Prognostic Value of Phase Analysis for Predicting Adverse Cardiac Events Beyond Conventional Single-Photon Emission Computed Tomography Variables: Results From the REFINE SPECT Registry.
Prognostic Value of Phase Analysis for Predicting Adverse Cardiac Events Beyond Conventional Single-Photon Emission Computed Tomography Variables: Results From the REFINE SPECT Registry.
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DOI：
10.1161/circimaging.120.012386
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Slomka PJ
中科院分区：
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作者：
Kuronuma K;Miller RJH;Otaki Y;Van Kriekinge SD;Diniz MA;Sharir T;Hu LH;Gransar H;Liang JX;Parekh T;Kavanagh PB;Einstein AJ;Fish MB;Ruddy TD;Kaufmann PA;Sinusas AJ;Miller EJ;Bateman TM;Dorbala S;Di Carli M;Tamarappoo BK;Dey D;Berman DS;Slomka PJ
单光子发射计算机断层心肌灌注成像（SPECT-MPI）的相位分析提供了与超声心动图评估相关的非同步化信息，但其独立预后意义尚不明确。本研究评估了SPECT-MPI阶段分析在迄今为止最大的跨国登记中对所有模式的独立预后价值。新一代SPECT快速心肌灌注成像登记处（REFINE SPECT）共纳入19210例患者（平均年龄63.8±12.0岁，56%为男性）。自动获得应激后总灌注差（TPD）、左室射血分数（LVEF）和相位变量（相位熵、带宽和标准差[SD]）。采用Cox比例风险分析来评估与主要不良心脏事件（MACE）的关联。在4.5±1.7年的随访中，2673例（13.9%）患者经历了MACE。年化MACE率随着阶段变量的增加而增加，在熵的第二和最高十分位数组之间大约高出4倍（1.7%对6.7%）。TPD和LVEF正常和异常患者MACE风险的最佳期变量临界值分层。只有熵与MACE独立相关。在TPD和LVEF模型中加入相熵显著提高了MACE预测的判别能力（p <0.0001）。在一项迄今为止规模最大、具有广泛代表性的国际队列成像研究中，阶段变量与MACE独立相关，并改善了MACE的风险分层，超出了仅通过灌注和LVEF评估预测的范围。相位分析可以完全自动获得，不需要额外的辐射暴露或成本来改善MACE风险预测，因此应该在SPECT-MPI研究中常规报告。在RIFINE SPECT登记的19210例患者中，我们探索了预测主要不良心脏事件（MACE）的相位变量（熵、带宽和相位标准差[SD]）的增量值，超出了常规SPECT参数，如总灌注缺陷（TPD）或左心室射血分数（LVEF）。在分类分析中，在校正常规冠状动脉疾病危险因素、TPD、LVEF和左室舒张末期容积后，所有三个阶段变量均与MACE独立相关。然而，只有熵与MACE作为一个连续变量有显著的关联。此外，考虑熵而不考虑带宽或相位SD后，风险预测得到了显著改善。当只考虑全因死亡或非致死性心肌梗死时，结果相似。本研究的结果表明，熵是最有希望改善MACE预测的相位变量，当加入常规SPECT参数时。相位变量可以通过ecg门控SPECT自动获得，无需额外的辐射暴露，因此，应该在SPECT研究中常规报告并用于临床管理。
Phase analysis of single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI) provides dyssynchrony information which correlates well with assessments by echocardiography, but the independent prognostic significance is not well defined. This study assessed the independent prognostic value of SPECT-MPI phase analysis in the largest multinational registry to date across all modalities. From the REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT (REFINE SPECT), a total of 19,210 patients were included (mean age 63.8 ± 12.0 years and 56% males). Post-stress total perfusion deficit (TPD), left ventricular ejection fraction (LVEF), and phase variables (phase entropy, bandwidth, and standard deviation [SD]) were obtained automatically. Cox proportional hazards analyses were performed to assess associations with major adverse cardiac events (MACE). During a follow-up of 4.5 ± 1.7 years, 2,673 (13.9%) patients experienced MACE. Annualized MACE rates increased with phase variables and were approximately 4-fold higher between the second and highest decile group for entropy (1.7% vs. 6.7%). Optimal phase variable cut-off values stratified MACE risk in patients with normal and abnormal TPD and LVEF. Only entropy was independently associated with MACE. The addition of phase entropy significantly improved the discriminatory power for MACE prediction when added to the model with TPD and LVEF (p <0.0001). In a largest to date imaging study, widely representative, international cohort, phase variables were independently associated with MACE and improved risk stratification for MACE beyond the prediction by perfusion and LVEF assessment alone. Phase analysis can be obtained fully-automatically, without additional radiation exposure or cost to improve MACE risk prediction and therefore should be routinely reported for SPECT-MPI studies. In a population of 19,210 patients from the RIFINE SPECT registry, we explored the incremental value of phase variables (entropy, bandwidth, and phase standard deviation [SD]) for predicting major adverse cardiac events (MACE) beyond conventional SPECT parameters such as total perfusion deficit (TPD) or left ventricular ejection fraction (LVEF). All three phase variables were independently associated with MACE after adjustment for conventional coronary artery disease risk factors, TPD, LVEF, and left ventricular end diastolic volume in categorical analysis. However, only entropy showed a significant association with MACE as a continuous variable. In addition, risk prediction was significantly improved by including entropy but not bandwidth or phase SD. Findings were similar when considering only all-cause death or non-fatal myocardial infarction. Findings from the present study suggest that entropy is the most promising phase variable for improving MACE prediction when added to conventional SPECT parameters. Phase variables can be automatically gained by ECG-gated SPECT without extra radiation exposure, therefore, should be routinely reported for SPECT studies and utilized for clinical management.