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Machine-Learning Algorithm for Prediction of Blood Pressure, Glycated Haemoglobin and Estimated Glomerular Filtration Rate

NCT04814680 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 301

Last updated 2023-01-04

No results posted yet for this study

Summary

This is a non-interventional pilot study with the following objectives:

* Establish scalable methodology for collection of retinal images, blood pressure (BP) and laboratory-based assessments
* Compare the results of a machine-learning algorithm in predicting BP, glycated haemoglobin (HbA1c) and estimated glomerular filtration rate (eGFR) from digital retinal images with clinical and laboratory-based measures
* Determine the required sample size needed to support a future study to fully validate the machine-learning algorithm

Conditions

  • Prediction of Blood Pressure, Glycated Haemoglobin and Estimated Glomerular Filtration Rate From Digital Retinal Images

Sponsors & Collaborators

  • Iqvia Pty Ltd

    collaborator INDUSTRY

  • AstraZeneca

    lead INDUSTRY

Eligibility

Min Age
35 Years
Max Age
130 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-11-08
Primary Completion
2022-02-10
Completion
2022-02-10

Countries

  • Kenya

Study Locations

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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 NCT04814680 on ClinicalTrials.gov