Predicting Mortality and Periprocedural Adverse Events in Patients Undergoing Complex and Higher Risk Percutaneous...

Area of Science:

• Cardiology
• Interventional Cardiology
• Health Services Research

Background:

• Estimating periprocedural risk in complex and higher-risk percutaneous coronary intervention (CHIP-PCI) is challenging.
• Existing risk-scoring systems require validation in diverse patient populations undergoing CHIP-PCI.
• Accurate risk prediction is crucial for patient selection and procedural planning in CHIP-PCI.

Purpose of the Study:

• To compare the predictive performance of three risk-scoring systems: National Cardiovascular Data Registry (NCDR) CathPCI, British Cardiovascular Intervention Society (BCIS)-CHIP, and Blue Cross Blue Shield of Michigan Cardiovascular Consortium 2 (BMC2).
• To evaluate the ability of these scores to predict in-hospital mortality and major adverse cardiac and cerebrovascular events (MACCEs) in patients undergoing CHIP-PCI.
• To identify the most accurate risk score for this specific patient group.

Main Methods:

• A retrospective analysis of 4287 patients undergoing CHIP-PCI at a specialized center.
• Evaluation of three risk scores (NCDR CathPCI, BCIS-CHIP, BMC2) for predicting in-hospital mortality and MACCE.
• Performance assessment using discrimination (Area Under the Curve - AUC) and calibration metrics.

Main Results:

• The BMC2 score demonstrated superior discrimination for both mortality (AUC=0.93) and MACCE (AUC=0.87) compared to NCDR CathPCI and BCIS-CHIP.
• The BMC2 score showed even greater superiority in subanalyses of chronic total occlusion and multivessel PCI.
• Calibration analysis indicated good accuracy but a trend toward overestimation of risk in lower-risk patients for the BMC2 score.

Conclusions:

• The BMC2 risk score is superior for predicting in-hospital mortality and MACCE in patients undergoing CHIP-PCI.
• The findings support the use of the BMC2 score for enhanced risk stratification in complex PCI procedures.
• Further refinement of risk scores may be needed to address potential overestimation in low-risk populations.