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Um modelo preditivo para lesão renal aguda moderada a grave em pessoas com insuficiência cardíaca

  • 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*

Resumo

Palavras-chave

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.

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