Machine learning integration of circulating and imaging biomarkers for explainable patient-specific prediction of cardiac events: A prospective study.
Machine learning integration of circulating and imaging biomarkers for explainable patient-specific prediction of cardiac events: A prospective study.
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机器学习的整合循环和成像生物标志物，用于可解释的患者特定于心脏事件的预测：一项前瞻性研究。
DOI：
10.1016/j.atherosclerosis.2020.11.008
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发表时间：
2021-03
期刊：
影响因子：
5.3
通讯作者：
Dey D
中科院分区：
文献类型：
--
作者：
Tamarappoo BK;Lin A;Commandeur F;McElhinney PA;Cadet S;Goeller M;Razipour A;Chen X;Gransar H;Cantu S;Miller RJ;Achenbach S;Friedman J;Hayes S;Thomson L;Wong ND;Rozanski A;Slomka PJ;Berman DS;Dey D
我们试图评估综合机器学习（ML）风险评分的性能，该评分整合了循环生物标志物和计算机断层扫描（CT）测量，用于长期预测无症状受试者的硬心脏事件。我们研究了来自前瞻性EISNER试验的1069例受试者（年龄58.2±8.2岁，54.0%为男性），这些受试者接受了冠状动脉钙化（CAC）评分CT、血清生物标志物评估和长期随访。使用全自动深度学习软件从CT量化心外膜脂肪组织（EAT）。测定了48种血清生物标志物，包括已建立的和新的。使用临床风险因素、CT测量（CAC评分、冠状动脉病变数量、主动脉瓣钙评分、EAT体积和衰减）和循环生物标志物训练ML算法（XGBoost），并使用重复10倍交叉验证进行验证。在14.5±2.0年时，有50例严重心脏事件（心肌梗死或心源性死亡）。ML风险评分（受试者工作特征曲线下面积[AUC] 0.81）在预测严重心脏事件方面优于CAC评分（0.75）和ASCVD风险评分（0.74;均p=0.02）。在ML模型中，血清生物标志物提供了超出临床数据和CT测量的增量预后价值（净重新分类指数0.53 [95%CI：0.23-0.81]，p<0.0001）。在新的生物标志物中，MMP-9、正五聚蛋白3、PIGR和GDF-15对ML具有最高的可变重要性，并反映了炎症、细胞外基质重塑和纤维化的途径。在这项前瞻性研究中，与目前的风险评估工具相比，ML整合新的循环生物标志物和无创成像测量提供了上级的心脏事件长期风险预测。
We sought to assess the performance of a comprehensive machine learning (ML) risk score integrating circulating biomarkers and computed tomography (CT) measures for the long-term prediction of hard cardiac events in asymptomatic subjects. We studied 1069 subjects (age 58.2±8.2 years, 54.0% males) from the prospective EISNER trial who underwent coronary artery calcium (CAC) scoring CT, serum biomarker assessment, and long-term follow-up. Epicardial adipose tissue (EAT) was quantified from CT using fully automated deep learning software. Forty-eight serum biomarkers, both established and novel, were assayed. A ML algorithm (XGBoost) was trained using clinical risk factors, CT measures (CAC score, number of coronary lesions, aortic valve calcium score, EAT volume and attenuation), and circulating biomarkers, and validated using repeated 10-fold cross validation. At 14.5±2.0 years, there were 50 hard cardiac events (myocardial infarction or cardiac death). The ML risk score (area under the receiver operator characteristic curve [AUC] 0.81) outperformed the CAC score (0.75) and ASCVD risk score (0.74; both p=0.02) for the prediction of hard cardiac events. Serum biomarkers provided incremental prognostic value beyond clinical data and CT measures in the ML model (net reclassification index 0.53 [95% CI: 0.23–0.81], p<0.0001). Among novel biomarkers, MMP-9, pentraxin 3, PIGR, and GDF-15 had highest variable importance for ML and reflect the pathways of inflammation, extracellular matrix remodeling, and fibrosis. In this prospective study, ML integration of novel circulating biomarkers and noninvasive imaging measures provided superior long-term risk prediction for cardiac events compared to current risk assessment tools.
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DOI：
10.1161/atvbaha.112.301174
发表时间：
2013-07
期刊：
Arteriosclerosis, thrombosis, and vascular biology
影响因子：
--
作者：
Schnabel RB;Yin X;Larson MG;Yamamoto JF;Fontes JD;Kathiresan S;Rong J;Levy D;Keaney JF Jr;Wang TJ;Murabito JM;Vasan RS;Benjamin EJ
通讯作者：
Benjamin EJ
影响因子：
2
作者：
Pencina, Michael J.;D'Agostino, Ralph B., Sr.;Steyerberg, Ewout W.
通讯作者：
Steyerberg, Ewout W.
影响因子：
5.4
作者：
Goeller, Markus;Achenbach, Stephan;Dey, Damini
通讯作者：
Dey, Damini
DOI：
10.1016/0735-1097(90)90282-t
发表时间：
1990-03-15
影响因子：
24
作者：
AGATSTON, AS;JANOWITZ, WR;DETRANO, R
通讯作者：
DETRANO, R
影响因子：
5.8
作者：
Molinaro, AM;Simon, R;Pfeiffer, RM
通讯作者：
Pfeiffer, RM