Impact of diabetes on long-term mortality following multivessel percutaneous interventions: an insight into optimal...

Area of Science:

• Cardiology
• Biostatistics
• Public Health

Background:

• Drug-eluting stents (DES) offer better long-term outcomes than bare metal stents (BMS) for diabetics with coronary artery disease (CAD).
• Statistical analysis of stent data presents challenges due to heterogeneity.
• Percutaneous coronary intervention (PCI) data involves complex spatial and temporal clustering.

Purpose of the Study:

• To compare long-term mortality between DES and BMS in diabetic CAD patients undergoing PCI.
• To identify the optimal statistical strategy for analyzing clustered PCI data.
• To evaluate the impact of stent type on mortality using robust statistical methods.

Main Methods:

• Retrospective analysis of a PCI registry (2003-2009) including diabetics with CAD undergoing multi-vessel PCI.
• Assessment of long-term mortality via the Social Security Death Index.
• Application and comparison of six different statistical analytical strategies, including Generalized Estimating Equations (GEE).

Main Results:

• The study included 756 diabetics with 1568 DES and 336 BMS interventions.
• Significant differences in outcomes were observed across analytical methods.
• The Generalized Estimating Equation (GEE) approach with an autoregressive correlation structure proved robust for clustered data.
• Diabetics receiving BMS-PCI had a 47% higher hazard of long-term mortality compared to those receiving DES-PCI (Hazard Ratio: 1.47, 95% CI: 1.04-2.09).

Conclusions:

• Complex clustering in PCI data can lead to misinterpretation.
• GEE with an autoregressive correlation matrix and robust variance is the optimal method for analyzing clustered PCI data.
• Diabetics undergoing BMS-PCI face a significantly higher mortality risk compared to those undergoing DES-PCI.