The incidence of acute kidney injury (AKI) after major abdominal surgery ranges from 3.5%1 to 13.4%2 and is associated with morbidity and mortality.2,3 Although the mechanisms underlying the development of AKI are multifactorial,4,5 the toxic effects of the perioperative inflammatory response may play an important role.6–8 Although the inflammatory responses induced by surgery are important for maintaining postoperative homeostasis, severe dysregulation of the inflammatory process, such as complement and leukocytic cell activation producing inflammatory cytokines, may predispose to end-organ complications such as AKI.8,9
Propofol and sevoflurane, widely used general anesthetics, have been shown to modulate the inflammatory responses to surgical stimulations in both clinical and experimental studies.7,10–12 Previous reports have demonstrated that propofol reduces the lipopolysaccharide proinflammatory cytokine response and improves cell survival in an in vitro neuroinflammatory model.13 Sevoflurane, another anesthetic with anti-inflammatory action,14 suppresses local alveolar inflammatory response in patients undergoing thoracic surgery.10
The effects of propofol and sevoflurane on postoperative AKI are incompletely understood. In animal models of renal ischemia–reperfusion (IR), propofol reduces oxidative stress,11 and in a 2014 clinical study of AKI after cardiac surgery, propofol exerted a protective effect relative to sevoflurane.12 The effect of sevoflurane on renal function is less clear. In rat models, sevoflurane decreased AKI and reduced inflammatory responses during liver transplantation.15 However, older studies link sevoflurane to increased perioperative renal toxicity.10,11,16,17 It is not clear which anesthetic more effectively prevents postoperative AKI after major abdominal surgery.
The aim of the present study was to compare the incidence of AKI diagnosed by Acute Kidney Injury Network (AKIN) and risk, injury, failure, loss, and end-stage renal disease (RIFLE) criteria after colorectal surgery in patients anesthetized with propofol or sevoflurane and to identify the predictors of AKI in our patient population. We also evaluated the impact of AKI on short-term and long-term morbidity and mortality.
METHODS
Patient Population
We reviewed the electronic medical records and laboratory results of all patients who underwent colorectal surgery in Asan Medical Center between January 2008 and December 2011. A total of 4958 consecutive adult patients who had undergone colorectal surgeries were identified through our electrical medical recording system. Of these, 638 were excluded, including 39 patients with repeated operations, 96 patients with combined operations on parts other than large or small bowel, 11 patients anesthetized with anesthetics other than sevoflurane or propofol (1 with desflurane, 1 with isoflurane, and 9 with regional anesthesia), and 492 patients for whom preoperative or postoperative serum creatinine (sCr) values were not available. Thus, 4320 patients were included in the final analysis. Patients were divided into 2 groups according to the anesthetic type (propofol group: n = 3055 and the sevoflurane group: n = 1265). Both groups of patients received remifentanil as the primary intraoperative opioid. The study protocol was approved by the IRB of our institution (2012–0132), which waived the requirement for informed consent because of the retrospective design of the analysis.
Clinical Data
The computerized patient record system of our institution (Asan Medical Center Information System Electronic Medical Records) was reviewed retrospectively to obtain demographic, laboratory, surgical, and anesthetic data on all patients and their postoperative outcomes. Demographic data included patient age, sex, body mass index (BMI), smoking history, comorbidities (hypertension [HTN], diabetes mellitus [DM], ischemic heart disease, chronic obstructive pulmonary disease, cerebrovascular accident, and chronic kidney disease), and use of prescribed medications (calcium channel blockers, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, β-blockers, aspirin, antiplatelet agents, oral hypoglycemic agents, and 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitor). HTN was defined as the use of any antihypertensive medications at admission, DM as the use of any hypoglycemic agents, ischemic heart disease as positive coronary angiography or compatible electrocardiographic or perfusion scan findings, cerebrovascular accident as positive magnetic resonance imaging findings or neurologic sequelae, and chronic kidney disease as a baseline estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2. Chronic obstructive pulmonary disease was documented by certified pulmonologists.
Laboratory data included hemoglobin, sCr, albumin, serum uric acid, and eGFR. Serum albumin concentrations at baseline were measured by the use of the bromocresol green dye-binding method.18 eGFR was estimated from the preoperative sCr concentration with the Modification of Diet in Renal Disease study equation for adult patients and adjusted for each 1.73 m2 of body surface area.19 Surgical and anesthetic data included the type of surgical procedure (laparoscope or open), urgency of the operation, volume and type of fluids (crystalloid and colloid), volume of packed red blood cells, duration of anesthesia, lowest intraoperative mean blood pressure, anesthetic agents (sevoflurane and propofol), and the intraoperative use of furosemide. Colloid agents used during surgery included 10% hydroxyl ethyl starch 260/0.45 (Pentaspan™; Bristol-Myers Squibb, Montreal, Canada), 6% hydroxyl ethyl starch 130/0.4 (Voluven®; Fresenius Kabi, Bad Homburg, Germany), and albumin.
Definition of Outcomes
Outcome variables included postoperative AKI, postoperative intensive care unit (ICU) admission, 30-day major adverse cardiovascular events (MACE), 30-day mortality, and overall survival. Postoperative AKI was diagnosed by AKIN and RIFLE criteria via the alteration of the sCr concentration on postoperative 1 to 7 days compared with the baseline sCr concentration defined as the last concentration measured before surgery. If sCr was measured more than once per day within postoperative 7 days, the highest reading of that day was used. MACE included cardiovascular complications, including myocardial infarction, atrial fibrillation with rapid ventricular response, new onset or aggravating heart failure, and recurrent stroke, which occurred postoperatively.20 In-hospital mortality was determined by review of electrical medical records. For validation of complete follow-up data regarding mortality, information about the date of death was obtained from the National Population Registry of the Korea National Statistical Office using a unique personal identification number for each patient.
Statistical Analysis
Continuous variables were reported as mean ± SD or median and interquartile range (IQR). All continuous data were evaluated for normality using the Shapiro-Wilk test, and parametric and nonparametric tests were applied when appropriate for inferential statistics. Patient age, height, weight, BMI, laboratory data, amounts of administered packed red blood cells and fluids, urine output, lowest intraoperative mean blood pressure, and duration of anesthesia were compared with the Student t test. Categorical variables are expressed as frequencies and percentages and analyzed with the χ2 test or the Fisher exact test, as appropriate. Multiple logistic regression analysis was performed to identify independent predictors of AKI and to evaluate the impact of anesthetic type on outcome variables. All variables with P < 0.1 on univariate analysis were included in the multivariable analysis. Discrimination of the model was assessed by C statistics, and calibration was evaluated with the Hosmer-Lemeshow statistics.
Weighted logistic regression and multivariate Cox’s proportional hazard regression were performed to assess the adjusted odds ratios (ORs) and hazard ratios for the effect of anesthetic type on outcome variables. The proportional-hazards assumption for each variable was checked by Schoenfeld residuals and the double-log method. To reduce the influence of confounding variables, propensity score (PS) matching method was used to adjust intergroup differences between sevoflurane and propofol group. We calculated the PS for each patient by modeling the probability of being anesthetized with propofol or sevoflurane. We subsequently used the derived PS values to match 1265 patients in the sevoflurane group with the patients in propofol group at a ratio of 1:1 using a greedy matching algorithm.21 All variables shown in Table 1 without regard to outcomes were used to obtain the PS. After all PS matches were performed, we assessed the balance in baseline covariates through paired t tests/McNemar tests and standardized mean difference as appropriate for continuous and categorical variables.
Table 1.: Demographic, Preoperative, and Intraoperative Characteristics
All P values <0.05 were considered statistically significant. Data manipulation and statistical analyses were done using SAS® version 9.1 (SAS Institute Inc, Cary, NC) or R software version 2.10.1.
RESULTS
A total of 4320 patients were included in the final analysis. The median follow-up for the overall patient population was 3.1 (IQR, 2.2–3.9) years. The baseline characteristics of patients according to the anesthetic type are summarized in Table 1. Laboratory variables did not differ between the 2 groups, except that the eGFR was greater in the sevoflurane group. Before propensity matching, patients in the sevoflurane group were more likely to undergo emergent surgery, require diuretics during the operation, and have laparoscopic surgery than patients in the propofol group (Table 1). The balance between matched patients is summarized in Table 2.
Table 2.: Propensity Score–Matched Data
The demographic characteristics and the outcomes of patients who underwent emergent surgery are summarized in Supplementary Table S1 (Supplemental Digital Content, https://links.lww.com/AA/B405). The patients who underwent emergent surgery showed significantly greater ASA physical status, lower eGFR, serum albumin level, and lowest intraoperative mean blood pressure. In addition, they were anesthetized with sevoflurane more often, required more diuretics, fluid administration, and intraoperative pRBCs transfusion.
Overall, AKI occurred in 414 (9.6%) of patients using AKIN criteria. The prevalence of AKI was greater in the sevoflurane group (n = 142, 11.2%) than that in the propofol group (n = 272, 8.9%) (P = 0.02). When RIFLE criteria were used, AKI occurred in 251 patients (5.8%) overall. Again, the sevoflurane group had a greater incidence of AKI (n = 94, 7.4%) compared with the propofol group (n = 157, 5.1%) (P = 0.004). Multivariate analysis identified the following independent predictors of AKI: age (OR, 1.02; 95% confidence interval [CI], 1.01–1.03; P = 0.0078 by AKIN; OR, 1.01; 95% CI, 1.00–1.03; P = 0.01 by RIFLE), sevoflurane (OR, 1.29; 95% CI, 1.03–1.61; P = 0.03 by AKIN; OR, 1.44; 95% CI, 1.09–1.91; P = 0.02), emergent operation (OR, 1.89; 95% CI, 1.03–3.49; P = 0.04 by AKIN; OR, 3.52; 95% CI, 1.92–6.45; P = 0.0024 by RIFLE), diuretics (OR, 2.44; 95% CI, 1.15–5.17; P = 0.02 by AKIN; OR, 3.26; 95% CI, 1.45–7.30; P = 0.005 by RIFLE), preoperative albumin level (OR, 0.76; 95% CI, 0.63–0.92; P = 0.0042 by AKIN; OR, 0.74; 95% CI, 0.59–0.93; P = 0.0022 by RIFLE), and DM (OR, 1.53; 95% CI, 1.17–2.00; P = 0.0005 by AKIN; OR, 1.51; 95% CI, 1.08–2.11; P = 0.02 by RIFLE; Table 3; Supplemental Digital Content, Supplemental Table S3, https://links.lww.com/AA/B405). Moreover, HTN (by AKIN) and β-blocker (by RIFLE) were associated with AKI prediction.
Table 3.: Logistic Regression Analysis Identifying Predictors for Acute Kidney Injury
Univariate and multivariate predictors of overall mortality are shown in Supplementary Table S2 (Supplemental Digital Content, https://links.lww.com/AA/B405). In multivariate analysis, BMI, ASA physical status, emergent operation, anesthetic time, and DM were associated with overall mortality. However, AKI was not associated with overall mortality. Patients who received sevoflurane had longer hospital stays (median [IQR], 7 [6–9] days) than those who received propofol (median [IQR], 7 [6–8] days) (P < 0.01) and higher ICU admission rates (6.4% vs 4.7%, P = 0.025).
The unadjusted (univariate analysis) effect of anesthetics on the incidence of outcome variables is shown in Figure 1. Mortality within 30 postoperative days was higher in the sevoflurane group than that in the propofol group (P = 0.01), but the incidence of MACE (P = 0.07) and overall mortality (P = 0.14) did not differ between the 2 groups. The incidence of AKI according to the diagnosis and type of operation is summarized in Table 4. Most patients were diagnosed with colorectal cancer (n = 3843, 90%). The remainder was diagnosed with the Crohn disease, benign lesion, ileus, or ischemia.
Table 4.: The Incidence of Acute Kidney Injury According to the Diagnosis and Type of Operation
Figure 1.: The incidences of outcome variables in the propofol and sevoflurane group. The incidences of acute kidney injury (AKI) diagnosed by Acute Kidney Injury Network (AKIN) and risk, injury, failure, loss, and end-stage renal disease (RIFLE) criteria, intensive care unit (ICU) admission, major adverse cardiovascular event (MACE), and 30-day mortality were significantly higher in the sevoflurane group.
Table 5.: The Impact of Sevoflurane on Outcome Variables: AKIN, RIFLE, ICU Admission, 30-Day MACE, and 30-Day Mortality
The association between sevoflurane and postoperative outcomes is shown in Tables 5 and 6. Before propensity matching was performed, multivariate-adjusted analysis identified sevoflurane as the only predictor of postoperative AKI by AKIN (OR, 1.29; 95% CI, 1.03–1.61; P = 0.03) and RIFLE (OR, 1.44; 95% CI, 1.09–1.91; P = 0.02) criteria. After PS matching (n = 1249 pairs), the association was considerably weaker. AKI defined by RIFLE criteria remained weakly correlated (OR, 1.41; 95% CI, 1.02–1.95; P = 0.04) with sevoflurane use but not AKI by AKIN criteria (P = 0.06). However, differences in ICU admission, 30-day mortality, and overall mortality did not differ between the 2 groups (P = 0.41, P = 0.18, and P = 0.68, each respectively).
DISCUSSION
In this retrospective study, we explored the relationship between anesthetic agent and postoperative outcomes in patients who underwent colorectal surgery. Overall, we found a lower incidence of AKI in the propofol group according to AKIN (5.1%) and RIFLE (7.4%) criteria compared with sevoflurane group (8.9% and 11.2%, respectively). Multivariate analysis revealed that sevoflurane was a risk factor of postoperative AKI by AKIN (P = 0.03) and RIFLE (P = 0.02) criteria. After propensity matching to adjust for potential confounding factors, sevoflurane may be associated with postoperative AKI when the RIFLE criteria are used (P = 0.04) and not associated with AKI when AKIN criteria are used. Patients presenting for emergent surgery were more likely to be anesthetized with sevoflurane, which was not associated with an increase in mortality or ICU admission.
Although postoperative AKI after cardiovascular surgery has been investigated extensively,22,23 only a few reports have discussed how AKI evolves after major noncardiac surgery.1,5,24 Known predictors of AKI include older age, male sex, hypoalbuminemia, active congestive heart failure, ascites, HTN, greater ASA physical status, emergency surgery, intraperitoneal surgery, renal insufficiency, and DM.4,5,25 Our multivariate analysis revealed similar risk factors, finding, in addition to sevoflurane versus propofol, that age, emergency operation, diuretics, preoperative albumin level, DM, HTN, and β-blockers were associated with AKI by AKIN and RIFLE criteria. To our knowledge, propofol and sevoflurane for postoperative AKI development have not been systematically compared after colorectal surgery.
Propofol (2,6-diisopropylphenol), which has antioxidant activity, is used widely for general anesthesia26 and inhibits the secretion of proinflammatory cytokines in septic animal models6,27–29 and during clinical sepsis.30 In a small 2014 study of cardiac surgery patients, propofol was associated with lower incidence of AKI than sevoflurane.12 In critically ill patients, propofol has also been associated with a lower risk of AKI than midazolam.31 The cause of AKI has been hypothesized to involve IR injury-induced or massive inflammation.12 One potential mechanism by which propofol might affect AKI incidence is by modulating the inflammatory response induced by IR injury and/or cardiopulmonary bypass and decreasing the concentration of inflammatory markers, such as renal myeloperoxidase, C-reactive protein, and proinflammatory cytokines, such as tumor necrosis factor-α, interleukin-1 and -6.11,12
In this study, the incidence of AKI was greater than that in previous studies of noncardiac surgeries,5,24 possibly because different AKI criteria were used. We also found that AKI incidence was greater in patients who had mechanical obstruction (24.1% and 16.1% by AKIN and RIFLE criteria, respectively) or ischemia (31.3% and 25.0% by AKIN and RIFLE criteria, respectively) of the bowel. Our results are consistent with animal data finding a relationship between bowel ischemia and renal function.32 Moreover, intestinal IR injury triggers multiorgan dysfunction and systemic inflammation.33,34 In addition, patients undergoing colorectal resection often receive mechanical bowel preparation,35 predisposing to volume depletion and, higher risk of AKI in patients who undergo colorectal surgery.36
In our study, after PS matching, sevoflurane was not associated with an increase in AKI using AKIN criteria (OR, 1.28; 95% CI, 0.99–1.67; P = 0.06) and only weakly associated with AKI using the RIFLE criteria (OR, 1.41; 95% CI, 1.02–1.95; P = 0.04). This discrepancy may be because AKIN criteria use a smaller change in sCr (0.3 mg/dL versus a percentage increase in sCr ≥ 50%) over a shorter time window (within 48 hours) than RIFLE criteria (within 7 days).37,38 A delayed increase in creatinine may thus trigger RIFLE, but not AKIN, metrics. Other studies have found discrepancies between AKIN and RIFLE in AKI incidence.
Another potential confounder may have been the greater use of sevoflurane for emergency surgery. Emergent operation is a strong predictor of postoperative AKI,24,25 and in our study, emergency patients had a higher ASA physical status, lower serum albumin level, required more diuretics, fluid administration, intraoperative pRBCs transfusion, and low intraoperative mean blood pressure; all risk factors of AKI.24 In addition, emergent patients were more frequently anesthetized with sevoflurane. Also, because the incidence of overall mortality of our study was low (1.9 %), a larger sample size may have better been able to detect the difference between 2 groups.
Our study has several limitations. First, because of its retrospective design, we could not control for all confounding parameters that might have affected our results. Although we performed PS analysis to control for selection bias, we could not entirely remove residual confounding. Second, because we enrolled patients with colorectal surgery, our results cannot be applied to other patient types, and care should be taken when interpreting our data.
Table 6.: The Impact of Sevoflurane on Outcome Variable: Overall Mortality
In conclusion, when propensity matching is used, sevoflurane may be associated with a modest increase in the incidence of AKI when the RIFLE criteria but not AKIN criteria are used. Sevoflurane was not associated with an increase in mortality or ICU admission, and patients presenting for emergent surgery were at greater risk for AKI and more likely to be anesthetized with sevoflurane. Further large, multicenter, prospective trials are needed to establish such an association. E
DISCLOSURES
Name: Ji-Yeon Bang, MD, PhD.
Contribution: This author helped conduct the study, analyze the data, and write the manuscript.
Name: JungBok Lee, PhD.
Contribution: This author helped analyze the data and Interpretation of data.
Name: Jimi Oh, MD
Contribution: This author helped analyze the data.
Name: Jun-Gol Song, MD, PhD.
Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.
Name: Gyu-Sam Hwang, MD, PhD.
Contribution: This author helped design the study and analyze the data.
This manuscript was handled by: Avery Tung, MD.
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