Predicting Post-Cardiac Surgery Acute Kidney Disease: A Machine Learning Approach
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Acute Kidney Disease. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Development and Validation of a Machine Learning-Based Risk Prediction Model for Acute Kidney Disease After Cardiac Surgery
Overview
Renal injury after cardiac surgery is one of the common complications with high incidence rate, high risk of death and progression to chronic kidney disease (CKD). Previous evaluations of perioperative renal function mainly focused on acute kidney injury (AKI) related to cardiac surgery within seven days after surgery. The newly proposed concept of acute kidney disease (AKD) in recent years refers to acute or subacute kidney injury lasting seven to ninety days. Research has found that AKD can occur after AKI or in patients without AKI, and the two are both related and independent of each other, possibly indicating different subtypes of kidney injury. AKD is not uncommon and is a more significant predictor of mortality and end-stage kidney disease (ESKD). Therefore, AKD may be an important window for identifying and managing high-risk patients after cardiac surgery. Due to limited research on AKD after cardiac surgery, the risk factors for AKD are currently unclear, and there are no clinically practical and effective risk stratification tools available. This study aims to establish a multimodal perioperative data platform through a retrospective cohort, and use machine learning methods to construct a risk prediction model for AKD after cardiac surgery. The accuracy and stability of the model will be validated in a prospective study cohort, and an online risk prediction and clinical decision-making tool will be developed to help clinicians quickly conduct personalized risk assessments and optimize diagnosis and treatment strategies, thereby improving patient prognosis and reducing medical costs.
Primary outcome measures
- Number of Participants with acute kidney disease after cardiac surgery Assessed by KDIGO guideline [Time frame: within 90 days after cardiac surgery]
- acute kidney disease after cardiac surgery [Time frame: within 90 days after cardiac surgery]
Eligibility criteria
Inclusion criteria
- Age ≥ 18 years;
- Undergoing coronary artery bypass grafting and/or heart valve surgery, with or without aortic surgery;
- Baseline serum creatinine level < 354 umol/L;
- Informed consent obtained.
Exclusion criteria
- Emergency surgery;
- Multiple surgeries or reoperation;
- End-stage kidney disease (ESKD), renal replacement therapy, or kidney transplantation;
- Occurrence of AKI within 1 week before surgery or unresolved AKI;
- Death within 1 week after surgery.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- National Center for Cardiovascular Diseases — Beijing
Identifiers
NCT: NCT07482683 · 2025-I2M-C&T-B-027