Development and Validation of a Nomogram for Predicting Acute Kidney Injury in Septic Patients

被引:0
|
作者
Zhao, Li [1 ]
Zhang, Tuo [2 ]
Li, Xunliang [3 ]
Chen, Li [1 ]
Zhou, Shenglin [1 ]
Meng, Zhaoli [1 ]
Fang, Wei [1 ]
Xu, Jianle [4 ]
Zhang, Jicheng [1 ]
Chen, Man [1 ,2 ]
机构
[1] Shandong First Med Univ, Dept Crit Care Med, Shandong Prov Hosp, 324 Jingwu Weiqi Rd, Jinan 250021, Shandong, Peoples R China
[2] Shandong Univ, Shandong Prov Hosp, Cheeloo Coll Med, Dept Crit Care Med, Jinan 250021, Peoples R China
[3] Shandong First Med Univ, Dept Intens Care Unit, Cent Hosp, Jinan 250013, Peoples R China
[4] Shandong First Med Univ, Shandong Prov Hosp, Dept Stat & Med Records Management, Jinan, Peoples R China
关键词
sepsis; acute kidney injury; neutrophil gelatinase-associated lipocalin; platelet-to-lymphocyte ratio; vasopressor use; nomogram; GELATINASE-ASSOCIATED LIPOCALIN; SEPSIS; BIOMARKER; SHOCK; DIAGNOSIS; PROGNOSIS; MORTALITY; DISEASE; MODEL;
D O I
10.2147/JIR.S470773
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
Purpose: Sepsis-associated acute kidney injury (S-AKI) is associated with increased morbidity and mortality. We aimed to develop a nomogram for predicting the risk of S-AKI patients. Patients and Methods: We collected data from septic patients admitted to the Provincial Hospital Affiliated with Shandong First Medical University from January 2019 to September 2022. Septic patients were divided into two groups based on the occurrence of AKI. A nomogram was developed by multiple logistic regression analyses. The performance of the nomogram was evaluated using C-statistics, calibration curves, and decision curve analysis (DCA). The validation cohort contained 70 patients between December 2022, and March 2023 in the same hospital. Results: 198 septic patients were enrolled in the training cohort. Multivariate logistic regression analysis showed that neutrophil gelatinase-associated lipocalin (NGAL), platelet-to-lymphocyte ratio (PLR), and vasopressor use were independent risk factors for S-AKI. A nomogram was developed based on these factors. C-statistics for the training and validation cohorts were respectively 0.873 (95% CI 0.825-0.921) and 0.826 (95% CI 0.727-0.924), indicating high prediction accuracy. The calibration curves showed good concordance. DCA revealed that the nomogram was of great clinical value. Conclusion: The nomogram presents early and effective prediction for the S-AKI patients, and provides optimal intervention to improve patient outcomes.
引用
收藏
页码:5653 / 5662
页数:10
相关论文
共 50 条
  • [21] Development and validation of a nomogram for postoperative severe acute kidney injury in acute type A aortic dissection
    Luo, Cong-Cong
    Zhong, Yong-Liang
    Qiao, Zhi-Yu
    Li, Cheng-Nan
    Liu, Yong-Min
    Zheng, Jun
    Sun, Li-Zhong
    Ge, Yi-Peng
    Zhu, Jun-Ming
    JOURNAL OF GERIATRIC CARDIOLOGY, 2022, 19 (10) : 734 - 742
  • [22] Predicting the risk of postoperative acute kidney injury: development and assessment of a novel predictive nomogram
    Wu, Yukun
    Chen, Junxing
    Luo, Cheng
    Chen, Lingwu
    Huang, Bin
    JOURNAL OF INTERNATIONAL MEDICAL RESEARCH, 2021, 49 (08)
  • [23] Dynamic nomogram for predicting acute kidney injury in patients with acute ischemic stroke: A retrospective study
    Zhu, Ganggui
    Fu, Zaixiang
    Jin, Taian
    Xu, Xiaohui
    Wei, Jie
    Cai, Lingxin
    Yu, Wenhua
    FRONTIERS IN NEUROLOGY, 2022, 13
  • [24] Development and validation of a nomogram to predict the risk of vancomycin-related acute kidney injury in critical care patients
    Bao, Peng
    Sun, Yuzhen
    Qiu, Peng
    Li, Xiaohui
    FRONTIERS IN PHARMACOLOGY, 2024, 15
  • [25] Dynamic nomogram for predicting acute kidney injury in patients with community-acquired pneumonia
    Chen, Dawei
    Zhao, Jing
    Ma, Mengqing
    Jiang, Lingling
    Tan, Yan
    Wan, Xin
    BMJ OPEN RESPIRATORY RESEARCH, 2023, 10 (01)
  • [26] A nomogram based on serum cystatin C for predicting acute kidney injury in patients with traumatic brain injury
    Wang, Ruo Ran
    He, Min
    Gui, Xiying
    Kang, Yan
    RENAL FAILURE, 2021, 43 (01) : 206 - 215
  • [27] Predicting recovery from acute kidney injury in critically ill patients: development and validation of a prediction model
    Itenov, Theis S.
    Berthelsen, Rasmus Ehrenfried
    Jensen, Jens-Ulrik
    Gerds, Thomas A.
    Pedersen, Lars M.
    Strange, Ditte
    Thormar, Katrin
    Loken, Jesper
    Andersen, Mads H.
    Tousi, Hamid
    Reiter, Nanna
    Lundgren, Jens D.
    Bestle, Morten H.
    CRITICAL CARE AND RESUSCITATION, 2018, 20 (01) : 54 - 60
  • [28] Development and validation of a risk stratification model for predicting the mortality of acute kidney injury in critical care patients
    Huang, Haofan
    Liu, Yong
    Wu, Ming
    Gao, Yi
    Yu, Xiaxia
    ANNALS OF TRANSLATIONAL MEDICINE, 2021, 9 (04)
  • [29] Development and validation of a model for predicting acute kidney injury after cardiac surgery in patients of advanced age
    Hu, Penghua
    Chen, Yuanhan
    Wu, Yanhua
    Song, Li
    Zhang, Li
    Li, Zhilian
    Fu, Lei
    Liu, Shuangxin
    Ye, Zhiming
    Shi, Wei
    Liang, Xinling
    JOURNAL OF CARDIAC SURGERY, 2021, 36 (03) : 806 - 814
  • [30] A nomogram incorporating functional and tubular damage biomarkers to predict the risk of acute kidney injury for septic patients
    Jianchao Ma
    Yujun Deng
    Haiyan Lao
    Xin Ouyang
    Silin Liang
    Yifan Wang
    Fen Yao
    Yiyu Deng
    Chunbo Chen
    BMC Nephrology, 22