Pei Jianfeng, Zaid Maryam, Wu Yiling, Wang Na, Liu Xuebo, Zhao Qi, Jiang Yonggen, Xu Wang Hong, Zhao Genming
Department of Epidemiology, Fudan University School of Public Health, Shanghai, China.
Shanghai Songjiang District Center for Disease Control and Prevention, Shanghai, China.
BMJ Open. 2025 Sep 15;15(9):e091516. doi: 10.1136/bmjopen-2024-091516.
The development of simple tools to identify individuals at high risk of coronary heart disease (CHD) would enable rapid implementation of preventive measures. This study was designed to construct predictive models and scoring systems for CHD using monocyte count and its ratio to high-density lipoprotein cholesterol (HDL-C) (MHR).
Population-based prospective cohort study.
The Shanghai Suburban Adult Cohort and Biobank (SSACB).
This prospective study included 44 013 CHD-free participants of the SSACB. The Songjiang subcohort served as the training set, in which three predictive models and corresponding scoring systems were developed with monocyte count or MHR using stepwise Cox regression. The models and algorithms were tested internally using 10-fold cross-validation and externally in the Jiading subcohort. Discriminations were assessed based on area under the curve (AUC) values, while calibrations were evaluated using the Hosmer-Lemeshow goodness-of-fit test.
During a mean follow-up period of 4.8 years, 883 CHD events occurred, with an incidence of 415.7/100 000. Monocyte count and MHR were significantly associated with the risk of CHD. The constructed model incorporating monocyte count (Model 2) achieved AUC values of 0.746 (0.726, 0.766) for 4-year CHD prediction in the training set, 0.746 (0.690, 0.796) in the cross-validation, and 0.717 (0.674, 0.761) in the external validation, comparable to the models including HDL-C (model 1) or MHR (model 3). Calibration plots demonstrated good agreement between predicted and actual probabilities. Similar results were observed for the corresponding scoring algorithms.
The monocyte-based model is a simple, low-cost and well-calibrated risk-stratification tool for CHD. However, the declined discrimination in external validation indicates limited generalisability. Prospective multicentre validation and recalibration are therefore warranted before clinical adoption.
开发简单工具以识别冠心病（CHD）高危个体，将有助于快速实施预防措施。本研究旨在利用单核细胞计数及其与高密度脂蛋白胆固醇（HDL-C）的比值（MHR）构建冠心病预测模型和评分系统。
基于人群的前瞻性队列研究。
上海郊区成人队列与生物样本库（SSACB）。
这项前瞻性研究纳入了44013名无冠心病的SSACB参与者。松江子队列作为训练集，利用单核细胞计数或MHR，通过逐步Cox回归建立了三个预测模型及相应评分系统。模型和算法在内部采用10倍交叉验证进行测试，并在嘉定子队列中进行外部验证。基于曲线下面积（AUC）值评估区分度，使用Hosmer-Lemeshow拟合优度检验评估校准度。
在平均4.8年的随访期内，发生883例冠心病事件，发病率为415.7/10万。单核细胞计数和MHR与冠心病风险显著相关。构建的包含单核细胞计数的模型（模型2）在训练集中对4年冠心病预测的AUC值为0.746（0.726, 0.766），交叉验证中为0.746（0.690, 0.796），外部验证中为0.717（0.674, 0.761），与包含HDL-C的模型（模型1）或MHR的模型（模型3）相当。校准图显示预测概率与实际概率之间具有良好的一致性。相应的评分算法也观察到类似结果。
基于单核细胞的模型是一种简单、低成本且校准良好的冠心病风险分层工具。然而，外部验证中区分度下降表明其普遍性有限。因此，在临床应用前需要进行前瞻性多中心验证和重新校准。
