이전|다음

심부전 환자의 중등도에서 중증 급성 신손상 예측 모델

  • role： Contribute equally, First author同等贡献者, 第一作者
  • Affiliation：

    Department of Nephrology, Guizhou Provincial People’s Hospital, Guiyang 550002, China

Yu-Qi Yang1,  
  • role： Contribute equally同等贡献者
  • Affiliation：

    Department of Nephrology, Guizhou Provincial People’s Hospital, Guiyang 550002, China

Jing-Jing Da1,  
  • role： Contribute equally同等贡献者
  • Affiliation：

    National Clinical Research Center for Kidney Disease, State Laboratory of Organ Failure Research, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China

Sheng Nie2,  
  • Affiliation：

    Department of Nephrology, Guizhou Provincial People’s Hospital, Guiyang 550002, China

Jing Yuan1,  
  • Affiliation：

    Institute of Nephrology, Zhongda Hospital, Southeast University School of Medicine, Nanjing 210000, China

Bi-Cheng Liu3,  
  • Affiliation：

    Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Institute of Nephrology, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524000, Guangdong, China

Hua-Feng Liu4,  
  • Affiliation：

    Department of Nephrology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510515, China

Qiong-Qiong Yang5,  
  • Affiliation：

    Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China

Hua Li6,  
  • Affiliation：

    Division of Nephrology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China

Gang Xu7,  
  • Affiliation：

    Department of Endocrinology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230000, China

Jian-Ping Weng8,  
  • Affiliation：

    Department of Nephrology, the First People’s Hospital of Foshan, Foshan 528000, Guangdong, China

Yao-Zhong Kong9,  
  • Affiliation：

    The Second People’s Hospital of Shenzhen, Shenzhen University, Shenzhen 518000, Guangdong, China

Qi-Jun Wan10,  
  • Affiliation：

    Department of Nephrology and Nephrology Institute, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610072, China

Gui-Sen Li11,  
  • Affiliation：

    Department of Critical Care Medicine, Maoming People’s Hospital, Maoming 525000, Guangdong, China

Chun-Bo Chen12,  
  • Affiliation：

    Children’s Hospital of Fudan University, Shanghai 200000, China

Hong Xu13,  
  • Affiliation：

    The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310000, China

Ying Hu14,  
  • Affiliation：

    Huizhou Municipal Central Hospital, Sun Yat-Sen University, Huizhou 516000, Guangdong, China

Yong-Jun Shi15,  
  • Affiliation：

    Department of Nephrology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100000, China

Yi-Lun Zhou16,  
  • Affiliation：

    Department of Nephrology, Guangdong Provincial Hospital of Chinese Medicine, the Second Affiliated Hospital, the Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510000, China

Guo-Bin Su17,  
  • Affiliation：

    The Third Affiliated Hospital of Southern Medical University, Guangzhou 510000, China

Ying Tang18,  
  • role： Corresponding author通信作者
  • Affiliation：

    National Clinical Research Center for Kidney Disease, State Laboratory of Organ Failure Research, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China

  • Email：ffhouguangzhou@163.com
  • Introduction：* ffhouguangzhou@163.com
Fan-Fan Hou2*,  
  • role： Corresponding author通信作者
  • Affiliation：

    Department of Nephrology, Guizhou Provincial People’s Hospital, Guiyang 550002, China

  • Email：zhayan72@126.com
  • Introduction：zhayan72@126.com
Yan Zha1*

추상적인

키워드

Acute kidney injury (AKI);heart failure;Prediction model;Machine learning

References

  1. 1..
    Damman K, Valente MAE, Voors AA, O’Connor CM, van Veldhuisen DJ, Hillege HL. Renal impairment, worsening renal function, and outcome in patients with heart failure: an updated meta-analysis. Eur Heart J. 2014;35(7):455–69.
  2. 2..
    Ronco C, Bellomo R, Kellum JA. Acute kidney injury. Lancet. 2019;394(10212):1949–64.
  3. 3..
    Yang L, Xing G, Wang L, Wu Y, Li S, Xu G, et al. Acute kidney injury in China: a cross-sectional survey. Lancet. 2015;386(10002):1465–71.
  4. 4..
    Hong C, Sun Z, Hao Y, Dong Z, Gu Z, Huang Z. Identifying patients with heart failure who are susceptible to de novo acute kidney injury: machine learning approach. JMIR Med Inform. 2022;10(10):e37484.
  5. 5..
    Liu WT, Liu XQ, Jiang TT, Wang MY, Huang Y, Huang YL, et al. Using a machine learning model to predict the development of acute kidney injury in patients with heart failure. Front Cardiovasc Med. 2022;9:911987.

더 읽기

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website