Krittayaphong Rungroj, Chichareon Ply, Komoltri Chulalak, Sairat Poom, Lip Gregory Y H
Department of Medicine, Division of Cardiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.
Cardiology Unit, Department of Internal Medicine, Faculty of Medicine, Prince of Songkla University, Songkla 90110, Thailand.
J Clin Med. 2023 Feb 6;12(4):1265. doi: 10.3390/jcm12041265.
This study aimed to determine risk factors and incidence rate and develop a predictive risk model for heart failure for Asian patients with atrial fibrillation (AF).
This is a prospective multicenter registry of patients with non-valvular AF in Thailand conducted between 2014 and 2017. The primary outcome was the occurrence of an HF event. A predictive model was developed using a multivariable Cox-proportional model. The predictive model was assessed using C-index, D-statistics, Calibration plot, Brier test, and survival analysis.
There were a total of 3402 patients (average age 67.4 years, 58.2% male) with mean follow-up duration of 25.7 ± 10.6 months. Heart failure occurred in 218 patients during follow-up, representing an incidence rate of 3.03 (2.64-3.46) per 100 person-years. There were ten HF clinical factors in the model. The predictive model developed from these factors had a C-index and D-statistic of 0.756 (95% CI: 0.737-0.775) and 1.503 (95% CI: 1.372-1.634), respectively. The calibration plots showed a good agreement between the predicted and observed model with the calibration slope of 0.838. The internal validation was confirmed using the bootstrap method. The Brier score indicated that the model had a good prediction for HF.
We provide a validated clinical HF predictive model for patients with AF, with good prediction and discrimination values.
本研究旨在确定亚洲房颤（AF）患者发生心力衰竭的危险因素、发病率，并建立预测风险模型。
这是一项2014年至2017年在泰国进行的非瓣膜性房颤患者前瞻性多中心登记研究。主要结局是心力衰竭事件的发生。使用多变量Cox比例模型建立预测模型。使用C指数、D统计量、校准图、Brier检验和生存分析对预测模型进行评估。
共有3402例患者（平均年龄67.4岁，58.2%为男性），平均随访时间为25.7±10.6个月。随访期间218例患者发生心力衰竭，发病率为每100人年3.03（2.64 - 3.46）。模型中有10个心力衰竭临床因素。由这些因素建立的预测模型的C指数和D统计量分别为0.756（95%CI：0.737 - 0.775）和1.503（95%CI：1.372 - 1.634）。校准图显示预测模型与观察模型之间具有良好的一致性，校准斜率为0.838。使用自助法进行内部验证。Brier评分表明该模型对心力衰竭具有良好的预测能力。
我们为房颤患者提供了一个经过验证的临床心力衰竭预测模型，具有良好的预测和区分价值。
