ArticleMedicine2026
Risk factors for post-PCI restenosis in patients with coronary heart disease and the development and validation of a nomogram prediction model.
Article in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study aimed to analyze risk factors for in-stent restenosis (ISR) following percutaneous coronary intervention (PCI) in patients with coronary heart disease and develop and validate a nomogram prediction model. Clinical data were retrospectively collected from 1135 patients with coronary heart disease who underwent PCI at our hospital between January 2015 and December 2024. The occurrence of ISR within 1 year after PCI was defined as the outcome event. Using R software, the patients were randomly divided into a modeling group (795 cases) and a validation group (340 cases) in a 7:3 ratio. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for ISR, and a nomogram prediction model was constructed based on the results of the multivariate analysis. The model's discriminatory ability was evaluated using receiver operating characteristic curves and the area under the curve (AUC). Model calibration and clinical utility were assessed using calibration curves and decision curve analysis (DCA). Internal validation was conducted using Bootstrap resampling combined with 10-fold cross-validation. A total of 61 patients developed ISR, with an incidence rate of 5.37%, including 43 cases in the modeling group and 18 cases in the validation group. Results of multivariate logistic regression analysis showed that diabetes (odds ratio [OR] = 2.578, 95% confidence interval [CI]: 2.143-3.528), multivessel disease (OR = 2.361, 95% CI: 1.689-3.490), low-density lipoprotein cholesterol (LDL-C) ≥ 1.8 mmol/L (OR = 1.462, 95% CI: 1.290-2.160), total stent length ≥ 30 mm (OR = 3.142, 95% CI: 2.322-4.657), and minimum stent diameter < 3.0 mm (OR = 3.633, 95% CI: 2.588-5.102) were identified as independent risk factors for post-PCI ISR (all P < .05). The AUC of the model was 0.833 in the modeling group and 0.822 in the validation group, indicating that the model demonstrated good discriminatory ability in both groups. The calibration curve and DCA results demonstrate that the model has good clinical utility. Post-PCI ISR is associated with multiple factors. The nomogram demonstrated good predictive performance in the present cohort and may assist in the early identification of patients at high risk of post-PCI ISR. Further external validation is required before broader clinical application.
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