Key result
Novel 27-variable model moderately predicts 1-year post-PCI bleeding with a c-statistic of ~0.67.
Why the study?
There are no models to predict long-term bleeding events after PCI despite the need to balance ischemic benefits and bleeding risks with dual antiplatelet therapy.
Can pre-procedural patient characteristics predict long-term bleeding events in patients following percutaneous coronary intervention?
Observational (n=3,128)
Can pre-procedural patient characteristics predict long-term bleeding events in patients following percutaneous coronary intervention?
Effect estimate: c-statistic 0.674
A newly developed 10-variable model offers moderate discrimination for predicting long-term bleeding events after PCI, which may aid in shared decision-making regarding antiplatelet therapy duration.
Should not yet guide antiplatelet duration post-PCI; leaves open whether refined models improve risk stratification.
OBJECTIVES: To construct a model to predict long-term bleeding events following percutaneous coronary intervention (PCI). BACKGROUND: Treatment with dual antiplatelet therapy following PCI involves balancing the benefits of preventing ischemic events with the risks of bleeding. There are no models to predict long-term bleeding events after PCI. METHODS: We analyzed 1-year bleeding outcomes from 3,128 PCI procedures in the Patient Risk Information Services Manager (PRISM) observational study. Patient-reported bleeding events were categorized according to Bleeding Academic Research Consortium (BARC) definitions. Logistic regression analysis was used to develop a model predicting BARC ≥ 1 bleeding. RESULTS: BARC 0, 1, 2 or 3 bleeding was observed in 574 (18.4%); 2382 (76.2%); 114 (3.6%); and 58 (1.8%) patients, respectively. Compared to patients who had no bleeding, patients with BARC ≥ 1 bleeding were more often female (30 vs. 23%), Caucasian (94 vs. 83%), had a higher incidence of drug eluting stent (DES) implantation (83 vs. 76%) and warfarin therapy (7.4 vs. 3.9%), and a lower incidence of diabetes (31 vs. 45%; P-value <0.01 for all comparisons). A 27-variable model had moderate discrimination (c-statistic of 0.674), and good calibration, as did a parsimonious model with 10 variables (c-statistic = 0.667). This model performed well in predicting BARC ≥ 2 bleeding events as well (c-statistic = 0.653). CONCLUSIONS: Bleeding is common in the first year after PCI, and can be predicted by pre-procedural patient characteristics and use of DES. Objective estimates of bleeding risk may help support shared decision-making with respect to stent selection and duration of antiplatelet therapy following PCI.
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Sharma et al. (2016) conducted an observational in Percutaneous coronary intervention (n=3,128). Pre-procedural patient characteristics and drug-eluting stent use was evaluated on BARC ≥ 1 bleeding (c-statistic 0.674). A 27-variable clinical model predicted BARC ≥ 1 bleeding at 1 year after percutaneous coronary intervention with moderate discrimination (c-statistic 0.674).
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