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NCT07515807: QCRC-AI
Qatar Cardiometabolic Retrospective Cohort-Analysis Using Artificial Intelligence
trial in Cardio Vascular Disease in 10,000 participants. Not yet recruiting.
16 July 2030
Quick facts
| Lead sponsor | Weill Cornell Medical College in Qatar |
|---|---|
| Status | Not yet recruiting |
| Study type | OBSERVATIONAL |
| Enrollment | 10,000 |
| Start date | 16 July 2026 |
| Primary completion | 16 July 2030 |
| Estimated completion | 16 July 2030 |
| Sites | 1 location across Qatar |
Conditions studied
- Cardio Vascular Disease — all drugs for Cardio Vascular Disease →
- Acute Coronary Syndromes (ACS) — all drugs for Acute Coronary Syndromes (ACS) →
- Type 2 Diabetes — all drugs for Type 2 Diabetes →
- Pre Diabetes — all drugs for Pre Diabetes →
Sponsor
Weill Cornell Medical College in Qatar
Who can join
18 and older, any sex, with Cardio Vascular Disease or Acute Coronary Syndromes (ACS). Patients with the condition only — healthy volunteers not accepted.
Sponsor's own 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 patients 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 patients with cardiometabolic disease. These models will integrate clinical, biochemical, imaging, and other non-invasive data routinely collected during patient care to identify predictors of cardiovascular events.
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
Verify or expand the search:
- PubMed search for NCT07515807
- Europe PMC full search
- ASCO Meeting Library
- ESMO Meeting Library
- bioRxiv preprints
- medRxiv preprints
- Google Scholar
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Other Weill Cornell Medical College in Qatar trials
Trials by the same sponsor.
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT07515807 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by Weill Cornell Medical College in Qatar
- Last refreshed: 7 April 2026
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT07515807.
Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing