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. 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.

Acute kidney injury

“…Some may be persuaded that other tools may help identify AKI early, including artificial intelligence (AI). The role of AI will undoubtedly influence the practice of medicine significantly in the future and recent meta-analyses of currently published models showed promising performance for early prediction of postoperative AKI [15]. However, a word of caution.…”

Comparing machine learning and logistic regression for acute kidney injury prediction in trauma patients: A retrospective observational study at a single tertiary medical center

“…[21] However, a simple decision tree model may have a low classification performance for other ML models, and it is difficult to process noisy datasets. [10,[21][22][23] To address these challenges, we used a dataset with no missing data, and feature selection was aided by the least absolute shrinkage, and selection operator regression method. [13] In the unbalanced data, the specificity or local accuracy of the majority class exceeded that of the minority class.…”

“…[16] This study provides additional evidence to the finding of previous studies that ML models outperform logistic regression in predicting AKI. [10,11,26] Although the XG Boost algorithm is known to exhibit better performance than other algorithms, it requires more time for model training. [25,27] Our study demonstrated a good classification performance with only hyperparameter tuning through Grid Search CV, without using XG Boost or gradient boosting tree algorithms.…”

“…[4,7,8] With recent advancements in computer technology, studies have reported that artificial intelligence (AI) can improve the performance of existing tests for diagnosing various diseases. [9][10][11] Machine learning (ML) is a type of AI involving a set of algorithms that are not explicitly programmed but instead, learn and improve through a set of predefined objectives. [11] Numerous AI algorithms have been developed to support predictive models that demonstrate improved accuracy compared to traditional multivariate regression models.…”

“…[11] Numerous AI algorithms have been developed to support predictive models that demonstrate improved accuracy compared to traditional multivariate regression models. [2,5,[10][11][12] Using logistic regression, we previously analyzed the risk factors of AKI in major patients with trauma with torso injuries. [3] We hypothesized that the prediction model using ML would have a better predictive performance in this patient population than the traditional logistic regression model.…”