Diagnostic accuracy of bone scintigraphy imaging for transthyretin cardiac amyloidosis: systematic review and meta-analysis

Prognostic Value of [99mTc]Tc-DPD Quantitative SPECT/CT in Patients with Suspected and Confirmed Amyloid Transthyretin-Related Cardiomyopathy and Preserved Left Ventricular Function

Quantitative 99mTc-3,3-diphosphono-1,2-propanodicarboxylic acid ([99mTc]Tc-DPD) SPECT may be used for risk-stratifying patients with amyloid transthyretin-related cardiomyopathy (ATTR-CM). We aimed to analyze the predictive value of quantitative [99mTc]Tc-DPD SPECT/CT in suspected and confirmed ATTR-CM according to different disease stages. Methods: The study enrolled consecutive patients with suspected ATTR-CM who were referred to a single tertiary center and underwent quantitative [99mTc]Tc-DPD SPECT/CT allowing SUVmax and SUVpeak analysis. Patients were divided into 2 groups according to left ventricular ejection fraction (LVEF) at baseline (i.e., ≥50% and <50%). Clinical, laboratory, and echocardiographic parameters and major adverse cardiac events (i.e., all-cause death, sustained ventricular tachyarrhythmia, hospitalization for heart failure, implantation of a cardioverter defibrillator) were investigated for any correlation with quantitative uptake values. Results: In total, 144 patients with suspected ATTR-CM were included in the study (98 with LVEF ≥ 50% and 46 with LVEF < 50%), of whom 99 were diagnosed with ATTR-CM (68.8%; 69 with LVEF ≥ 50% and 30 with LVEF < 50%). A myocardial SUVmax of at least 7 was predictive of major adverse cardiac events at 21.9 ± 13.0 mo of follow-up (hazard ratio, 2.875; 95% CI, 1.23-6.71; P = 0.015) in patients with suspected or confirmed ATTR-CM (global χ2 = 6.892, P = 0.02) and an LVEF of at least 50%. SUVmax was not predictive in patients with an LVEF of less than 50% and suspected or confirmed ATTR-CM. Conclusion: In patients with suspected or confirmed ATTR-CM and preserved LVEF, representing an early disease stage, quantitative [99mTc]Tc-DPD SPECT should be considered to improve early-stage risk stratification.

How Artificial Intelligence Can Enhance the Diagnosis of Cardiac Amyloidosis: A Review of Recent Advances and Challenges

Cardiac amyloidosis (CA) is an underdiagnosed form of infiltrative cardiomyopathy caused by abnormal amyloid fibrils deposited extracellularly in the myocardium and cardiac structures. There can be high variability in its clinical manifestations, and diagnosing CA requires expertise and often thorough evaluation; as such, the diagnosis of CA can be challenging and is often delayed. The application of artificial intelligence (AI) to different diagnostic modalities is rapidly expanding and transforming cardiovascular medicine. Advanced AI methods such as deep-learning convolutional neural networks (CNNs) may enhance the diagnostic process for CA by identifying patients at higher risk and potentially expediting the diagnosis of CA. In this review, we summarize the current state of AI applications to different diagnostic modalities used for the evaluation of CA, including their diagnostic and prognostic potential, and current challenges and limitations.

Current and Evolving Multimodality Cardiac Imaging in Managing Transthyretin Amyloid Cardiomyopathy

Amyloid transthyretin (ATTR) amyloidosis is a protein-misfolding disease characterized by fibril accumulation in the extracellular space that can result in local tissue disruption and organ dysfunction. Cardiac involvement drives morbidity and mortality, and the heart is the major organ affected by ATTR amyloidosis. Multimodality cardiac imaging (ie, echocardiography, scintigraphy, and cardiac magnetic resonance) allows accurate diagnosis of ATTR cardiomyopathy (ATTR-CM), and this is of particular importance because ATTR-targeting therapies have become available and probably exert their greatest benefit at earlier disease stages. Apart from establishing the diagnosis, multimodality cardiac imaging may help to better understand pathogenesis, predict prognosis, and monitor treatment response. The aim of this review is to give an update on contemporary and evolving cardiac imaging methods and their role in diagnosing and managing ATTR-CM. Further, an outlook is presented on how artificial intelligence in cardiac imaging may improve future clinical decision making and patient management in the setting of ATTR-CM.

High-Sensitivity Cardiac Troponin T to Exclude Cardiac Involvement in TTR Variant Carriers and ATTRv Amyloidosis Patients

(1) Background: Individuals carrying a pathogenic transthyretin gene variant (TTRv) are at high risk for developing hereditary transthyretin (ATTRv) amyloidosis and are routinely screened for the development of cardiomyopathy (ATTRv-CM). This study aims to evaluate whether the cardiac biomarkers N-terminal pro B-type natriuretic peptide (NT-proBNP) and high-sensitivity cardiac troponin T (hs-cTnT) can be used to rule out ATTRv-CM. (2) Methods: In this retrospective case-control study, data from 46 ATTRv-CM patients and 101 TTRv carriers and ATTRv amyloidosis patients without cardiomyopathy were included. Binary logistic regression models were used to assess the ability of NT-proBNP and hs-cTnT to predict the diagnosis of ATTRv-CM. An optimal cutoff for the relevant biomarker(s) was determined based on a sensitivity of ≥99% and the highest possible percentage of additional tests avoided (%ATA) in the index dataset. (3) Results: Hs-cTnT demonstrated the highest predictive capabilities for ATTRv-CM. The addition of NT-proBNP did not improve the predictive model. A hs-cTnT cutoff of <6 ng/L resulted in a 97% sensitivity and a negative predictive value of 95% with a %ATA of 30% in the validation dataset. (4) Conclusion: In conclusion, hs-cTnT is a useful biomarker for excluding cardiac involvement in TTRv carriers and ATTRv amyloidosis patients and it has the potential to prevent unnecessary diagnostic procedures.