Risk Factor Analysis and Construction of a Prognostic Model after Percutaneous Coronary Intervention in Patients with Chronic Total Occlusion

Abstract

Background and Objective. Percutaneous coronary intervention (PCI) is expansively used in the treatment of chronic total occlusion (CTO), but there is a lack of effective means to predict the prognosis of CTO patients undergoing PCI. The aim of this study was to construct a risk model that could effectively predict disease progression after PCI treatment in patients with CTO and enrich the means of clinical prediction of prognosis. Methods. The clinical data of 82 patients with CTO who underwent PCI in Lianshui County People’s Hospital were collected in this study. The patients were divided into training set ( n = 54 ) and validation set ( n = 28 ) by random sampling method. Statistical difference test was performed for clinical features of patients. Univariate and multivariate Cox regression analyses were performed to determine the risk factors affecting progression of CTO. Nomogram was used to construct a prediction model for disease progression. C -index was calculated, and the accuracy of the model was tested by calibration curve. Results. No statistically significant differences were incorporated in baseline characteristics of included patients ( p > 0.05 ). There were 25 patients with adverse cardiac events during follow-up in the training set and 13 in the validation set. The results of multivariate Cox regression analysis demonstrated that the important factors affecting postoperative disease progression mainly came down to age, BMI, diabetes, creatinine clearance rate, and left ventricular fraction < 40 % . A nomogram was constructed and C -index was calculated. The calibration curve was then used to evaluate and predict risk model of disease progression. The result showed an internal validation C -index of 0.6219 and an external validation C -index of 0.6453, which indicated the good prediction performance of the model. Conclusion. The risk of disease progression in CTO patients treated with PCI can be effectively predicted by the risk model constructed in this study, which opens up a great possibility for enriching the means of predicting the prognosis of these patients in clinical practice.

Predicting long-term prognosis after percutaneous coronary intervention in patients with acute coronary syndromes: a prospective nested case-control analysis for county-level health services

“…Previous research has linked the clinical profile of ACS patients at admission to prognosis, with age, cardiac insufficiency, and blood pressure clinically recognized as independent markers of poor prognosis following PCI in ACS patients (34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53).…”