xDAPT: Patient Centered Machine Learning Model for Bleeding and Ischemic Risk
Study Details
Study Description
Brief Summary
Dual antiplatelet therapy (DAPT) is indicated in all patients undergoing coronary stent implantation to prevent ischemic recurrencies despite an increased risk of bleeding. Accordingly, clinical practice guidelines advocate tailoring DAPT duration according to the patient's individual ischemic and bleeding risk profile.
Data from 11 clinical trials involving patients who underwent percutaneous coronary intervention (PCI) with an everolimus-eluting stent will be pooled and analyzed to develop a machine learning-based algorithm to predict the probability of an ischemic or bleeding event up to 1 year. These predictive risk models aim to support clinical decision-making on DAPT management after PCI.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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Detailed Description
Dual antiplatelet therapy (DAPT) with aspirin and a P2Y12 inhibitor is the standard of care for secondary prevention after percutaneous coronary intervention (PCI). DAPT has demonstrated its efficacy in reducing ischemic complications (including stent thrombosis) after PCI although at the cost of an increased risk of bleeding. As both event types have been independently linked with excess morbidity and mortality, international guidelines emphasize the need to tailor DAPT duration and intensity according to the individual ischemic and bleeding risk profile of each patient. In this context, several predictive risk models for bleeding and thrombosis have been developed with the aim of guiding clinical decisions on DAPT management post-PCI. However, many of these risk models have shown only modest performance and limited applicability in real-world clinical practice. Such limitations can be attributed, at least in part, to the analytical approaches used for their development, mostly based on linear models unable to capture the complex interplay between different clinical covariates. Machine learning methods offer the potential to overcome these limitations by leveraging computer algorithms to large datasets that capture high-dimensional, non-linear relationships among variables. However, the feasibility and usefulness of machine learning-based prognostic risk models in PCI patients remain relatively unexplored.
The present study will analyze data from 11 clinical trials encompassing approximately 19,000 patients undergoing percutaneous coronary intervention (PCI) with an everolimus-eluting stent to develop a machine learning-based algorithm. Institutional review board approval or informed patient consent was not required as this study is an analysis of previously published clinical trials and all individual patient data were deidentified. The goal is to predict the probability of an ischemic or bleeding event up to 1 year after PCI. Patient-level data from the eligible clinical trials listed per the XIENCE Machine Learning Data Acquisition Protocol (90961902) will be pooled and randomly split into a training cohort (~75%) and a validation cohort (~25%). These include both Abbott- sponsored and investigator-initiated XIENCE studies (i.e., XIENCE V, XIENCE 28 USA, XIENCE 28 GLOBAL, XIENCE 90, ABSORB III, ABSORB IV, Compare ABSORB, Compare Acute, EXAMINATION, SIERRA-75 and ITALIC). The performance of different trials of machine learning classifiers will be compared with traditional statistical approaches for the prediction of ischemic and bleeding outcomes. The best-performing machine learning model will then be selected and tested against a pre-defined performance goal to assess its clinical usefulness. Based on existing literature on established risk scores that currently inform clinical practice, the performance goal for the model is set at C-index value equal or greater than 0.65 at the 97.5% lower confidence interval of the bootstrap C-index distribution. This is to ensure that the true value of the C-index is still within a clinically relevant range and to validate the clinical usefulness of the risk prediction model.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Ischemic cohort All patients sustaining an ischemic event (i.e., cardiovascular death, myocardial infarction, stroke, or stent thrombosis) within 1 year of PCI. |
Device: Percutaneous coronary intervention
Percutaneous coronary intervention (PCI) is a catheter-based technique performed under fluoroscopic guidance to treat coronary artery disease and restore blood flow to the myocardium. During PCI, coronary vessel patency is generally achieved with drug-eluting stents, which are metallic scaffolds coated with a polymer that carry and gradually release an antiproliferative drug. In the present study, all participants underwent PCI with implantation of a thin-strut, cobalt-chromium, durable-fluorinated polymer, everolimus-eluting stent (XIENCE, Abbott), and received a course of dual antiplatelet therapy (DAPT) ranging from 28 to 365 days after PCI.
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| Bleeding cohort All patients sustaining a major bleeding event (i.e., Bleeding Academic Research Consortium type 3-5) within 1 year of PCI. |
Device: Percutaneous coronary intervention
Percutaneous coronary intervention (PCI) is a catheter-based technique performed under fluoroscopic guidance to treat coronary artery disease and restore blood flow to the myocardium. During PCI, coronary vessel patency is generally achieved with drug-eluting stents, which are metallic scaffolds coated with a polymer that carry and gradually release an antiproliferative drug. In the present study, all participants underwent PCI with implantation of a thin-strut, cobalt-chromium, durable-fluorinated polymer, everolimus-eluting stent (XIENCE, Abbott), and received a course of dual antiplatelet therapy (DAPT) ranging from 28 to 365 days after PCI.
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Outcome Measures
Primary Outcome Measures
- Ischemic event [12 months after PCI]
Composite of cardiovascular death, myocardial infarction, stroke, or stent thrombosis.
- Major bleeding [12 months after PCI]
Bleeding Academic Research Consortium (BARC) type 3-5 bleeding
Eligibility Criteria
Criteria
Inclusion criteria:
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Subject must be at least 18 years of age
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Subject must provide informed consent to participate in the XIENCE study
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Subject underwent PCI with the XIENCE stent
Exclusion criteria:
• none
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Icahn School of Medicine at Mount Sinai | New York | New York | United States | 10029 |
Sponsors and Collaborators
- Abbott Medical Devices
- Icahn School of Medicine at Mount Sinai
Investigators
- Principal Investigator: Roxana Mehran, Abbott
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- SVTP90997943 and SVTP90996971