Spanish
Albanian
Arabic
Armenian
Azerbaijani
Belarusian
Bengali
Bosnian
Catalan
Czech
Danish
Deutsch
Dutch
English
Estonian
Finnish
Français
Greek
Haitian Creole
Hebrew
Hindi
Hungarian
Icelandic
Indonesian
Irish
Italian
Japanese
Korean
Latvian
Lithuanian
Macedonian
Mongolian
Norwegian
Persian
Polish
Portuguese
Romanian
Russian
Serbian
Slovak
Slovenian
Spanish
Swahili
Swedish
Turkish
Ukrainian
Vietnamese
Български
中文(简体)
中文(繁體)
Chinese Journal of Cardiology 2015-Jul

[Left ventricular systolic synchrony assessed by phase analysis of gated myocardial perfusion imaging in patients with old myocardial infarction].

Solo los usuarios registrados pueden traducir artículos
Iniciar sesión Registrarse
El enlace se guarda en el portapapeles.
Jianfeng Wang
Yuetao Wang
Xiaoli Zhang
Ruijue Zhou
Rong Niu
Peiqi Lu

Palabras clave

Abstracto

OBJECTIVE

To assess the left ventricular (LV) systolic synchrony by phase analysis of gated myocardial perfusion imaging (GMPI) with SPECT/CT in patients with old myocardial infarction (OMI) and further to identify independent predictors for LV dyssynchrony.

METHODS

Seventy-six OMI patients and seventy-four healthy volunteers (control group) underwent resting GMPI from October 2010 to September 2013 in our hospital were included in this study. The left ventricular systolic synchrony parameters including phase histogram bandwidth (BW) and phase standard deviation (SD) were obtained by Cedars-Sinai quantitative gated SPECT (QGS) phase analysis technique, and LV cardiac function was also measured. The extent of myocardial perfusion defect was analyzed by the Quantitative Perfusion SPECT (QPS) software. The value of BW and SD were compared between OMI and the control groups, between LVEF ≤ 35% and LVEF > 35% groups in OMI patients. Dyssynchrony was defined when the BW exceeded the abnormality threshold derived from a normal control group (threshold = x ± 2s for normal BW).

RESULTS

(1) The BW ((91.3 ± 58.6)° vs. (37.2 ± 11.7)°) and SD ((27.3 ± 20.8)° vs. (11.8 ± 5.4)°) were significantly higher and the LVEF was significantly lower in OMI group than in the normal control group (all P < 0.01). In addition, BW ((136.0 ± 52.9)° vs. (51.0 ± 24.0)°) and SD ((38.7 ± 21.3)° vs. (17.1 ± 14.0)°) were significantly higher in patients with LVEF ≤ 35% than in patients with LVEF > 35% (all P < 0.001). (2) Dyssynchrony (BW > 60.6°) prevalence was 57.9% (44/76) in OMI patients. Compared with the synchrony group, LVEF was significantly lower, while the left ventricular end-diastolic volume, end-systolic volume, summed motion score, summed thickening score and extent were significantly higher in dyssynchrony group (all P < 0.001). (3) Additionally, dyssynchrony prevalence was significantly higher in patients with LVEF ≤ 35% compared with patients with LVEF > 35% (91.7% (33/36) vs. 27.5% (11/40), P < 0.001). (4) Pearson correlation analysis showed that LVEF was negatively correlated with BW (r = -0.807, P < 0.001). (5) Multivariate logistic regression analysis revealed that the extent of myocardial perfusion defect was an independent predictor for dyssynchrony in OMI patients (OR = 1.076, 95% CI: 1.015-1.141, P = 0.015).

CONCLUSIONS

GMPI phase analysis can reliably reflect left ventricular systolic synchrony. The left ventricular systolic dyssynchrony in OMI patients is significantly increased. Left ventricular dyssynchrony is closely related to LVEF. The extent of myocardial perfusion defect (Extent) is an independent predictor for left ventricular systolic dyssynchrony in OMI patients.

Únete a nuestra
página de facebook

La base de datos de hierbas medicinales más completa respaldada por la ciencia

  • Funciona en 55 idiomas
  • Curas a base de hierbas respaldadas por la ciencia
  • Reconocimiento de hierbas por imagen
  • Mapa GPS interactivo: etiquete hierbas en la ubicación (próximamente)
  • Leer publicaciones científicas relacionadas con su búsqueda
  • Buscar hierbas medicinales por sus efectos.
  • Organice sus intereses y manténgase al día con las noticias de investigación, ensayos clínicos y patentes.

Escriba un síntoma o una enfermedad y lea acerca de las hierbas que podrían ayudar, escriba una hierba y vea las enfermedades y los síntomas contra los que se usa.
* Toda la información se basa en investigaciones científicas publicadas.

Google Play badgeApp Store badge