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Intensive care
Protocol
Prediction models for acute kidney injury in critically ill patients: a protocol for systematic review and critical appraisal
  1. Danqiong Wang1,
  2. Weiwen Zhang1,
  3. Jian Luo1,
  4. Honglong Fang1,
  5. Shanshan Jing1,
  6. Zubing Mei2
  1. 1Department of Critical Care Medicine, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People’s Hospital, Quzhou, China
  2. 2Department of Anorectal Surgery, Anorectal Disease Institute of Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
  1. Correspondence to Dr Danqiong Wang; danqiong.w{at}hotmail.com; Dr Zubing Mei; herrmayor{at}126.com

Abstract

Introduction Acute kidney injury (AKI) has high morbidity and mortality in intensive care units, which can lead to chronic kidney disease, more costs and longer hospital stay. Early identification of AKI is crucial for clinical intervention. Although various risk prediction models have been developed to identify AKI, the overall predictive performance varies widely across studies. Owing to the different disease scenarios and the small number of externally validated cohorts in different prediction models, the stability and applicability of these models for AKI in critically ill patients are controversial. Moreover, there are no current risk-classification tools that are standardised for prediction of AKI in critically ill patients. The purpose of this systematic review is to map and assess prediction models for AKI in critically ill patients based on a comprehensive literature review.

Methods and analysis A systematic review with meta-analysis is designed and will be conducted according to the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). Three databases including PubMed, Cochrane Library and EMBASE from inception through October 2020 will be searched to identify all studies describing development and/or external validation of original multivariable models for predicting AKI in critically ill patients. Random-effects meta-analyses for external validation studies will be performed to estimate the performance of each model. The restricted maximum likelihood estimation and the Hartung-Knapp-Sidik-Jonkman method under a random-effects model will be applied to estimate the summary C statistic and 95% CI. 95% prediction interval integrating the heterogeneity will also be calculated to pool C-statistics to predict a possible range of C-statistics of future validation studies. Two investigators will extract data independently using the CHARMS checklist. Study quality or risk of bias will be assessed using the Prediction Model Risk of Bias Assessment Tool.

Ethics and dissemination Ethical approval and patient informed consent are not required because all information will be abstracted from published literatures. We plan to share our results with clinicians and publish them in a general or critical care medicine peer-reviewed journal. We also plan to present our results at critical care international conferences.

OSF registration number 10.17605/OSF.IO/X25AT.

  • adult intensive & critical care
  • acute renal failure
  • risk management
  • intensive & critical care
http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjopen-2020-046274

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    Footnotes

    • DW and ZM contributed equally.

    • Contributors ZM and DW: full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. ZM and DW: study concept and design. DW, WZ, JL, HF, SJ and ZM: acquisition, analysis or interpretation of data. ZM and DW: drafting of the manuscript. DW, WZ, JL, HF, SJ and ZM: critical revision of the manuscript for important intellectual content. DW, WZ and ZM: statistical analysis. ZM and DW: administrative, technical or material support. All authors: study supervision.

    • Funding This study was supported by the Commonweal Project of Science and Technology Department of Zhejiang Province (grant no. LGF19H150002), the National Natural Science Foundation of China (grant no. 81774112) and a grant from Siming Scholars from Shuguang Hospital (grant no. SGXZ-201913).

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer reviewed.