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OpenTrials
Completed

NCT Number: NCT06146829

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

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.

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Key information

Conditions

Postoperative Acute Kidney Injury

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Rao Sun

Wuhan, Hubei, 430030, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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

Exclusion criteria

  • 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).

Treatment and study plan

No intervention

Other

no intervention

Primary outcomes

  1. Postoperative acute kidney injury

    Time frame: Within 7 days after surgery

    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.

Sponsors and collaborators

Lead sponsor

Rao Sun

Other

Registry information

Official study title

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

Important dates

Study start
2023
Primary completion
2023
Study completion
2023
First posted
Nov 27, 2023
Registry last updated
Apr 10, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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