Study design and ethical considerations
We performed a pilot, single-center, randomized controlled trial in a tertiary hospital in Melbourne, Australia. This study was approved by Austin Health Human Research Ethics Committee (HREC/59893/Austin-2019) and the trial was retrospectively registered on the 5th of July 2023 in the Australian New Zealand Clinical Trial Registry (ACTRN12623000726651). Informed consent was obtained from all patients during the first pre-operative visit. The study was conducted following the Declaration of Helsinki principles.
Study population
We screened all patients booked for upcoming general surgery from October 2020 to October 2022. We included all adult patients (≥ 18 years old) requiring a urinary catheter insertion as part of routine clinical care and who provided informed consent. We excluded patients with a baseline serum creatinine greater than 200 µmol/L or an estimated glomerular filtration rate according to the CKD-EPI method [28] below 30 ml/min/1.73m2, patients with acute kidney injury as defined by the KDIGO consensus criteria [22], pregnant women and patient with pre-anesthetics MAP greater than 100 mmHg or lower than 65 mmHg.
Sample size
In this pilot study, we aimed to include a convenience sample of 40 patients.
Intervention
Each surgery group (laparoscopic and open) was divided into a usual MAP and a high MAP arm. In the usual MAP arm, the anesthetic team targeted a MAP between 65 and 75 mmHg, while in the high MAP arm, they targeted a MAP within 10% of the patient's pre-operative baseline or above 75 mmHg, whichever was the greater number.
Randomization
Group allocation was determined by fixed block randomization of four with a 1:1 allocation generated by a web-based computer random sequence generator (https://www.sealedenvelope.com). The randomization was stratified according to the laparoscopic or open surgery group. The treatment allocation was placed in an opaque, sealed envelope by an independent party not involved in collecting or analyzing the data and given to the clinical anesthetist.
Continuous measurement of PuO2
The urine oximetry values were measured using a sterile fibre optic luminescence optode (NX-LAS-1/O/E-5 m, Oxford Optronix, Abingdon, UK), measuring PuO2 every 0.6 seconds. The data were then recorded on the OxyLite™ Pro device (Oxford Optronix, Abingdon, UK) (Supplemental Figure S1).
Outcomes
To assess the renal medullary stress during the procedure and whether it differed within the laparoscopic and open surgery groups, we studied the PuO2 mean difference between those two surgery types among patients allocated to the control group for MAP management. Then we studied the PuO2 slope and mean difference according to the intervention group (high vs. usual MAP management) in patients undergoing laparoscopic surgery. Finally, we evaluated if the same intervention (high MAP versus usual MAP management) was beneficial among patients who had open surgery.
Statistical analysis
Descriptive statistics were used to summarise patient characteristics for each group. Continuous variables were reported as median with interquartile range (IQR) or mean (SD) as appropriate and categorical variables as proportions. We fitted linear mixed-effect models to investigate the slope and mean PuO2 differences between the groups. The models included random intercepts for both time and patients to account for the repeated measures nature of the data. We studied only the first 150 minutes after incision, which represented the mean surgery duration. For the exploratory analyses, we divided the time scale, if necessary, for descriptive purposes. The difference in arterial blood pressure among the groups was also assessed by linear mixed models with the same random intercepts. The model assumptions, including linearity and normality of the residuals, were checked using diagnostics plots (Supplemental Figure S2). As a sensitivity analysis, we robustly estimated the difference in area under the curves (AUC) for PuO2 levels between the groups by a non-parametric bootstrapping procedure. This technique generated 10,000 bootstrap samples by randomly resampling our original data with replacement. For each sample, we calculated the AUC for both groups and the difference between these two values. This process generated a distribution of 10,000 bootstrapped differences in AUC from which we could determine the mean difference and construct a 95% confidence interval [29]. We calculated bias-corrected and accelerated (BCa) confidence intervals to account for any potential bias or skewness in the bootstrap distribution [30]. Statistical analyses were performed using R version 4.2.2 (R Foundation for Statistical Computing) with the packages: 'dplyr,' 'ggplot2,' 'ggpubr,' 'ggstatplot30,' 'boot,' 'nlme,' 'dygraphs' and 'tableone.'