%0 Journal Article %A Nikolaus Jander %A Willibald Hochholzer %A Beat A Kaufmann %A Edda Bahlmann %A Eva Gerdts %A Kurt Boman %A John B Chambers %A Christoph A Nienaber %A Simon Ray %A Anne Rossebo %A Terje R Pedersen %A Kristian Wachtell %A Christa Gohlke-Bärwolf %A Franz-Josef Neumann %A Jan Minners %T Velocity ratio predicts outcomes in patients with low gradient severe aortic stenosis and preserved EF %D 2014 %R 10.1136/heartjnl-2014-305763 %J Heart %P 1946-1953 %V 100 %N 24 %X Objective To evaluate the usefulness of velocity ratio (VR) in patients with low gradient severe aortic stenosis (LGSAS) and preserved EF. Background LGSAS despite preserved EF represents a clinically challenging entity. Reliance on mean pressure gradient (MPG) may underestimate stenosis severity as has been reported in the context of paradoxical low flow, LGSAS. On the other hand, grading of stenosis severity by aortic valve area (AVA) may overrate stenosis severity due to erroneous underestimation of LV outflow tract (LVOT) diameter, small body size or inconsistencies in cut-off values for severe stenosis. We hypothesised that VR may have conceptual advantages over MPG and AVA, predict clinical outcomes and thereby be useful in the management of patients with LGSAS. Methods Patients from the prospective Simvastatin and Ezetimibe in Aortic Stenosis (SEAS) study with an AVA<1.0 cm2, MPG≤40 mm Hg and EF≥55% and asymptomatic at baseline were stratified according to VR with a cut-off value of 0.25. Outcomes were evaluated according to aortic valve-related events and cardiovascular death. Results Of 435 patients with LGSAS, 197 (45%) had VR<0.25 suggesting severe and 238 (55%) had VR≥0.25 suggesting non-severe stenosis. Aortic valve-related events (mean follow-up 42±14 months) were more frequent in patients with VR<0.25 (57% vs 41%; p<0.001) as was cardiovascular death within the first 24 months (p<0.05). In multivariable Cox regression analysis, MPG was the strongest independent predictor of aortic valve events (p<0.001) followed by VR (p<0.02). Adjusting AVA by VR increased predictive accuracy for aortic valve events (area under the receiver operating curve 0.62 (95% CI 0.57 to 0.67) vs 0.56 (95% CI 0.51 to 0.61) for AVA, p=0.02) with net reclassification improvement calculated at 0.36 (95% CI 0.17 to 0.54, p<0.001). VR did not improve the prediction of clinical events by MPG. Conclusions In the difficult setting of LGSAS, VR shows a strong association with valve-related events and—although not outperforming MPG—may be particularly useful in guiding clinical management. Trial registration number NCT00092677. %U https://heart.bmj.com/content/heartjnl/100/24/1946.full.pdf