Machine Learning Predict Renal Replacement Therapy After Cardiac Surgery
Study Details
Study Description
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.
Condition or Disease | Intervention/Treatment | Phase |
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Study Design
Outcome Measures
Primary Outcome Measures
- 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
Eligibility Criteria
Criteria
Inclusion Criteria:
- age over 18 years who underwent cardiac surgery
Exclusion Criteria:
- data miss greater than 10%
Contacts and Locations
Locations
Site | City | State | Country | Postal Code | |
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1 | Chinese PLA General hospital | Beijing | Beijing | China | 100853 |
Sponsors and Collaborators
- Chinese PLA General Hospital
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- chinaPLAGH-08983219