Division of Cardiovascular Medicine, Department of Medicine (A.T.S., F.R., S.N., D.J.M.), Stanford University, CA.
Center for Digital Health, Department of Medicine (A.T.S., F.R.), Stanford University, CA.
Circulation. 2023 Feb 28;147(9):703-714. doi: 10.1161/CIRCULATIONAHA.122.062746. Epub 2022 Nov 7.
Coronary artery calcium (CAC) can be identified on nongated chest computed tomography (CT) scans, but this finding is not consistently incorporated into care. A deep learning algorithm enables opportunistic CAC screening of nongated chest CT scans. Our objective was to evaluate the effect of notifying clinicians and patients of incidental CAC on statin initiation.
NOTIFY-1 (Incidental Coronary Calcification Quality Improvement Project) was a randomized quality improvement project in the Stanford Health Care System. Patients without known atherosclerotic cardiovascular disease or a previous statin prescription were screened for CAC on a previous nongated chest CT scan from 2014 to 2019 using a validated deep learning algorithm with radiologist confirmation. Patients with incidental CAC were randomly assigned to notification of the primary care clinician and patient versus usual care. Notification included a patient-specific image of CAC and guideline recommendations regarding statin use. The primary outcome was statin prescription within 6 months.
Among 2113 patients who met initial clinical inclusion criteria, CAC was identified by the algorithm in 424 patients. After chart review and additional exclusions were made, a radiologist confirmed CAC among 173 of 194 patients (89.2%) who were randomly assigned to notification or usual care. At 6 months, the statin prescription rate was 51.2% (44/86) in the notification arm versus 6.9% (6/87) with usual care (<0.001). There was also more coronary artery disease testing in the notification arm (15.1% [13/86] versus 2.3% [2/87]; =0.008).
Opportunistic CAC screening of previous nongated chest CT scans followed by clinician and patient notification led to a significant increase in statin prescriptions. Further research is needed to determine whether this approach can reduce atherosclerotic cardiovascular disease events.
URL: https://www.
gov; Unique identifier: NCT04789278.
非门控胸部计算机断层扫描(CT)可识别冠状动脉钙(CAC),但该发现并未一致纳入治疗中。深度学习算法可实现非门控胸部 CT 扫描的机会性 CAC 筛查。我们的目的是评估通知临床医生和患者偶然 CAC 对启动他汀类药物的影响。
NOTIFY-1(偶然冠状动脉钙化质量改进项目)是斯坦福医疗保健系统中的一项随机质量改进项目。2014 年至 2019 年,对无已知动脉粥样硬化性心血管疾病或既往他汀类药物处方的患者进行了先前的非门控胸部 CT 扫描 CAC 筛查,使用经过验证的深度学习算法并经放射科医生确认。偶然 CAC 患者被随机分配至通知初级保健临床医生和患者与常规护理。通知包括 CAC 的患者特异性图像和他汀类药物使用的指南建议。主要结局是在 6 个月内开具他汀类药物处方。
在符合初始临床纳入标准的 2113 例患者中,算法在 424 例患者中识别出 CAC。在进行图表审查和进一步排除后,在随机分配至通知或常规护理的 194 例患者中的 173 例(89.2%)中,放射科医生确认 CAC。在 6 个月时,通知组的他汀类药物处方率为 51.2%(44/86),而常规护理组为 6.9%(6/87)(<0.001)。通知组还进行了更多的冠状动脉疾病检查(15.1%[13/86]比 2.3%[2/87];=0.008)。
对先前的非门控胸部 CT 扫描进行机会性 CAC 筛查,然后通知临床医生和患者,显著增加了他汀类药物的处方。需要进一步研究以确定这种方法是否可以减少动脉粥样硬化性心血管疾病事件。
网址:https://www.
gov;独特标识符:NCT04789278。