Clinical trial NCT04517201: AI-Assisted Insulin Titration System on Inpatients Glucose Control
Official title: Study to Assess Artificial Intelligence-Assisted Insulin Titration System on Inpatients Glucose Control
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Type 2 Diabetes
Keywords: iNCDSS
T2D
AI
Lead sponsor: Shanghai Zhongshan Hospital
Sponsor class: OTHER
First posted: 2020-08-18
Enrollment: 120
Sex: ALL
Minimum age (years): 18
Maximum age (years): 99
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: TREATMENT

Brief summary: This is a multi-center, open-labeled, parallel group, randomized controlled trial to access the effect and safety of the Artificial Intelligence Assisted Insulin Titration System (iNCDSS) in patients with Type 2 Diabetes Mellitus.

Detailed description: As one of the most common treatments for diabetes inpatients, insulin regimens often vary due to different physicians. Since 2016, Zhongshan Hospital has set up the Internet-based glucose management system (iGMS) to monitor plasma glucose of diabetes patients, and further set up an Artificial Intelligence Assisted Insulin Titration System (iNCDSS) to recommend insulin regime in 2019. Previous single-center clinical trial (NCT04053959) have demonstrated the efficacy and safety of iNCDSS in glycemic management in patients with type 2 diabetes.

This multi-center study enrolls 120 patients with Type 2 Diabetes from 20 wards of Zhongshan Hospital who are on treatment with insulin for at least 3 months. They are randomly allocated into 2 groups at a ratio of 1:1 after screening for the inclusion and exclusion criteria. Patients in the Intervention group (AI group) receive insulin regimen set by iNCDSS and patients in Control group receive insulin regimen recommended by endocrinologists.

This study will be conducted in the Department of Endocrinology, Zhongshan Hospital,Fudan University and consist of a 7-day intervention period. Patient allocation will be stratified by HbA1c, BMI and previous total insulin doses. The primary endpoint is the fasting plasma glucose level after the 7-days trial period.

Eligibility criteria: Inclusion Criteria:

* Men or women aged 18-99 years old;
* Inpatients who had been diagnosed with type 2 diabetes;
* Subjects who are on treatment with insulin for at least 3 months;
* HbA1c: 7.0%-10.0%.

Exclusion Criteria:

* Patients who were diagnosed with T1D, gestational diabetes or other specific types of diabetes.
* Subjects with acute complications of diabetes such as ketoacidosis or hyperglycemic hyperosmolar state;
* Patients with severe cardiac, hepatic, renal or general diseases;
* Subjects that are, in the judgement of the investigator, unlikely to comply with the protocol.
* Absence of informed consent.

Primary outcomes:
measure: Fasting plasma glucose of T2D patients
timeFrame: After 7-day intervention
Secondary outcomes:
measure: Well-controlled rate of fasting blood glucose
timeFrame: During 7-day intervention
measure: Incidence of hypoglycemia
timeFrame: During 7-day intervention
measure: Total insulin dose
timeFrame: During 7-day intervention
Other outcomes:
Locations:
city: Shanghai
country: China
facility: Zhongshan Hospital
state: Shanghai
city: Shanghai
country: China
facility: Zhongshan Hospital, Fudan University
Study officials:
affiliation: Shanghai Zhongshan Hospital
name: Hua Bian
role: PRINCIPAL_INVESTIGATOR
References:
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citation: Wang T, Xu Y, Xu M, Wang W, Bi Y, Lu J, Dai M, Zhang D, Ding L, Xu B, Sun J, Zhao W, Jiang Y, Wang L, Li Y, Zhang M, Lai S, Wang L, Ning G. Awareness, treatment and control of cardiometabolic disorders in Chinese adults with diabetes: a national representative population study. Cardiovasc Diabetol. 2015 Feb 26;14:28. doi: 10.1186/s12933-015-0191-6.
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citation: Rawshani A, Rawshani A, Gudbjornsdottir S. Mortality and Cardiovascular Disease in Type 1 and Type 2 Diabetes. N Engl J Med. 2017 Jul 20;377(3):300-301. doi: 10.1056/NEJMc1706292. No abstract available.
citation: Contreras I, Vehi J. Artificial Intelligence for Diabetes Management and Decision Support: Literature Review. J Med Internet Res. 2018 May 30;20(5):e10775. doi: 10.2196/10775.