Clinical trial NCT06089304: Patient Centered Machine Learning Model for Bleeding and Ischemic Risk
Official title: Predicting Ischemic and Bleeding Risk In Patients On Dual Antiplatelet Therapy After Percutaneous Coronary Intervention: An Individualized Patient Centered Machine Learning Model
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Coronary Artery Disease
Percutaneous Coronary Intervention
Keywords: PCI
DAPT
Thrombosis
Bleeding
Lead sponsor: Abbott Medical Devices
Sponsor class: INDUSTRY
Collaborators: name: Icahn School of Medicine at Mount Sinai
First posted: 2023-10-18
Enrollment: 19000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 99
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients with coronary artery disease undergoing percutaneous coronary intervention with an everolimus-eluting stent who were enrolled in one of the 11 XIENCE clinical trials, including the XIENCE V, XIENCE 28 USA, XIENCE 28 USA Global, XIENCE 90, ABSORB III, ABSORB IV, Compare ABSORB, Compare Acute, EXAMINATION, SIERRA-75 and ITALIC. The combined pooled dataset will be randomly split into a training cohort (\~75%) and a validation cohort (\~25%).

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.

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.

Eligibility criteria: Inclusion criteria:

* Subject must be at least 18 years of age
* Subject must provide informed consent to participate in the XIENCE study
* Subject underwent PCI with the XIENCE stent

Exclusion criteria:

• none

Primary outcomes:
measure: Ischemic event
timeFrame: 12 months after PCI
measure: Major bleeding
timeFrame: 12 months after PCI
Secondary outcomes:
Other outcomes:
Locations:
city: New York
country: United States
facility: Icahn School of Medicine at Mount Sinai
state: New York
Study officials:
affiliation: Abbott
name: Roxana Mehran
role: PRINCIPAL_INVESTIGATOR
References: