Differentiation of light-chain cardiac amyloidosis from hypertrophic cardiomyopathy using myocardial mechanical parameters by velocity vector imaging echocardiography

Myocardial Mechanics and Valvular and Vascular Abnormalities in Cardiac Amyloidosis

Cardiac amyloidosis is an infiltrative disease primarily caused by extracellular tissue deposition of amyloid fibrils in the myocardial interstitium. The aim of the present review was to summarize findings regarding changes in myocardial mechanics, valvular abnormalities, and vascular remodeling detected in patients with cardiac amyloidosis.

Research trends and hotspots evolution of cardiac amyloidosis: a bibliometric analysis from 2000 to 2022

In the new century, cardiac amyloidosis has received more attention from many countries and institutions, leading to innovations in the essence of the pathology, biological markers, noninvasive tests, and staging diagnoses and treatments for this disease. However, few reviews have summarized the research trends and hotspots in cardiac amyloidosis. Bibliometrics analysis is a statistically based approach to research that visualizes the contributions of academic institutions and changes in research hotspots. Therefore, in this paper, we used Citespace and VOSviewer software to conduct co-occurrence analysis and collaborative network analysis on the countries, institutions, and authors in the articles related to cardiac amyloidosis since the new century. And further find out burst keywords and references to obtain the research history, disciplinary development, and new hotspots and topics.

Deep learn-based computer-assisted transthoracic echocardiography: approach to the diagnosis of cardiac amyloidosis

Myocardial amyloidosis (CA) differs from other etiological pathologies of left ventricular hypertrophy in that transthoracic echocardiography is challenging to assess the texture features based on human visual observation. There are few studies on myocardial texture based on echocardiography. Therefore, this paper proposes an adaptive machine learning method based on ultrasonic image texture features to identify CA. In this retrospective study, a total of 289 participants (50 cases of myocardial amyloidosis; Hypertrophic cardiomyopathy: 70 cases; Uremic cardiomyopathy: 92 cases; Hypertensive heart disease: 77 cases). We extracted the myocardial ultrasonic imaging features of these patients and screened the features, and four models of random forest (RF), support vector machine (SVM), logistic regression (LR) and gradient decision-making lifting tree (GBDT) were established to distinguish myocardial amyloidosis from other diseases. Finally, the diagnostic efficiency of the model was evaluated and compared with the traditional ultrasonic diagnostic methods. In the overall population, the four machine learning models we established could effectively distinguish CA from nonCA diseases, AUC (RF 0.77, SVM 0.81, LR 0.81, GBDT 0.71). The LR model had the best diagnostic efficiency with recall, F1-score, sensitivity and specificity of 0.21, 0.34, 0.21 and 1.0, respectively. Slightly better than the traditional ultrasonic diagnosis model. In further subgroup analysis, the myocardial amyloidosis group was compared one-by-one with the patients with hypertrophic cardiomyopathy, uremic cardiomyopathy, and hypertensive heart disease groups, and the same method was used for feature extraction and data modeling. The diagnostic efficiency of the model was further improved. Notably, in identifying of the CA group and HHD group, AUC values reached more than 0.92, accuracy reached more than 0.87, sensitivity equal to or greater than 0.81, specificity 0.91, and F1 score higher than 0.84. This novel method based on echocardiography combined with machine learning may have the potential to be used in the diagnosis of CA.

The Clinical Characteristics of Immunoglobulin Light Chain Amyloidosis in the Chinese Population: A Systematic Scoping Review

Immunoglobulin light chain (AL) amyloidosis is the most common type of systemic amyloidosis in China and is associated with increased morbidity and a poor prognosis. However, the clinical characteristics of Chinese patients with AL amyloidosis have not been systematically investigated. This scoping review aimed to summarize the available literature regarding the clinical characteristics of patients with AL amyloidosis and identify potential knowledge gaps. We searched three electronic databases from inception to 7 February 2021. PICOS (Patient, Intervention, Comparison, Outcome and Study) design structure was used to formulate the data extraction. All statistical calculations and analyses were performed with R (version 3.6.0). Sixty-seven articles with 5022 patients were included. Results suggest Chinese patients were younger (57 years) at the time of diagnosis when compared with other patient populations and were predominantly male (61.2%). The time interval from the onset of symptoms to diagnosis was between 6 and 12 months. It was found that 41.1% of Chinese patients with AL amyloidosis were diagnosed with an advanced stage III disease when diagnosed, and 20.2% had a concurrent disease. The most involved organs were the kidneys (84.3%) and the heart (62.5%). In conclusion, our study shows some similarities and differences with other studies on the clinical characteristics of Chinese patients with AL amyloidosis, including the age at diagnosis, Mayo stage, and organ involvement. However, a nationwide epidemiological investigation is still needed to provide a comprehensive overview of this patient population in China.

Multiparametric Echocardiography Scores for the Diagnosis of Cardiac Amyloidosis

OBJECTIVES

This study aimed to investigate the accuracy of a broad range of echocardiographic variables to develop multiparametric scores to diagnose CA in patients with proven light chain (AL) amyloidosis or those with increased heart wall thickness who had amyloid was suspected. We also aimed to further characterize the structural and functional changes associated with amyloid infiltration.

BACKGROUND

Cardiac amyloidosis (CA) is a serious but increasingly treatable cause of heart failure. Diagnosis is challenging and frequently unclear at echocardiography, which remains the most often used imaging tool.

METHODS

We studied 1,187 consecutive patients evaluated at 3 referral centers for CA and analyzed morphological, functional, and strain-derived echocardiogram parameters with the aim of developing a score-based diagnostic algorithm. Cardiac amyloid burden was quantified by using extracellular volume measurements at cardiac magnetic resonance.

RESULTS

A total of 332 patients were diagnosed with AL amyloidosis and 339 patients with transthyretin CA. Concentric remodeling and strain-derived parameters displayed the best diagnostic performance. A multivariable logistic regression model incorporating relative wall thickness, E wave/e' wave ratio, longitudinal strain, and tricuspid annular plane systolic excursion had the greatest diagnostic performance in AL amyloidosis (area under the curve: 0.90; 95% confidence interval: 0.87 to 0.92), whereas the addition of septal apical-to-base ratio yielded the best diagnostic accuracy in the increased heart wall thickness group (area under the curve: 0.80; 95% confidence interval: 0.85 to 0.90).

CONCLUSIONS

Specific functional and structural parameters characterize different burdens of CA deposition with different diagnostic performances and enable the definition of 2 scores that are sensitive and specific tools with which diagnose or exclude CA.

Characterization of hypertrophic cardiomyopathy according to global, regional, and multi-layer longitudinal strain analysis, and prediction of sudden cardiac death

To evaluate the severity of hypertrophic cardiomyopathy (HCM) according to global, regional, and multi-layer longitudinal strain (LS) analysis using speckle-tracking echocardiography. From February 2007 to November 2014, we prospectively evaluated 375 consecutive HCM patients referred to our specialized cardiomyopathy center. Demographics, clinical, and rest and exercise echocardiographic parameters were collected according to a completely standardized protocol. Global, regional, and multilayer strain analyses were performed. Correlations between LS and other characteristics were evaluated, and we assessed their prognostic value to predict sudden cardiac death (SCD) or appropriate implantable cardioverter defibrillator (ICD) shocks during follow-up, using Cox proportional hazards analyses. We finally included 217 patients (50.1 ± 15.6 years, 67% male) but only 179 (82%) had LS analysis of sufficient quality. An inverse relation was observed between the mean basal left ventricular (LV) LS and diastolic parameters [E/Ea (r = - 0.30) and left atrium indexed volume (r = - 0.23)], as well as between the resting LV outflow-tract maximal gradient (r = - 0.26) or during peak exercise (r = - 0.20). Mean LS in the LV hypertrophic area was particularly related with maximal wall thickness (r = - 0.47) and transmural global LS with the degree of myocardial fibrosis in cardiac magnetic resonance (r = - 0.32). During a median follow-up of 2.8 ± 1.5 years, mean transmural LS in the hypertrophic area was predictor of SCD and appropriate ICD shock (10 events/179 patients, hazard ratio = 0.83 [95% CI 0.72-0.95], p = 0.01). Basal LS and hypertrophic area LS are valuable parameters to evaluate HCM severity. Mean hypertrophic area LS particularly seems predictive of SCD occurrence and appropriate ICD shocks.