The clinical presentation of idiopathic dilated cardiomyopathy (IDCM) heart failure (HF) patients who will respond to medical therapy (responders) and those who will not (non-responders) is often similar. A machine learning (ML)-based clinical tool to identify responders would prevent unnecessary surgery, while targeting non-responders for early intervention. We used regional left ventricular (LV) contractile injury patterns in ML models to identify IDCM HF non-responders. MRI-based multiparametric strain analysis was performed in 178 test subjects (140 normal subjects and 38 IDCM patients), calculating longitudinal, circumferential, and radial strain over 18 LV sub-regions for inclusion in ML analyses. Patients were identified as responders based upon symptomatic and contractile improvement on medical therapy. We tested the predictive accuracy of support vector machines (SVM), logistic regression (LR), random forest (RF), and deep neural networks (DNN). The DNN model outperformed other models, predicting response to medical therapy with an area under the receiver operating characteristic curve (AUC) of 0.94. The top features were longitudinal strain in (1) basal: anterior, posterolateral and (2) mid: posterior, anterolateral, and anteroseptal sub-regions. Regional contractile injury patterns predict response to medical therapy in IDCM HF patients, and have potential application in ML-based HF patient care.

特发性扩张型心肌病（IDCM）心脏衰竭（HF）患者的临床表现谁将会对药物治疗反应（反应）和那些谁也不会（非-应答）往往是相似的。机器学习（ML）为基础的临床工具来识别应答器会阻止不必要的手术，而针对非-应答进行早期干预。我们使用的ML车型区域左心室（LV）收缩损伤类型，以确定IDCM HF非-应答. 在 178 名受试对象（140 名正常受试者和 38 名 IDCM 患者）中进行了基于 MRI 的多参数应变分析，计算了 18 个 LV 子区域的纵向、圆周和径向应变，以纳入 ML 分析。患者被确定为响应者基于药物治疗的症状和收缩改善。我们测试了支持向量机 (SVM)、逻辑回归 (LR)、随机森林 (RF) 和深度神经网络 (DNN) 的预测准确性。DNN 模型优于其他模型，以 0.94 的受试者操作特征曲线 (AUC) 下面积预测对药物治疗的反应。顶部特征是 (1) 基底：前、后外侧和 (2) 中：后、前外侧和前间隔子区域的纵向应变。区域收缩损伤模式可预测 IDCM HF 患者对药物治疗的反应，并在基于 ML 的 HF 患者护理中具有潜在应用。