Using hospital records and AI to study heart attacks in people with diabetes
Reviews past records of people with acute coronary syndrome and diabetes or prediabetes.
Plain-language title generated from the registry record
Registry title: Qatar Cardiometabolic Retrospective Cohort-Analysis Using Artificial Intelligence
Key facts
- Where
- Doha, QatarSee the site
Where in Qatar
- Hamad Medical CorporationDoha, QatarNo longer taking part
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Weill Cornell Medical College in Qatar
- Trial code
- NCT07515807
- Record updated
- 21 April 2026
- Original description
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.Read full original descriptionShow less
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.
Inclusion: 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: Non-diabetic: HbA1C < 5.7%; Non-Qatari or non-Arab participants; 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.