MRI-based multiparametric strain analysis predicts contractile recovery after aortic valve replacement for aortic insufficiency.
MRI-based multiparametric strain analysis predicts contractile recovery after aortic valve replacement for aortic insufficiency.
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DOI:
10.1111/j.1540-8191.2012.01477.x
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发表时间:
2012-07
影响因子:
1.6
通讯作者:
Pasque MK
中科院分区:
文献类型:
--
作者:
Brady BD;Knutsen AK;Ma N;Gardner R;Taggar AK;Cupps BP;Kouchoukos NT;Pasque MK
Guidelines for referral of chronic aortic insufficiency (AI) patients for aortic valve replacement (AVR) suggest that surgery can be delayed until symptoms or reduction in left ventricular (LV) contractile function occur. The frequent occurrence of reduced LV contractile function after AVR for chronic AI suggests that new contractile metrics for surgical referral are needed. In 16 chronic AI patients, cardiac MRI tagged images were analyzed before and 21.5 ± 13.8 months after AVR to calculate LV systolic strain. Average measurements of three strain parameters were obtained for each of 72 LV regions, normalized using a normal human strain database (n=63), and combined into a composite index (multi-parametric strain z score [MSZ]) representing standard deviation from the normal regional average. Preoperative global MSZ (72-region average) correlated with post-AVR global MSZ (R2 = .825, p < .001). Preoperative global MSZ also predicts improvement of impaired regions (N=271 regions from 14 AI patients, R2 = .392, p < .001). Preoperative MRI-based left ventricular ejection fraction (LVEF) is also predictive (r = .410, p < .001). Although global preoperative MSZ had a significantly higher correlation than preoperative LVEF with improvement of injured regions (p < .001), both measures convey the same phenomenon. Global preoperative MRI-based multi-parametric strain predicts global strain postoperatively, as well as improvement of regions (n=72/LV) with impaired contractile function. Global contractile function is an important correlate with improvement in regionally impaired contractile function, perhaps reflecting total AI volume-overload burden (severity/duration of AI).
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影响因子:
--
作者:
Cupps BP;Taggar AK;Reynolds LM;Lawton JS;Pasque MK
通讯作者:
Pasque MK
影响因子:
19.7
作者:
AXEL, L;DOUGHERTY, L
通讯作者:
DOUGHERTY, L
影响因子:
4.6
作者:
Cupps, Brian P.;Bree, Douglas R.;Pasque, Michael K.
通讯作者:
Pasque, Michael K.
影响因子:
37.8
作者:
FIORETTI, P;ROELANDT, J;HUGENHOLTZ, PG
通讯作者:
HUGENHOLTZ, PG
影响因子:
37.8
作者:
Chaliki, HP;Mohty, D;Enriquez-Sarano, M
通讯作者:
Enriquez-Sarano, M