Clinical trial NCT04240652: The Diabetic Retinopathy Screening, Prevention and Control Program
Official title: The Diabetic Retinopathy Screening, Prevention and Control Program
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Diabetic Retinopathy
Lead sponsor: Shanghai Jiao Tong University School of Medicine
Sponsor class: OTHER
First posted: 2020-01-27
Enrollment: 500000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Diabetes patients under the management of MMC, and diabetes, non-diabetic, and healthy participants from other medical institutes who meet the In-/Ex-clusion Criteria will be included in this study.

Brief summary: The greatest harm of diabetes is various acute and chronic complications, especially diabetic retinopathy(DR), leading to extremely high rates of disability and blindness. Early screening, early diagnosis, and early treatment are the keys to maintaining vision in patients with DR. However, compared with the high prevalence of diabetes in China, the DR screening ability is relatively inadequate. To change this situation, deep learning(DL), a form of artificial intelligence (AI), might be a potential effective method to solve this dilemma.

Detailed description: The greatest harm of diabetes is various acute and chronic complications, especially DR, leading to extremely high rates of disability and blindness. However, if the fundus examination is carried out regularly in the early stages of onset, the risk of blindness can be significantly reduced. Therefore, early screening, early diagnosis, and early treatment are the keys to maintaining vision in patients with DR. However, compared with the high prevalence of diabetes in China, the DR screening ability is relatively inadequate.

The Diabetic Retinopathy Screening and Prevention Program is a branch project of MMC. Its purpose is to carry out an efficient workflow for early detecting, timely managing of DR, and to establish a referral system for implementing treatment and the long-term follow-up of DR by means of DL. First, In order to improve its sensitivity and specificity, more participants are involved in other medical institutes besides MMCs, then we can effectively explore the prevalance of DR in China and helps to early screening, prevention, treatment and referal process of DR. Secend, we collect participants' serum, plasma,DNA, several medical stastistics and life styles to explore genetics, new biomarkers, risk factors of DR.

Objective:

1. To validate the methodology and feasibility of DR screening using a DL based automated DR grading system in clinical practice.
2. To explore the prevalence of DR and subgroup identification, and fundus images analysis, etc.
3. To explore the genetics, new biomarkers, risk factors of DR.
4. To explore the methods of early screening, prevention, treatment and referal process of DR.

Eligibility criteria: Inclusion Criteria:

* Meet the diagnostic criteria for type 2 diabetes according to the World Health Organization (WHO) in 1999; Type 1 diabetes, single gene mutation diabetes, secondary diabetes caused by pancreatic damage, Cushing's syndrome, thyroid dysfunction, or acromegaly;
* Subjects from other medical institutes are diabetes, non-diabetic patients and healthy participants who are invited to participate in the study.

Exclusion Criteria:

* Those who have a history of drug abuse;
* Sexually transmitted diseases such as AIDS and syphilis, and infectious diseases such as viral hepatitis and tuberculosis which are at active phase;
* Any condition that the investigator think that the subject is not suitable for participating in the study.

For detailed In-/Ex-clusion criteria please see the study protocol.

Primary outcomes:
measure: Diabetic retinopathy
timeFrame: through study completion, up to 20 years
measure: Referable diabetic retinopathy
timeFrame: through study completion, up to 20 years
measure: Vision threatening diabetic retinopathy
timeFrame: through study completion, up to 20 years
measure: Diabetic macular edema
timeFrame: through study completion, up to 20 years
Secondary outcomes:
measure: HbA1c (%)
timeFrame: through study completion, up to 20 years
measure: Smoking history
timeFrame: through study completion, up to 20 years
measure: Alcohol intake
timeFrame: through study completion, up to 20 years
measure: Salt intake
timeFrame: through study completion, up to 20 years
measure: Vegetable and fruits intake
timeFrame: through study completion, up to 20 years
measure: Physical activity
timeFrame: through study completion, up to 20 years
measure: Blood pressures (mmHg)
timeFrame: through study completion, up to 20 years
measure: Lipids (mg/dl)
timeFrame: through study completion, up to 20 years
measure: Cardiolvascular diseases
timeFrame: through study completion, up to 20 years
measure: Body mass index (BMI)
timeFrame: through study completion, up to 20 years
measure: Systolic blood pressure
timeFrame: through study completion, up to 20 years
measure: Diastolic blood pressure
timeFrame: through study completion, up to 20 years
measure: Visceral fat (cm^2)
timeFrame: through study completion, up to 20 years
measure: Fasting glucose (mmol/L)
timeFrame: through study completion, up to 20 years
measure: Postprandial glucose (mmol/L)
timeFrame: through study completion, up to 20 years
measure: Fasting serum C peptide (ug/L)
timeFrame: through study completion, up to 20 years
measure: Postprandial serum C peptide (ug/L)
timeFrame: through study completion, up to 20 years
measure: Fasting serum insuline (μIU/mL)
timeFrame: through study completion, up to 20 years
measure: Postprandial serum insuline (μIU/mL)
timeFrame: through study completion, up to 20 years
measure: Intimal medial thikness (mm)
timeFrame: through study completion, up to 20 years
measure: Pulse wave velocity (cm/s)
timeFrame: through study completion, up to 20 years
measure: Albumin-creatinine-ratio (mg/mmol)
timeFrame: through study completion, up to 20 years
Other outcomes:
Locations:
city: Shanghai
country: China
facility: Ruijin hospital, Shanghai Jiao-Tong University School of Medicine
state: Shanghai
city: Shanghai
country: China
facility: Shanghai Jiao-Tong University School of Medicine
Study officials:
affiliation: Shanghai Jiao Tong University School of Medicine Shanghai, Shanghai, China
name: Guang Ning, MD,PHD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Zhang Y, Shi J, Peng Y, Zhao Z, Zheng Q, Wang Z, Liu K, Jiao S, Qiu K, Zhou Z, Yan L, Zhao D, Jiang H, Dai Y, Su B, Gu P, Su H, Wan Q, Peng Y, Liu J, Hu L, Ke T, Chen L, Xu F, Dong Q, Terzopoulos D, Ning G, Xu X, Ding X, Wang W. Artificial intelligence-enabled screening for diabetic retinopathy: a real-world, multicenter and prospective study. BMJ Open Diabetes Res Care. 2020 Oct;8(1):e001596. doi: 10.1136/bmjdrc-2020-001596.