4.6 Article

Late gadolinium enhancement entropy as a new measure of myocardial tissue heterogeneity for prediction of adverse cardiac events in patients with hypertrophic cardiomyopathy

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INSIGHTS INTO IMAGING
卷 14, 期 1, 页码 -

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SPRINGER WIEN
DOI: 10.1186/s13244-023-01479-6

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Magnetic resonance imaging; Prognosis; Hypertrophic cardiomyopathy

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This study investigated the prognostic value of left ventricular (LV) entropy in patients with hypertrophic cardiomyopathy (HCM). The results showed that increased LV entropy was associated with a higher risk of adverse events such as heart failure and cardiac death in HCM patients.
Objectives Entropy is a new late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR)-derived parameter that is independent of signal intensity thresholds. Entropy can be used to measure myocardial tissue heterogeneity by comparing full pixel points of tissue images. This study investigated the incremental prognostic value of left ventricular (LV) entropy in patients with hypertrophic cardiomyopathy (HCM). Methods This study enrolled 337 participants with HCM who underwent 3.0-T CMR. The LV entropy was obtained by calculating the probability distribution of the LV myocardial pixel signal intensities of the LGE sequence. Patients who underwent CMR imaging were followed up for endpoints. The primary endpoint was defined as readmission to the hospital owing to heart failure. The secondary endpoint was the composite of the primary endpoint, sudden cardiac death and non-cardiovascular death. Results During the median follow-up of 24 months +/- 13 (standard deviation), 43 patients who reached the primary and secondary endpoints had a higher entropy (6.20 +/- 0.45, p < 0.001). The patients with increased entropy (>= 5.587) had a higher risk of the primary and secondary endpoints, compared with HCM patients with low entropy (p < 0.001 for both). In addition, Cox analysis showed that LV entropy provided significant prognostic value for predicting both primary and secondary endpoints (HR: 1.291 and 1.273, all p < 0.001). Addition of LV entropy to the multivariable model improved model performance and risk reclassification (p < 0.05). Conclusion LV entropy assessed by CMR was an independent predictor of primary and secondary endpoints. LV entropy assessment contributes to improved risk stratification in patients with HCM.

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