Analysis of influential factors for poor prognosis in patients with acute myocardial infarction and construction of a risk prediction nomograph model
10.3760/cma.j.cn341190-20230316-00202
- VernacularTitle:急性心肌梗死患者预后不良的影响因素分析及其风险预测列线图模型构建
- Author:
Guodong LI
1
;
Haibin XU
;
Qiyin SUN
Author Information
1. 湖州师范学院附属第一医院心内科，湖州，313000
- Keywords:
Acute myocardial infarction;
Poor prognosis;
Influencing factors;
ASSO regression;
Logistic regression;
Column line model;
ROC curve;
Decision curve
- From:
Chinese Journal of Primary Medicine and Pharmacy
2023;30(10):1483-1488
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the influential factors for poor prognosis in patients with acute myocardial infarction and construct a risk prediction nomograph model.Methods:A total of 173 patients with acute myocardial infarction who received treatment in The First People's Hospital of Huzhou from June 2018 to June 2021 were included in this study. They were divided into a good prognosis group ( n = 130) and a poor prognosis group ( n = 43) according to the follow-up results at 6 months after developing acute myocardial infarction. The clinical data of the two groups were compared using retrospective analysis methods. The potential influential factors were preliminarily screened using LASSO regression analysis. The influential factors of poor prognosis for acute myocardial infarction were investigated using logistic regression analysis. The risk prediction nomograph model was constructed using the "rms" package of R 4.2.6 language. The discriminability, calibration, and effectiveness of the model were evaluated by drawing the receiver operating characteristic curve, calibration curve, and decision curve. Model validation was conducted internally using the Bootstrap method (repeated sampling 1 000 times). Results:There were significant differences in the culprit vessel, Killip classification, vessel opening time, cardiac troponin I (cTnI), hypertension history, N-terminal pro-brain natriuretic peptide (NT-proBNP), diabetes history, creatinine, hyperlipidemia history, left ventricular ejection fraction, smoking history and creatine kinase isoenzymes-MB between the two groups (all P < 0.05). Seven potential influential factors were screened using LASSO regression model, including diabetes history, infarcted vessel anterior descending branch, Killip IV, vascular opening time, cTnI, NT-proBNP, and left ventricular ejection fraction. Logistic regression analysis showed that vascular opening time ( OR = 0.171, 95% CI: 0.053-0.548, P = 0.003), cTnI ( OR = 0.201, 95% CI: 0.079-0.510, P = 0.001), left ventricular ejection fraction ( OR = 1.469, 95% CI: 1.167-1.847, P = 0.001), NT-proBNP ( OR = 0.996, 95% CI: 0.993-1.00, P = 0.025) were independent influential factors of poor prognosis in patients with acute myocardial infarction (all P < 0.05). Linear regression analysis results indicate that the regression model did not exhibit significant multicollinearity (variance inflation factor < 10). Based on the four influential factors identified by logistic regression analysis, a nomogram model for predicting the poor prognosis of patients with acute myocardial infarction was developed. The area under the receiver operating characteristic curve was 0.979 [95% CI (0.959, 0.999)], and the consistency index was 0.934. The calibration curve of the model was close to the ideal curve. Decision curve analysis revealed that when the probability threshold predicted by the model ranged from 0.61 to 0.99, the predictive value of the model was superior. Conclusion:Factors influencing the poor prognosis of acute myocardial infarction include the time of vessel opening, cTnI, NT-proBNP, and left ventricular ejection fraction. The constructed nomogram model demonstrates good efficacy in predicting the poor prognosis of patients with acute myocardial infarction and can provide some reference for clinical doctors and nurses to identify patients with poor prognosis as soon as possible.
