Artificial Intelligence for the prediction of Acute Kidney Injury during the perioperative period: Systematic Review and Meta-Analysis of Diagnostic Test Accuracy

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. Objective 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. Two independent evaluators extracted data. 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. Further studies should focus on the improvement of existing models, novel biomarkers, and clinical effectiveness.