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Abstract：
BACKGROUND: Coronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC™) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC cardiac chambers for prediction of incident heart failure (HF).
METHODS: We applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% female, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We used the 15-year outcomes data and compared the time-dependent area under the curve (AUC) of AI-CAC volumetry versus NT-proBNP, Agatston score, and 9 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL for predicting incident HF over 15 years.
RESULTS: Over 15 years of follow-up, 256 HF events accrued. The time-dependent AUC [95% CI] at 15 years for predicting HF with AI-CAC all chambers volumetry (0.86 [0.82,0.91]) was significantly higher than NT-proBNP (0.74 [0.69, 0.77]) and Agatston score (0.71 [0.68, 0.78]) (p < 0.0001), and comparable to clinical risk factors (0.85, p = 0.4141). Category-free Net Reclassification Index (NRI) [95% CI] adding AI-CAC LV significantly improved on clinical risk factors (0.32 [0.16,0.41]), NT-proBNP (0.46 [0.33,0.58]), and Agatston score (0.71 [0.57,0.81]) for HF prediction at 15 years (p < 0.0001).
CONCLUSIONS: AI-CAC volumetry significantly outperformed NT-proBNP and the Agatston CAC score, and significantly improved the AUC and category-free NRI of clinical risk factors for incident HF prediction.
METHODS: We applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% female, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We used the 15-year outcomes data and compared the time-dependent area under the curve (AUC) of AI-CAC volumetry versus NT-proBNP, Agatston score, and 9 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL for predicting incident HF over 15 years.
RESULTS: Over 15 years of follow-up, 256 HF events accrued. The time-dependent AUC [95% CI] at 15 years for predicting HF with AI-CAC all chambers volumetry (0.86 [0.82,0.91]) was significantly higher than NT-proBNP (0.74 [0.69, 0.77]) and Agatston score (0.71 [0.68, 0.78]) (p < 0.0001), and comparable to clinical risk factors (0.85, p = 0.4141). Category-free Net Reclassification Index (NRI) [95% CI] adding AI-CAC LV significantly improved on clinical risk factors (0.32 [0.16,0.41]), NT-proBNP (0.46 [0.33,0.58]), and Agatston score (0.71 [0.57,0.81]) for HF prediction at 15 years (p < 0.0001).
CONCLUSIONS: AI-CAC volumetry significantly outperformed NT-proBNP and the Agatston CAC score, and significantly improved the AUC and category-free NRI of clinical risk factors for incident HF prediction.
摘要：
背景：冠状动脉钙（CAC）扫描包含的有用信息超出了目前未报告的AgatstonCAC评分。我们最近报道了在CAC扫描中启用人工智能（AI）的心腔容积（AI-CAC™）预测了多种族动脉粥样硬化研究（MESA）中的房颤事件。在这项研究中,我们调查了AI-CAC心腔在预测心力衰竭(HF)中的表现.
方法：我们将AI-CAC应用于无症状个体的5750个CAC扫描（52％为女性，白色40%,黑色26%,西班牙裔22％中国12％）在MESA基线检查（2000-2002）中没有已知的心血管疾病。我们使用了15年的结果数据，并比较了AI-CAC容量与NT-proBNP的时间依赖性曲线下面积（AUC）。Agatston得分,和9个已知的临床危险因素(年龄，性别,糖尿病,目前吸烟,高血压药物，收缩压和舒张压,LDL,HDL用于预测15年以上的HF事件。
结果：经过15年的随访，产生256个高频事件。使用AI-CAC预测HF的15年时间依赖性AUC[95％CI]所有腔室容量（0.86[0.82,0.91]）显着高于NT-proBNP（0.74[0.69,0.77]）和Agatston评分（0.71[0.68,0.78]）（p<0.0001）。与临床危险因素相当（0.85，p=0.4141）。无类别净重新分类指数（NRI）[95%CI]添加AI-CACLV对临床危险因素（0.32[0.16,0.41]）有显著改善，NT-proBNP（0.46[0.33,0.58]），和Agatston评分（0.71[0.57,0.81]）用于15年的HF预测（p<0.0001）。
结论：AI-CAC容量显着优于NT-proBNP和AgatstonCAC评分，并显著提高了临床危险因素预测HF事件的AUC和无类别NRI。
方法：我们将AI-CAC应用于无症状个体的5750个CAC扫描（52％为女性，白色40%,黑色26%,西班牙裔22％中国12％）在MESA基线检查（2000-2002）中没有已知的心血管疾病。我们使用了15年的结果数据，并比较了AI-CAC容量与NT-proBNP的时间依赖性曲线下面积（AUC）。Agatston得分,和9个已知的临床危险因素(年龄，性别,糖尿病,目前吸烟,高血压药物，收缩压和舒张压,LDL,HDL用于预测15年以上的HF事件。
结果：经过15年的随访，产生256个高频事件。使用AI-CAC预测HF的15年时间依赖性AUC[95％CI]所有腔室容量（0.86[0.82,0.91]）显着高于NT-proBNP（0.74[0.69,0.77]）和Agatston评分（0.71[0.68,0.78]）（p<0.0001）。与临床危险因素相当（0.85，p=0.4141）。无类别净重新分类指数（NRI）[95%CI]添加AI-CACLV对临床危险因素（0.32[0.16,0.41]）有显著改善，NT-proBNP（0.46[0.33,0.58]），和Agatston评分（0.71[0.57,0.81]）用于15年的HF预测（p<0.0001）。
结论：AI-CAC容量显着优于NT-proBNP和AgatstonCAC评分，并显著提高了临床危险因素预测HF事件的AUC和无类别NRI。
