Establishment of a Predictive Model for Poor Prognosis of Incomplete Revascularization in Patients with Coronary Heart Disease and Multivessel Disease

OBJECTIVE: To establish a predictive model for poor prognosis after incomplete revascularization (ICR) in patients with multivessel coronary artery disease (MVD). METHODS: Clinical data of 757 patients with MVD and ICR after percutaneous coronary intervention (PCI) in the Affiliated Hospital of Chengde Medical University from January 2020 to August 2021 were retrospectively collected. The least absolute shrinkage and selection operator regression method was used to screen variables, and multivariate logistic regression was used to establish a predictive model. An independent cohort was used to validate the model. The C-statistic was used to verify and evaluate the discriminative ability of the model; the calibration curve was drawn, and the decision curve analysis (DCA) was performed to evaluate the calibration degree, the clinical net benefit, and the practicability of the model. RESULTS: The predictive factors included female, age, unconjugated bilirubin, uric acid, low-density lipoprotein, hyperglycemia, total occlusion, and severe tortuosity lesion on coronary angiography. The C-statistic of the training and validation sets were 0.628 and 0.745, respectively. The statistical value of the Hosmer–Lemeshow test for the calibration curve of the training and validation sets were 5.27(P = 0.873) and 6.27 (P = 0.792), respectively. DCA showed that the model was clinically applicable when the predicted probability value of major adverse cardiovascular events(MACEs) ranged from 0.07 to 0.68. CONCLUSIONS: We established a predictive model for poor prognosis after ICR in patients with MVD. The predictive and calibration ability and the clinical net benefit of the predictive model were good, indicating that it can be used as an effective tool for the early prediction of poor prognosis after ICR in patients with MVD.