Scholarlink

<p>Radiocontrast media is a major cause of nephrotoxic acute kidney injury(AKI). Contrast-enhanced CT(CE-CT) is commonly performed in emergency departments(ED). Predicting individualized risks of contrast-associated AKI(CA-AKI) in ED patients is challenging due to a narrow time window and rapid patient turnover. We aimed to develop machine-learning(ML) models to predict CA-AKI in ED patients. Adult ED patients who underwent CE-CT between 2016 and 2020 at an academic, tertiary, referral hospital were included. Demographic, clinical, and laboratory data were collected from electronic medical records. Five ML models based on logistic regression; random forest; extreme gradient boosting; light gradient boosting; and multilayer perceptron were developed, using 42 features. Among 22,984 ED patients who underwent CE-CT; 1,862(8.1%) developed CA-AKI. The LGB model performed the best (AUROC = 0.731). Its top 10 features, in order of importance for predicting CA-AKI, were baseline serum creatinine; systolic blood pressure; serum albumin; estimated glomerular filtration rate; blood urea nitrogen; body weight; serum uric acid; hemoglobin; triglyceride; and body temperature. Given the difficulty of predicting risk of CA-AKI in ED, this model can help clinicians with early recognition of AKI and nephroprotective point-of-care interventions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Prediction of contrast-associated acute kidney injury with machine-learning in patients undergoing contrast-enhanced computed tomography in emergency department

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月27日

查看全文
  • Kyungho Lee,
  • Weon Jung,
  • Junseok Jeon,
  • Hansol Chang,
  • Jung Eun Lee,
  • Wooseong Huh,
  • Won Chul Cha,
  • Hye Ryoun Jang

摘要

Radiocontrast media is a major cause of nephrotoxic acute kidney injury(AKI). Contrast-enhanced CT(CE-CT) is commonly performed in emergency departments(ED). Predicting individualized risks of contrast-associated AKI(CA-AKI) in ED patients is challenging due to a narrow time window and rapid patient turnover. We aimed to develop machine-learning(ML) models to predict CA-AKI in ED patients. Adult ED patients who underwent CE-CT between 2016 and 2020 at an academic, tertiary, referral hospital were included. Demographic, clinical, and laboratory data were collected from electronic medical records. Five ML models based on logistic regression; random forest; extreme gradient boosting; light gradient boosting; and multilayer perceptron were developed, using 42 features. Among 22,984 ED patients who underwent CE-CT; 1,862(8.1%) developed CA-AKI. The LGB model performed the best (AUROC = 0.731). Its top 10 features, in order of importance for predicting CA-AKI, were baseline serum creatinine; systolic blood pressure; serum albumin; estimated glomerular filtration rate; blood urea nitrogen; body weight; serum uric acid; hemoglobin; triglyceride; and body temperature. Given the difficulty of predicting risk of CA-AKI in ED, this model can help clinicians with early recognition of AKI and nephroprotective point-of-care interventions.