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NCT Number: NCT07515807

Qatar Cardiometabolic Retrospective Cohort-Analysis Using Artificial Intelligence

Cardiovascular disease is the leading cause of death worldwide, and individuals with diabetes or other cardiometabolic conditions are at increased risk of adverse cardiovascular outcomes. Although advances in prevention and treatment have reduced cardiovascular events globally, cardiometabolic disease continues to represent a significant health burden, particularly in regions with high diabetes prevalence. In Qatar and other Gulf Cooperation Council countries, the prevalence of diabetes and obesity is increasing, contributing to a high proportion of participants presenting with acute coronary syndrome who have type 2 diabetes or prediabetes. This observational study will use electronic medical record data from patients hospitalized at the Heart Hospital with acute coronary syndrome and a concomitant diagnosis of diabetes or prediabetes. The study will assess trends in cardiovascular risk factors and cardiovascular events, including readmission and mortality. An artificial intelligence component will be used to develop and validate machine learning based risk prediction models to forecast adverse cardiovascular outcomes in participants with cardiometabolic disease. These models will integrate clinical, biochemical, imaging, and other non-invasive data routinely collected during participants care to identify predictors of cardiovascular events.

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Key information

Conditions

Cardio Vascular Disease Acute Coronary Syndrome Acute Coronary Syndromes (ACS) Artifical Intelligence Cardiovascular Diseases Diabetes Mellitus Diabetes Mellitus, Type 2 Endocrine System Diseases Glucose Intolerance Glucose Metabolism Disorders Heart Diseases Hyperglycemia Metabolic Diseases Myocardial Ischemia Nutritional and Metabolic Diseases Pre Diabetes Type 2 Diabetes Vascular Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

About this study

This study combines retrospective and prospective designs.

Retrospective:

We use past electronic medical records to identify participants and collect baseline information from their initial visit (using their code or health card number).

Prospective:

From that starting point, we follow the same participants forward in time, updating data every two years and recording new outcomes (mortality, cardiovascular events, rehospitalizations, treatment-related outcomes) at planned checkpoints (approximately 6 months, 1 year, and 2 years).

New eligible participants identified in later data extractions are added and followed in the same manner. Because we only observe and record participants existing records and outcomes without assigning interventions, the study is observational.

Who can participate

Healthy volunteers accepted: No

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

Inclusion criteria

  • Age ≥ 18
  • Qatari and Arab participants
  • Participants admitted for Acute Coronary Syndrome (ACS) or Acute Heart Failure (AHF)
  • Metabolic disease: Diabetes (HbA1C ≥ 6.5% or any HbA1C if a patient is on an antidiabetic agent) or pre-diabetes: 5.7% ≤ HbA1c ≤ 6.4%

Exclusion criteria

  • Non-Qatari or non-Arab participants
  • Non-diabetic: HbA1C < 5.7%
  • This chart review involves no direct interaction with individuals. Prisoners are not a focus of this study, and incarceration status is not identifiable in the records reviewed.

Treatment and study plan

Primary outcomes

  1. Incidence of 3-point Major Adverse Cardiovascular Events (MACE) in Acute Coronary Syndrome Patients

    Time frame: 5 years

    Composite endpoint defined as the occurrence of cardiovascular death, non-fatal myocardial infarction, or non-fatal stroke. Events will be identified using electronic medical records, hospital admission data, and follow-up assessments during the study period. These outcomes will serve as endpoints for the development and validation of predictive machine learning models.

  2. Incidences of 2-point Major Adverse Cardiovascular Events (MACE) in Heart Failure Patients

    Time frame: 5 years

    Composite endpoint defined as cardiovascular death or hospitalization for heart failure. Events will be ascertained through hospital records, clinical documentation, and follow-up data collection. These outcomes will be used as endpoints for predictive model development and validation.

Secondary outcomes

  1. Major Adverse Cardiovascular Events

    Time frame: 5 years

    MACE - Major Adverse Cardiovascular Events

  2. Unstable angina requiring hospitalization

    Time frame: 5 years

  3. Arrhythmic events

    Time frame: 5 years

  4. Coronary revascularization

    Time frame: 5 years

  5. Post-Percutaneous Coronary Intervention (PCI) or Coronary Artery Bypass Grafting (CABG) complications (stent/graft thrombosis or repeat revascularization)

    Time frame: 5 years

  6. Development or progression of valvular heart disease (aortic or mitral stenosis/regurgitation)

    Time frame: 5 years

  7. New-onset diabetes mellitus

    Time frame: 5 years

  8. Change in glycated Hemoglobin A1c (HbA1c)

    Time frame: 5 years

  9. Newly diagnosed medical or surgical conditions not present at baseline

    Time frame: 5 years

Study contacts

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

Charbel Abi Khalil Prof

CONTACT

[email protected]

+ 974 4492 8484

Sponsors and collaborators

Lead sponsor

Weill Cornell Medical College in Qatar

Other

Collaborators

  • Hamad Medical Corporation

Registry information

Acronym: QCRC-AI

Important dates

Study start
2026
Primary completion
2030
Study completion
2030
First posted
Apr 7, 2026
Registry last updated
Apr 21, 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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