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

A Non-Interventional Pilot Study to Evaluate A Machine-Learning Algorithm for Prediction of Blood Pressure, Glycated Haemoglobin and Estimated Glomerular Filtration Rate From Digital Retinal Images

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

Inclusion Criteria:

• Participant must be ≥ 35 years of age, at the time of signing the informed consent.
• With or without DM (at least 50% of participants will have an HbA1c ≥ 6.5%* and/or a known diagnosis of DM)
• Male or female (at least 40% of participants must be male, and at least 40% must be female)
• Capable of giving signed informed consent as described in Appendix A

Exclusion Criteria:

• As judged by the investigator, any evidence of a medical condition which in the investigator's opinion makes it undesirable for the participant to participate in the study for example, but not limited to, if a participant is critically unwell or requiring ongoing emergency treatment
• Any known eye condition that may preclude clear imaging of the retina for example; cataracts
• Involvement in the planning and/or conduct of the study (applies to both AstraZeneca staff and/or staff at the study site).
• Judgment by the investigator that the participant should not participate in the study if the participant is unlikely to comply with study procedures, restrictions and requirements.
• Previous enrolment in the present study with the exception of roll-in participants who may be re-screened and re-enrolled.