Development and Validation of a Nomogram for Predicting Long-Term Net Adverse Clinical Events in High Bleeding Risk Patients Undergoing Percutaneous Coronary Intervention

Junyan Zhang; Zhongxiu Chen; Ran Liu; Yuxiao Li; Hongsen Zhao; Yanning Li; Minggang Zhou; Hua Wang; Chen Li; Li Rao; Yong He

doi:10.31083/RCM25352

Development and Validation of a Nomogram for Predicting Long-Term Net Adverse Clinical Events in High Bleeding Risk Patients Undergoing Percutaneous Coronary Intervention

Rev Cardiovasc Med. 2025 Jan 17;26(1):25352. doi: 10.31083/RCM25352. eCollection 2025 Jan.

Authors

Junyan Zhang¹, Zhongxiu Chen¹, Ran Liu², Yuxiao Li^{1

3}, Hongsen Zhao², Yanning Li², Minggang Zhou¹, Hua Wang¹, Chen Li¹, Li Rao¹, Yong He¹

Affiliations

¹ Department of Cardiology, West China Hospital of Sichuan University, 610041 Chengdu, Sichuan, China.
² Information Center of West China Hospital, Sichuan University, 610041 Chengdu, Sichuan, China.
³ Department of Nursing, West China Hospital of Sichuan University, 610041 Chengdu, Sichuan, China.

Abstract

Background: Patients with a high risk of bleeding undergoing percutaneous coronary intervention (PCI-HBR) were provided consensus-based criteria by the Academic Research Consortium for High Bleeding Risk (ARC-HBR). However, the prognostic predictors in this group of patients have yet to be fully explored. Thus, an effective prognostic prediction model for PCI-HBR patients is required.

Methods: We prospectively enrolled PCI-HBR patients from May 2022 to April 2024 at West China Hospital of Sichuan University. The cohort was randomly divided into training and internal validation sets in a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression algorithm was employed to select variables in the training set. Subsequently, a prediction model for 1-year net adverse clinical events (NACEs)-free survival was developed using a multivariable Cox regression model, and a nomogram was constructed. The outcome of the NACEs is defined as a composite endpoint that includes death, myocardial infarction, ischemic stroke, and Bleeding Academic Research Consortium (BARC) grade 3-5 major bleeding. Validation was conducted exclusively using the internal validation cohort, assessing the discrimination, calibration, and clinical utility of the nomogram.

Results: This study included 1512 patients with PCI-HBR, including 1058 in the derivation cohort and 454 in the validation cohort. We revealed five risk factors after LASSO regression, Cox regression, and clinical significance screening. These were then utilized to construct a prognostic prediction nomogram, including chronic kidney disease, left main stem lesion, multivessel disease, triglycerides (TG), and creatine kinase-myocardial band (CK-MB). The nomogram exhibited strong predictive ability (the area under the curve (AUC) to predict 1-year NACE-free survival was 0.728), displaying favorable levels of accuracy, discrimination, and clinical usefulness in the internal validation cohort.

Conclusions: This study presents a nomogram to predict 1-year NACE outcomes in PCI-HBR patients. Internal validation showed strong predictive capability and clinical utility. Future research should validate the nomogram in diverse populations and explore new predictors for improved accuracy.

Clinical trial registration: The data for this study were obtained from the PPP-PCI registry, NCT05369442 (https://clinicaltrials.gov/study/NCT05369442).

Keywords: high bleeding risk; internal validation; nomogram; percutaneous coronary intervention; prediction model; prognosis.

Associated data

ClinicalTrials.gov/NCT05369442

Grants and funding

This study was funded by a grant from the Natural Science Foundation of China (grant number 82100282), the Sichuan Provincial Department of Science and Technology Natural Science Foundation Youth Fund Project (grant number 24NSFSC2970), the 1.3.5 project for disciplines of excellence–Clinical Research Incubation Project, West China Hospital, Sichuan University (grant number 2021HXFH021).