背景:冠状动脉钙(CAC)扫描包含CAC评分以外的可操作信息,目前尚未报告。
方法:我们已经对5535名无症状个体(52.2%女性,45-84岁),先前在多种族动脉粥样硬化研究(MESA)的基线检查(2000-2002)中获得的CAC评分。AI-CAC平均每次CAC扫描花费21s。我们使用了房颤(AF)的5年结局数据,并使用AI-CACLA体积的时间依赖性曲线下面积(AUC)与已知的AF预测因子进行了区分。CHARGE-AF风险评分和NT-proBNP。房颤事件的平均随访时间为2.9±1.4年。
结果:在1、2、3、4和5年的随访中,发现了36、77、123、182和236例房颤,分别。AI-CACLA体积的AUC在1年、2年和3年显著高于CHARGE-AF(0.83vs.0.74,0.84vs.0.80和0.81vs.分别为0.78,所有p<0.05),但4年和5年相似,在1-5年显著高于NT-proBNP(所有p<0.01),但不是任何一年的联合CHARGE-AF和NT-proBNP。当添加到CHARGE-AF风险评分(0.60、0.28、0.32、0.19、0.24)时,AI-CACLA显着提高了1-5年房颤预测的连续净分类指数,和NT-proBNP(0.68,0.44,0.42,0.30,0.37)(均p<0.01)。
结论:AI-CACLA体积能够早在一年内预测房颤,并显著改善CHARGE-AF风险评分和NT-proBNP的风险分类。
BACKGROUND: Coronary artery calcium (CAC) scans contain actionable information beyond CAC scores that is not currently reported.
METHODS: We have applied artificial intelligence-enabled automated cardiac chambers volumetry to CAC scans (AI-CACTM) to 5535 asymptomatic individuals (52.2% women, ages 45-84) that were previously obtained for CAC scoring in the baseline examination (2000-2002) of the Multi-Ethnic Study of Atherosclerosis (MESA). AI-CAC took on average 21 s per CAC scan. We used the 5-year outcomes data for incident atrial fibrillation (AF) and assessed discrimination using the time-dependent area under the curve (AUC) of AI-CAC LA volume with known predictors of AF, the CHARGE-AF Risk Score and NT-proBNP. The mean follow-up time to an AF event was 2.9 ± 1.4 years.
RESULTS: At 1,2,3,4, and 5 years follow-up 36, 77, 123, 182, and 236 cases of AF were identified, respectively. The AUC for AI-CAC LA volume was significantly higher than CHARGE-AF for Years 1, 2, and 3 (0.83 vs. 0.74, 0.84 vs. 0.80, and 0.81 vs. 0.78, respectively, all p < 0.05), but similar for Years 4 and 5, and significantly higher than NT-proBNP at Years 1-5 (all p < 0.01), but not for combined CHARGE-AF and NT-proBNP at any year. AI-CAC LA significantly improved the continuous Net Reclassification Index for prediction of AF over years 1-5 when added to CHARGE-AF Risk Score (0.60, 0.28, 0.32, 0.19, 0.24), and NT-proBNP (0.68, 0.44, 0.42, 0.30, 0.37) (all p < 0.01).
CONCLUSIONS: AI-CAC LA volume enabled prediction of AF as early as one year and significantly improved on risk classification of CHARGE-AF Risk Score and NT-proBNP.