Regional Myocardial Remodeling Characteristics Correlates With Cardiac Events in Sarcoidosis

Left ventricular concentric hypertrophy with cardiac magnetic resonance imaging improves risk stratification in patients with Duchenne muscular dystrophy: a prospective cohort study

BACKGROUND

The development of left ventricular (LV) remodeling has been associated with an increased cardiovascular risk and cardiogenic death, and different patterns of remodeling result in varying levels of prognosis.

OBJECTIVE

To investigate the association between different patterns of LV remodeling and clinical outcomes in the preclinical stage of patients with Duchenne muscular dystrophy (DMD).

MATERIALS AND METHODS

A total of 148 patients with DMD and 43 sex- and age-matched healthy participants were enrolled. We used the four-quadrant analysis method to investigate LV remodeling based on cardiac magnetic resonance (MR) imaging. Kaplan-Meier curves were generated to illustrate the event-free survival probability stratified by the LV remodeling pattern. Cox regression models were constructed and compared to evaluate the incremental predictive value of the LV remodeling pattern.

RESULTS

During the median follow-up period of 2.2 years, all-cause death, cardiomyopathy, and ventricular arrhythmia occurred in 5, 35, and 7 patients, respectively. LV concentric hypertrophy (hazard ratio 2.91, 95% confidence interval 1.47-5.75, P=0.002) was an independent predictor of composite endpoint events. Compared to the model without LV concentric hypertrophy, the model with LV concentric hypertrophy had significant incremental predictive value (chi-square value 33.5 vs. 25.2, P=0.004).

CONCLUSION

Age and late gadolinium enhancement positivity were positively correlated with clinical outcomes according to the prediction models. LV concentric hypertrophy was also an independent predictor for risk stratification and provided incremental value for predicting clinical outcomes in the preclinical stage of patients with DMD.

Comparison of cardiac magnetic resonance imaging and fluorodeoxyglucose positron emission tomography in the assessment of cardiac sarcoidosis: Meta-analysis and systematic review

AIM

Fluorine-18 fluorodeoxyglucose-positron emission tomography (FDG-PET) and cardiac magnetic resonance (CMR) are frequently used advanced cardiac imaging to diagnose cardiac sarcoidosis (CS). We conducted a meta-analysis and systematic review to compare diagnostic parameters of FDG-PET and CMR in the diagnosis of cardiac sarcoidosis (CS).

METHODS

We searched PubMed, EMBASE, and Scopus databases from their inception to 9/30/2021 with search terms "cardiac sarcoidosis" AND "cardiac magnetic resonance imaging" AND "positronemission tomography". We extracted patient characteristics, results of the FDG-PET and CMR, and adverse outcomes from the included studies. Adverse outcomes served as a reference standard for the evaluation of FDG-PET and CMR.

RESULTS

We included 4 studies in the meta-analysis which provided adverse outcomes and all patients underwent FDG-PET and CMR. There were 237 patients, 60.3% male, and ages ranged from 50-53 years. There were 45 events in 237 patients from four studies included in the meta-analyses. The pooled sensitivity (95% confidence interval-CI) and specificity (CI) of CMR in predicting an adverse event were 0.94 (0.79-0.98) and 0.49 (0.40-0.59), respectively. The pooled sensitivity (CI) and specificity (CI) of FDG-PET in predicting an adverse event were 0.51 (0.26-0.75) and 0.60 (0.35-0.81), respectively.

CONCLUSION

CMR was more sensitive but less specific than FDG-PET in predicting adverse events; however, the study population and definition of a positive test need to be considered while interpreting the results.

Predicting adverse cardiac events in sarcoidosis: deep learning from automated characterization of regional myocardial remodeling

Recognizing early cardiac sarcoidosis (CS) imaging phenotypes can help identify opportunities for effective treatment before irreversible myocardial pathology occurs. We aimed to characterize regional CS myocardial remodeling features correlating with future adverse cardiac events by coupling automated image processing and data analysis on cardiac magnetic resonance (CMR) imaging datasets. A deep convolutional neural network (DCNN) was used to process a CMR database of a 10-year cohort of 117 consecutive biopsy-proven sarcoidosis patients. The maximum relevance - minimum redundancy method was used to select the best subset of all the features-24 (from manual processing) and 232 (from automated processing) left ventricular (LV) structural/functional features. Three machine learning (ML) algorithms, logistic regression (LogR), support vector machine (SVM) and multi-layer neural networks (MLP), were used to build classifiers to categorize endpoints. Over a median follow-up of 41.8 (inter-quartile range 20.4-60.5) months, 35 sarcoidosis patients experienced a total of 43 cardiac events. After manual processing, LV ejection fraction (LVEF), late gadolinium enhancement, abnormal segmental wall motion, LV mass (LVM), LVMI index (LVMI), septal wall thickness, lateral wall thickness, relative wall thickness, and wall thickness of 9 (out of 17) individual LV segments were significantly different between patients with and without endpoints. After automated processing, LVEF, end-diastolic volume, end-systolic volume, LV mass and wall thickness of 92 (out of 216) individual LV segments were significantly different between patients with and without endpoints. To achieve the best predictive performance, ML algorithms selected lateral wall thickness, abnormal segmental wall motion, septal wall thickness, and increased wall thickness of 3 individual segments after manual image processing, and selected end-diastolic volume and 7 individual segments after automated image processing. LogR, SVM and MLP based on automated image processing consistently showed better predictive accuracies than those based on manual image processing. Automated image processing with a DCNN improves data resolution and regional CS myocardial remodeling pattern recognition, suggesting that a framework coupling automated image processing with data analysis can help clinical risk stratification.