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Choi S, You J, Kim YJ, Lee HC, Park HP, Park CK, Oh H. High Intraoperative Serum Lactate Level is Associated with Acute Kidney Injury after Brain Tumor Resection. J Neurosurg Anesthesiol 2024:00008506-990000000-00095. [PMID: 38291797 DOI: 10.1097/ana.0000000000000954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/03/2024] [Indexed: 02/01/2024]
Abstract
BACKGROUND Postoperative acute kidney injury (AKI) is associated with poor clinical outcomes. Identification of risk factors for postoperative AKI is clinically important. Serum lactate can increase in situations of inadequate oxygen delivery and is widely used to assess a patient's clinical course. We investigated the association between intraoperative serum lactate levels and AKI after brain tumor resection. METHODS Demographics, medical and surgical history, tumor characteristics, surgery, anesthesia, preoperative and intraoperative blood test results, and postoperative clinical outcomes were retrospectively collected from 4131 patients who had undergone brain tumor resection. Patients were divided into high (n=1078) and low (n=3053) lactate groups based on an intraoperative maximum serum lactate level of 3.35 mmol/L. After propensity score matching, 1005 patients were included per group. AKI was diagnosed using the Kidney Disease Improving Global Outcomes criteria, based on serum creatinine levels within 7 days after surgery. RESULTS Postoperative AKI was observed in 53 (1.3%) patients and was more frequent in those with high lactate both before (3.2% [n=35] vs. 0.6% [n=18]; P < 0.001) and after (3.3% [n=33] vs. 0.6% [n=6]; P < 0.001) propensity score matching. Intraoperative predictors of postoperative AKI were maximum serum lactate levels > 3.35 mmol/L (odds ratio [95% confidence interval], 3.57 [1.45-8.74], P = 0.005), minimum blood pH (odds ratio per 1 unit, 0.01 [0.00-0.24], P = 0.004), minimum hematocrit (odds ratio per 1%, 0.91 [0.84-1.00], P = 0.037), and mean serum glucose levels > 200 mg/dL (odds ratio, 6.22 [1.75-22.16], P = 0.005). CONCLUSION High intraoperative serum lactate levels were associated with AKI after brain tumor resection.
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Affiliation(s)
| | - Jiwon You
- Department of Anesthesiology and Pain Medicine
| | | | | | | | - Chul-Kee Park
- Department of Neurosurgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hyongmin Oh
- Department of Anesthesiology and Pain Medicine
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Zhang H, Wang AY, Wu S, Ngo J, Feng Y, He X, Zhang Y, Wu X, Hong D. 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; 23:405. [PMID: 36536317 PMCID: PMC9761969 DOI: 10.1186/s12882-022-03025-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
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.
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Affiliation(s)
- Hanfei Zhang
- grid.54549.390000 0004 0369 4060School of Medicine, University of Electronic Science and Technology of China, Chengdu, China ,grid.54549.390000 0004 0369 4060Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Amanda Y. Wang
- grid.1004.50000 0001 2158 5405The faculty of medicine and health sciences, Macquarie University, Sydney, NSW Australia
| | - Shukun Wu
- grid.54549.390000 0004 0369 4060School of Medicine, University of Electronic Science and Technology of China, Chengdu, China ,grid.54549.390000 0004 0369 4060Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Johnathan Ngo
- grid.1013.30000 0004 1936 834XConcord Clinical School, University of Sydney, Sydney, Australia
| | - Yunlin Feng
- grid.54549.390000 0004 0369 4060School of Medicine, University of Electronic Science and Technology of China, Chengdu, China ,grid.54549.390000 0004 0369 4060Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Xin He
- grid.54549.390000 0004 0369 4060Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China ,grid.488387.8Department of Nephrology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yingfeng Zhang
- grid.54549.390000 0004 0369 4060School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xingwei Wu
- grid.54549.390000 0004 0369 4060School of Medicine, University of Electronic Science and Technology of China, Chengdu, China ,grid.54549.390000 0004 0369 4060Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China ,grid.54549.390000 0004 0369 4060Department of Pharmacy, Sichuan Provincial Peoples Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Daqing Hong
- grid.54549.390000 0004 0369 4060School of Medicine, University of Electronic Science and Technology of China, Chengdu, China ,grid.54549.390000 0004 0369 4060Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China ,grid.54549.390000 0004 0369 4060Renal Department and Nephrology Institute, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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