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Clinical Decision Support Algorithm to Predict Diabetic Retinopathy

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ClinicalTrials.gov Identifier: NCT03769948
Recruitment Status : Not yet recruiting
First Posted : December 10, 2018
Last Update Posted : December 10, 2018
Sponsor:
Collaborators:
Oklahoma State University
University of Oklahoma
Information provided by (Responsible Party):
William Paiva, Oklahoma State University

Brief Summary:
Diabetic retinopathy (DR), a complication of diabetes, is a leading cause of blindness among working-aged adults globally. In its early stages, DR is symptomless, and can only be detected by an annual eye exam. Once the disease has progressed to the point where vision loss has occurred, the damage is irreversible. Consequently, early detection is quintessential in treating DR. Two barriers to early detection are poor patient compliance with the annual exam and lack of access to specialists in rural areas. This research is focused on developing and validating new, cost-effective predictive technologies that can improve early screening of DR. Our overall objective is to develop and implement an entire suite of tools to detect diabetes complications in order to augment care for underserved rural populations in the US and internationally.

Condition or disease Intervention/treatment
Diabetic Retinopathy Other: risk factors for diabetic retinopathy

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Study Type : Observational
Estimated Enrollment : 500 participants
Observational Model: Cohort
Time Perspective: Retrospective
Official Title: Validating a Clinical Decision Support Algorithm Developed With Demographic, Co-morbidity, and Lab Data to Diagnose, Stage, Prevent, and Monitor a Patient's Diabetic Retinopathy
Estimated Study Start Date : February 2019
Estimated Primary Completion Date : August 2019
Estimated Study Completion Date : July 2020

Resource links provided by the National Library of Medicine



Intervention Details:
  • Other: risk factors for diabetic retinopathy
    Demographic variables: gender, race, marital status, urban rural status. Co-morbidity variable: neuropathy, nephropathy, peripheral circulatory, ketoacidosis, hyperosmolarity Lab tests variables: alanine aminotransferase (ALT), albumin serum, anion gap, aspartate aminotransferase, blood urea nitrogen, calcium serum, chloride serum, creatinine serum, glucose serum plasma, hematocrit, hemoglobin, mean corpuscular hemoglobin concentration (MCHC), mean platelet volume (MPV), potassium serum, protein total serum, red blood cell (RBC) count, sodium serum, white blood cell (WBC) count


Primary Outcome Measures :
  1. Diabetic retinopathy indicator (yes/no) [ Time Frame: March, 2019 ]
    Diabetic patients with 362.0x ICD-9 codes are classified as DR patient



Information from the National Library of Medicine

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Ages Eligible for Study:   Child, Adult, Older Adult
Sexes Eligible for Study:   All
Sampling Method:   Probability Sample
Study Population
The cohort will be selected from diabetic patients that have Electronic Medical Records in Harold Hamm Diabetes Center.
Criteria

Inclusion Criteria:

For diabetic patients with DR:

  • With 250.xx diabetes and 362.0x DR ICD-9 codes
  • All variables are complete within the observation window

For diabetic patients without DR:

  • With 250.xx diabetes ICD-9 codes
  • Without 362.0x DR ICD-9 codes
  • All variables are complete within the observation window

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Responsible Party: William Paiva, Executive Director, Oklahoma State University
ClinicalTrials.gov Identifier: NCT03769948     History of Changes
Other Study ID Numbers: HR-18-087
First Posted: December 10, 2018    Key Record Dates
Last Update Posted: December 10, 2018
Last Verified: December 2018

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No

Additional relevant MeSH terms:
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Retinal Diseases
Diabetic Retinopathy
Eye Diseases
Diabetic Angiopathies
Vascular Diseases
Cardiovascular Diseases
Diabetes Complications
Diabetes Mellitus
Endocrine System Diseases