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Use of AI in Cardiometabolic Risk Prediction in Asian Indians

NCT05939869 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 500

Last updated 2023-07-12

No results posted yet for this study

Summary

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:

1. Mediation and moderation analysis,
2. Machine Learning methods
3. Deep Learning and Neural Networks to devise prediction models for different metrics, including diabetes, blood pressure, and lipid control.
4. 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)

Conditions

  • Type2diabetes

Interventions

OTHER

Medical and Clinical History

Retrospective and prospective analysis of patients data will be done.

Sponsors & Collaborators

  • National Diabetes Obesity and Cholesterol Foundation

    collaborator OTHER

  • Johns Hopkins University

    collaborator OTHER

  • Diabetes Foundation, India

    lead OTHER

Eligibility

Min Age
25 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2023-07-01
Primary Completion
2024-07-31
Completion
2024-08-30

Countries

  • India

Study Locations

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Entities

Companies

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05939869 on ClinicalTrials.gov