Preventing Perioperative Acute Kidney Injury

High Intraoperative Serum Lactate Level is Associated with Acute Kidney Injury after Brain Tumor Resection

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.

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

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.