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Development of computer-based models (using machine learning) for improving identification and for better prediction of outcomes in patients with coronary artery disease (disease of the blood vessels of the heart).

Development of machine learning models to improve the accuracy of diagnosis and prognostication in ischemic heart disease

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2023/05/052610
Enrollment
5000
Registered
2023-05-12
Start date
Unknown
Completion date
Unknown
Last updated
2023-05-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Health Condition 1: I20-I25- Ischemic heart diseases Health Condition 2: I99- Other and unspecified disorders ofcirculatory system Health Condition 3: I30-I52- Other forms of heart disease

Interventions

None listed

Sponsors

Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: Ischemic heart disease (IHD) patients or those with symptoms suggestive of IHD visiting the cardiology department as outpatients or inpatients

Exclusion criteria

Exclusion criteria: 1. Critically ill patients with cardiogenic shock or acute pulmonary edema. 2. Pregnant women. 3. End-stage renal disease. 4. Terminal illnesses like cancer. 5. Patients who refuse to give consent.

Design outcomes

Primary

MeasureTime frame
1. Diagnosis of ischemic heart disease after evaluation (recorded as yes/no) along with specific diagnosis. 2. Mortality and Major adverse cardiac events (MACE)Timepoint: 1 year—

Secondary

MeasureTime frame
All cause mortalityTimepoint: 1 year;Heart failureTimepoint: 1 year;Myocardial infarctionTimepoint: 1 year;StrokeTimepoint: 1 year—

Countries

India

Contacts

Public ContactGanesh P

Kasturba Medical College and Hospital

ganeshbmc@gmail.com9914204224

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: Feb 4, 2026