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Predicting Acute Kidney Injury in Elderly Postoperative Patients with Machine Learning: Model Development and External Validation

Predicting Acute Kidney Injury in Elderly Postoperative Patients with Machine Learning: Model Development and External Validation

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400083229
Enrollment
Unknown
Registered
2024-04-18
Start date
2024-05-01
Completion date
Unknown
Last updated
2025-02-10

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

Conditions

acute kidney injury

Interventions

No AKI group:NA
AKI group:NA

Sponsors

The First Affiliated Hospital of Soochow University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
65 Years to 110 Years

Inclusion criteria

Inclusion criteria: 1. Age >=65 years old; 2. Surgery from January 1, 2013 to December 31, 2023; (3) receiving the first surgery;

Exclusion criteria

Exclusion criteria: 1. Trauma and emergency patients; 2. Acute kidney injury or end-stage renal disease before surgery; 3. Missing the necessary data for the diagnosis of AKI or missing 30% or more of the remaining data;

Design outcomes

Primary

MeasureTime frame
acute kidney injury;—

Secondary

MeasureTime frame
survival rate;—

Countries

China

Contacts

Public ContactJi Fuhai

The First Affiliated Hospital of Soochow University

jifuhai@suda.edu.cn+86 1656207331

Outcome results

None listed

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