Acute kidney injury (AKI) in critically ill patients is characterized by high incidence, delayed diagnosis and treatment, and high mortality. Early identification and precision management are key to improving prognosis. Currently, in China, the population with severe AKI faces prominent challenges, including a lack of standardized, localized specialized data, insufficient early warning and subtyping capabilities, and a shortage of high-quality evidence-based guidance for clinical decision-making. These issues constrain the application of artificial intelligence (AI) technologies in the precision diagnosis and treatment of AKI. Leveraging the Critical Care Medicine Specialty Alliance, which has been approved by the Beijing Hospital Management Center and consists of 19 tertiary hospital ICUs nationwide, this project will conduct a three-year prospective, observational registry study. The investigators plan to consecutively enroll 23,600 adult critically ill patients (with an anticipated \>3,000 AKI patients). The study will systematically collect clinical characteristics, time-series monitoring data, laboratory parameters, renal ultrasound imaging, biomarkers, and omics data, while concurrently retaining biological samples, to establish the largest multi-modal specialized disease dataset and biobank for severe AKI in China. Focusing on the entire AKI continuum of "early warning - diagnosis - phenotyping - treatment - prognosis," the study aims to: ① characterize the epidemiological features and disease burden of ICU-AKI in China; ② develop an early warning system for AKI; ③ identify AKI sub-phenotypes using machine learning and establish a precision management framework; ④ develop an intelligent decision support system for renal replacement therapy; ⑤ evaluate prognosis; and ⑥ promote medical-engineering collaborative translation. Expected outcomes include 3-5 early warning/prognostic models and one intelligent decision support system, along with applications for 3-5 invention patents and 2-3 software copyrights. The project aims to translate at least one outcome into practical application, provide high-level evidence-based support for developing national guidelines on severe AKI management tailored to China's context, and contribute to reducing the incidence and mortality of AKI.
Study Type
OBSERVATIONAL
Enrollment
23,600
Save the blood and urine samples
Beijing Chao Yang Hospital
Beijing, China
Incidence of acute kidney injury during ICU admission
Time frame: During ICU admission, assessed up to 1 year
Rate of complete renal recovery
Time frame: 7 days after AKI diagnosis
ICU length of stay (ICU LOS) in AKI patients
Time frame: Assessed at ICU discharge, up to 1 year
Total hospital length of stay (Total hospital LOS)
Time frame: Assessed at hospital discharge, up to 1 year
In-hospital survival rate
Time frame: Assessed at hospital discharge, up to 1 year
28-day survival rate
Time frame: 28 days after AKI diagnosis
RRT duration
Time frame: Assessed at RRT cessation or ICU discharge, up to 90 days
Rate of RRT dependence
Time frame: Rate of RRT dependence at 28 days after AKI diagnosis
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