Abstract：
BACKGROUND: With increasing numbers of patients and novel drugs for distinct causes of systolic and diastolic heart failure, automated assessment of cardiac function is important. We aimed to provide a non-invasive method to predict diagnosis of patients undergoing cardiac MRI (cMRI) and to obtain left ventricular end-diastolic pressure (LVEDP).
METHODS: For this modelling study, patients who had undergone cardiac catheterisation at University Hospital Heidelberg (Heidelberg, Germany) between July 15, 2004 and March 16, 2023, were identified, as were individual left ventricular pressure measurements. We used existing patient data from routine cardiac diagnostics. From this initial group, we extracted patients who had been diagnosed with ischaemic cardiomyopathy, dilated cardiomyopathy, hypertrophic cardiomyopathy, or amyloidosis, as well as control individuals with no structural phenotype. Data were pseudonymised and only processed within the university hospital\'s AI infrastructure. We used the data to build different models to predict either demographic (ie, AI-age and AI-sex), diagnostic (ie, AI-coronary artery disease and AI-cardiomyopathy [AI-CMP]), or functional parameters (ie, AI-LVEDP). We randomly divided our datasets via computer into training, validation, and test datasets. AI-CMP was not compared with other models, but was validated in a prospective setting. Benchmarking was also done.
RESULTS: 66 936 patients who had undergone cardiac catheterisation at University Hospital Heidelberg were identified, with more than 183 772 individual left ventricular pressure measurements. We extracted 4390 patients from this initial group, of whom 1131 (25·8%) had been diagnosed with ischaemic cardiomyopathy, 1064 (24·2%) had been diagnosed with dilated cardiomyopathy, 816 (18·6%) had been diagnosed with hypertrophic cardiomyopathy, 202 (4·6%) had been diagnosed with amyloidosis, and 1177 (26·7%) were control individuals with no structural phenotype. The core cohort only included patients with cardiac catherisation and cMRI within 30 days, and emergency cases were excluded. AI-sex was able to predict patient sex with areas under the receiver operating characteristic curves (AUCs) of 0·78 (95% CI 0·77-0·78) and AI-age was able to predict patient age with a mean absolute error of 7·86 years (7·77-7·95), with a Pearson correlation of 0·57 (95% CI 0·56-0·57). The AUCs for the classification tasks ranged between 0·82 (95% CI 0·79-0·84) for ischaemic cardiomyopathy and 0·92 (0·91-0·94) for hypertrophic cardiomyopathy.
CONCLUSIONS: Our AI models could be easily integrated into clinical practice and provide added value to the information content of cMRI, allowing for disease classification and prediction of diastolic function.
BACKGROUND: Informatics for Life initiative of the Klaus-Tschira Foundation, German Center for Cardiovascular Research, eCardiology section of the German Cardiac Society, and AI Health Innovation Cluster Heidelberg.
METHODS: For this modelling study, patients who had undergone cardiac catheterisation at University Hospital Heidelberg (Heidelberg, Germany) between July 15, 2004 and March 16, 2023, were identified, as were individual left ventricular pressure measurements. We used existing patient data from routine cardiac diagnostics. From this initial group, we extracted patients who had been diagnosed with ischaemic cardiomyopathy, dilated cardiomyopathy, hypertrophic cardiomyopathy, or amyloidosis, as well as control individuals with no structural phenotype. Data were pseudonymised and only processed within the university hospital\'s AI infrastructure. We used the data to build different models to predict either demographic (ie, AI-age and AI-sex), diagnostic (ie, AI-coronary artery disease and AI-cardiomyopathy [AI-CMP]), or functional parameters (ie, AI-LVEDP). We randomly divided our datasets via computer into training, validation, and test datasets. AI-CMP was not compared with other models, but was validated in a prospective setting. Benchmarking was also done.
RESULTS: 66 936 patients who had undergone cardiac catheterisation at University Hospital Heidelberg were identified, with more than 183 772 individual left ventricular pressure measurements. We extracted 4390 patients from this initial group, of whom 1131 (25·8%) had been diagnosed with ischaemic cardiomyopathy, 1064 (24·2%) had been diagnosed with dilated cardiomyopathy, 816 (18·6%) had been diagnosed with hypertrophic cardiomyopathy, 202 (4·6%) had been diagnosed with amyloidosis, and 1177 (26·7%) were control individuals with no structural phenotype. The core cohort only included patients with cardiac catherisation and cMRI within 30 days, and emergency cases were excluded. AI-sex was able to predict patient sex with areas under the receiver operating characteristic curves (AUCs) of 0·78 (95% CI 0·77-0·78) and AI-age was able to predict patient age with a mean absolute error of 7·86 years (7·77-7·95), with a Pearson correlation of 0·57 (95% CI 0·56-0·57). The AUCs for the classification tasks ranged between 0·82 (95% CI 0·79-0·84) for ischaemic cardiomyopathy and 0·92 (0·91-0·94) for hypertrophic cardiomyopathy.
CONCLUSIONS: Our AI models could be easily integrated into clinical practice and provide added value to the information content of cMRI, allowing for disease classification and prediction of diastolic function.
BACKGROUND: Informatics for Life initiative of the Klaus-Tschira Foundation, German Center for Cardiovascular Research, eCardiology section of the German Cardiac Society, and AI Health Innovation Cluster Heidelberg.
摘要：
背景：随着越来越多的患者和新型药物治疗收缩和舒张性心力衰竭的不同原因，心脏功能的自动评估很重要。我们旨在提供一种非侵入性方法来预测接受心脏MRI（cMRI）的患者的诊断并获得左心室舒张末期压（LVEDP）。
方法：对于这项建模研究，在海德堡大学医院接受过心脏导管插入术的患者(海德堡，德国)在2004年7月15日至2023年3月16日之间被确定，以及单独的左心室压力测量。我们使用常规心脏诊断的现有患者数据。从这个最初的群体中，我们提取了被诊断为缺血性心肌病的患者，扩张型心肌病,肥厚型心肌病,或者淀粉样变性,以及没有结构表型的对照个体。数据是假名的，只在大学医院的人工智能基础设施内处理。我们使用这些数据来构建不同的模型来预测人口统计(即，AI年龄和AI性别），诊断(即，AI-冠状动脉疾病和AI-心肌病[AI-CMP]），或功能参数(即，AI-LVEDP)。我们通过计算机将数据集随机分成训练，验证,和测试数据集。AI-CMP没有与其他型号进行比较，但在预期的环境中得到了验证。也做了基准。
结果：66936名在海德堡大学医院接受心导管插入术的患者被确认，超过183772个单独的左心室压力测量值。我们从这个初始组中提取了4390名患者，其中1131人(25%)被诊断为缺血性心肌病，1064（24·2%）被诊断为扩张型心肌病，816（18·6%）被诊断为肥厚型心肌病，202人(4.6%)被诊断为淀粉样变性，1177人(26·7%)为无结构表型的对照个体。核心队列仅包括30天内有心脏插管和cMRI的患者，紧急情况被排除在外。AI-sex能够预测患者性别，受试者工作特征曲线（AUC）下的面积为0·78（95％CI0·77-0·78），AI-年龄能够预测患者年龄，平均绝对误差为7·86岁（7·77-7·95），皮尔逊相关性为0·57（95%CI0·56-0·57）。分类任务的AUC范围为缺血性心肌病的0·82（95％CI0·79-0·84）和肥厚型心肌病的0·92（0·91-0·94）。
结论：我们的AI模型可以很容易地整合到临床实践中，并为cMRI的信息内容提供附加价值。允许疾病分类和预测舒张功能。
背景：Klaus-Tschira基金会的生命信息学倡议，德国心血管研究中心，德国心脏学会的心脏病学部分，和海德堡人工智能健康创新集群。
方法：对于这项建模研究，在海德堡大学医院接受过心脏导管插入术的患者(海德堡，德国)在2004年7月15日至2023年3月16日之间被确定，以及单独的左心室压力测量。我们使用常规心脏诊断的现有患者数据。从这个最初的群体中，我们提取了被诊断为缺血性心肌病的患者，扩张型心肌病,肥厚型心肌病,或者淀粉样变性,以及没有结构表型的对照个体。数据是假名的，只在大学医院的人工智能基础设施内处理。我们使用这些数据来构建不同的模型来预测人口统计(即，AI年龄和AI性别），诊断(即，AI-冠状动脉疾病和AI-心肌病[AI-CMP]），或功能参数(即，AI-LVEDP)。我们通过计算机将数据集随机分成训练，验证,和测试数据集。AI-CMP没有与其他型号进行比较，但在预期的环境中得到了验证。也做了基准。
结果：66936名在海德堡大学医院接受心导管插入术的患者被确认，超过183772个单独的左心室压力测量值。我们从这个初始组中提取了4390名患者，其中1131人(25%)被诊断为缺血性心肌病，1064（24·2%）被诊断为扩张型心肌病，816（18·6%）被诊断为肥厚型心肌病，202人(4.6%)被诊断为淀粉样变性，1177人(26·7%)为无结构表型的对照个体。核心队列仅包括30天内有心脏插管和cMRI的患者，紧急情况被排除在外。AI-sex能够预测患者性别，受试者工作特征曲线（AUC）下的面积为0·78（95％CI0·77-0·78），AI-年龄能够预测患者年龄，平均绝对误差为7·86岁（7·77-7·95），皮尔逊相关性为0·57（95%CI0·56-0·57）。分类任务的AUC范围为缺血性心肌病的0·82（95％CI0·79-0·84）和肥厚型心肌病的0·92（0·91-0·94）。
结论：我们的AI模型可以很容易地整合到临床实践中，并为cMRI的信息内容提供附加价值。允许疾病分类和预测舒张功能。
背景：Klaus-Tschira基金会的生命信息学倡议，德国心血管研究中心，德国心脏学会的心脏病学部分，和海德堡人工智能健康创新集群。
