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NCT Number: NCT07426107

Development and Prospective Validation of an AI Model for Prognosis in ITP Patients Undergoing Coronary Revascularization

This study employs a dual-cohort design to develop and validate a prognostic model for Major Adverse Cardiovascular Events (MACE) following revascularization in immune thrombocytopenia (ITP) patients with Coronary Artery Disease (CAD). The model will be developed and trained using a retrospective multi-center cohort (development/training cohort). Its performance will then be prospectively validated in a separate, consecutively enrolled prospective cohort (validation cohort). The goal is to create an AI-based tool to assist in personalized risk assessment and decision-making for this high-risk population.

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Key information

About this study

Study Design: This is a dual-phase, multi-center observational study. Phase 1 (Retrospective Cohort): A retrospective cohort will serve as the development and training set. Data from eligible patients treated in the past will be collected to identify predictors and develop the initial AI prediction model.

Phase 2 (Prospective Cohort): A prospective, observational cohort will serve as the validation set. Consecutively eligible patients will be enrolled and followed forward in time. The model derived from Phase 1 will be applied to this cohort to evaluate its predictive accuracy and clinical utility.

Treatment Groups: Within both cohorts, patients will be categorized based on actual clinical care:

  • Revascularization Group: Patients undergoing PCI or CABG.
  • Medical Therapy Group: Patients managed with guideline-directed medical therapy alone.

Objective: To compare MACE risk between groups and to develop and validate a model predicting MACE specifically in the revascularization group.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Age ≥ 18 years.
  • Diagnosis of primary Immune Thrombocytopenia (ITP) according to international working group criteria.
  • Diagnosis of Coronary Artery Disease (stable angina or Acute Coronary Syndrome) confirmed by coronary angiography.
  • The diagnosis of ITP must be established and documented prior to the diagnosis of CAD.
  • Capable of providing informed consent (for prospective enrollment and data collection).

Exclusion criteria

  • Secondary causes of thrombocytopenia (e.g., drug-induced, hematologic malignancy, hypersplenism, liver disease).
  • Conditions requiring long-term therapeutic anticoagulation (e.g., atrial fibrillation, mechanical heart valve).
  • Life expectancy less than 1 year due to non-cardiovascular disease.
  • Inability to comply with follow-up.
  • Inability to give informed consent.
  • Pregnancy or breastfeeding.
  • A history of Type 2 Myocardial Infarction prior to the index treatment decision.

Treatment and study plan

Primary outcomes

  1. 1-month incidence of Major Adverse Cardiovascular Events (MACE)

    Time frame: from the date of CAD diagnosis (index date) until 1 month of follow-up

    MACE is a composite endpoint defined as the occurrence of any of the following: all-cause mortality, non-fatal myocardial infarction [MI], urgent coronary revascularization [CRV] and ischemic stroke. The time frame for assessment is from the date of CAD diagnosis (index date) until 1 month of follow-up.

  2. 1-year incidence of Major Adverse Cardiovascular Events (MACE)

    Time frame: from the date of CAD diagnosis (index date) until 1 year of follow-up

    MACE is a composite endpoint defined as the occurrence of any of the following: all-cause mortality, non-fatal myocardial infarction [MI], urgent coronary revascularization [CRV] and ischemic stroke. The time frame for assessment is from the date of diagnosis of CAD (index date) until 1 year of follow-up.

Secondary outcomes

  1. key predictors of adverse outcomes following revascularization

    Time frame: from the date of CAD diagnosis (index date) until 1 month and 1 year of follow-up

    Identification of independent predictors for MACE in the revascularization group using a multivariate Cox proportional hazards regression model with stepwise selection or Lasso regularization. The model will include candidate clinical variables such as platelet count, type of CAD, comorbidities, and medication use. For each final predictor selected, the Hazard Ratio (HR), 95% confidence interval, and p-value will be reported. MACE is defined as a composite of all-cause death, non-fatal myocardial infarction, urgent coronary revascularization and ischemic stroke.

  2. BARC type ≥2 bleeding event

    Time frame: from the date of CAD diagnosis (index date) until 1 month and 1 year of follow-up

    clinical-related bleeding with a BARC type ≥2 bleeding event

  3. overall bleeding event

    Time frame: from the date of CAD diagnosis (index date) until 1 month and 1 year of follow-up

    the bleeding event identified according to the BARC standardized bleeding Criteria

  4. Hospitalization for CAD within 1 year.

    Time frame: from the date of CAD diagnosis (index date) until 1 year of follow-up

    Hospitalization for CAD within 1 year.

  5. 1-year overall survival

    Time frame: from the date of CAD diagnosis (index date) until 1 year of follow-up

    overall survival from the date of CAD diagnosis (index date) until 1 year of follow-up

Other outcomes

  1. Performance of the AI-based model in predicting Major Adverse Cardiovascular Events (MACE)

    Time frame: from the date of CAD diagnosis (index date) until 1 month and 1 year of follow-up

    Assessment of the discriminative power and calibration of the AI-based prognostic model for predicting MACE in the validation cohort. Discrimination will be quantified using the Area Under the Receiver Operating Characteristic Curve (AUC) or C-statistic. Calibration will be assessed using a calibration plot and the Hosmer-Lemeshow goodness-of-fit test.MACE is defined as a composite of all-cause death, non-fatal myocardial infarction, urgent coronary revascularization and ischemic stroke.

  2. Subgroup analysis: Impact of clinical factors on MACE incidence

    Time frame: from the date of CAD diagnosis (index date) until 1 month and 1 year of follow-up

    Incidence rates (number and proportion of events) of MACE will be calculated for subgroups stratified by pre-defined clinical factors. These factors include type of Coronary Artery Disease (Stable CAD vs. Acute Coronary Syndrome [ACS]), platelet count categories (e.g., <25×10⁹/L, 25-50×10⁹/L, >50×10⁹/L), age, and sex. Furthermore, the association between these factors and MACE will be evaluated using multivariate Cox proportional hazards or logistic regression models, reported as adjusted Hazard Ratios (HR) with 95% confidence intervals. MACE is defined as a composite of all-cause death, non-fatal myocardial infarction, urgent coronary revascularization and ischemic stroke.

Study contacts

Contact information is provided by the study sponsor or research team.

Sponsors and collaborators

Lead sponsor

Xiao Hui Zhang

Other

Collaborators

  • Beijing Anzhen Hospital
  • Chinese Academy of Medical Sciences, Fuwai Hospital
  • Chinese PLA General Hospital
  • Peking Union Medical College
  • Peking University Third Hospital

Registry information

Official study title

An Artificial Intelligence-Enhanced Longitudinal Cohort Study to Optimize Revascularization Decisions in Patients With Coronary Artery Disease and Immune Thrombocytopenia (The ITP-CAD AI-REVASC Study)

Acronym: ITP with CAD

Important dates

Study start
2026
Primary completion
2028
Study completion
2029
First posted
Feb 23, 2026
Registry last updated
Feb 23, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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