Machine Learning Predict Renal Replacement Therapy After Cardiac Surgery

Chinese PLA General Hospital2,108 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

2,108

Conditions

Machine Learning Acute Kidney Injury Renal Replacement Therapy Prediction Models

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

Inclusion Criteria: * age over 18 years who underwent cardiac surgery Exclusion Criteria: * data miss greater than 10%

Locations (1)

Outcomes

Primary Outcomes

patients required renal replacement therapy

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

Time frame: 14 days