Community-based participatory research application of an artificial intelligence-enhanced electrocardiogram for cardiovascular disease screening: A FAITH! Trial ancillary study.
Community-based participatory research application of an artificial intelligence-enhanced electrocardiogram for cardiovascular disease screening: A FAITH! Trial ancillary study.
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DOI：
10.1016/j.ajpc.2022.100431
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发表时间：
2022-12
影响因子：
4.1
通讯作者：
Brewer, LaPrincess C
中科院分区：
文献类型：
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作者：
Harmon, David M;Adedinsewo, Demilade;Van't Hof, Jeremy R;Johnson, Matthew;Hayes, Sharonne N;Lopez-Jimenez, Francisco;Jones, Clarence;Attia, Zachi I;Friedman, Paul A;Patten, Christi A;Cooper, Lisa A;Brewer, LaPrincess C
随着基于人工智能（AI）的卫生干预措施的出现，系统性种族主义仍然是一个令人担忧的问题，因为这些进步往往是在没有针对种族的数据分析或验证的情况下发展起来的。为了评估基于人工智能的心血管疾病（CVD）筛查工具在资源不足的非裔美国人队列中的潜在效用，我们回顾了一项社区临床试验参与者的人工智能增强心电图（ECG）数据，作为社区筛查的概念验证辅助研究。受试者完成了心血管测试，包括标准12导联心电图和有限超声心动图（TTE）。使用先前发表的基于机构的人工智能算法分析所有心电图。AI-ECG预测年龄、性别和左心室射血分数（LVEF）降低。使用受试者工作特征曲线下面积（AUC）量化AI-ECG对LVEF下降和性别的诊断准确性。使用Pearson相关系数评估实际年龄与AI-ECG预测年龄的相关性。54名参与者同时完成了心电图和TTE检查（平均年龄55岁[范围31-87岁]，其中66.7%为女性）。所有参与者都处于窦性心律，队列中位LVEF为60-65%。LVEF降低的AI-ECG表现优异，AUC为0.892(95%可信区间[CI] 0.708-1)；灵敏度=50% (95% CI 9.5-90.5%; n=1/2)，特异性=96% （95% CI 868 -98.9%; n=49/51）。受试者性别的AI-ECG表现相似，AUC为0.944 (95% CI 0.891-0.998)；敏感性=100% (95% CI 82.4-100.0%, n=18/18)，特异性=77.8% （95% CI 61.9-88.3%, n=28/36）。AI-ECG预测的平均年龄为55岁（26.9 ~ 72.6岁），与实际年龄有很强的相关性（R=0.769; p<0.001）。我们对先前开发的用于预测年龄、性别和LVEF下降的AI-ECG算法的分析表明，该算法在以社区为基础的非裔美国人队列中表现可靠。这种新颖的、以社区为中心的人工智能交付可以为资源不足的患者群体提供宝贵的筛查资源和适当的转诊，以早期发现高发病率的心血管疾病。
With the emergence of artificial intelligence (AI)-based health interventions, systemic racism remains a concern as these advancements are frequently developed without race-specific data analysis or validation. To evaluate the potential utility of an AI-based cardiovascular diseases (CVD) screening tool in an under-resourced African-American cohort, we reviewed the AI-enhanced electrocardiogram (ECG) data of participants enrolled in a community-based clinical trial as a proof-of-concept ancillary study for community-based screening. Enrollees completed cardiovascular testing including standard 12-lead ECG and a limited echocardiogram (TTE). All ECGs were analyzed using previously published institution-based AI algorithms. AI-ECG predictions were generated for age, sex, and decreased left ventricular ejection fraction (LVEF). Diagnostic accuracy of the AI-ECG for decreased LVEF and sex was quantified using area under the receiver operating characteristic curve (AUC). Correlation between actual age and AI-ECG predicted age was assessed using Pearson correlation coefficients. Fifty-four participants completed both an ECG and TTE (mean age 55 years [range 31-87 years]; 66.7% female). All participants were in sinus rhythm, and the median LVEF of the cohort was 60-65%. The AI-ECG for decreased LVEF demonstrated excellent performance with an AUC of 0.892 (95% confidence interval [CI] 0.708-1); sensitivity=50% (95% CI 9.5-90.5%; n=1/2) and specificity=96% (95% CI 86.8-98.9%; n=49/51). The AI-ECG for participant sex demonstrated similar performance with AUC of 0.944 (95% CI 0.891-0.998); sensitivity=100% (95% CI 82.4-100.0%; n=18/18) and specificity=77.8% (95% CI 61.9-88.3%; n=28/36). The AI-ECG predicted mean age was 55 years (range 26.9-72.6 years) with a strong correlation to actual age (R=0.769; p<0.001). Our analyses of previously developed AI-ECG algorithms for prediction of age, sex, and decreased LVEF demonstrated reliable performance in this community-based, African-American cohort. This novel, community-centric delivery of AI could provide valuable screening resources and appropriate referrals for early detection of highly-morbid CVD for under-resourced patient populations.