Use of AI in Cardiometabolic Risk Prediction in Asian Indians
The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details. |
ClinicalTrials.gov Identifier: NCT05939869 |
Recruitment Status :
Recruiting
First Posted : July 11, 2023
Last Update Posted : July 12, 2023
|
- Study Details
- Tabular View
- No Results Posted
- Disclaimer
- How to Read a Study Record
The Investigators are recruiting T2DM patients (n, 500) from Fortis-CDOC Hospital.
Patients' weight, BMI, lipid profile, liver and kidney function tests, EGG, glycemic parameters, blood pressure, etc. will be entered in MS Excel sheets and appropriate data coding will be performed. Additional information on sleep hygiene, self-perceived stress, environmental pollution, and socio-economic status (education, occupation, and family annual income) will be collected by phone interviews. The entered data will be filtered for outliers and missing data will be excluded from the final data sheet.
Johns Hopkins Team will perform the following:
- Mediation and moderation analysis,
- Machine Learning methods
- Deep Learning and Neural Networks to devise prediction models for different metrics, including diabetes, blood pressure, and lipid control.
- Traditional statistics like Propensity Score Matching and Multivariate Linear Regression
Data pre-processing The data pre-processing will be performed to standardize the variables and minimize the impact of non-normality. During this step, the raw data would be converted into appropriate transformations. Python and R programming will be used for AI and machine learning methods.
Data analysis Our research collaborators are well versed in techniques like multi-fold cross-validation, Synthetic Minority Oversampling Technique for Nominal and Continuous (SMOTE-NC), a widely used technique for balancing the observations only in the training dataset and not in the testing dataset, and hyper tuning of parameters. For our research, we would require a graphic processing unit (GPU) to perform high-quality and fast computing (especially important when analyzing large data sets through neural networks and machine learning). We have an understanding with ORACLE (a large software giant), for providing GPUs at no cost on a lease basis on the submission of a feasible proposal.
Key Milestones Expected
- During the initial three months of the study, the plan is to obtain all requisite permissions for data gathering from the Institutional Ethics Review Committees of the respective institutions. The research assistant would be recruited from FORTIS-CDOC Hospital.
- Over the next 12 months, there will be data tabulation and gathering
- The last 3-4 months will be allocated to data analysis, application of AI algorithms (using training and testing datasets), and reporting of the data (meetings and manuscripts)
Condition or disease | Intervention/treatment |
---|---|
Type2diabetes | Other: Medical and Clinical History |
Study Type : | Observational |
Estimated Enrollment : | 500 participants |
Observational Model: | Cohort |
Time Perspective: | Retrospective |
Official Title: | Use of Artificial Intelligence and Machine Learning in Cardiovascular Risk Prediction in Urban Asian Indians |
Actual Study Start Date : | July 1, 2023 |
Estimated Primary Completion Date : | July 31, 2024 |
Estimated Study Completion Date : | August 30, 2024 |
- Other: Medical and Clinical History
Retrospective and prospective analysis of patients data will be done.
- Risk of CVD Event [ Time Frame: 5 years ]How many T2DM patients will get MI or Stroke within next five years.
Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.
Ages Eligible for Study: | 25 Years to 80 Years (Adult, Older Adult) |
Sexes Eligible for Study: | All |
Accepts Healthy Volunteers: | Yes |
Sampling Method: | Non-Probability Sample |
Inclusion Criteria:
- T2DM
Exclusion Criteria:
- T1DM
- Genetic Diabetes
- Gestational Diabetes
- Terminal Illness (Cancer)
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT05939869
Contact: ANOOP MISRA, MD | 01149101222 | anoopmisra@gmail.com | |
Contact: IRSHAD AHMAD, M.Sc | 01149101222 | irshad.ahmad225@gmail.com |
India | |
Fortis Cdoc Hospital | Recruiting |
New Delhi, Delhi, India, 110048 | |
Contact: Anoop Misra, MD 01149101222 anoopmisra@gmail.com |
Responsible Party: | Dr Anoop Misra, Director, Diabetes Foundation, India |
ClinicalTrials.gov Identifier: | NCT05939869 |
Other Study ID Numbers: |
AI |
First Posted: | July 11, 2023 Key Record Dates |
Last Update Posted: | July 12, 2023 |
Last Verified: | July 2023 |
Individual Participant Data (IPD) Sharing Statement: | |
Plan to Share IPD: | No |
Studies a U.S. FDA-regulated Drug Product: | No |
Studies a U.S. FDA-regulated Device Product: | No |
Type 2 Diabetes Asian Indian CVD |
Diabetes Mellitus, Type 2 Diabetes Mellitus Glucose Metabolism Disorders Metabolic Diseases Endocrine System Diseases |