Creating a Predictive Model of Renal Failure in Type 2 Diabetes Including Patients Taking SGLT2 Inhibitors Using Machine Learning with Artificial Intelligence.

Creating a Predictive Model of Renal Failure in Type 2 Diabetes Including Patients Taking SGLT2 Inhibitors Using Machine Learning with Artificial Intelligence. - Creating a Predictive Model of Renal Failure in Type 2 Diabetes Including Patients Taking SGLT2 Inhibitors Using Machine Learning with Artificial Intelligence.

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000049178

Enrollment

24187

Registered

2022-10-25

Start date

2022-11-01

Completion date

Unknown

Last updated

2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Type 2 diabetes mellitus Diabetic kidney disease

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Patients who attended the outpatient clinic of the Department of Nephrology, Endocrinology and Metabolism of our hospital between January 2012 and March 2022, with an eGFR of at least 30 mL/min/1.73m2 at the time of the first visit and at least two eGFR measurements every six months for at least three years.

Exclusion criteria

Exclusion criteria: None in particular

Design outcomes

Primary

Measure	Time frame
50% reduction in eGFR from the mean value during the input period	—

Countries

Japan

Contacts

Public ContactShu Meguro

Keio University School of Medicine Division of Endocrinology, Metabolism and Nephrology, Department of Internal Medicine

shumeg@keio.jp03-3353-1211

Outcome results