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Development and Validation of Prediction Models for Diagnosis and Prognosis of Renal Disease: Applications of Artificial Intelligence Based on Real World Data

Development and Validation of Prediction Models for Diagnosis and Prognosis of Renal Disease: Applications of Artificial Intelligence Based on Real World Data

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2000030070
Enrollment
Unknown
Registered
2020-02-22
Start date
2020-03-01
Completion date
Unknown
Last updated
2020-02-25

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

Conditions

Renal Disease

Interventions

Gold Standard:Pathological diagnosis and clinical outcome
Index test:Prediction&#32
for&#32
Diagnosis&#32
and&#32
of&#32
Disease

Sponsors

Peking University Shenzhen Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
14 Years to 90 Years

Inclusion criteria

Inclusion criteria: Patients with kidney diseases and in whom a kidney biopsy was performed at Peking University Shenzhen Hospital between May 2008 and December 2020 will be included in the present project.

Exclusion criteria

Exclusion criteria: Patients whose data on personal ID, inpatient identification, or renal histopathology were not available will be excluded.

Design outcomes

Primary

MeasureTime frame
accuracy;C-statistic;Goodness of fit;

Secondary

MeasureTime frame
sensitivity;specificity;positive predictive value;negative predict value;

Countries

China

Contacts

Public ContactXiaoyan Huang

Peking University Shenzhen Hospital

huangxiaoyan@pku.org.cn+86 0755-83923333-2568

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026