Journal of clinical laboratory analysis · 2023
Development of a risk score model for the prediction of patients needing percutaneous coronary intervention.
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Wang Xi, Lin Yuping, Wang Feng
Аннотация
BACKGROUND: The incidence of coronary heart disease (CHD) is increasing worldwide. The need for percutaneous coronary intervention (PCI) is determined by coronary angiography (CAG). As coronary angiography is an invasive and risky test for patients, it will be of great help to develop a predicting model for the assessment of the probability of PCI in patients with CHD using the test indexes and clinical characteristics.
METHODS: A total of 454 patients with CHD were admitted to the cardiovascular medicine department of a hospital from January 2016 to December 2021, including 286 patients who underwent CAG and were treated with PCI, and 168 patients who only underwent CAG to confirm the diagnosis of CHD were set as the control group. Clinical data and laboratory indexes were collected. According to the clinical symptoms and the examination signs, the patients in the PCI therapy group were further split into three subgroups: chronic coronary syndrome (CCS), unstable angina pectoris (UAP), and acute myocardial infarction (AMI). The significant indicators were extracted by comparing the differences among the groups. A nomogram was drawn based on the logistic regression model, and predicted probabilities were performed using R software (version 4.1.3).
RESULTS: Twelve risk factors were selected by regression analysis; the nomogram was successfully constructed to predict the probability of needing PCI in patients with CHD. The calibration curve shows that the predicted probability is in good agreement with the actual probability (C-index = 0.84, 95% CI = 0.79-0.89). According to the results of the fitted model, the ROC curve was plotted, and the area under the curve was 0.801. Among the three subgroups of the treatment group, 17 indexes were statistically different, and the results of the univariable and multivariable logistic regression analysis revealed that cTnI and ALB were the two most important independent impact factors.
CONCLUSION: cTnI and ALB are independent factors for the classification of CHD. A nomogram with 12 risk factors can be used to predict the probability of requiring PCI in patients with suspected CHD, which provided a favorable and discriminative model for clinical diagnosis and treatment.
METHODS: A total of 454 patients with CHD were admitted to the cardiovascular medicine department of a hospital from January 2016 to December 2021, including 286 patients who underwent CAG and were treated with PCI, and 168 patients who only underwent CAG to confirm the diagnosis of CHD were set as the control group. Clinical data and laboratory indexes were collected. According to the clinical symptoms and the examination signs, the patients in the PCI therapy group were further split into three subgroups: chronic coronary syndrome (CCS), unstable angina pectoris (UAP), and acute myocardial infarction (AMI). The significant indicators were extracted by comparing the differences among the groups. A nomogram was drawn based on the logistic regression model, and predicted probabilities were performed using R software (version 4.1.3).
RESULTS: Twelve risk factors were selected by regression analysis; the nomogram was successfully constructed to predict the probability of needing PCI in patients with CHD. The calibration curve shows that the predicted probability is in good agreement with the actual probability (C-index = 0.84, 95% CI = 0.79-0.89). According to the results of the fitted model, the ROC curve was plotted, and the area under the curve was 0.801. Among the three subgroups of the treatment group, 17 indexes were statistically different, and the results of the univariable and multivariable logistic regression analysis revealed that cTnI and ALB were the two most important independent impact factors.
CONCLUSION: cTnI and ALB are independent factors for the classification of CHD. A nomogram with 12 risk factors can be used to predict the probability of requiring PCI in patients with suspected CHD, which provided a favorable and discriminative model for clinical diagnosis and treatment.