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Risk prediction model for in-stent restenosis following PCI: a systematic review
Qin Xiang1,2, Xiao-Yun Xiong1, Si Liu2
1Department of Nursing, The 2nd Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Frontiers in Cardiovascular Medicine
|September 13, 2024
Summary
This systematic review found high bias in current in-stent restenosis (ISR) risk prediction models after percutaneous coronary intervention (PCI). Improved study design and model validation are crucial for reliable ISR prediction.
Area of Science:
- Cardiovascular Medicine
- Medical Informatics
- Biostatistics
Background:
- Coronary heart disease (CHD) poses significant morbidity and mortality risks.
- Percutaneous coronary intervention (PCI) is a primary treatment for CHD.
- In-stent restenosis (ISR) following PCI reduces treatment effectiveness, necessitating accurate risk prediction.
Purpose of the Study:
- To systematically retrieve and evaluate existing risk prediction models for ISR after PCI.
- To assess the quality, risk of bias, and applicability of these models.
Main Methods:
- A comprehensive literature search was performed across 9 databases.
- Data extraction and quality appraisal used the CHARMS checklist.
- Risk of bias and applicability were assessed using the PROBAST tool.
Main Results:
- 17 studies comprising 29 ISR risk prediction models were included.
- Model performance varied, with AUC ranging from 0.530 to 0.953.
- Frequent predictors included diabetes, number of diseased vessels, age, LDL-C, and stent diameter.
Conclusions:
- Current ISR risk prediction models post-PCI exhibit a high overall risk of bias.
- Enhancing study design, reporting, and model validation is essential for improving predictive accuracy and clinical utility.
