Creating a Predictive Model of Renal Failure in Type 2 Diabetes Using Machine Learning with Artificial Intelligence

Creating a Predictive Model of Renal Failure in Type 2 Diabetes Using Machine Learning with Artificial Intelligence - Pred(o)minance study

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000045802

Enrollment

2533

Registered

2021-10-25

Start date

2021-11-01

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 2003 and March 2015, with an eGFR of at least 60 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
Deterioration of renal function (decrease in eGFR value from the beginning of observation to less than half)	—

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