Lead Investigator: Linong Ji, Peking University People’s Hospital
Title of Proposal Research: Identify treatment responders in patients with type 2 diabetes using a machine learning based dynamic cardiovascular risk assessment tool (ML-CVD) in clinical trials
Vivli Data Request: 10038
Funding Source: The National Natural Science Foundation of China (T2341011). Beijing Nova Cross Program (Z211100002121169).
Potential Conflicts of Interest: None
Summary of the Proposed Research:
Type 2 diabetes is a common health concern characterised by high blood sugar levels. It affects over 500 million adults worldwide and leads to serious health problems related to the heart and kidneys. To manage the diabetes, new types of medicine, such as sodium/glucose cotransporter 2 inhibitors (SGLT2i) which helps the kidneys get rid of extra sugar through urine, and glucagon-like peptide-1 receptor agonists (GLP-1 RAs) which helps increase the hormone that controls blood sugar and reduce appetite have been used. Now clinical guidelines recommend special tools to figure out who might get events because of diabetes. The tools are designed to categorize patients into low, medium, or high risk, and the initiation of novel drugs is preferred for patients at high risk. However, these predictive tools weren’t always accurate enough for people with type 2 diabetes, especially for those already have heart or kidney diseases.
In our previous study, we created a new tool called ML-CVD (Machine Learning for CardioVascular Diseases). This tool used patient information and health updates over time to have a more precise prediction on the 5-year probability of heart or kidney disease than the traditional tools. It also helped to identify which patient benefit more from SGLT2i. But whether this model could predict the treatment effect of other drugs is unknown.
In this study, we aim to validate the ML-CVD model in controlled clinical trials. We would estimate the risk score calculated by ML-CVD model and test the C-index (a popular statistic often applied to models with binary or survival outcome variables) of the model on predicting the risk in each trial to know if it’s effective to predict risk for a broader patient group. Observing the change of the risk score, we would also identify those who might have more benefit from specific medications. The study could lead to better care for people with type 2 diabetes by providing more accurate risk assessments and identifying more beneficial treatments for each individual.
Requested Studies:
A Randomized, Double-blind, Event-driven, Placebo-controlled, Multicenter Study of the Effects of Canagliflozin on Renal and Cardiovascular Outcomes in Subjects With Type 2 Diabetes Mellitus and Diabetic Nephropathy
Data Contributor: Johnson & Johnson
Study ID: NCT02065791
Sponsor ID: CR103517
A Randomized, Multicenter, Double-Blind, Parallel, Placebo-Controlled Study of the Effects of JNJ-28431754 on Cardiovascular Outcomes in Adult Subjects With Type 2 Diabetes Mellitus
Data Contributor: Johnson & Johnson
Study ID: NCT01032629
Sponsor ID: CR016627
A Randomized, Multicenter, Double-Blind, Parallel, Placebo-Controlled Study of the Effects of Canagliflozin on Renal Endpoints in Adult Subjects With Type 2 Diabetes Mellitus
Data Contributor: Johnson & Johnson
Study ID: NCT01989754
Sponsor ID: CR102647
A Multicenter, Randomized, Double-Blind, Placebo-Controlled Study to Evaluate Cardiovascular Outcomes Following Treatment With Alogliptin in Addition to Standard of Care in Subjects With Type 2 Diabetes and Acute Coronary Syndrome
Data Contributor: Takeda
Study ID: NCT00968708
Sponsor ID: SYR-322_402
The Effect of Dulaglutide on Major Cardiovascular Events in Patients With Type 2 Diabetes: Researching Cardiovascular Events With a Weekly INcretin in Diabetes (REWIND)
Data Contributor: Lilly
Study ID: NCT01394952
Sponsor ID: 13438
A Randomized, Double-blind, Placebo-controlled, Parallel-group, Multicenter, Event-driven Phase 3 Study to Investigate Efficacy and Safety of Finerenone on the Reduction of Cardiovascular Morbidity and Mortality in Subjects With Type 2 Diabetes Mellitus and the Clinical Diagnosis of Diabetic Kidney Disease in Addition to Standard of Care.
Data Contributor: Bayer
Study ID: NCT02545049
Sponsor ID: 17530
A Randomized, Double-blind, Placebo-controlled, Parallel-group, Multicenter Study to Evaluate Cardiovascular Outcomes During Treatment With Lixisenatide in Type 2 Diabetic Patients After an Acute Coronary Syndrome
Data Contributor: Sanofi
Study ID: NCT01147250
Sponsor ID: EFC11319
A Randomized, Double-blind, Placebo-controlled, Parallel-group, Multicenter, Event-driven Phase 3 Study to Investigate the Safety and Efficacy of Finerenone, in Addition to Standard of Care, on the Progression of Kidney Disease in Subjects With Type 2 Diabetes Mellitus and the Clinical Diagnosis of Diabetic Kidney Disease
Data Contributor: Bayer
Study ID: NCT02540993
Sponsor ID: 16244