Scuppa Diego, Colaceci Francesca, Sciandrone Marco, Arcari Luca, Mariano Enrica G, Musumeci Beatrice Maria, Barbato Emanuele, Galiuto Leonarda
Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy.
Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy.
Int J Cardiol. 2025 Oct 15;437:133509. doi: 10.1016/j.ijcard.2025.133509. Epub 2025 Jun 11.
Takotsubo syndrome (TTS) is an acute coronary syndrome characterized by a reversible, mostly apical dysfunction of the left ventricle. Based on the triggering event, TTS has been classified as primary due to emotional causes and secondary due to physical stress. Using a comprehensive machine-learning approach, we aimed to distinguish between these two types of TTS, an essential task for optimizing patient care.
Based on a dataset of 320 TTS patients from a research group in Rome, a logistic regression model was trained to develop an interpretable predictive model capable of accurately classifying the aetiology of TTS in individual patients using admission-based clinical markers.
The developed model achieved 74 % accuracy, 75 % precision and recall, 72 % specificity, and an area under the curve (AUC) of 0.78. Based on the studies conducted, chest pain, dyspnoea, atrial fibrillation, sex, chronic obstructive pulmonary disease, heart rate, and cancer were identified as key clinical features for differentiating between the two TTS types. An external validation cohort of 121 TTS patients has been employed further to assess the performance of the trained classification model, obtaining 74 % accuracy, 77 % precision, 91 % recall, 27 % specificity, and an AUC of 0.62.
An interpretable machine learning model has been developed, demonstrating the ability to distinguish between emotional versus physical aetiologies in TTS, highlighting the most impactful clinical factors. As built considering clinical variables recorded at admission, the model may serve as an immediate tool that can guide clinicians in their practice.
应激性心肌病（TTS）是一种急性冠状动脉综合征，其特征为左心室功能可逆性障碍，主要表现为心尖部功能障碍。根据诱发事件，TTS可分为因情绪因素导致的原发性和因身体应激导致的继发性。我们旨在通过综合机器学习方法区分这两种类型的TTS，这对于优化患者护理至关重要。
基于罗马一个研究小组的320例TTS患者数据集，训练了一个逻辑回归模型，以开发一个可解释的预测模型，该模型能够使用入院时的临床指标准确分类个体患者TTS的病因。
所开发的模型准确率达74%，精确率和召回率为75%，特异性为72%，曲线下面积（AUC）为0.78。基于所开展的研究，胸痛、呼吸困难、心房颤动、性别、慢性阻塞性肺疾病、心率和癌症被确定为区分两种TTS类型的关键临床特征。另外使用了121例TTS患者的外部验证队列来评估训练后的分类模型的性能，准确率为74%，精确率为77%，召回率为91%，特异性为27%，AUC为0.62。
已开发出一个可解释的机器学习模型，该模型显示出能够区分TTS中情绪性病因与身体性病因的能力，并突出了最具影响力的临床因素。由于该模型是基于入院时记录的临床变量构建的，它可作为一种即时工具，指导临床医生的实践。
