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.
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.
Study Type
OBSERVATIONAL
Enrollment
161,876
Peking University First Hospital
Beijing, Beijing Municipality, China
RECRUITINGPredictive performance of the prediction model for Acute Kidney Injury
Evaluated using AUC
Time frame: through study completion, varied from one to three years in different validation cohorts.
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