Cedars-Sinai Medical Center, Los Angeles, CA, USA.
University of Manitoba, Winnipeg, MB, Canada.
J Nucl Cardiol. 2018 Aug;25(4):1353-1360. doi: 10.1007/s12350-017-0840-0. Epub 2017 Mar 13.
Most prior studies assessing the prognostic value of SPECT myocardial perfusion imaging (MPI) have used semi-quantitative visual analysis. We assessed the feasibility of large-scale fully automated quantitative analysis of SPECT MPI to predict acute myocardial infarction (AMI). Additionally, we examined the impact of attenuation correction (AC) in automated strategies.
5960 patients underwent rest/stress SPECT MPI with AC. Left ventricular (LV) segmentation, contour QC check, and quantitation of stress and ischemic total perfusion deficit (sTPD, iTPD) were performed. Only contours flagged for potential errors by QC were visually checked (10%). During long-term follow-up (6.1 ± 2.7 years), 522 patients (9%) had AMI. In Cox models, adjusted for ejection fraction (LVEF) and other relevant covariates, there was a stepwise increase in risk hazard ratios by quartile for sTPD (Q1: 1.00, Q2: 1.26, Q3: 1.66, Q4: 1.79; P < 0.0001) and iTPD (Q1: 1.00, Q2: 1.26, Q3: 1.66, Q4: 1.79; P < 0.0001). Area under curve for AMI prediction by automated measures was similar for AC and non-AC data (sTPD: 0.63 vs 0.64, P = 0.85; iTPD: 0.61 vs 0.61, P = 0.70). Higher AUCs for both AC and non-AC data were seen for AMI occurring in the first 1 year of follow-up (sTPD: 0.71, 0.72; iTPD: 0.70, 0.68).
Fully automated sTPD was an independent predictor of future AMI events even after adjusting for LVEF and other relevant covariates. AC did not significantly impact predictive accuracy.
大多数评估单光子发射计算机断层扫描(SPECT)心肌灌注成像(MPI)预后价值的研究都使用了半定量视觉分析。我们评估了大规模全自动定量 SPECT MPI 分析预测急性心肌梗死(AMI)的可行性。此外,我们还研究了衰减校正(AC)在自动分析策略中的影响。
5960 例患者接受了静息/负荷 SPECT MPI 检查,并进行了 AC、左心室(LV)分段、轮廓 QC 检查以及应激和缺血性总灌注缺陷(sTPD、iTPD)的定量分析。仅对 QC 标记为可能存在误差的轮廓进行了视觉检查(10%)。在长期随访(6.1±2.7 年)中,522 例患者(9%)发生了 AMI。在 Cox 模型中,校正射血分数(LVEF)和其他相关协变量后,sTPD(Q1:1.00,Q2:1.26,Q3:1.66,Q4:1.79;P<0.0001)和 iTPD(Q1:1.00,Q2:1.26,Q3:1.66,Q4:1.79;P<0.0001)的四分位风险比呈逐步增加趋势。自动化测量值预测 AMI 的曲线下面积(AUC)在 AC 和非 AC 数据之间相似(sTPD:0.63 与 0.64,P=0.85;iTPD:0.61 与 0.61,P=0.70)。在随访的第 1 年发生 AMI 时,AC 和非 AC 数据的 AUC 更高(sTPD:0.71、0.72;iTPD:0.70、0.68)。
即使在校正 LVEF 和其他相关协变量后,全自动 sTPD 仍然是未来 AMI 事件的独立预测因素。AC 并未显著影响预测准确性。