BMC Cardiovascular Disorders (Jan 2021)

Predictors of cardiac involvement and survival in patients with primary systemic light-chain amyloidosis: roles of the clinical, chemical, and 3-D speckle tracking echocardiography parameters

  • Changhui Lei,
  • Xiaoli Zhu,
  • David H. Hsi,
  • Jing Wang,
  • Lei Zuo,
  • Shengjun Ta,
  • Qianli Yang,
  • Lei Xu,
  • Xueli Zhao,
  • Yan Wang,
  • Shiren Sun,
  • Liwen Liu

DOI
https://doi.org/10.1186/s12872-021-01856-3
Journal volume & issue
Vol. 21, no. 1
pp. 1 – 11

Abstract

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Abstract Background Light-chain (AL) amyloidosis is the most common type of systemic amyloidosis with poor prognosis. Currently, the predictors of cardiac involvement and prognostic staging systems are primarily based on conventional echocardiography and serological biomarkers. We used three-dimensional speckle tracking echocardiography (STE-3D) measurements of strain, hypothesizing that it could detect cardiac involvement and aid in prediction of mortality. Methods We retrospectively analysed 74 consecutive patients with biopsy-proven AL amyloidosis. Among them, 42 showed possible cardiac involvement and 32 without cardiac involvement. LV global longitudinal strain (GLS), global radial strain, global circumferential strain and global area strain (GAS) measurements were obtained. Results The GLS and GAS were considered significant predictors of cardiac involvement. The cut-off values discriminating cardiac involvement were 16.10% for GLS, 32.95% for GAS. During the median follow-up of 12.5 months (interquartile range 4–25 months), 20 (27%) patients died. For the Cox proportional model survival analysis, heart rate, cardiac troponin T, NT-proBNP levels, E/e’, GLS, and GAS were univariate predictors of death. Multivariate Cox model showed that GLS ≤ 14.78% and cardiac troponin T ≥ 0.049 mg/l levels were independent predictors of survival. Conclusions STE-3D measurements of LV myocardial mechanics could detect cardiac involvement in patients with AL amyloidosis; GLS and cardiac biomarkers can provided prognostic information for mortality prediction.

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