ArticleJournal of thoracic disease2026
Development and Internal validation of a simple echocardiography score for diagnosis of cardiac amyloidosis.
Article in Journal of thoracic disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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10 authors.
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Abstract
Background: Cardiac amyloidosis (CA) is a significant and increasingly treatable cause of heart failure. Although echocardiography is the first-line imaging modality for CA screening, conventional parameters alone have limited ability to predict the presence of CA. This study aimed to develop a simple echocardiographic scoring tool to predict CA. Methods: We retrospectively identified 71 patients with CA at Beijing Anzhen Hospital, Capital Medical University, between January 2018 and December 2023. A contemporaneous control group comprised 126 non-CA patients. Candidate predictors were screened out using univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression; a multivariable logistic regression model was then used to construct a nomogram. Internal validation employed bootstrap resampling to assess discrimination [receiver operating characteristic (ROC) curve/area under the curve (AUC)] and calibration (calibration plot and Hosmer-Lemeshow goodness-of-fit test). Clinical utility was evaluated using decision curve analysis (DCA) and clinical impact curve (CIC). Results: The final nomogram included five variables-biatrial enlargement, left ventricular ejection fraction (LVEF), left ventricular end diastolic diameter (LVEDD), left-ventricular hypertrophy, and a CA index-and showed excellent discrimination [AUC =0.95; 95% confidence interval (CI): 0.94-0.96]. Calibration curves and the Hosmer-Lemeshow test (P>0.05) indicated good agreement between predicted and observed risk. DCA and CIC demonstrated favorable net clinical benefit across plausible decision thresholds. Conclusions: Echocardiography-based screening for CA is feasible. A concise score derived from routine structural and functional measurements captures pathophysiologic hallmarks of CA and can help clinicians identify high-risk patients simply and effectively, thereby informing referral and treatment decisions.
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