BACKGROUND The heart's energy demand per gram of tissue is the body's highest and creatine kinase (CK) metabolism, its primary energy reserve, is compromised in common heart diseases. Here, neural-network analysis is used to test whether noninvasive phosphorus (31P) cardiovascular magnetic resonance spectroscopy (CMRS) measurements of cardiac adenosine triphosphate (ATP) energy, phosphocreatine (PCr), the first-order CK reaction rate kf, and the rate of ATP synthesis through CK (CK flux), can predict specific human heart disease and clinical severity. METHODS The data comprised the extant 178 complete sets of PCr and ATP concentrations, kf, and CK flux data from human CMRS studies performed on clinical 1.5 and 3 Tesla scanners. Healthy subjects and patients with nonischemic cardiomyopathy, dilated (DCM) or hypertrophic disease, New York Heart Association (NYHA) class I-IV heart failure (HF), or with anterior myocardial infarction are included. Three-layer neural-networks were created to classify disease and to differentiate DCM, hypertrophy and clinical NYHA class in HF patients using leave-one-out training. Network performance was assessed using 'confusion matrices' and 'area-under-the-curve' (AUC) analyses of 'receiver operating curves'. Possible methodological bias and network imbalance were tested by segregating 1.5 and 3 Tesla data, and by data augmentation by random interpolation of nearest neighbors, respectively. RESULTS The network differentiated healthy, HF and non-HF cardiac disease with an overall accuracy of 84% and AUC > 90% for each category using the four CK metabolic parameters, alone. HF patients with DCM, hypertrophy, and different NYHA severity were differentiated with ~ 80% overall accuracy independent of CMRS methodology. CONCLUSIONS While sample-size was limited in some sub-classes, a neural network classifier applied to noninvasive cardiac 31P CMRS data, could serve as a metabolic biomarker for common disease types and HF severity with clinically-relevant accuracy. Moreover, the network's ability to individually classify disease and HF severity using CK metabolism alone, implies an intimate relationship between CK metabolism and disease, with subtle underlying phenotypic differences that enable their differentiation. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT00181259.

中文翻译：

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根据肌酸激酶能量代谢的 31P 心血管磁共振波谱测量对心脏病进行神经网络分类。


背景技术心脏每克组织的能量需求是人体最高的，并且其主要能量储备肌酸激酶(CK)代谢在常见心脏病中受到损害。这里，神经网络分析用于测试心脏三磷酸腺苷 (ATP) 能量、磷酸肌酸 (PCr)、一级 CK 反应速率 kf 和速率的无创磷 (31P) 心血管磁共振波谱 (CMRS) 测量是否有效通过CK（CK通量）检测ATP合成，可以预测特定的人类心脏病和临床严重程度。方法 数据包括现有 178 套完整的 PCr 和 ATP 浓度、kf 和 CK 通量数据，这些数据来自在临床 1.5 和 3 Tesla 扫描仪上进行的人类 CMRS 研究。健康受试者和患有非缺血性心肌病、扩张型 (DCM) 或肥厚型疾病、纽约心脏协会 (NYHA) I-IV 级心力衰竭 (HF) 或前壁心肌梗死的患者均包括在内。创建三层神经网络以使用留一法训练对心力衰竭患者进行疾病分类并区分 DCM、肥厚和临床 NYHA 类别。使用“混淆矩阵”和“接收器操作曲线”的“曲线下面积”(AUC) 分析来评估网络性能。可能的方法偏差和网络不平衡分别通过分离 1.5 和 3 Tesla 数据以及通过最近邻随机插值进行数据增强来测试。结果 该网络仅使用四个 CK 代谢参数即可区分健康、心力衰竭和非心力衰竭心脏病，每个类别的总体准确度为 84%，AUC > 90%。具有 DCM、肥厚和不同 NYHA 严重程度的 HF 患者的总体准确度约为 80%，与 CMRS 方法无关。 结论 虽然某些子类的样本量有限，但应用于无创心脏 31P CMRS 数据的神经网络分类器可以作为常见疾病类型和心力衰竭严重程度的代谢生物标志物，并具有临床相关的准确性。此外，该网络能够单独使用 CK 代谢对疾病和 HF 严重程度进行单独分类，这意味着 CK 代谢与疾病之间存在密切关系，并且具有使它们能够区分的微妙的潜在表型差异。试验注册 ClinicalTrials.gov 标识符：NCT00181259。