Contrast-Induced Acute Kidney Injury in Lower Limb Percutaneous Transluminal Angioplasty: A Machine Learning Approach for Preoperative Risk Prediction.
Journal:
Annals of vascular surgery
PMID:
40081525
Abstract
BACKGROUND: Contrast-induced acute kidney injury (CI-AKI) is a common complication of lower limb percutaneous transluminal angioplasty (PTA). Common risk models are based on cardiology cohorts for percutaneous coronary intervention. They include a mix of preoperative and perioperative variables, but do not include important information such as inflammatory parameters and preoperative medications. None make use of machine learning. We aimed to develop an accurate preoperative risk model for CI-AKI in lower limb PTA using machine learning methods and comparing these with conventional logistic regression.
Authors
Keywords
Acute Kidney Injury
Aged
Angioplasty
Contrast Media
Decision Support Techniques
Female
Glomerular Filtration Rate
Humans
Lower Extremity
Machine Learning
Male
Middle Aged
Peripheral Arterial Disease
Predictive Value of Tests
Reproducibility of Results
Retrospective Studies
Risk Assessment
Risk Factors
Treatment Outcome