Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy

Hanfei Zhang; Amanda Y Wang; Shukun Wu; Johnathan Ngo; Yunlin Feng; Xin He; Yingfeng Zhang; Xingwei Wu; Daqing Hong

doi:10.1186/s12882-022-03025-w

Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy

BMC Nephrol. 2022 Dec 19;23(1):405. doi: 10.1186/s12882-022-03025-w.

Authors

Hanfei Zhang^#^{1

2}, Amanda Y Wang^#³, Shukun Wu^#^{1

2}, Johnathan Ngo⁴, Yunlin Feng^{1

2}, Xin He^{2

5}, Yingfeng Zhang¹, Xingwei Wu^{6

7

8}, Daqing Hong^{9

10

11}

Affiliations

¹ School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
² Department of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
³ The faculty of medicine and health sciences, Macquarie University, Sydney, NSW, Australia. Awang@georgeinstitute.org.au.
⁴ Concord Clinical School, University of Sydney, Sydney, Australia.
⁵ Department of Nephrology, Affiliated Hospital of Southwest Medical University, Luzhou, China.
⁶ School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. wuxw1998@126.com.
⁷ Department of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China. wuxw1998@126.com.
⁸ Department of Pharmacy, Sichuan Provincial Peoples Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. wuxw1998@126.com.
⁹ School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. hongdaqing11@126.com.
¹⁰ Department of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China. hongdaqing11@126.com.
¹¹ Renal Department and Nephrology Institute, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. hongdaqing11@126.com.

^# Contributed equally.

Abstract

Background: Acute kidney injury (AKI) is independently associated with morbidity and mortality in a wide range of surgical settings. Nowadays, with the increasing use of electronic health records (EHR), advances in patient information retrieval, and cost reduction in clinical informatics, artificial intelligence is increasingly being used to improve early recognition and management for perioperative AKI. However, there is no quantitative synthesis of the performance of these methods. We conducted this systematic review and meta-analysis to estimate the sensitivity and specificity of artificial intelligence for the prediction of acute kidney injury during the perioperative period.

Methods: Pubmed, Embase, and Cochrane Library were searched to 2nd October 2021. Studies presenting diagnostic performance of artificial intelligence in the early detection of perioperative acute kidney injury were included. True positives, false positives, true negatives and false negatives were pooled to collate specificity and sensitivity with 95% CIs and results were portrayed in forest plots. The risk of bias of eligible studies was assessed using the PROBAST tool.

Results: Nineteen studies involving 304,076 patients were included. Quantitative random-effects meta-analysis using the Rutter and Gatsonis hierarchical summary receiver operating characteristics (HSROC) model revealed pooled sensitivity, specificity, and diagnostic odds ratio of 0.77 (95% CI: 0.73 to 0.81),0.75 (95% CI: 0.71 to 0.80), and 10.7 (95% CI 8.5 to 13.5), respectively. Threshold effect was found to be the only source of heterogeneity, and there was no evidence of publication bias.

Conclusions: Our review demonstrates the promising performance of artificial intelligence for early prediction of perioperative AKI. The limitations of lacking external validation performance and being conducted only at a single center should be overcome.

Trial registration: This study was not registered with PROSPERO.

Keywords: Acute kidney failure; Acute kidney injury; Artificial intelligence; Machine learning; Perioperative period.

Publication types

Meta-Analysis
Systematic Review
Research Support, Non-U.S. Gov't

MeSH terms

Acute Kidney Injury* / diagnosis
Artificial Intelligence*
Diagnostic Tests, Routine
Humans
ROC Curve
Sensitivity and Specificity