersh BJ, Bailey KR, Seward JB. Left atrial volume as a morphophysiologic expression of left ventricular diastolic dysfunction and relation to cardiovascular risk burden. Am J Cardiol 2002;90:1284-9. [Crossref] [PubMed]
33. Pritchett AM, Mahoney DW, Jacobsen SJ, Rodeheffer RJ, Karon BL, Redfield MM. Diastolic dysfunction and left atrial volume: a population-based study. J Am Coll Cardiol 2005;45:87-92. [Crossref] [PubMed]
34. Tsang TS, Barnes ME, Bailey KR, Leibson CL, Montgomery SC, Takemoto Y, Diamond PM, Marra MA, Gersh BJ, Wiebers DO, Petty GW, Seward JB. Left atrial volume: important risk marker of incident atrial fibrillation in 1655 older men and women. Mayo Clin Proc 2001;76:467-75. [Crossref] [PubMed]
35. Tsang TS, Abhayaratna WP, Barnes ME, Miyasaka Y, Gersh BJ, Bailey KR, Cha SS, Seward JB. Prediction of cardiovascular outcomes with left atrial size: is volume superior to area or diameter? J Am Coll Cardiol 2006;47:1018-23. [Crossref] [PubMed]
36. Abhayaratna WP, Seward JB, Appleton CP, Douglas PS, Oh JK, Tajik AJ, Tsang TS. Left atrial size: physiologic determinants and clinical applications. J Am Coll Cardiol 2006;47:2357-63. [Crossref] [PubMed]
37. Manning WJ, Silverman DI, Katz SE, Douglas PS. Atrial ejection force: a noninvasive assessment of atrial systolic function. J Am Coll Cardiol 1993;22:221-5. [Crossref] [PubMed]
38. Murata M, Iwanaga S, Tamura Y, Kondo M, Kouyama K, Murata M, Ogawa S. A real-time three-dimensional echocardiographic quantitative analysis of left atrial function in left ventricular diastolic dysfunction. Am J Cardiol 2008;102:1097-102. [Crossref] [PubMed]
39. Kobayashi Y, Wheeler M, Finocchiaro G, Ariyama M, Kobayashi Y, Perez MV, Liang D, Kuznetsova T, Schnittger I, Ashley E, Haddad F. Left atrial function and phenotypes in asymmetric hypertrophic cardiomyopathy. Echocardiography 2017;34:843-50. [Crossref] [PubMed]
40. Savage DD, Seides SF, Maron BJ, Myers DJ, Epstein SE. Prevalence of arrhythmias during 24-hour electrocardiographic monitoring and exercise testing in patients with obstructive and nonobstructive hypertrophic cardiomyopathy. Circulation 1979;59:866-75. [Crossref] [PubMed]
41. Gladstone DJ, Dorian P, Spring M, Panzov V, Mamdani M, Healey JS, Thorpe KEEMBRACE Steering Committee and Investigators. Atrial premature beats predict atrial fibrillation in cryptogenic stroke: results from the EMBRACE trial. Stroke 2015;46:936-41. [Crossref] [PubMed]
42. Himmelreich JCL, Lucassen WAM, Heugen M, Bossuyt PMM, Tan HL, Harskamp RE, van Etten-Jamaludin FS, van Weert HCPM. Frequent premature atrial contractions are associated with atrial fibrillation, brain ischaemia, and mortality: a systematic review and meta-analysis. Europace 2019;21:698-707. [Crossref] [PubMed]
43. O'Neal WT, Kamel H, Kleindorfer D, Judd SE, Howard G, Howard VJ, Soliman EZ. Premature Atrial Contractions on the Screening Electrocardiogram and Risk of Ischemic Stroke: The Reasons for Geographic and Racial Differences in Stroke Study. Neuroepidemiology 2016;47:53-8. [Crossref] [PubMed]
44. Haïssaguerre M, Jaïs P, Shah DC, Takahashi A, Hocini M, Quiniou G, Garrigue S, Le Mouroux A, Le Métayer P, Clémenty J. Spontaneous initiation of atrial fibrillation by ectopic beats originating in the pulmonary veins. N Engl J Med 1998;339:659-66. [Crossref] [PubMed]
45. Debonnaire P, Joyce E, Hiemstra Y, Mertens BJ, Atsma DE, Schalij MJ, Bax JJ, Delgado V, Marsan NA. Left Atrial Size and Function in Hypertrophic Cardiomyopathy Patients and Risk of New-Onset Atrial Fibrillation. Circ Arrhythm Electrophysiol 2017;10:e004052. [Crossref] [PubMed]
46. Losi MA, Betocchi S, Barbati G, Parisi V, Tocchetti CG, Pastore F, Migliore T, Contaldi C, Caputi A, Romano R, Chiariello M. Prognostic significance of left atrial volume dilatation in patients with hypertrophic cardiomyopathy. J Am Soc Echocardiogr 2009;22:76-81. [Crossref] [PubMed]