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, ar...
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| Format: | Article |
| Language: | English |
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BMC
2022-12-01
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| Series: | BMC Nephrology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12882-022-03025-w |
| _version_ | 1797977578958487552 |
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| author | Hanfei Zhang Amanda Y. Wang Shukun Wu Johnathan Ngo Yunlin Feng Xin He Yingfeng Zhang Xingwei Wu Daqing Hong |
| author_facet | Hanfei Zhang Amanda Y. Wang Shukun Wu Johnathan Ngo Yunlin Feng Xin He Yingfeng Zhang Xingwei Wu Daqing Hong |
| author_sort | Hanfei Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T05:09:12Z |
| format | Article |
| id | doaj.art-f642ac913fc04910b1370f322acf4bfa |
| institution | Directory Open Access Journal |
| issn | 1471-2369 |
| language | English |
| last_indexed | 2024-04-11T05:09:12Z |
| publishDate | 2022-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Nephrology |
| spelling | doaj.art-f642ac913fc04910b1370f322acf4bfa2022-12-25T12:07:57ZengBMCBMC Nephrology1471-23692022-12-0123111310.1186/s12882-022-03025-wArtificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracyHanfei Zhang0Amanda Y. Wang1Shukun Wu2Johnathan Ngo3Yunlin Feng4Xin He5Yingfeng Zhang6Xingwei Wu7Daqing Hong8School of Medicine, University of Electronic Science and Technology of ChinaThe faculty of medicine and health sciences, Macquarie UniversitySchool of Medicine, University of Electronic Science and Technology of ChinaConcord Clinical School, University of SydneySchool of Medicine, University of Electronic Science and Technology of ChinaDepartment of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of ChinaSchool of Medicine, University of Electronic Science and Technology of ChinaSchool of Medicine, University of Electronic Science and Technology of ChinaSchool of Medicine, University of Electronic Science and Technology of ChinaAbstract 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.https://doi.org/10.1186/s12882-022-03025-wArtificial intelligenceMachine learningAcute kidney injuryAcute kidney failurePerioperative period |
| spellingShingle | Hanfei Zhang Amanda Y. Wang Shukun Wu Johnathan Ngo Yunlin Feng Xin He Yingfeng Zhang Xingwei Wu Daqing Hong Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy BMC Nephrology Artificial intelligence Machine learning Acute kidney injury Acute kidney failure Perioperative period |
| title | Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy |
| title_full | Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy |
| title_fullStr | Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy |
| title_full_unstemmed | Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy |
| title_short | Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy |
| title_sort | artificial intelligence for the prediction of acute kidney injury during the perioperative period systematic review and meta analysis of diagnostic test accuracy |
| topic | Artificial intelligence Machine learning Acute kidney injury Acute kidney failure Perioperative period |
| url | https://doi.org/10.1186/s12882-022-03025-w |
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