Ren Wenbo, Zhang Zichen, Wang Yifei, Wang Jiangyuan, Li Li, Shi Lin, Zhai Taiyu, Huang Jing
Department of Clinical Laboratory, The First Hospital of Jilin University, Changchun, 130000, China.
College of Medical Technology, Beihua University, Jilin, 132000, China.
J Transl Med. 2025 Jan 6;23(1):22. doi: 10.1186/s12967-024-06043-4.
Recent studies suggest a connection between immunoglobulin light chains (IgLCs) and coronary heart disease (CHD). However, current diagnostic methods using peripheral blood IgLCs levels or subtype ratios show limited accuracy for CHD, lacking comprehensive assessment and posing challenges in early detection and precise disease severity evaluation. We aim to develop and validate a Coronary Health Index (CHI) incorporating total IgLCs levels and their distribution. Additionally, we aim to evaluate its effectiveness by integrating patient data and using machine learning models through diagnostic trial.
The CHI was developed and combined with other clinical data. Nine machine learning models were screened to identify optimal diagnostic performance, with the XGBoost model emerging as the top performer. Performance was assessed based on accuracy, sensitivity, and its ability to identify severe CHD cases characterized by complex lesions (SYNTAX score > 33).
The XGBoost model demonstrated high accuracy and sensitivity in diagnosing CHD, with an area under the curve (AUC) of 0.927. It also accurately identified patients with severe CHD, achieving an AUC of 0.991. An online web tool was introduced for broader external validation, confirming the model's effectiveness.
Combining the CHI with the XGBoost model offers significant advantages in diagnosing CHD and assessing disease severity. This approach can guide clinical interventions and improve large-scale CHD screening.
近期研究表明免疫球蛋白轻链（IgLCs）与冠心病（CHD）之间存在关联。然而，目前使用外周血IgLCs水平或亚型比例的诊断方法对冠心病的诊断准确性有限，缺乏全面评估，在早期检测和精确的疾病严重程度评估方面存在挑战。我们旨在开发并验证一种纳入总IgLCs水平及其分布的冠状动脉健康指数（CHI）。此外，我们旨在通过整合患者数据并在诊断试验中使用机器学习模型来评估其有效性。
开发CHI并将其与其他临床数据相结合。筛选了九个机器学习模型以确定最佳诊断性能，其中XGBoost模型表现最佳。基于准确性、敏感性及其识别以复杂病变为特征的严重冠心病病例（SYNTAX评分>33）的能力来评估性能。
XGBoost模型在诊断冠心病方面表现出高准确性和敏感性，曲线下面积（AUC）为0.927。它还准确识别了严重冠心病患者，AUC达到0.991。引入了一个在线网络工具进行更广泛的外部验证，证实了该模型的有效性。
将CHI与XGBoost模型相结合在诊断冠心病和评估疾病严重程度方面具有显著优势。这种方法可以指导临床干预并改善大规模冠心病筛查。
