Machine Learning Predict Renal Replacement Therapy After Cardiac Surgery

NCT04977687 | Completed

• 1 other identifier: chinaPLAGH-08983219
• Study Type: observational
• Target: 2,108
• Locations: 1 country
• Sites: 1

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.

Conditions

Machine Learning; Acute Kidney Injury; Renal Replacement Therapy; Prediction Models

Outcome Measures

Primary Outcomes (1)

• 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

  14 days

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

age over 18 years who underwent cardiac surgery

You may qualify if:

• age over 18 years who underwent cardiac surgery

You may not qualify if:

• data miss greater than 10%

Sponsors & Collaborators

• Chinese PLA General Hospital: lead

Study Sites (1)

Chinese PLA General hospital

Beijing, Beijing Municipality, 100853, China

Location

MeSH Terms

Conditions

Acute Kidney Injury

Condition Hierarchy (Ancestors)

Renal Insufficiency; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinical Professor

Study Record Dates

• First Submitted: July 16, 2021
• First Posted: July 27, 2021
• Study Start: September 1, 2020
• Primary Completion: January 1, 2021
• Study Completion: January 1, 2021
• Last Updated: August 2, 2021

Record last verified: 2021-07

Locations

CN (1)