NCT06596785

Risk Factors and Deep Learning Model for CI-AKI

Investigation and Early Risk Prediction of Acute Kidney Injury Caused by Iodinated Contrast Agents: Development of CI-AKIDPi Model Based on Interpretable Deep Learning

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 2,000 (actual)
• Sponsor: Xiao Li，MD · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

Summary

Contrast-associated acute kidney injury (CI-AKI) is a sudden and significant decline in renal function resulting from the use of a contrast agent.

Detailed description

Contrast-associated acute kidney injury (CI-AKI) is a sudden and significant decline in renal function resulting from the use of a contrast agent. Despite the frequent use of iodinated contrast agents in medical investigations, predictive models for CI-AKI are scarce. This study aimed to construct and validate interpretable deep learning models for the early risk prediction of acute kidney injury (AKI) associated with iodinated contrast agents.

Conditions

• AKI - Acute Kidney Injury

Timeline

• Start date: 2024-06-01
• Primary completion: 2025-03-01
• Completion: 2025-06-01
• First posted: 2024-09-19
• Last updated: 2024-09-19

Locations

1 site across 1 country: China