Papillary and Trabecular Muscles Have Substantial Impact on Quantification of Left Ventricle in Patients with Hypertrophic Obstructive Cardiomyopathy

Comparison of left ventricular mass and wall thickness between cardiac computed tomography angiography and cardiac magnetic resonance imaging using machine learning algorithms

Aims

Cardiac magnetic resonance imaging (MRI) is the gold standard in the assessment of left ventricle (LV) mass and wall thickness. In recent years, cardiac computed tomography angiography (CCTA) has gained widespread usage as an imaging modality. Despite this, limited previous investigations have specifically addressed the potential of CCTA as an alternative modality for quantitative LV assessment. The aim of this study was to compare CCTA derived LV mass and wall thickness with cardiac MRI utilizing machine learning algorithms.

Methods and results

Fifty-seven participants who underwent both CCTA and cardiac MRI were identified. LV mass and wall thickness was calculated using LV contours which were automatically placed using in-house developed machine learning models. Pearson's correlation coefficients were calculated along with Bland-Altman plots to assess the agreement between the LV mass and wall thickness per region on CCTA and cardiac MRI. Inter-observer correlations were tested using Pearson's correlation coefficient. Average LV mass and wall thickness for CCTA and cardiac MRI were 127 g, 128 g, 7, and 8 mm, respectively. Bland-Altman plots demonstrated mean differences and corresponding 95% limits of agreement of -1.26 (25.06; -27.58) and -0.57 (1.78; -2.92), for LV mass and average LV wall thickness, respectively. Mean differences and corresponding 95% limits of agreement for wall thickness per region were -0.75 (1.34; -2.83), -0.58 (2.14; -3.30), and -0.29 (3.21; -3.79) for the basal, mid, and apical regions, respectively. Inter-observer correlations were excellent.

Conclusion

Quantitative assessment of LV mass and wall thickness on CCTA using machine learning algorithms seems feasible and shows good agreement with cardiac MRI.

Automatic left ventricle volume and mass quantification from 2D cine-MRI: Investigating papillary muscle influence

OBJECTIVE

Early detection of cardiovascular diseases is based on accurate quantification of the left ventricle (LV) function parameters. In this paper, we propose a fully automatic framework for LV volume and mass quantification from 2D-cine MR images already segmented using U-Net.

METHODS

The general framework consists of three main steps: Data preparation including automatic LV localization using a convolution neural network (CNN) and application of morphological operations to exclude papillary muscles from the LV cavity. The second step consists in automatically extracting the LV contours using U-Net architecture. Finally, by integrating temporal information which is manifested by a spatial motion of myocytes as a third dimension, we calculated LV volume, LV ejection fraction (LVEF) and left ventricle mass (LVM). Based on these parameters, we detected and quantified cardiac contraction abnormalities using Python software.

RESULTS

CNN was trained with 35 patients and tested on 15 patients from the ACDC database with an accuracy of 99,15 %. U-Net architecture was trained using ACDC database and evaluated using local dataset with a Dice similarity coefficient (DSC) of 99,78 % and a Hausdorff Distance (HD) of 4.468 mm (p < 0,001). Quantification results showed a strong correlation with physiological measures with a Pearson correlation coefficient (PCC) of 0,991 for LV volume, 0.962 for LVEF, 0.98 for stroke volume (SV) and 0.923 for LVM after pillars' elimination. Clinically, our method allows regional and accurate identification of pathological myocardial segments and can serve as a diagnostic aid tool of cardiac contraction abnormalities.

CONCLUSION

Experimental results prove the usefulness of the proposed method for LV volume and function quantification and verify its potential clinical applicability.

Allelic heterogeneity of TTNtv dilated cardiomyopathy can be modeled in adult zebrafish

Allelic heterogeneity (AH) has been noted in truncational TTN-associated (TTNtv-associated) dilated cardiomyopathy (DCM); i.e., mutations affecting A-band-encoding exons are pathogenic, but those affecting Z-disc-encoding exons are likely benign. The lack of an in vivo animal model that recapitulates AH hinders the deciphering of the underlying mechanism. Here, we explored zebrafish as a candidate vertebrate model by phenotyping a collection of zebrafish ttntv alleles. We noted that cardiac function and sarcomere structure were more severely disrupted in ttntv-A than in ttntv-Z homozygous embryos. Consistently, cardiomyopathy-like phenotypes were present in ttntv-A but not ttntv-Z adult heterozygous mutants. The phenotypes observed in ttntv-A alleles were recapitulated in null mutants with the full titin-encoding sequences removed. Defective autophagic flux, largely due to impaired autophagosome-lysosome fusion, was also noted only in ttntv-A but not in ttntv-Z models. Moreover, we found that genetic manipulation of ulk1a restored autophagy flux and rescued cardiac dysfunction in ttntv-A animals. Together, our findings presented adult zebrafish as an in vivo animal model for studying AH in TTNtv DCM, demonstrated TTN loss of function is sufficient to trigger ttntv DCM in zebrafish, and uncovered ulk1a as a potential therapeutic target gene for TTNtv DCM.

Role of Biomarkers of Myocardial Injury to Predict Adverse Outcomes in Hypertrophic Cardiomyopathy

BACKGROUND

Serum troponins and CK-MB (creatine kinase-MB) are readily detectable and reliable cardiac-specific biomarkers of subclinical myocardial injury. This study explores the roles of cTnI (cardiac troponin I) and CK-MB in hypertrophic cardiomyopathy (HCM).

METHODS

This study included 1045 patients with HCM who had baseline cTnI and CK-MB measurements at Fuwai Hospital between 1999 and 2019. Patients were excluded if they had undergone percutaneous coronary intervention or coronary artery bypass grafting, or had renal failure. Five end points were studied: all-cause death, cardiovascular death, noncardiovascular death, sudden cardiac death, and other cardiovascular death. Cox regression was used to assess the associations of cTnI and CK-MB levels with outcomes.

RESULTS

Nine hundred seventy patients with available follow-up data were finally analyzed (mean age, 49.3 years; 36.4% female). During the median 4.3-year follow-up period, 87 patients reached the end points. Higher cTnI (per 0.05 ng/mL increase) and CK-MB (per 1 IU/L increase) levels were associated with increased risks of all-cause death (cTnI: adjusted hazard ratio [HR], 1.038, P<0.001; CK-MB: adjusted HR, 1.021, P=0.004), cardiovascular death (cTnI: adjusted HR, 1.040, P<0.001; CK-MB: adjusted HR, 1.025, P=0.006), and sudden cardiac death (cTnI: adjusted HR, 1.045, P<0.001; CK-MB: adjusted HR, 1.032, P=0.001). Patients with elevated levels of both cTnI and CK-MB had worse prognoses than patients with an elevated level of either biomarker alone and patients who did not have an elevated level of either biomarker. Addition of the binary indicator elevation of both cTnI and CK-MB significantly improved the discrimination and reclassification abilities of the standard HCM Risk- sudden cardiac death model (C statistics: P=0.002; net reclassification improvement, 0.652; integrated discrimination improvement, 0.064).

CONCLUSIONS

Comprehensive evaluations of biomarkers of myocardial injury, cTnI and CK-MB, have considerable value for predicting adverse outcomes among patients with HCM. Routine cTnI and CK-MB assessments may help to guide implantable cardioverter defibrillator implantation for primary prevention in HCM.