Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering and Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates; Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering and Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates.
Comput Methods Programs Biomed. 2024 May;248:108107. doi: 10.1016/j.cmpb.2024.108107. Epub 2024 Mar 6.
Heart failure (HF) is a multi-faceted and life-threatening syndrome that affects more than 64.3 million people worldwide. Current gold-standard screening technique, echocardiography, neglects cardiovascular information regulated by the circadian rhythm and does not incorporate knowledge from patient profiles. In this study, we propose a novel multi-parameter approach to assess heart failure using heart rate variability (HRV) and patient clinical information.
In this approach, features from 24-hour HRV and clinical information were combined as a single polar image and fed to a 2D deep learning model to infer the HF condition. The edges of the polar image correspond to the timely variation of different features, each of which carries information on the function of the heart, and internal illustrates color-coded patient clinical information.
Under a leave-one-subject-out cross-validation scheme and using 7,575 polar images from a multi-center cohort (American and Greek) of 303 coronary artery disease patients (median age: 58 years [50-65], median body mass index (BMI): 27.28 kg/m [24.91-29.41]), the model yielded mean values for the area under the receiver operating characteristics curve (AUC), sensitivity, specificity, normalized Matthews correlation coefficient (NMCC), and accuracy of 0.883, 90.68%, 95.19%, 0.93, and 92.62%, respectively. Moreover, interpretation of the model showed proper attention to key hourly intervals and clinical information for each HF stage.
The proposed approach could be a powerful early HF screening tool and a supplemental circadian enhancement to echocardiography which sets the basis for next-generation personalized healthcare.
心力衰竭（HF）是一种多方面的、危及生命的综合征，影响着全球超过 6430 万人。目前的金标准筛查技术——超声心动图——忽略了受昼夜节律调节的心血管信息，也没有纳入患者个人资料的知识。在这项研究中，我们提出了一种使用心率变异性（HRV）和患者临床信息评估心力衰竭的新的多参数方法。
在这种方法中，24 小时 HRV 和临床信息的特征被组合成一个单一的极坐标图像，并输入到一个 2D 深度学习模型中，以推断 HF 状况。极坐标图像的边缘对应于不同特征的时间变化，每个特征都携带有关心脏功能的信息，内部则用颜色编码表示患者的临床信息。
在一项基于留一受试者外交叉验证方案下，使用来自美国和希腊的 303 名冠心病患者（中位年龄：58 岁[50-65]，中位体重指数（BMI）：27.28kg/m[24.91-29.41]）的多中心队列中的 7575 个极坐标图像，该模型产生的受试者工作特征曲线（AUC）下面积、敏感性、特异性、归一化马修斯相关系数（NMCC）和准确性的平均值分别为 0.883、90.68%、95.19%、0.93 和 92.62%。此外，对模型的解释表明，它适当关注了每个 HF 阶段的关键小时间隔和临床信息。
所提出的方法可以成为一种强大的早期 HF 筛查工具，也是超声心动图的补充性昼夜节律增强工具，为下一代个性化医疗奠定了基础。
