Karamchandani Rahul R, Yang Hongmei, Rhoten Jeremy B, Strong Dale, Satyanarayana Sagar, Asimos Andrew W
Neurology, Neurosciences Institute, Atrium Health, Charlotte, NC, USA.
Information and Analytics Services, Atrium Health, Charlotte, NC, USA.
Interv Neuroradiol. 2025 Feb;31(1):80-87. doi: 10.1177/15910199221149563. Epub 2023 Jan 9.
The Charlotte large artery occlusion endovascular therapy outcome score (CLEOS) predicts poor 90-day outcomes for patients presenting with internal carotid artery (ICA) or middle cerebral artery (MCA) occlusions. It incorporates RAPID-derived cerebral blood volume (CBV) index, a marker of collateral circulation. We validated the predictive ability of CLEOS with Viz.ai-processed computed tomography perfusion (CTP) imaging.
The original CLEOS derivation cohort was compared to a validation cohort consisting of all ICA and MCA thrombectomy patients treated at a large health system with Viz.ai-processed CTP. Rates of poor 90-day outcome (mRS 4-6) were compared in the derivation and validation cohorts, stratified by CLEOS. CLEOS was compared to previously described prediction models using area under the curve (AUC) analyses. Calibration of CLEOS was performed to compare predicted risk of poor outcomes with observed outcomes.
One-hundred eighty-one patients (mean age 66.4 years, median NIHSS 16) in the validation cohort were included. The validation cohort had higher median CTP core volumes (24 vs 8 ml) and smaller median mismatch volumes (81 vs 101 ml) than the derivation cohort. CLEOS-predicted poor outcomes strongly correlated with observed outcomes ( = 0.82). AUC for CLEOS in the validation cohort (0.72, 95% CI 0.64-0.80) was similar to the derivation cohort (AUC 0.75, 95% CI 0.70-0.80) and was comparable or superior to previously described prognostic models.
CLEOS can predict risk of poor 90-day outcomes in ICA and MCA thrombectomy patients evaluated with pre-intervention, Viz.ai-processed CTP.
夏洛特大动脉闭塞血管内治疗结局评分（CLEOS）可预测颈内动脉（ICA）或大脑中动脉（MCA）闭塞患者90天预后不良。它纳入了RAPID衍生的脑血容量（CBV）指数，这是侧支循环的一个标志物。我们使用Viz.ai处理的计算机断层扫描灌注（CTP）成像验证了CLEOS的预测能力。
将原始的CLEOS推导队列与一个验证队列进行比较，验证队列由在一个大型医疗系统接受Viz.ai处理的CTP治疗的所有ICA和MCA血栓切除术患者组成。在推导队列和验证队列中比较90天不良结局（改良Rankin量表评分4 - 6分）的发生率，并按CLEOS进行分层。使用曲线下面积（AUC）分析将CLEOS与先前描述的预测模型进行比较。对CLEOS进行校准，以比较不良结局的预测风险与观察到的结局。
验证队列纳入了181例患者（平均年龄66.4岁，美国国立卫生研究院卒中量表[NIHSS]中位数为16）。与推导队列相比，验证队列的CTP核心体积中位数更高（24 vs 8 ml），错配体积中位数更小（81 vs 101 ml）。CLEOS预测的不良结局与观察到的结局高度相关（r = 0.82）。验证队列中CLEOS的AUC（0.72，95%可信区间0.64 - 0.80）与推导队列（AUC 0.75，95%可信区间0.70 - 0.80）相似，且与先前描述的预后模型相当或更优。
CLEOS可预测在干预前接受Viz.ai处理的CTP评估的ICA和MCA血栓切除术患者90天不良结局的风险。
