# A risk prediction nomogram for in-stent restenosis in patients with coronary heart disease: first exploratory analysis based on the substrate materials of drug-eluting stents

## TL;DR

This study creates a risk prediction tool for in-stent restenosis in heart disease patients, highlighting the importance of stent material for the first time.

## Contribution

The first quantification of the clinical effect of drug-eluting stent substrate materials on in-stent restenosis risk.

## Key findings

- A predictive nomogram model for in-stent restenosis showed good performance with an AUC of 0.734 in the training set.
- Stent substrate materials were found to be a significant predictor, with their removal worsening model calibration and reclassification metrics.
- The model included BMI, SBP, LVDD, number of target vessels, and mean stent diameter as independent predictors of ISR.

## Abstract

We aimed to develop and validate a predictive nomogram for identifying the risk factors of in-stent restenosis (ISR). In addition, for the first time, we quantified the clinical effect of the substrate materials of DES.

We retrospectively enrolled 402 patients with coronary heart disease (CHD) who underwent initial percutaneous coronary intervention (PCI) at the Affiliated Hospital of Qingdao University between January 1, 2012, and June 1, 2022. LASSO regression and logistic regression analyses were conducted to identify the independent risk factors of ISR. A predictive nomogram was subsequently developed. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), clinical impact curve (CIC), and calibration curves. Furthermore, nested modeling was conducted to evaluate the incremental predictive value of the substrate materials of DES.

BMI, SBP, LVDD, number of target vessels, mean diameter of stent and substrate materials of DES were identified as independent predictors of ISR. A predictive nomogram model was successfully developed, exhibiting good performance in the training set (AUC = 0.734, 95% CI: 0.676–0.792; Brier score = 0.193, 95% CI: 0.173–0.213; calibration slope = 1.000, 95% CI: 0.706–1.359; Hosmer-Lemeshow χ2 = 8.087, P = 0.088). In addition, the nomogram model maintained stable performance in the validation set (AUC = 0.707, 95% CI: 0.562–0.837; Brier score = 0.207, 95% CI: 0.161–0.258; calibration slope = 0.842, 95% CI: 0.229–1.991; Hosmer-Lemeshow χ2 = 2.641, P = 0.620). The base model, including the substrate materials of DES in the nested analysis, was well-calibrated (χ2 = 8.087, P = 0.088; Brier score = 0.1929). However, the removal of this predictor significantly deteriorated calibration(χ2 = 14.0, P = 0.007; Brier score = 0.1962, Δ =  + 0.0033) and worsened reclassification metrics (continuous NRI = −0.2549, 95% CI: −0.4635 to −0.0481, P = 0.021; IDI = −0.0134, 95% CI: −0.0507 to −0.003, P = 0.033).

BMI, SBP, LVDD, number of target vessels, mean diameter of stent, and substrate materials of DES are independent predictors of ISR. The nomogram model exhibited good predictive value for ISR. This is the first study demonstrating the significance of substrate material selection for assessing the risk of ISR in patients. Future validation through prospective studies or larger sample sizes is still needed.