Clinical Interventions in Aging (Aug 2021)
Development and Validation of a Risk Nomogram Model for Predicting Revascularization After Percutaneous Coronary Intervention in Patients with Acute Coronary Syndrome
Abstract
Shengjue Xiao,1,* Linyun Zhang,1,2,* Qi Wu,1,* Yue Hu,3 Xiaotong Wang,1 Qinyuan Pan,1 Ailin Liu,1 Qiaozhi Liu,1 Jie Liu,1 Hong Zhu,1 Yufei Zhou,4,5 Defeng Pan1 1Department of Cardiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221004, People’s Republic of China; 2Department of Cardiology, The People’s Hospital of Suzhou New District, Suzhou, Jiangsu, 215000, People’s Republic of China; 3Department of General Practice, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221004, People’s Republic of China; 4Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, People’s Republic of China; 5Department of Cardiology, Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Shanghai Medical College of Fudan University, Shanghai, 200030, People’s Republic of China*These authors contributed equally to this workCorrespondence: Defeng PanDepartment of Cardiology, The Affiliated Hospital of Xuzhou Medical University, 99 Huaihai West Road, Xuzhou, 221004, Jiangsu, People’s Republic of ChinaTel +86 516-83262017Email [email protected]: Percutaneous coronary intervention (PCI) is one of the most effective treatments for acute coronary syndrome (ACS). However, the need for postoperative revascularization remains a major problem in PCI. This study was to develop and validate a nomogram for prediction of revascularization after PCI in patients with ACS.Methods: A retrospective observational study was conducted using data from 1083 patients who underwent PCI (≥ 6 months) at a single center from June 2013 to December 2019. They were divided into training (70%; n = 758) and validation (30%; n = 325) sets. Multivariate logistic regression analysis was used to establish a predictive model represented by a nomogram. The nomogram was developed and evaluated based on discrimination, calibration, and clinical efficacy using the concordance statistic (C-statistic), calibration plot and decision curve analysis (DCA), respectively.Results: The nomogram was comprised of ten variables: follow-up time (odds ratio (OR): 1.01; 95% confidence interval (CI): 1.00– 1.03), history of diabetes mellitus (OR: 1.83; 95% CI: 1.25– 2.69), serum creatinine level on admission (OR: 0.99; 95% CI: 0.98– 1.00), serum uric acid level on admission (OR: 1.005; 95% CI: 1.002– 1.007), lipoprotein-a level on admission (OR: 1.0021; 95% CI: 1.0013– 1.0029), low density lipoprotein cholesterol level on re-admission (OR: 1.33; 95% CI: 0.10– 0.47), the presence of chronic total occlusion (OR: 3.30; 95% CI: 1.93– 5.80), the presence of multivessel disease (OR: 4.48; 95% CI: 2.85– 7.28), the presence of calcified lesions (OR: 1.63; 95% CI: 1.11– 2.39), and the presence of bifurcation lesions (OR: 1.82; 95% CI: 1.20– 2.77). The area under the receiver operating characteristic curve values for the training and validation sets were 0.765 (95% CI: 0.732– 0.799) and 0.791 (95% CI: 0.742– 0.830), respectively. The calibration plots showed good agreement between prediction and observation in both the training and validation sets. DCA also demonstrated that the nomogram was clinically useful.Conclusion: We developed an easy-to-use nomogram model to predict the risk of revascularization after PCI in patients with ACS. The nomogram may provide useful assessment of risk for subsequent treatment of ACS patients undergoing PCI.Keywords: acute coronary syndrome, percutaneous coronary intervention, revascularization, nomogram, prediction model
