Predicting acute kidney injury prognosis

被引:6
|
作者
Szerlip, Harold M. [1 ]
Chawla, Lakhmir S. [2 ]
机构
[1] Baylor Univ, Med Ctr, Dallas, TX USA
[2] Vet Affairs Med Ctr, Dept Med, 50 Irving St, Washington, DC 20037 USA
来源
关键词
acute kidney injury; biomarkers; clinical predictive models; risk stratification; CRITICALLY-ILL PATIENTS; ACUTE-RENAL-FAILURE; INTENSIVE-CARE-UNIT; LONG-TERM OUTCOMES; EXTERNAL VALIDATION; AKIN CRITERIA; MORTALITY; EPIDEMIOLOGY; DIALYSIS; MULTICENTER;
D O I
10.1097/MNH.0000000000000223
中图分类号
R5 [内科学]; R69 [泌尿科学(泌尿生殖系疾病)];
学科分类号
1002 ; 100201 ;
摘要
Purpose of review The incidence of acute kidney injury has been steadily increasing. The development of any degree of kidney injury is associated with worse outcomes. Therefore, the ability to risk stratify patients and to predict prognosis is essential to properly educate the patient and family, appropriately utilize healthcare resources, and provide therapeutic interventions that may improve outcomes. Recent findings Numerous biomarkers and clinical prediction models have been developed that improve our ability to predict which patients will progress to higher stages of chronic kidney disease, require dialysis, or survive. The integration of biomarkers in predictive models will likely provide the best information. Further investigation will be required to validate the utility of these tools. Summary Early risk stratification for acute kidney injury can aid clinical decision making. The use of various biomarkers and predictive clinical models will improve the ability to appropriately utilize resources and provide useful prognostic information.
引用
收藏
页码:226 / 231
页数:6
相关论文
共 50 条
  • [41] Predicting and preventing acute kidney injury after cardiac surgery
    Harel, Ziv
    Chan, Christopher T.
    CURRENT OPINION IN NEPHROLOGY AND HYPERTENSION, 2008, 17 (06): : 624 - 628
  • [42] Predicting acute kidney injury: do we need biomarkers?
    Chadwick, David R.
    Post, Frank A.
    AIDS, 2023, 37 (15) : 2419 - 2420
  • [43] Furosemide Stress Test in Predicting Acute Kidney Injury Outcomes
    Rajasekaran, Kishore K.
    Venkataraman, Ramesh
    INDIAN JOURNAL OF CRITICAL CARE MEDICINE, 2020, 24 : S100 - S101
  • [44] Predicting in-hospital outcomes of patients with acute kidney injury
    Changwei Wu
    Yun Zhang
    Sheng Nie
    Daqing Hong
    Jiajing Zhu
    Zhi Chen
    Bicheng Liu
    Huafeng Liu
    Qiongqiong Yang
    Hua Li
    Gang Xu
    Jianping Weng
    Yaozhong Kong
    Qijun Wan
    Yan Zha
    Chunbo Chen
    Hong Xu
    Ying Hu
    Yongjun Shi
    Yilun Zhou
    Guobin Su
    Ying Tang
    Mengchun Gong
    Li Wang
    Fanfan Hou
    Yongguo Liu
    Guisen Li
    Nature Communications, 14
  • [45] Acute Kidney Injury: Predicting 30-Day Readmissions
    Keyes, Michael C.
    Bieniek, Joanna
    Richey, Allison
    Seetan, Raed
    2018 17TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), 2018, : 1408 - 1412
  • [46] Predicting Acute Kidney Injury Following Mitral Valve Repair
    Chang, Chih-Hsiang
    Lee, Cheng-Chia
    Chen, Shao-Wei
    Fan, Pei-Chun
    Chen, Yung-Chang
    Chang, Su-Wei
    Chen, Tien-Hsing
    Wu, Victor Chien-Chia
    Lin, Pyng-Jing
    Tsai, Feng-Chun
    INTERNATIONAL JOURNAL OF MEDICAL SCIENCES, 2016, 13 (01): : 19 - 24
  • [47] Predicting in-hospital outcomes of patients with acute kidney injury
    Wu, Changwei
    Zhang, Yun
    Nie, Sheng
    Hong, Daqing
    Zhu, Jiajing
    Chen, Zhi
    Liu, Bicheng
    Liu, Huafeng
    Yang, Qiongqiong
    Li, Hua
    Xu, Gang
    Weng, Jianping
    Kong, Yaozhong
    Wan, Qijun
    Zha, Yan
    Chen, Chunbo
    Xu, Hong
    Hu, Ying
    Shi, Yongjun
    Zhou, Yilun
    Su, Guobin
    Tang, Ying
    Gong, Mengchun
    Wang, Li
    Hou, Fanfan
    Liu, Yongguo
    Li, Guisen
    NATURE COMMUNICATIONS, 2023, 14 (01)
  • [48] Commentary: Predicting acute kidney injury in pediatric cardiac surgery
    Roy, Nathalie
    JOURNAL OF THORACIC AND CARDIOVASCULAR SURGERY, 2019, 157 (06): : 2452 - 2453
  • [49] Comparing Machine Learning Algorithms for Predicting Acute Kidney Injury
    Parreco, Joshua
    Soe-Lin, Hahn
    Parks, Jonathan J.
    Byerly, Saskya
    Chatoor, Matthew
    Buicko, Jessica L.
    Namias, Nicholas
    Rattan, Rishi
    AMERICAN SURGEON, 2019, 85 (07) : 725 - 729