Key result
Random Forest model accurately identifies cardiac amyloidosis among restrictive cardiomyopathies with an AUC of ~0.98.
Why the study?
Amyloidosis, a major subtype of restrictive cardiomyopathy, involves diagnostic complexity, economic burden, and prognostic implications.
Does a Random Forest machine learning model using clinical and multimodality imaging features accurately distinguish amyloidosis from other causes of restrictive cardiomyopathy?
Observational (n=83)
No
Does a Random Forest machine learning model using clinical and multimodality imaging features accurately distinguish amyloidosis from other causes of restrictive cardiomyopathy?
An interpretable machine learning model based on the Random Forest algorithm successfully distinguished amyloidosis from other causes of restrictive cardiomyopathy using TTE and MRI features, which could streamline diagnosis and reduce unnecessary laboratory test costs.
BACKGROUND: Restrictive cardiomyopathy (RCM) is an uncommon condition with heterogeneous causes. Amyloidosis, a major subtype, presents with diagnostic complexity, economic burden, and prognostic implications. This study aimed to apply machine learning (ML) techniques to improve the diagnosis of amyloidosis among RCM patients and assess the cost-effectiveness of laboratory tests. METHODS: This study included patients with RCM who underwent transthoracic echocardiography (TTE) and cardiac magnetic resonance imaging (MRI). Feature selection was performed using the least absolute shrinkage and selection operator regression based on variables that showed statistical differences between groups. These selected features were then used to construct eight ML models, which were trained and validated using leave-one-out cross-validation. The best-performing model was evaluated for sample size and interpreted using Shapley additive explanations to enhance model transparency. Laboratory testing costs related to autoimmune, infection, tumor, and amyloidosis evaluations were compared across subgroups. RESULTS: The Random Forest (RF) model achieved the best performance, with an area under the curve of 0.977, an accuracy of 0.908, a sensitivity of 0.869, and a specificity of 0.927. The model also showed a favorable Brier score and a satisfying effect size, indicating good performance in distinguishing amyloidosis from other RCM subtypes. Cost analysis revealed that patients without underlying autoimmune, infectious, or tumor-related etiologies incurred unnecessary expenditures. Multivariate regression identified key imaging features associated with amyloidosis, including left ventricular posterior wall thickness and left ventricular ejection fraction (LVEF) from TTE, and left ventricular short axis diameter, LVEF, and interventricular septum thickness from cardiac MRI. CONCLUSION: This study established an interpretable ML model based on the RF algorithm and accurately distinguished amyloidosis among RCM patients. By guiding more targeted use of amyloidosis-specific testing, the model offers a potential cost-saving strategy while improving diagnostic efficiency. These findings support the clinical integration of ML-based tools to streamline decision-making and optimize the allocation of healthcare resources.
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Chen et al. (2025) conducted an observational in Restrictive cardiomyopathy (n=83). Random Forest machine learning model vs. Other machine learning models was evaluated on Area under the curve (AUC) for distinguishing amyloidosis from other RCM subtypes. The Random Forest machine learning model accurately distinguished amyloidosis from other restrictive cardiomyopathy subtypes, achieving an area under the curve of 0.977, sensitivity of 86.9%, and specificity of 92.7%.
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