Department of Laboratory Medicine, The Affiliated Lihuili Hospital, Ningbo University, Ningbo, China.
Department of Cardiovascular Medicine, The Affiliated Lihuili Hospital, Ningbo University, Ningbo, China.
J Clin Lab Anal. 2023 Apr;37(4):e24849. doi: 10.1002/jcla.24849. Epub 2023 Feb 17.
The incidence of coronary heart disease (CHD) is increasing worldwide. The need for percutaneous coronary intervention (PCI) is determined by coronary angiography (CAG). As coronary angiography is an invasive and risky test for patients, it will be of great help to develop a predicting model for the assessment of the probability of PCI in patients with CHD using the test indexes and clinical characteristics.
A total of 454 patients with CHD were admitted to the cardiovascular medicine department of a hospital from January 2016 to December 2021, including 286 patients who underwent CAG and were treated with PCI, and 168 patients who only underwent CAG to confirm the diagnosis of CHD were set as the control group. Clinical data and laboratory indexes were collected. According to the clinical symptoms and the examination signs, the patients in the PCI therapy group were further split into three subgroups: chronic coronary syndrome (CCS), unstable angina pectoris (UAP), and acute myocardial infarction (AMI). The significant indicators were extracted by comparing the differences among the groups. A nomogram was drawn based on the logistic regression model, and predicted probabilities were performed using R software (version 4.1.3).
Twelve risk factors were selected by regression analysis; the nomogram was successfully constructed to predict the probability of needing PCI in patients with CHD. The calibration curve shows that the predicted probability is in good agreement with the actual probability (C-index = 0.84, 95% CI = 0.79-0.89). According to the results of the fitted model, the ROC curve was plotted, and the area under the curve was 0.801. Among the three subgroups of the treatment group, 17 indexes were statistically different, and the results of the univariable and multivariable logistic regression analysis revealed that cTnI and ALB were the two most important independent impact factors.
cTnI and ALB are independent factors for the classification of CHD. A nomogram with 12 risk factors can be used to predict the probability of requiring PCI in patients with suspected CHD, which provided a favorable and discriminative model for clinical diagnosis and treatment.
冠心病（CHD）的发病率在全球范围内呈上升趋势。经皮冠状动脉介入治疗（PCI）的需求取决于冠状动脉造影（CAG）。由于冠状动脉造影对患者来说是一种有创和有风险的检查，因此开发一种使用测试指标和临床特征评估 CHD 患者 PCI 概率的预测模型将非常有帮助。
2016 年 1 月至 2021 年 12 月，共有 454 例 CHD 患者入住我院心血管内科，其中 286 例行 CAG 并接受 PCI 治疗，168 例行 CAG 仅确诊为 CHD 患者作为对照组。收集临床资料和实验室指标。根据临床症状和检查体征，将 PCI 治疗组患者进一步分为慢性冠状动脉综合征（CCS）、不稳定型心绞痛（UAP）和急性心肌梗死（AMI）三个亚组。通过比较各组之间的差异提取显著指标。基于逻辑回归模型绘制列线图，并使用 R 软件（版本 4.1.3）进行预测概率。
通过回归分析选择了 12 个危险因素；成功构建了预测 CHD 患者需要 PCI 的概率的列线图。校准曲线表明预测概率与实际概率吻合良好（C 指数=0.84，95%CI=0.79-0.89）。根据拟合模型的结果绘制 ROC 曲线，曲线下面积为 0.801。在治疗组的三个亚组中，有 17 个指标具有统计学差异，单变量和多变量逻辑回归分析的结果表明 cTnI 和 ALB 是两个最重要的独立影响因素。
cTnI 和 ALB 是 CHD 分类的独立因素。使用 12 个风险因素的列线图可以预测疑似 CHD 患者需要 PCI 的概率，为临床诊断和治疗提供了一个有利和有区别的模型。
