Wang Peiwei, Yang Li, Lin Maohuan, Deng Bingqing, Wei Yulin, Liu Yingmei, Zheng Shaoxin, Lv Hanlu, Wu Maoxiong, Chen Yangxin, Qiu Qiong
Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China.
Quant Imaging Med Surg. 2025 Jun 6;15(6):5396-5409. doi: 10.21037/qims-2024-2913. Epub 2025 Jun 3.
Atrial functional mitral regurgitation (AFMR) is a recently identified subtype of functional mitral regurgitation (MR), which necessitates a distinct therapeutic approach to that of traditional functional MR. However, diagnosing AFMR remains a complex challenge. Thus, this study aimed to establish a straightforward and effective method for the accurate diagnosis of AFMR using a nomogram.
In total, 489 patients with clinically significant (moderate-to-severe or severe) functional MR who were admitted to the Sun Yat-sen Memorial Hospital of Sun Yat-sen University from January 2020 to May 2023 were enrolled in the study. The patients were randomly divided into training and validation groups at a 7:3 ratio. The predictors for AFMR were screened out by uni- and multivariate logistic regression analyses, and a nomogram model was constructed. The model's predictive accuracy and discriminative capacity were subsequently assessed.
The multivariate logistic regression analysis revealed that the following factors were independent predictors of AFMR: left atrial diameter (LAd) [odds ratio (OR): 1.14, 95% confidence interval (CI): 1.04-1.24, P=0.004], left ventricular diastolic diameter (LVDd) (OR: 0.73, 95% CI: 0.65-0.82, P<0.001), left ventricular ejection fraction (LVEF) (OR: 1.21, 95% CI: 1.13-1.29, P<0.001), previous atrial fibrillation (AF) (OR: 9.34, 95% CI: 2.89-30.45, P<0.001), and myocardial infarction (MI) (OR: 0.04, 95% CI: 0.00-0.40, P=0.007). These factors were integrated into the diagnostic nomogram model. The area under the curve (AUC) values of the model were 0.993 and 0.979 in the training and testing cohorts, respectively.
This study developed a simple way to diagnose AFMR using a nomogram model that incorporated the LAd, LVDd, LVEF, AF, and MI. This model could help cardiologists in treatment determination and prognosis evaluation.
心房功能性二尖瓣反流(AFMR)是最近发现的功能性二尖瓣反流(MR)的一种亚型,其治疗方法与传统功能性MR不同。然而,诊断AFMR仍然是一项复杂的挑战。因此,本研究旨在建立一种使用列线图准确诊断AFMR的简单有效方法。
2020年1月至2023年5月期间入住中山大学孙逸仙纪念医院的489例具有临床意义(中度至重度或重度)功能性MR患者纳入本研究。患者按7:3的比例随机分为训练组和验证组。通过单因素和多因素逻辑回归分析筛选出AFMR的预测因素,并构建列线图模型。随后评估该模型的预测准确性和判别能力。
多因素逻辑回归分析显示,以下因素是AFMR的独立预测因素:左心房直径(LAd)[比值比(OR):1.14,95%置信区间(CI):1.04-1.24,P = 0.004]、左心室舒张直径(LVDd)(OR:0.73,95%CI:0.65-0.82,P < 0.001)、左心室射血分数(LVEF)(OR:1.21,95%CI:1.13-1.29,P < 0.001)、既往心房颤动(AF)(OR:9.34,95%CI:2.89-30.45,P < 0.001)和心肌梗死(MI)(OR:0.04,95%CI:0.00-0.40,P = 0.007)。这些因素被纳入诊断列线图模型。该模型在训练队列和测试队列中的曲线下面积(AUC)值分别为0.993和0.979。
本研究开发了一种使用包含LAd、LVDd、LVEF、AF和MI的列线图模型诊断AFMR的简单方法。该模型可帮助心脏病专家进行治疗决策和预后评估。