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Research on establishing dynamic decision-making model of acute kidney injury based on deep learning algorithm

Research on establishing dynamic decision-making model of acute kidney injury based on deep learning algorithm

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2200058407
Enrollment
Unknown
Registered
2022-04-08
Start date
2022-09-01
Completion date
Unknown
Last updated
2024-01-08

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

Conditions

Acute Kidney Injury

Interventions

None listed

Sponsors

Shanghai General Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. Patients were admitted to the ICU of the south hospital of Shanghai General Hospital from January 2017 to December 2021.

Exclusion criteria

Exclusion criteria: 1. The age at admission was less than 18 years old; 2. Lack of creatinine or urine volume information; 3. Lack of the information of fluid input and output and medication information; 4. Lack of necessary clinical data related to modeling.

Design outcomes

Primary

MeasureTime frame
28-day mortality;—

Secondary

MeasureTime frame
Progressed to AKI Phase III;Total hospital stay;ICU stay;—

Countries

China

Contacts

Public ContactDaonan Chen

Shanghai General Hospital

dnchen1994@163.com+86 17621226779

Outcome results

None listed

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