Machine Learning Models for Prediction of Acute Kidney Injury After Noncardiac Surgery

NCT06146829 | Completed

• 1 other identifier: TJH-20230608C
• Study Type: observational
• Target: 88,367
• Locations: 1 country
• Sites: 1

Brief Summary

Acute kidney injury (AKI) is a common surgical complication characterized by a rapid decline in renal function. Patients with AKI are at an increased risk of developing chronic kidney disease and end-stage renal disease, which has been associated with an increased risk of morbidity, mortality and financial burdens. Identifying high-risk patients for postoperative AKI early can facilitate the development of preventive and therapeutic management strategies, and prediction models can be helpful in this regard. The goal of this retrospective study is to develop prediction models for postoperative AKI in noncardiac surgery using machine learning algorithms, and to simplify the models by including only preoperative variables or only important predictors.

Conditions

Postoperative Acute Kidney Injury

Outcome Measures

Primary Outcomes (1)

• Postoperative acute kidney injury

  In accordance with the KDIGO creatinine criteria: a serum creatinine increases of 26.5 mmol/L within 48 hours or 1.5 times baseline within 7 days after surgery.

  Within 7 days after surgery

Interventions

no intervention OTHER

no intervention

Eligibility Criteria

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

Study Population

Adult patients who underwent noncardiac surgical procedures at Tongji hospital between July 2018 and October 2022 were included.

You may qualify if:

• Adult patients (age ≥ 18 years) who had a serum creatinine measurement within 10 days before surgery and at least one measurement within 7 days after surgery.
• Eligible surgeries encompassed general, thoracic, orthopedic, obstetric, gynecology, and neurosurgery procedures lasting longer than 1 hour

You may not qualify if:

• Patients with concurrent cardiac, vascular, urological, or transplant surgeries.
• Patients with an American Society of Anesthesiologists (ASA) physical status V.
• Patients with end-stage renal disease (i.e., a glomerular filtration rate \[eGFR\] of 15 mL/min/1.73 m² or receiving hemodialysis).

Sponsors & Collaborators

• Rao Sun: lead

Study Sites (1)

Rao Sun

Wuhan, Hubei, 430030, China

Location

Related Publications (1)

• Sun R, Li S, Wei Y, Hu L, Xu Q, Zhan G, Yan X, He Y, Wang Y, Li X, Luo A, Zhou Z. Development of interpretable machine learning models for prediction of acute kidney injury after noncardiac surgery: a retrospective cohort study. Int J Surg. 2024 May 1;110(5):2950-2962. doi: 10.1097/JS9.0000000000001237.

  PMID: 38445452 DERIVED

Study Officials

• Rao Sun

  Tongji Hospital

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Associate chief physician

Study Record Dates

• First Submitted: November 19, 2023
• First Posted: November 27, 2023
• Study Start: November 27, 2023
• Primary Completion: December 15, 2023
• Study Completion: December 15, 2023
• Last Updated: April 10, 2024

Record last verified: 2024-04

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)