Feng Rong, Lu Jiahui, Cui Honggen, Li Yaqin
Department of Cardiology, Affiliated Hospital of Hebei University, 071030 Baoding, Hebei, China.
Department of Endocrinology, Affiliated Hospital of Hebei University, 071030 Baoding, Hebei, China.
Rev Cardiovasc Med. 2025 Jul 23;26(7):36608. doi: 10.31083/RCM36608. eCollection 2025 Jul.
The incidence of silent myocardial infarction (SMI) is increasing. Meanwhile, due to the atypical clinical symptoms and signs associated with SMI, the prognosis for patients is often poor.
This prediction model used the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analyses to screen variables. Predictive accuracy was assessed using the area under the receiver operating characteristic (ROC) curve (AUC). The clinical decision curve analysis (DCA), alongside the calibration curve and clinical impact curve (CIC) analyses, were used to assess model validity.
This study included 174 patients, 64 (36.8%) of whom experienced SMI; logistic regression analysis identified six variables: gender, age, high-density lipoprotein cholesterol (HDL-C), apolipoprotein B/apolipoprotein A1 (ApoB/A1), uric acid (UA), and triglyceride glucose-body mass index (TyG-BMI). The results identified the TyG-BMI as a predictor of SMI (odds ratios (OR) = 1.02, 95% CI: 1.01-1.03; = 0.003). The ROC curve of the model demonstrated an AUC of 0.772 (95% CI: 0.699-0.844), which increased to 0.774 (95% CI: 0.707-0.841) following a bootstrap analysis with 1000 repetitions. The calibration curve of the model was in high agreement with the ideal curve. The DCA demonstrated that the prediction probability threshold of the model ranged from 12% to 83%, where the patient achieved a significant net clinical benefit. The CIC showed that the model effectively identified high-risk SMI patients when the threshold probability exceeded 0.7.
The TyG-BMI is an independent predictor of SMI. A prediction model based on the TyG-BMI showed good predictive ability for SMI.
无症状心肌梗死（SMI）的发病率正在上升。同时，由于与SMI相关的非典型临床症状和体征，患者的预后往往较差。
本预测模型使用最小绝对收缩和选择算子（LASSO）和多变量逻辑回归分析来筛选变量。使用受试者操作特征（ROC）曲线下面积（AUC）评估预测准确性。临床决策曲线分析（DCA）以及校准曲线和临床影响曲线（CIC）分析用于评估模型有效性。
本研究纳入174例患者，其中64例（36.8%）发生SMI；逻辑回归分析确定了6个变量：性别、年龄、高密度脂蛋白胆固醇（HDL-C）、载脂蛋白B/载脂蛋白A1（ApoB/A1）、尿酸（UA）和甘油三酯-葡萄糖体重指数（TyG-BMI）。结果确定TyG-BMI为SMI的预测因子（比值比（OR）=1.02，95%置信区间：1.01-1.03；P=0.003）。该模型的ROC曲线显示AUC为0.772（95%置信区间：0.699-0.844），在进行1000次重复的自抽样分析后增加至0.774（95%置信区间：0.707-0.841）。该模型的校准曲线与理想曲线高度吻合。DCA表明，该模型的预测概率阈值范围为12%至83%，在此范围内患者获得显著的净临床获益。CIC显示，当阈值概率超过0.7时，该模型能够有效识别高危SMI患者。
TyG-BMI是SMI的独立预测因子。基于TyG-BMI的预测模型对SMI具有良好的预测能力。
