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Development and Validation of an Early Warning Model for Sepsis-Associated AKI in Children Using Big Data and Machine Learning: A Cohort Study

Development and Validation of an Early Warning Model for Sepsis-Associated AKI in Children Using Big Data and Machine Learning: A Cohort Study

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600131387
Enrollment
Unknown
Registered
2026-09-01
Start date
2025-02-01
Completion date
Unknown
Last updated
2026-09-07

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

Conditions

sepsis-associated acute kidney injury

Interventions

Sponsors

Children's Hospital of Chongqing Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
No minimum to 18 Years

Inclusion criteria

Inclusion criteria: 1. Age 1 month to 18 years old (including 18 years old); 2. PICU management; 3. Diagnosis of sepsis or septic shock (the diagnostic criteria for sepsis refer to the 2024 Phoenix Sepsis Diagnostic Criteria).

Exclusion criteria

Exclusion criteria: 1. Primary glomerular disease; 2. Neoplastic diseases; 3. PICU management time is less than 24 hours; 4. The proportion of missing values of variables is greater than 10%.

Design outcomes

Primary

MeasureTime frame
Acute Kidney Injury;

Secondary

MeasureTime frame
Inhalation oxygen concentration;Transcutaneous oxygen saturation;Consciousness;Diastolic pressure;Systolic blood pressure;Mean arterial pressure;Breathe;Heart rate;28-day all-cause mortality after PICU admission;

Countries

China

Contacts

Public ContactShaojun Li

Children's Hospital of Chongqing Medical University

lishaojun1980@hotmail.com+86 23 63632085

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Sep 19, 2026