Literature DB >> 31896444

Predicting mortality among critically ill patients with acute kidney injury treated with renal replacement therapy: Development and validation of new prediction models.

Daniel H Li1, Ron Wald2, Daniel Blum1, Eric McArthur3, Matthew T James4, Karen E A Burns5, Jan O Friedrich5, Neill K J Adhikari6, Danielle M Nash3, Gerald Lebovic7, Andrea K Harvey1, Stephanie N Dixon8, Samuel A Silver9, Sean M Bagshaw10, William Beaubien-Souligny11.   

Abstract

PURPOSE: Severe acute kidney injury (AKI) is associated with a significant risk of mortality and persistent renal replacement therapy (RRT) dependence. The objective of this study was to develop prediction models for mortality at 90-day and 1-year following RRT initiation in critically ill patients with AKI.
METHODS: All patients who commenced RRT in the intensive care unit for AKI at a tertiary care hospital between 2007 and 2014 constituted the development cohort. We evaluated the external validity of our mortality models using data from the multicentre OPTIMAL-AKI study.
RESULTS: The development cohort consisted of 594 patients, of whom 320(54%) died and 40 (15% of surviving patients) remained RRT-dependent at 90-day Eleven variables were included in the model to predict 90-day mortality (AUC:0.79, 95%CI:0.76-0.82). The performance of the 90-day mortality model declined upon validation in the OPTIMAL-AKI cohort (AUC:0.61, 95%CI:0.54-0.69) and showed modest calibration. Similar results were obtained for mortality model at 1-year.
CONCLUSIONS: Routinely collected variables at the time of RRT initiation have limited ability to predict mortality in critically ill patients with AKI who commence RRT.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Acute kidney injury; Dialysis; Prediction model; Renal recovery; Renal replacement therapy

Year:  2019        PMID: 31896444     DOI: 10.1016/j.jcrc.2019.12.015

Source DB:  PubMed          Journal:  J Crit Care        ISSN: 0883-9441            Impact factor:   3.425


  7 in total

1.  Development and validation of clinical prediction models for acute kidney injury recovery at hospital discharge in critically ill adults.

Authors:  Chao-Yuan Huang; Fabian Güiza; Greet De Vlieger; Pieter Wouters; Jan Gunst; Michael Casaer; Ilse Vanhorebeek; Inge Derese; Greet Van den Berghe; Geert Meyfroidt
Journal:  J Clin Monit Comput       Date:  2022-05-09       Impact factor: 2.502

2.  Interhospital Transfer and Outcomes in Patients with AKI: A Population-Based Cohort Study.

Authors:  Abhijat Kitchlu; Joshua Shapiro; Justin Slater; K Scott Brimble; Jade S Dirk; Nivethika Jeyakumar; Stephanie N Dixon; Amit X Garg; Ziv Harel; Andrea Harvey; S Joseph Kim; Samuel A Silver; Ron Wald
Journal:  Kidney360       Date:  2020-09-17

3.  Dissipating the Fog at the Crossroad: Predicting Survival after the Initiation of Kidney Replacement Therapy.

Authors:  Jean-Maxime Côté; William Beaubien-Souligny
Journal:  Kidney360       Date:  2022-03-25

4.  Application of interpretable machine learning for early prediction of prognosis in acute kidney injury.

Authors:  Chang Hu; Qing Tan; Qinran Zhang; Yiming Li; Fengyun Wang; Xiufen Zou; Zhiyong Peng
Journal:  Comput Struct Biotechnol J       Date:  2022-06-03       Impact factor: 6.155

5.  Association of Intradialytic Hypotension and Ultrafiltration with AKI-D Outcomes in the Outpatient Dialysis Setting.

Authors:  Emaad M Abdel-Rahman; Ernst Casimir; Genevieve R Lyons; Jennie Z Ma; Jitendra K Gautam
Journal:  J Clin Med       Date:  2022-06-01       Impact factor: 4.964

6.  Association between predialysis creatinine and mortality in acute kidney injury patients requiring dialysis.

Authors:  Hsin-Hsiung Chang; Chia-Lin Wu; Chun-Chieh Tsai; Ping-Fang Chiu
Journal:  PLoS One       Date:  2022-09-26       Impact factor: 3.752

7.  An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation.

Authors:  Yihan Zhang; Dong Yang; Zifeng Liu; Xiaodong Zhang; Shaoli Zhou; Ziqing Hei; Chaojin Chen; Mian Ge; Xiang Li; Tongsen Luo; Zhengdong Wu; Chenguang Shi; Bohan Wang; Xiaoshuai Huang
Journal:  J Transl Med       Date:  2021-07-28       Impact factor: 5.531

  7 in total

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