Logistic regression analysis of factors influencing NT-proBNP expression levels in patients with unstable angina pectoris and intervention strategies

This study aims to analyze the influencing factors of N-terminal pro-B-type natriuretic peptide (NT-proBNP) expression levels in patients with unstable angina pectoris (UAP) using a logistic regression model, establish a prediction model with internal validation, and propose corresponding intervention strategies. A retrospective analysis was conducted on the clinical data of 100 UAP patients admitted to our hospital from November 2021 to October 2023. Patients were randomly divided into a training set (n = 80) and a validation set (n = 20) at a ratio of 8:2. The training set was further divided into an low NT-proBNP group (n = 40) and a high NT-proBNP group (n = 40) based on serum NT-proBNP levels at admission. Differences in baseline and clinical data between the 2 groups were compared. Variables with significant differences in univariate analysis were included in the logistic regression model to identify influencing factors of NT-proBNP expression levels in UAP patients. A nomogram prediction model was constructed. The predictive value of the model was evaluated using the receiver operating characteristic curve, calibration curve, and decision curve, and corresponding intervention strategies were formulated. There were no significant differences in baseline data between the training and validation sets ( P > .05). In the training set, age, history of myocardial infarction, left ventricular ejection fraction (LVEF), New York Heart Association (NYHA) classification, and serum creatinine showed statistically significant differences between the observation and high NT-proBNP groups ( P < .05). The odds ratio values for age, history of myocardial infarction, LVEF, NYHA, and serum creatinine were 1.175 (95% confidence interval CI: 1.096–1.248), 1.244 (95% CI: 1.185–1.460), 0.085 (95% CI: 0.074–0.096), 13.183 (95% CI: 2.824–61.532), and 1.118 (95% CI: 1.015–1.183), respectively. The nomogram model was internally validated using validation set data. The area under the receiver operating characteristic curve area under the curve for the training and validation sets was 0.866 (0.789–0.944) and 0.876 (0.801–0.951). The model showed high net benefit within the threshold range, demonstrating good applicability. Conclusion Age, history of myocardial infarction, LVEF, NYHA classification, and serum creatinine level are influencing factors of NT-proBNP levels in UAP patients.