Xing Yuanming, Wang Chen, Wu Haoyu, Ding Yiming, Chen Siying, Yuan Zuyi
Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, People's Republic of China.
Key Laboratory of Environment and Genes Related to Diseases, Ministry of Education, Xi'an, People's Republic of China.
Int J Gen Med. 2022 Jul 6;15:6085-6096. doi: 10.2147/IJGM.S372158. eCollection 2022.
Left ventricular aneurysm (LVA) is a severe and common mechanical comorbidity with acute myocardial infarction (AMI) that can present high mortality and serious adverse outcomes. Accordingly, there is a need for early identification and prevention of patients at risk of LVA. The aim of this study was to develop and validate a risk prediction model for LVA among AMI patients in Northwest China.
A total of 509 patients with AMI were retrospectively collected between January 2018 and August 2021. All patients were randomly divided into a training group (n=356) and a validation group (n=153). Potential risk factors for LVA were screened for predictive modelling using least absolute shrinkage and selection operator regression, multivariate logistic regression, clinical relevance, and represented by a comprehensive nomogram. Receiver operating characteristic curve, calibration curve, and decision-curve analysis (DCA) were used to assess the discrimination capacity, calibration, and clinical validity, respectively.
Seven predictors were finally identified for the establishment of prediction model, including age, cardiovascular disease history, left ventricular ejection fraction, ST-segment elevation, percutaneous coronary intervention history, mean platelet volume, and aspartate aminotransferase. The prediction model achieved acceptable areas under the curves of 0.901 (95% confidence interval [CI]=0.868-0.933) and 0.908 (95% CI=0.861-0.956) in the training and validation groups, respectively, and the calibration curves fit well in our model. The DCA result indicated that this nomogram exhibited a favorable performance in terms of clinical utility.
An accurate prediction model for LVA development established, which can be applied to rapidly assess the risk of LVA in patients with AMI. Our findings will aid clinical decision-making to reduce the incidence of LVA in high-risk patients, and counteract adverse cardiovascular outcomes.
左心室室壁瘤（LVA）是急性心肌梗死（AMI）严重且常见的机械性合并症，可导致高死亡率和严重不良后果。因此，有必要早期识别和预防有LVA风险的患者。本研究旨在建立并验证中国西北地区AMI患者LVA的风险预测模型。
回顾性收集2018年1月至2021年8月期间共509例AMI患者。所有患者随机分为训练组（n = 356）和验证组（n = 153）。采用最小绝对收缩和选择算子回归、多因素逻辑回归、临床相关性筛选LVA的潜在风险因素用于预测建模，并以综合列线图表示。分别采用受试者工作特征曲线、校准曲线和决策曲线分析（DCA）评估辨别能力、校准和临床有效性。
最终确定7个预测因子用于建立预测模型，包括年龄、心血管疾病史、左心室射血分数、ST段抬高、经皮冠状动脉介入治疗史、平均血小板体积和天门冬氨酸氨基转移酶。预测模型在训练组和验证组的曲线下面积分别为0.901（95%置信区间[CI]=0.868 - 0.933）和0.908（95%CI = 0.861 - 0.956），校准曲线在我们的模型中拟合良好。DCA结果表明，该列线图在临床实用性方面表现良好。
建立了LVA发生的准确预测模型，可用于快速评估AMI患者发生LVA的风险。我们的研究结果将有助于临床决策，以降低高危患者LVA的发生率，并对抗不良心血管结局。
