Machine Learning Predict Renal Replacement Therapy After Cardiac Surgery
- Conditions
- Prediction ModelsMachine LearningAcute Kidney InjuryRenal Replacement Therapy
- Registration Number
- NCT04977687
- Lead Sponsor
- Chinese PLA General Hospital
- Brief Summary
Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication which may result in adverse impact on short- and long-term mortality. The researcher here developed several prediction models based on machine learning technique to allow early identification of patients who at the high risk of unfavorable kidney outcomes. The retrospective study comprised 2108 consecutive patients who underwent cardiac surgery from January 2017 to December 2020.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 2108
- age over 18 years who underwent cardiac surgery
- data miss greater than 10%
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method patients required renal replacement therapy 14 days The primary outcome was patients with the requirement for acute dialysis within 14 days after cardiac surgery. Renal replacement therapy is recommended for patients with severe acute kidney injury as well as hemodynamic instability or severe electrolyte disturbances (e.g. blood potassium \> 6) or acid-base balance disturbances (e.g. H value less than or equal to 7.15).
Prior to the start of renal replacement therapy, the investigator invited a consultation with the nephrology department to assess the condition
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
Chinese PLA General hospital
🇨🇳Beijing, Beijing, China