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.
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%
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.
Participants admitted at Hamad Medical Corporation for Acute Coronary Syndrome (ACS) or Acute Heart Failure (AHF).
Cohort
Other
Not listed
Incidence of 3-point Major Adverse Cardiovascular Events (MACE) in Acute Coronary Syndrome Patients
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.
Incidences of 2-point Major Adverse Cardiovascular Events (MACE) in Heart Failure Patients
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.
Major Adverse Cardiovascular Events
MACE - Major Adverse Cardiovascular Events
Unstable angina requiring hospitalization
Arrhythmic events
Coronary revascularization
Click on the lead sponsor to view all of their trials.
Weill Cornell Medical College in Qatar
Lead sponsor
Hamad Medical Corporation
Collaborator
This trial is not recruiting at the moment. You can still explore other options: