陆军军医大学学报 (Jun 2022)
Risk prediction model of in-hospital heart failure in patients with coronary heart disease after percutaneous coronary intervention: development and validation
Abstract
Objective To analyze the risk factors for heart failure in hospitalized patients with coronary heart disease (CHD) after percutaneous coronary intervention (PCI) and establish a personalized risk prediction model for heart failure after PCI. Methods The clinical data of 640 patients with CHD who were hospitalized and underwent PCI in our department from January 2017 to December 2018 were collected and retrospectively analyzed. The subjects were divided into heart failure group (n=44) and non-heart failure group (n=596) according to the occurrence of postoperative heart failure. Logistic multivariate regression analysis and R software were used respectively to screen the independent predictors and plot the nomogram prediction model of heart failure risk. Then, consistency index (C-index), area under the receiver operating characteristic (ROC) curve and calibration curve were subsequently adopted to evaluate the prediction efficiency of the model. Results Multivariate analysis showed that left ventricular ejection fraction (LVEF), age, contrast agent dosage, concomitant cerebral infarction, and grade of New York Heart Association (NYHA) functional classification were independent predictors of heart failure after PCI (P < 0.05). The C-index of the nomogram for predicting heart failure risk was 0.796 (95%CI: 0.723~0.869), and the area under the ROC curve was 0.797 after internal validation (95%CI: 0.726~0.861). Hosmer-Lemeshow test and calibration curves indicated that the nomogram had good prediction consistency (Chi-square=12.360, P=0.135). Conclusion The nomogram model for predicting heart failure risk is successfully constructed, which can assist the medical staff to screen out the population with high risk of heart failure after PCI, and then to implement targeted intervention measures.
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