Acute Coronary Syndromes (ACS), Artifical Intelligence, Cardio Vascular Disease, Pre Diabetes, Type 2 Diabetes
Conditions
Brief summary
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.
Detailed description
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.
Interventions
None listed
Sponsors
Weill Cornell Medical College in Qatar
Hamad Medical Corporation
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Incidence of 3-point Major Adverse Cardiovascular Events (MACE) in Acute Coronary Syndrome Patients | 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. |
| Incidences of 2-point Major Adverse Cardiovascular Events (MACE) in Heart Failure Patients | 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
| Measure | Time frame | Description |
|---|---|---|
| Major Adverse Cardiovascular Events | 5 years | MACE - Major Adverse Cardiovascular Events |
| Unstable angina requiring hospitalization | 5 years | — |
| Arrhythmic events | 5 years | — |
| Coronary revascularization | 5 years | — |
| Post-Percutaneous Coronary Intervention (PCI) or Coronary Artery Bypass Grafting (CABG) complications (stent/graft thrombosis or repeat revascularization) | 5 years | — |
| Development or progression of valvular heart disease (aortic or mitral stenosis/regurgitation) | 5 years | — |
| New-onset diabetes mellitus | 5 years | — |
| Change in glycated Hemoglobin A1c (HbA1c) | 5 years | — |
| Newly diagnosed medical or surgical conditions not present at baseline | 5 years | — |
Countries
Qatar
Contacts
CONTACTCharbel Abi Khalil Prof
PRINCIPAL_INVESTIGATORCharbel Abi Khalil
Weill Cornell Medicine-Qatar
Outcome results
None listed