Machine Learning Predict Acute Kidney Injury in Patients Following Cardiac Surgery

Yunlong Fan2,108 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 investigatorshere 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

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)

Chinese PLA General hospital

Beijing, Beijing Municipality, China

Outcomes

Primary Outcomes

acute kidney injury

postoperative AKI was defined according to KDIGO criteria during the first 7 days after operation. Postoperative AKI was defined as either at an increase of at least 50% within 7 days or 0.3 mg/dL elevation within 48 h compared with the reference serum creatinine level.

Time frame: 7 days

Data from ClinicalTrials.gov

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