Machine Learning Predict Acute Kidney Injury in Patients Following Cardiac Surgery

Using Machine Learning to Predict Acute Kidney Injury in Patients Following Cardiac Surgery

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT04966598

Enrollment

2108

Registered

2021-07-19

Start date

2020-09-01

Completion date

2021-01-01

Last updated

2021-07-22

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Acute Kidney Injury, Machine Learning

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 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.

Interventions

None listed

Study design

Observational model

OTHER

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Inclusion criteria

* age over 18 years who underwent cardiac surgery

Exclusion criteria

* data miss greater than 10%

Design outcomes

Primary

Measure	Time frame	Description
acute kidney injury	7 days	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.

Countries

China

Outcome results