A nomogram model based on serological indicators for predicting in-hospital major adverse cardiovascular events in elderly patients with acute coronary syndrome
10.3760/cma.j.issn.0254-9026.2025.03.009
- VernacularTitle:基于血清学指标构建预测老年急性冠状动脉综合征患者院内不良心血管事件的列线图模型
- Author:
Xiang ZHOU
1
;
Ruihan LIU
1
;
Yutong LIU
1
;
Fan TIAN
1
;
Jie ZHANG
1
;
Xiaomao WANG
1
;
Jian CAO
1
Author Information
1. 解放军总医院第二医学中心保健四科 国家老年疾病临床医学研究中心，北京 100853
- Publication Type:Journal Article
- Keywords:
Acute coronary syndrome;
Elderly;
Serological indicators;
Adverse cardiovascular events;
Nomogram
- From:
Chinese Journal of Geriatrics
2025;44(3):289-296
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop a nomogram model utilizing serological indicators for predicting in-hospital major adverse cardiovascular events(MACE)in elderly patients diagnosed with acute coronary syndrome(ACS).Methods:This study involved a retrospective analysis of clinical data from 1, 818 elderly patients with ACS who were treated at the First Medical Center of the General Hospital of the People's Liberation Army from January 2022 to May 2024.The patients were randomly assigned to a training set(1, 272 cases)and a validation set(546 cases)in a 7: 3 ratio.Following a comparison of the two groups, the training set was further categorized into non-MACE and MACE groups based on the occurrence of endpoint events.Univariate analysis, Lasso regression, and multivariate logistic regression analyses were sequentially employed to identify factors influencing in-hospital MACE and to construct the nomogram model.The performance of the model was assessed using receiver operating characteristic(ROC)curves, calibration curves, and decision curves.Results:Among the 1, 818 ACS patients, the mean age was 67 years(interquartile range: 61.0 to 73.0), with 70.4% being male.Almost all indicators(except platelet count)exhibited no statistically significant differences between the training and validation sets(all P>0.05).However, statistically significant differences(all P<0.05)were observed in age, body mass index, neutrophil count, lymphocyte count, monocyte count, white blood cell count, hemoglobin, red blood cell distribution width, mean platelet volume, C-reactive protein(CRP), fibrinogen, D-dimer, albumin, direct bilirubin, troponin T(TnT), fasting blood glucose(FBG), estimated glomerular filtration rate(eGFR), uric acid, N-terminal pro-B-type natriuretic peptide(NT-proBNP), glycated hemoglobin(HbA1c), and high-density lipoprotein cholesterol(HDL-C)between the non-MACE and MACE groups in the training set.Ultimately, seven variables—neutrophil count, hemoglobin, red blood cell distribution width, CRP, TnT, FBG, and NT-proBNP—were selected to construct the nomogram model.The model demonstrated high discrimination in both the training and validation sets, with an area under the curve of 0.86(95% CI: 0.82-0.90)for the training set and 0.85(95% CI: 0.81-0.90)for the validation set.Furthermore, the calibration curves for both cohorts indicated a close agreement between predicted and actual risk estimates, suggesting improved model calibration.Decision curve analysis indicated that the predictive model has notable clinical utility. Conclusions:The constructed nomogram enhances the accuracy of predicting in-hospital MACE in elderly patients with ACS, thereby offering a valuable reference for clinical practice.
