模型采用机电耦合时间来评估肥厚型心肌病患者的心力衰竭情况。
Model Embraced Electromechanical Coupling Time for Estimation of Heart Failure in Patients With Hypertrophic Cardiomyopathy.
作者信息
Hu Su, Mi Lan, Fu Jianli, Ma Wangxia, Ni Jingsong, Zhang Zhenxia, Li Botao, Guan Gongchang, Wang Junkui, Zhao Na
机构信息
Department of Cardiovascular Medicine, Shaanxi Provincial People's Hospital, Xi'an, China.
Department of Cardiovascular Medicine, The Second Affiliated Hospital of Xi'an Medical University, Xi'an, China.
出版信息
Front Cardiovasc Med. 2022 Jun 16;9:895035. doi: 10.3389/fcvm.2022.895035. eCollection 2022.
OBJECTIVE
This study aimed to establish a model embraced electromechanical coupling time (EMC-T) and assess the value of the model for the prediction of heart failure (HF) in patients with hypertrophic cardiomyopathy (HCM).
MATERIALS AND METHODS
Data on 82 patients with HCM at Shaanxi Provincial People's Hospital between February 2019 and November 2021 were collected and then formed the training dataset ( = 82). Data were used to screen predictors of HF using univariate and multivariate analyses. Predictors were implemented to discover the optimal cut-off value, were incorporated into a model, and shown as a nomogram. The cumulative HF curve was calculated using the Kaplan-Meier method. Additionally, patients with HCM at other hospitals collected from March 2019 to March 2021 formed the validation dataset. The model's performance was confirmed both in training and validation sets.
RESULTS
During a median of 22.91 months, 19 (13.38%) patients experienced HF. Cox analysis showed that EMC-T courses in the lateral wall, myoglobin, PR interval, and left atrial volume index were independent predictors of HF in patients with HCM. Five factors were incorporated into the model and shown as a nomogram. Stratification of patients into two risk subgroups by applying risk score (<230.65, ≥230.65) allowed significant distinction between Kaplan-Meier curves for cumulative incidence of HF events. In training dataset, the model had an AUC of 0.948 (95% CI: 0.885-1.000, < 0.001) and achieved a good C-index of 0.918 (95% CI: 0.867-0.969). In validation dataset, the model had an AUC of 0.991 (95% CI: 0.848-1.000, < 0.001) and achieved a strong C-index of 0.941 (95% CI: 0.923-1.000). Calibration plots showed high agreement between predicted and observed outcomes in both two datasets.
CONCLUSION
We established and validated a novel model incorporating electromechanical coupling time courses for predicting HF in patients with HCM.
目的
本研究旨在建立一个包含机电耦合时间（EMC-T）的模型，并评估该模型在预测肥厚型心肌病（HCM）患者心力衰竭（HF）方面的价值。
材料与方法
收集了2019年2月至2021年11月期间在陕西省人民医院就诊的82例HCM患者的数据，形成训练数据集（n = 82）。通过单因素和多因素分析，利用这些数据筛选HF的预测因素。将预测因素用于确定最佳截断值，纳入模型并以列线图形式展示。采用Kaplan-Meier法计算累积HF曲线。此外，收集2019年3月至2021年3月期间在其他医院就诊的HCM患者形成验证数据集。在训练集和验证集中均对该模型的性能进行了验证。
结果
在中位随访22.91个月期间，19例（13.38%）患者发生HF。Cox分析显示，侧壁的EMC-T过程、肌红蛋白、PR间期和左心房容积指数是HCM患者发生HF的独立预测因素。将这五个因素纳入模型并以列线图形式展示。通过应用风险评分（<230.65，≥230.65）将患者分为两个风险亚组，使得HF事件累积发生率的Kaplan-Meier曲线之间有显著差异。在训练数据集中，该模型的AUC为0.948（95%CI：0.885 - 1.000，P < 0.001），C指数为0.918（95%CI：0.867 - 0.969），表现良好。在验证数据集中，该模型的AUC为0.991（95%CI：0.848 - 1.000，P < 0.001），C指数为0.941（95%CI：0.923 - 1.000），表现较强。校准图显示在两个数据集中预测结果与观察结果之间具有高度一致性。
结论
我们建立并验证了一个包含机电耦合时间过程的新型模型，用于预测HCM患者的HF。
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