Xuzhou Medical University, Jiangsu, 221004, China.
Department of Cardiac Care Unit, The Affiliated Hospital of Xuzhou Medical University, Jiangsu, 221006, China.
BMC Cardiovasc Disord. 2024 Mar 25;24(1):179. doi: 10.1186/s12872-024-03847-6.
The aim of this study is to develop a nomogram model for predicting the occurrence of intramyocardial hemorrhage (IMH) in patients with Acute Myocardial Infarction (AMI) following Percutaneous Coronary Intervention (PCI). The model is constructed utilizing clinical data and the SYNTAX Score (SS), and its predictive value is thoroughly evaluated.
A retrospective study was conducted, including 216 patients with AMI who underwent Cardiac Magnetic Resonance (CMR) within a week post-PCI. Clinical data were collected for all patients, and their SS were calculated based on coronary angiography results. Based on the presence or absence of IMH as indicated by CMR, patients were categorized into two groups: the IMH group (109 patients) and the non-IMH group (107 patients). The patients were randomly divided in a 7:3 ratio into a training set (151 patients) and a validation set (65 patients). A nomogram model was constructed using univariate and multivariate logistic regression analyses. The predictive capability of the model was assessed using Receiver Operating Characteristic (ROC) curve analysis, comparing the predictive value based on the area under the ROC curve (AUC).
In the training set, IMH post-PCI was observed in 78 AMI patients on CMR, while 73 did not show IMH. Variables with a significance level of P < 0.05 were screened using univariate logistic regression analysis. Twelve indicators were selected for multivariate logistic regression analysis: heart rate, diastolic blood pressure, ST segment elevation on electrocardiogram, culprit vessel, symptom onset to reperfusion time, C-reactive protein, aspartate aminotransferase, lactate dehydrogenase, creatine kinase, creatine kinase-MB, high-sensitivity troponin T (HS-TnT), and SYNTAX Score. Based on multivariate logistic regression results, two independent predictive factors were identified: HS-TnT (Odds Ratio [OR] = 1.61, 95% Confidence Interval [CI]: 1.21-2.25, P = 0.003) and SS (OR = 2.54, 95% CI: 1.42-4.90, P = 0.003). Consequently, a nomogram model was constructed based on these findings. The AUC of the nomogram model in the training set was 0.893 (95% CI: 0.840-0.946), and in the validation set, it was 0.910 (95% CI: 0.823-0.970). Good consistency and accuracy of the model were demonstrated by calibration and decision curve analysis.
The nomogram model, constructed utilizing HS-TnT and SS, demonstrates accurate predictive capability for the risk of IMH post-PCI in patients with AMI. This model offers significant guidance and theoretical support for the clinical diagnosis and treatment of these patients.
本研究旨在建立一个预测经皮冠状动脉介入治疗（PCI）后急性心肌梗死（AMI）患者发生心肌内出血（IMH）的列线图模型。该模型利用临床数据和 SYNTAX 评分（SS）构建，并对其预测价值进行了全面评估。
本研究为回顾性研究，纳入了 216 例 PCI 后一周内行心脏磁共振（CMR）检查的 AMI 患者。收集所有患者的临床数据，并根据冠状动脉造影结果计算其 SS。根据 CMR 检查是否存在 IMH，将患者分为 IMH 组（109 例）和非 IMH 组（107 例）。患者按 7:3 的比例随机分为训练集（151 例）和验证集（65 例）。采用单因素和多因素逻辑回归分析建立列线图模型。通过受试者工作特征（ROC）曲线分析评估模型的预测能力，比较基于 ROC 曲线下面积（AUC）的预测价值。
在训练集中，78 例 AMI 患者在 CMR 上显示 PCI 后发生 IMH，73 例患者未显示 IMH。使用单因素逻辑回归分析筛选出有统计学意义（P<0.05）的变量。对 12 个指标进行多因素逻辑回归分析：心率、舒张压、心电图 ST 段抬高、罪犯血管、症状发作至再灌注时间、C 反应蛋白、天门冬氨酸氨基转移酶、乳酸脱氢酶、肌酸激酶、肌酸激酶同工酶、高敏肌钙蛋白 T（HS-TnT）和 SYNTAX 评分。基于多因素逻辑回归结果，确定了两个独立的预测因素：HS-TnT（优势比[OR] = 1.61，95%置信区间[CI]：1.21-2.25，P = 0.003）和 SS（OR = 2.54，95%CI：1.42-4.90，P = 0.003）。因此，根据这些发现构建了一个列线图模型。该模型在训练集的 AUC 为 0.893（95%CI：0.840-0.946），在验证集的 AUC 为 0.910（95%CI：0.823-0.970）。校准和决策曲线分析表明该模型具有良好的一致性和准确性。
该列线图模型利用 HS-TnT 和 SS 构建，对 AMI 患者 PCI 后发生 IMH 的风险具有准确的预测能力。该模型为这些患者的临床诊断和治疗提供了重要的指导和理论支持。
