The noninvasive diagnosis models and its clinical significance of acute myocardial infarction in emergency patients with high-risk chest pain established based on chest pain database
10.3760/cma.j.cn115455-20230316-00271
- VernacularTitle:基于胸痛数据库构建急诊急性高危胸痛患者急性心肌梗死的无创诊断模型及临床意义
- Author:
Yan WANG
1
;
Xiyun WANG
;
Dongqin ZHANG
;
Meng SHI
Author Information
1. 济宁医学院附属医院急诊科，济宁 272029
- Keywords:
Chest pain;
Myocardial infarction;
Noninvasive diagnostic model;
Database
- From:
Chinese Journal of Postgraduates of Medicine
2024;47(8):673-679
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the noninvasive diagnosis model and its clinical significance of acute myocardial infarction(AMI) in emergency patients with high-risk chest pain established based on chest pain database.Methods:A total of 467 patients with acute high-risk chest pain admitted to the Affiliated Hospital of Jining Medical University from January 2020 to October 2022 were selected. The patients were divided into AMI group (317 cases) and non-AMI group (150 cases) according to the occurrence of AMI. The clinical data of the two groups were compared, and Lasso regression and Logistic regression were used to analyze the related risk factors of AMI. R language was used to establish a diagnostic model, and concordance index (C-index) was used to evaluate the predictive ability of the model. Calibration curve and decision analysis curve (DCA) were used to verify and evaluate the established model externally.Results:The results of the univariate analysis showed that the proportion of patients with coronary heart disease, respiratory rate, myoglobin, creatine kinase isoenzyme-MB (CK-MB), cardiac troponin I (cTnI), D-dimer, N-terminal pro-brain natriuretic peptide, C- reactive protein, fibrinogen, lactic acid, ST-segment elevation and abnormal ventricular wall movement in the AMI group were higher than those in the non-AMI group: 51.10 % (162/317) vs. 21.33%(32/150), (19.25 ± 2.44) times/min vs. (16.30 ± 2.15) times/min, (270.03 ± 26.59) μg/L vs. (71.44 ± 19.85) μg/L, (30.51 ± 8.22) μg/L vs. (3.22 ± 0.88) μg/L, (4.51 ± 1.38) μg/L vs. (0.04 ± 0.01) μg/L, (1.69 ± 0.51) mg/L vs. (0.32 ± 0.09) mg/L, (2 085.66 ± 561.24) ng/L vs. (964.39 ± 257.40) ng/L, (13.98 ± 4.52) mg/L vs. (7.11 ± 2.26) mg/L, (4.07 ± 0.83) g/L vs. (2.95 ± 0.78) g/L, (2.20 ± 0.49) mmol/L vs. (1.36 ± 0.35) mmol/L, 80.76%(256/317) vs. 16.67% (25/150), 95.27%(302/317) vs. 17.33% (26/150); the platelet count, activited partial thomboplastin time, prothombin time and left ventricular ejection fractionin in the AMI group were lower than those in the non-AMI group: (168.97 ± 29.66) × 10 9/L vs. (230.58 ± 30.57) × 10 9/L, (30.25 ± 4.59) s vs. (33.59 ± 4.16) s, (11.82 ± 0.74) s vs. (13.25 ± 1.02) s, (47.25 ± 5.33)% vs. (58.49 ± 5.07)%, there were statistical differences ( P<0.05). Using 17 variables with P<0.05 in univariate analysis as independent variables, Lasso regression analysis selected 7 predictive variables as coronary heart disease, myoglobin, CK-MB, cTnI, D-dimer, ST segment elevation and abnormal ventricular wall movement. Multivariate Logistic regression analysis showed that coronary heart disease, myoglobin, CK-MB, cTnI, D-dimer, ST-segment elevation and abnormal ventricular wall movement were the related risk factors of AMI ( P<0.05). Hosmer-Lemeshow goodness of fit test showed that the fit was good ( χ2 = 2.56, df = 9, P = 0.860); R language was used to draw the non-invasive diagnosis model of AMI, and the C-index was 0.945, indicated good predictive ability. Calibration curve analysis showed that the calibration degrees of the model establishment population and the external verification population were 0.918 and 0.924, respectively, indicated that the model was in good agreement with the actual observation results. The DCA curve showed that the column graph model for diagnosing AMI had significant positive net benefit and good clinical utility. Conclusions:Coronary heart disease, myoglobin, CK-MB, cTnI, D-dimer, ST-segment elevation and abnormal ventricular wall movement can be used as non-invasive diagnostic markers for AMI in patients with acute high-risk chest pain in emergency department. The prediction performance of the diagnostic model based on the above factors is good.
