Intraoperative serum neutrophil gelatinase-associated lipocalin and lactate levels predict acute renal injury and early allograft dysfunction after liver transplantation

This study was reported according to the STROBE statement checklist of observational studies.Ten. We obtained approval for a retrospective cohort study from the Institutional Review Board of Seoul National University Hospital (H-2205-084-1324). We received a written informed consent waiver from the Board given the retrospective nature of the study. All methods were performed in accordance with relevant guidelines and regulations.

We reviewed an institutional electronic database of 450 consecutive patients who underwent deceased or living liver transplantation from January 2019 to April 2022 at a tertiary care university hospital. Patients with preoperative renal dysfunction (n = 24), patients who lacked baseline or outcome parameters (n = 24) = 21), reimplantation due to graft failure after previous transplantation (n = 4 ), and deceased donor transplants (n = 48) were excluded. The remaining 353 patients were included in the analysis.

We extracted demographic or perioperative data from an institutional electronic medical record database whose association with postoperative EAD and AKI after liver transplantation has been previously reported (Table 1).5, 6, 7, 8, 11, 12. Early allograft dysfunction was defined if one or more of the following were present within 7 days postoperatively: total bilirubin ≥10 mg/dL, prothrombin time:international normalized ratio ≥1.6.13. AKI, as determined by renal disease global outcome improvement criteria, maximum change in serum creatinine levels during the first 7 days after surgery (Stage 1: 1.5-1.9; Stage 2: 2-2.9; Stage 3: 2-2.9 or greater) was diagnosed according to 3-fold increase from baseline, or increase in serum creatinine to ≥4.0 mg/dL, or initiation of renal replacement therapy)14,15. The most recent serum creatinine value measured before surgery was collected as baseline.

Table 1 Patient characteristics and perioperative parameters for all patients (n = 353).

Serum lactate levels at the end of surgery were used because previous studies had reported prognostic values ​​at that time.3,4. Serum NGAL levels were measured on his two occasions during surgery at baseline and at the end of surgery. Because baseline NGAL levels were not significantly different between her patients with and without AKI or EAD in preliminary analyses, levels at the end of surgery were used in the analysis. The combination of lactate and NGAL was defined as lactate-adjusted NGAL, and EAD and AKI were calculated respectively according to the following formulas: It was calculated as the sum of each measure multiplied by the respective odds ratio for EAD or AKI calculated by multivariate logistic regression analysis.

$$ \begin{aligned} & {\text{LAD adjusted NGAL for EAD }} = { 1}. {41}*{\text{lactate }} + { 1}.0{3}*{\text{NGAL}} \\ & {\text{lactate adjusted NGAL of AKI }} = { 1}.0{ 2 }*{\text{lactic acid }} + { 1}. {27}*{\text{NGAL}} \\ \end{align} $$

statistical analysis

Before statistical analysis, the Shapiro-Wilk test was used to determine the normality of each continuous variable. Continuous data were reported as medians (25th and 75th percentiles) and compared by the Mann-Whitney method. U test. Incidence data were compared by or χ.2 Test or Fisher’s exact test depending on expected counts. Baseline characteristics or outcome data were missing in 4.5% of the records. We excluded these missing cases before the main statistical analysis. Baseline characteristics did not differ significantly between missing and missing parameters in preliminary analyses.

Below are the main analyzes of our study. First, we performed a binary multivariate logistic regression analysis to separately investigate the association between serum NGAL and lactate levels and the risk of postoperative EAD and AKI after liver transplantation. All covariates previously reported as risk factors for EAD and AKI were included. No variable selection process was used in the regression analysis. Calibration and discrimination of regression models were assessed by the Hosmer–Lemeshow goodness-of-fit test and the c-statistic, respectively.

We then compared the area under the receiver operating characteristic curve (AUC) for each logistic regression analysis to compare the diagnostic value of combinations of serum NGAL, lactate values, and clinical outcome lactate-adjusted NGAL. Compare AUCs of multivariate-adjusted regression models with and without NGAL, lactate, and lactate-adjusted NGAL levels to identify EAD or AKI by adding NGAL, lactate, or lactate-adjusted NGAL levels to the multivariate model I checked to see if it improved. DeLong’s method was used to compare different AUCs16. To determine meaningful cutoffs for serum lactate-adjusted NGAL, the Youden index, which maximizes the sum of sensitivity and specificity, was used for EAD and AKI, respectively.17.

Third, we plotted cubic spline curves to explore multivariate-adjusted relationships between serum NGAL, lactate and lactate-adjusted NGAL levels as continuous variables and the risk of EAD and AKI.

Fourth, propensity score matching was performed between the two lactate-adjusted NGAL groups to adjust for potential confounding effects of baseline patient characteristics and anesthesia- and surgery-related parameters. Matching was performed against his two lactate-adjusted NGAL groups of EAD and AKI, respectively. The following variables were used for matching: patient demographics, previous history of hypertension, diabetes, baseline laboratory values ​​including hemoglobin, serum albumin levels, model of end-stage liver disease (MELD) score, pediatric classification, previous history of abdominal surgery, baseline left ventricular ejection fraction, preoperative medication of β-blockers, diuretics, estimated graft-to-recipient weight ratio, operative time, cold versus warm ischemic time, intraoperative crystalloid and albumin doses, and estimated intraoperative blood loss. A caliper width of 0.2 standard deviation of logit-transformed propensity scores was used. We then compared clinical outcomes between the two corresponding groups.

Data were presented as median (interquartile range) or numbers (%). All P-values ​​were calculated with a two-sided hypothesis test and statistical significance was determined at the significance level of 0.05. Multiple comparisons were adjusted by Bonferroni correction. Stata 15.1 (StataCorp, College Station, TX, USA) was used for statistical analysis.

ethics statement

We obtained approval for a retrospective cohort study from the Institutional Review Board of Seoul National University Hospital (H-2205-084-1324). We received a written informed consent waiver from the Board given the retrospective nature of the study.

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