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Development and Validation of a Real-time Prediction Model for Acute Kidney Injury in Hospitalized Patients

Recruiting
Conditions
Prediction Model
Registration Number
NCT06597838
Brief Summary

Early prediction of acute kidney injury (AKI) may provide a crucial opportunity for AKI prevention. To date, no prediction model targeting AKI among general hospitalized patients in developing countries has been published. We developed a simple, real-time, interpretable AKI prediction model for general hospitalized patients from a large tertiary hospital in China, and validated it across five independent, geographically distinct, different tiered hospitals.

Detailed Description

Early prediction of acute kidney injury (AKI) may provide a crucial opportunity for AKI prevention. To date, no prediction model targeting AKI among general hospitalized patients in developing countries has been published. We developed a simple, real-time, interpretable AKI prediction model for general hospitalized patients from a large tertiary hospital in China using the machine learning technique, and then validated the performance of the prediction model across five independent, geographically distinct, different tiered hospitals in China.

Recruitment & Eligibility

Status
RECRUITING
Sex
All
Target Recruitment
161876
Inclusion Criteria
  • Adult patients (18 years and older) admitted to five hospitals during the study period
Exclusion Criteria
  • Have less than 2 documented serum creatinine (Scr) measurements during hospitalization
  • Being diagnosed with end-stage renal disease (ESRD)
  • Maintained on dialysis or had an initial Scr greater than or equal to 4.0 mg/dL at admission
  • Developed AKI prior to admission or within 24 hours after admission
  • Length of stay shorter than 24 hours
  • Underwent kidney transplantation or nephrectomy during hospitalization
  • With all Scr measurements lower than or equal to 0.6 mg/dL from 90 days prior to admission until discharge.

Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Primary Outcome Measures
NameTimeMethod
Predictive performance of the prediction model for Acute Kidney Injurythrough study completion, varied from one to three years in different validation cohorts.

Evaluated using AUC

Secondary Outcome Measures
NameTimeMethod
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