Machine-Learning Algorithm for Prediction of Blood Pressu... | NCT04814680

Status Module

Record Verification Date

Dec 2022

Overall Recruitment Status or Expanded Access Status

Completed

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Nov 8, 2021Actual

Primary Completion Date

Feb 10, 2022Actual

Completion Date

Feb 10, 2022Actual

First Submitted Date

Mar 23, 2021

First Submission Date that Met QC Criteria

Mar 23, 2021

First Posted Date

Mar 24, 2021Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Jan 3, 2023

Last Update Posted Date

Jan 4, 2023Actual

Name	Class
IQVIA Pty Ltd	INDUSTRY

Oversight Module

Has Data Monitoring Committee (DMC)

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

Brief 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

Detailed Description

Not provided

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

301Actual

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
To assess successful use of methodology for retinal image acquisition, BP (ABPM or manual), HbA1C (laboratory based or point of care test) and e-GFR measurement.	Proportion of participants with completed study procedures and interpretable results including both retinal images, systolic and diastolic BP (ABPM or manual), HbA1c (laboratory based or point of care test) and eGFR at Visit 1	up to 66 day

Secondary Outcomes

Not provided

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

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.

Accepts Healthy Volunteers

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

35 Years

Maximum Age

130 Years

Standard Ages

AdultOlder Adult

Study Population

Participants have to be more than 35 years, able to provide written consent and meet the following inclusion criteria:

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)

Sampling Method

Non-Probability Sample

Facility	Status	City	State	ZIP	Country	Contacts
Research Site		Nairobi		00100	Kenya
Research Site

References Module

Citations

PubMed Identifier	Type	Citation	Retractions
38618987	Derived	White T, Selvarajah V, Wolfhagen-Sand F, Svangard N, Mohankumar G, Fenici P, Rough K, Onyango N, Lyons K, Mack C, Nduba V, Noorali Saleh M, Abayo I, Siddiqui A, Majdanska-Strzalka M, Kaszubska K, Hegelund-Myrback T, Esterline R, Manzur A, Parker VER. Prediction of cardiovascular risk factors from retinal fundus photographs: Validation of a deep learning algorithm in a prospective non-interventional study in Kenya. Diabetes Obes Metab. 2024 Jul;26(7):2722-2731. doi: 10.1111/dom.15587. Epub 2024 Apr 15.

See Also Links

Label	URL
Redacted CSR synopsis	View source

Available IPD Information

Not provided

IPD Sharing Statement Module

Plan to Share IPD

Yes

Description

Qualified researchers can request access to anonymized individual patient-level data from AstraZeneca group of companies sponsored clinical trials via the request portal.

All request will be evaluated as per the AZ disclosure commitment:

https://astrazenecagrouptrials.pharmacm.com/ST/Submission/Disclosure.

Types

Not provided

Time Frame

AstraZeneca will meet or exceed data availability as per the commitments made to the EFPIA Pharma Data Sharing Principles. For details of our timelines, please rerefer to our disclosure commitment at https://astrazenecagrouptrials.pharmacm.com/ST/Submission/Disclosure.

Access Criteria

When a request has been approved AstraZeneca will provide access to the deidentified individual patient-level data in an approved sponsored tool . Signed Data Sharing Agreement (non-negotiable contract for data accessors) must be in place before accessing requested information. Additionally, all users will need to accept the terms and conditions of the SAS MSE to gain access. For additional details, please review the Disclosure Statements at https://astrazenecagrouptrials.pharmacm.com/ST/Submission/Disclosure.

URL

https://astrazenecagroup-dt.pharmacm.com/DT/Home