Low Left Atrial Strain Is Associated With Adverse Outcomes in Hypertrophic Cardiomyopathy Patients

Vasquez, Nestor; Ostrander, Benjamin T.; Lu, Dai‐Yin; Ventoulis, Ioannis; Haileselassie, Bereketeab; Goyal, Sagar; Greenland, Gabriela V.; Vakrou, Styliani; Olgin, Jeffrey E.; Abraham, Theodore P.; Abraham, M. Roselle

doi:10.1016/j.echo.2019.01.007

Cited by 72 publications

(64 citation statements)

References 46 publications

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“…LA strain has been reported to correlate with LV filling pressure [12][13][14] and is a sensitive marker detecting early LV diastolic dysfunction [14]. It is also recognised as a predictor of adverse cardiovascular outcomes in women in the general population [15] and in diseases that are associated with ventricular diastolic dysfunction [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Reproducibility of left atrial function using cardiac magnetic resonance imaging

et al. 2020

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Objectives To determine the test-retest reproducibility and observer variability of CMR-derived LA function, using (i) LA strain (LAS) and strain rate (LASR), and (ii) LA volumes (LAV) and emptying fraction (LAEF). Methods Sixty participants with and without cardiovascular disease (aortic stenosis (AS) (n = 16), type 2 diabetes (T2D) (n = 28), end-stage renal disease on haemodialysis (n = 10) and healthy volunteers (n = 6)) underwent two separate CMR scans 7–14 days apart. LAS and LASR, corresponding to LA reservoir, conduit and contractile booster-pump function, were assessed using Feature Tracking software (QStrain v2.0). LAEF was calculated using the biplane area length method (QMass v8.1). Both were assessed using 4- and 2-chamber long-axis standard steady-state free precession cine images, and average values were calculated. Intra- and inter-observer variabilities were assessed in 10 randomly selected participants. Results The test-retest reproducibility was moderate to poor for all strain and strain rate parameters. Overall, strain and strain rate corresponding to reservoir phase (LAS_r, LASR_r) were the most reproducible, yielding the smallest coefficient of variance (CoV) (29.9% for LAS_r, 28.9% for LASR_r). The test-retest reproducibility for LAVs and LAEF was good: LAVmax CoV = 19.6% ICC = 0.89, LAVmin CoV = 27.0% ICC = 0.89 and total LAEF CoV = 15.6% ICC = 0.78. The inter- and intra-observer variabilities were good for all parameters except for conduit function. Conclusion The test-retest reproducibility of LA strain and strain rate assessment by CMR utilising Feature Tracking is moderate to poor across disease states, whereas LA volume and emptying fraction are more reproducible on CMR. Further improvements in LA strain quantification are needed before widespread clinical application. Key Points • LA strain and strain rate assessment using Feature Tracking on CMR has moderate to poor test-retest reproducibility across disease states. • The test-retest reproducibility for the biplane method of assessing LA function is better than strain assessment, with lower coefficient of variances and narrower limits of agreement on Bland-Altman plots. • Biplane LA volumetric measurement also has better intra- and inter-observer variability compared to strain assessment.

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Section: Introductionmentioning

confidence: 99%

Reproducibility of left atrial function using cardiac magnetic resonance imaging

et al. 2020

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“…In previous studies, the LA strain was found to be an independent predictor of the development of AF in HCM patients 19,20 . In our study; although LA strain was found to be lower in the group, in which AF developed, a statistically significant difference was not observed between the groups in the regression analysis.…”

Section: Resultsmentioning

confidence: 83%

Left Atrial Dispersion Predicts Atrial Fibrillation in Patients with Hypertrophic Cardiomyopathy: A 5-year Follow-up study

Candan

Çakmak

Geçmen

et al. 2020

Preprint

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Purpose:The aim of our study was to examine whether left atrial dispersion and left atrial strain as measured by speckle tracking echocardiography and clinic paramaters are predictors for the development of atrial fibrillation (AF) in patients with hypertrophic cardiomyopathy. Methods: A total of 137 patients (70% male, mean age 49.6 ± 14.2 years) with HCM were included in the study. Patients' clinical, electrocardiographic, 2D classic and speckle tracking echocardiographic (STE) data were collected. AF was searched by 12-lead electrocardiograms or 24-hour Holter recordings during follow-up period. Atrial dispersion was defined as the standard deviation of time to peak strain in 12 left atrial segments Results: During a follow-up period of 5 years 37 patients (16.9%) developed AF. At follow-up, the patients with occurrence of AF were older than in patients without AF. Atrial dispersion was observed to be higher in the AF developing group (61.4 ± 23.2 vs 43.1±15.8, p=<0.001). The multivariate in Cox regression analysis (including atrial dispersion, PALS, age, LA) atrial dispersion (msn) (HR 1.017, 95% CI: 1.001-1.03, p= 0.035) and age were found to be independent predictors of AF occurrence. In the ROC analysis atrial dispersion > 44.7 msn predicted occurrence of AF with 82.4% sensitivity and 64 % specificity. Conclusion: In patients with hypertrophic cardiomyopathy, atrial dispersion and age are predictive of the development of atrial fibrillation. Atrial dispersion measured by the speckle tracking-based method may provide further information in evaluating left atrial functions in patients with hypertrophic cardiomyopathy or other disease states

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“…Similar results of exercise intolerance in HCM patients with PAF have been reported in a previous study of 265 HCM patients during sinus rhythm [57] -here, the authors did not observe an association between lower exercise capacity and diastolic dysfunction or LA volume. Additionally, ECHO [58] and CMR [5,56] studies in HCM patients have revealed greater impairment of LA function and greater degree of LA fibrosis in HCM patients with PAF, suggesting that PAF is a marker of LA myopathy.…”

Section: Discussionmentioning

confidence: 99%

“…We grouped all AF cases (paroxysmal, persistent, permanent), as well as prevalent and incident AF into one set (because of low event number) and only included ECHO/CMR imaging features obtained at the patients' first clinic visit in the model. Left atrial volume, [46] LA strain, [58] LA fibrosis, [56] EKG/blood biomarkers, [66] genotype, [67][68][69] or sleep apnea were not included in our model, because this data is not available for a large proportion of our cohort. Lastly, we were unable to assess the generalizability of our approach by applying our developed model to additional HCM patients -beyond the cross-validation study, due to the unavailability of data from other HCM cohorts reported in other studies.…”

Section: Limitationsmentioning

confidence: 99%

HCM-AF-Risk Model to Identify Cases and Predictors of Atrial Fibrillation in Hypertrophic Cardiomyopathy

Bhattacharya

Greenland

et al. 2020

Preprint

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Background AF in HCM is associated with high stroke risk despite low CHA2DS2-VASc scores. Hence, there is need to understand AF pathophysiology and predict AF in HCM. We develop/apply a data-driven, machine learning-based method to identify AF cases and clinical features associated with AF in HCM, using electronic health record (EHR) data. Methods Patients with documented paroxysmal/ persistent/permanent AF (n=191) were considered AF cases, and the remaining patients in sinus rhythm (n=640) were tagged as No-AF. We evaluated 93 clinical variables; the most informative variables useful for distinguishing AF from No-AF cases were selected based on the 2-sample t-test and information gain criterion. Results We identified 18 highly informative clinical variables: 11 are positively associated (e.g. LA-diameter, LV-diastolic dysfunction, LV-LGE), and 7 are negatively correlated (e.g. several exercise parameters) with AF in HCM. Next, patient records were represented via these 18 variables. Data imbalance resulting from the relatively low number of AF cases was addressed via a combination of over-and under-sampling strategies. We trained and tested multiple classifiers under this sampling approach, showing effective classification. Specifically, an ensemble of logistic regression and naïve Bayes classifiers, trained based on the 18 variables and corrected for data imbalance, proved most effective for separating AF from No-AF cases (sensitivity=0.74, specificity=0.72, C-index=0.80). Conclusions Our model (HCM-AF-Risk Model), the first machine learning-based method for identification of AF cases in HCM, demonstrates good performance, and suggests that AF is associated with a more severe cardiac HCM-phenotype.

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Low Left Atrial Strain Is Associated With Adverse Outcomes in Hypertrophic Cardiomyopathy Patients

Cited by 72 publications

References 46 publications

Reproducibility of left atrial function using cardiac magnetic resonance imaging

Reproducibility of left atrial function using cardiac magnetic resonance imaging

Left Atrial Dispersion Predicts Atrial Fibrillation in Patients with Hypertrophic Cardiomyopathy: A 5-year Follow-up study

HCM-AF-Risk Model to Identify Cases and Predictors of Atrial Fibrillation in Hypertrophic Cardiomyopathy

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