| CTRI Number |
CTRI/2025/11/097636 [Registered on: 18/11/2025] Trial Registered Prospectively |
| Last Modified On: |
18/11/2025 |
| Post Graduate Thesis |
No |
| Type of Trial |
Observational |
|
Type of Study
|
Cross Sectional Study |
| Study Design |
Other |
|
Public Title of Study
|
Collecting data on health status of people with diabetes and hypertension to develop easy-to-use tests to screen for heart complications. |
|
Scientific Title of Study
|
: A prospective, Cross-Sectional Study to Support Machine Learning Model Development for Screening Cardiovascular Disease Conditions in Populations with Diabetes and Hypertension: Mapping Mobile ECG Device Signals to Gold-Standard Tests |
| Trial Acronym |
NIL |
|
Secondary IDs if Any
|
| Secondary ID |
Identifier |
| NIL |
NIL |
|
|
Details of Principal Investigator or overall Trial Coordinator (multi-center study)
|
| Name |
Sushil Mathew John |
| Designation |
Professor |
| Affiliation |
Christian Medical College Vellore |
| Address |
Low Cost Effective Care Unit
Schell Campus, Arani Road
Christian Medical College
Vellore. Tamil Nadu
Vellore
TAMIL NADU
632001
India |
| Phone |
9443038848 |
| Fax |
|
| Email |
rikkisush@cmcvellore.ac.in |
|
Details of Contact Person
Scientific Query
|
| Name |
Sushil Mathew John |
| Designation |
Professor |
| Affiliation |
Christian Medical College Vellore |
| Address |
Low Cost Effective Care Unit
Schell Campus, Arani Road
Christian Medical College
Vellore. Tamil Nadu
TAMIL NADU
632001
India |
| Phone |
9443038848 |
| Fax |
|
| Email |
rikkisush@cmcvellore.ac.in |
|
Details of Contact Person
Public Query
|
| Name |
Sushil Mathew John |
| Designation |
Professor |
| Affiliation |
Christian Medical College Vellore |
| Address |
Low Cost Effective Care Unit
Schell Campus, Arani Road
Christian Medical College
Vellore. Tamil Nadu
TAMIL NADU
632001
India |
| Phone |
9443038848 |
| Fax |
|
| Email |
rikkisush@cmcvellore.ac.in |
|
|
Source of Monetary or Material Support
|
| Gates Foundation
500 Fifth Avenue North,
Seattle, WA
USA - 98109
|
|
|
Primary Sponsor
|
| Name |
Gates Foundation |
| Address |
P.O. Box 23350
Seattle, Washington 98102
USA |
| Type of Sponsor |
Other [Private, non-profit foundation] |
|
|
Details of Secondary Sponsor
|
|
|
Countries of Recruitment
|
India |
|
Sites of Study
|
| No of Sites = 1 |
| Name of Principal
Investigator |
Name of Site |
Site Address |
Phone/Fax/Email |
| Dr Sushil Mathew John |
Christian Medical College Vellore |
Principals Office
Christian Medical College
Vellore. 632002
Vellore
TAMIL NADU |
9443038848
rikkisush@cmcvellore.ac.in |
|
|
Details of Ethics Committee
|
| No of Ethics Committees= 1 |
| Name of Committee |
Approval Status |
| Institutional Review Board, Christian Medical College Vellore |
Approved |
|
|
Regulatory Clearance Status from DCGI
|
|
|
Health Condition / Problems Studied
|
| Health Type |
Condition |
| Patients |
(1) ICD-10 Condition: E11||Type 2 diabetes mellitus, |
|
|
Intervention / Comparator Agent
|
| Type |
Name |
Details |
| Intervention |
Nil |
Nil |
| Intervention |
Nil |
Nil |
| Intervention |
Nil |
Nil |
|
|
Inclusion Criteria
|
| Age From |
30.00 Year(s) |
| Age To |
99.00 Year(s) |
| Gender |
Both |
| Details |
Adult patients aged 30 years or above with diabetes and/or hypertension |
|
| ExclusionCriteria |
| Details |
Currently has a pacemaker implanted
Severe comorbid conditions – such as cancers, advanced liver or kidney disease, cognitive impairment
Pregnant
Type 1 Diabetes
|
|
|
Method of Generating Random Sequence
|
Not Applicable |
|
Method of Concealment
|
Not Applicable |
|
Blinding/Masking
|
Not Applicable |
|
Primary Outcome
|
| Outcome |
TimePoints |
| Based on population studies we anticipate detection of about 10% of the study cohort (approximately 500 subjects) to have cardiovascular disease conditions among those with risk factors of diabetes and/or hypertension to be predicted by the algorithm. |
This is an observational study and the time point is at the time of recruitment of the participant into the study only. There are no follow up time points. |
|
|
Secondary Outcome
|
| Outcome |
TimePoints |
| Nil |
NA |
|
|
Target Sample Size
|
Total Sample Size="5500"
Sample Size from India="5500"
Final Enrollment numbers achieved (Total)= "Applicable only for Completed/Terminated trials"
Final Enrollment numbers achieved (India)="Applicable only for Completed/Terminated trials" |
|
Phase of Trial
|
N/A |
|
Date of First Enrollment (India)
|
01/01/2026 |
| Date of Study Completion (India) |
Applicable only for Completed/Terminated trials |
| Date of First Enrollment (Global) |
Date Missing |
| Date of Study Completion (Global) |
Applicable only for Completed/Terminated trials |
|
Estimated Duration of Trial
|
Years="1"
Months="6"
Days="0" |
|
Recruitment Status of Trial (Global)
|
Not Applicable |
| Recruitment Status of Trial (India) |
Not Yet Recruiting |
|
Publication Details
|
N/A |
|
Individual Participant Data (IPD) Sharing Statement
|
Will individual participant data (IPD) be shared publicly (including data dictionaries)?
Response - NO
|
|
Brief Summary
|
Given the high burden of cardiovascular disease, and difficulties in accessing and providing diagnostic services for CVDs among the rising numbers of patients with diabetes and hypertension in India, an Artificial Intelligence point of care tool that can detect high risk cardiovascular conditions will be useful for primary care. The goal of the study is to develop machine learning model to detect common cardiovascular disease conditions such as coronary artery disease in populations with diabetes and/or hypertension. This would improve early detection and enable timely treatment, especially in populations with limited healthcare access. The study proposes to collect data from patients seeking care for diabetes and/or hypertension at various levels of healthcare facilities at Christian Medical College Vellore. Patients will undergo point-of-care tests like 1 to 12 lead mobile ECGs, that can be used in primary care setting, as well as gold-standard diagnostic measurements such as echocardiograms and 12 lead ECGs, all of which will be reviewed by cardiologists for diagnosis of abnormalities. Standard assessments like vitals and POC blood sugar will also be collected. Additionally, we will gather information on medical history, treatment history, and health related behaviours to provide a comprehensive representation of the patient, which will help with model development. Christian Medical College Vellore will manage, clean and anonymize the dataset, following which it will be shared with Indian Institute of Technology Madras for model development. |