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Variations in the Risk of Acute Kidney Injury Across Intraabdominal Surgery Procedures

Kim, Minjae MD, MS*; Brady, Joanne E. PhD*†; Li, Guohua MD, DrPH*†

doi: 10.1213/ANE.0000000000000425
Critical Care, Trauma, and Resuscitation: Research Report

BACKGROUND: The literature on perioperative acute kidney injury (AKI) focuses mainly on cardiac and major vascular surgery. Among noncardiac general surgery procedures, intraabdominal general surgery has been identified as high risk for developing AKI, but variations in AKI risk and its impact on 30-day mortality among different types of abdominal surgeries are not well characterized.

METHODS: We used the American College of Surgeons National Surgical Quality Improvement Program (2005–2010) to identify patients in 15 intraabdominal general surgery procedure categories (n = 457,656). AKI was defined as an increase in the creatinine level of >2 mg/dL above baseline and/or dialysis. Relative risk regression modeling was used to assess the relative risks of AKI across the procedures. The relationships among surgical procedure, AKI, and 30-day mortality stratified by procedure type were assessed using relative risk regression.

RESULTS: The overall incidence of AKI among intraabdominal surgery patients was 1.1%, which varied from 0.2% in appendectomy and 0.3% in gastric bypass patients to 2.6% in small bowel resection and 3.5% in exploratory laparotomy patients. Of the patients who developed AKI, 31.3% died within 30 days, compared with 1.9% of those who did not develop AKI. After adjusting for comorbidities and operative factors, AKI was associated with a 3.5-fold increase in the risk of 30-day mortality (adjusted risk ratio, 3.51, 95% confidence interval [CI], 3.29–3.74). Among individual procedures, the estimated adjusted risk ratio of 30-day mortality associated with AKI ranged from 1.87 (95% CI, 1.62–2.17) in exploratory laparotomy to 31.6 (95% CI, 17.9–55.9) in gastric bypass.

CONCLUSIONS: The incidence of AKI and the impact of AKI on 30-day mortality vary markedly across procedures within intraabdominal general surgery. This highlights the importance of preoperative risk stratification and identifies procedure type as a significant risk factor for AKI and 30-day mortality.

Published ahead of print September 4, 2014.Supplemental Digital Content is available in the text.

From the *Department of Anesthesiology, Columbia University Medical Center, New York, New York; and Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York.

Published ahead of print September 4, 2014.

Accepted for publication July 17, 2014.

Funding: This work was supported through intramural sources.

The authors declare no conflicts of interest.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website.

Reprints will not be available from the authors.

Address correspondence to Minjae Kim, MD, MS, Department of Anesthesiology, Columbia University Medical Center, 622 West 168th St., PH 5, Suite 505C, New York, NY 10032. Address e-mail to:

The literature on perioperative acute kidney injury (AKI) has mainly focused on high-risk procedures such as cardiac or major vascular surgery,1,2 but the risk of AKI in other surgical populations is not well established. Among patients undergoing noncardiac general surgery, the prevalence of AKI has been reported to be approximately 1%,3 and those developing AKI have an 8-fold increase in 30-day mortality risk.4 Intraperitoneal surgery has been identified as a risk factor for AKI3,4 and cardiac complications,5 but this group, ranging from gastric bypass to small bowel resection, is a heterogeneous mix of procedures that each has a unique risk of perioperative AKI; differences in AKI risk among these procedures have not been previously examined. At least 4 million patients undergo open abdominal surgical procedures annually in the United States,6 and perioperative AKI significantly impacts morbidity and mortality,7,8 utilization of hospital resources, and higher costs.9 Thus, characterizing the risk of AKI in this large population of patients will allow physicians to better understand its epidemiology and identify those at high risk for developing AKI and associated sequelae.

In this study, we establish the variation in AKI risk among different intraabdominal general surgery procedures so that both patients and perioperative physicians may better estimate the risk of developing this devastating complication after a specific intraabdominal surgical procedure. In addition, we establish the differential risks of AKI on 30-day mortality so that the effects of AKI on operative mortality may be better understood, again in the context of the specific intraabdominal procedure. We used data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP), a large, multicenter database of surgical outcomes from hospitals throughout North America. A more thorough understanding of the relationships among specific intraabdominal surgical procedures, the development of AKI, and 30-day mortality will provide further insight into the role of AKI on adverse perioperative outcomes and may help guide efforts on prevention and treatment of AKI.

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The Columbia University Medical Center IRB (New York, NY) determined that this study was not subject to review because it did not require access to protected health information. The ACS-NSQIPa is a validated, prospectively collected national dataset aimed at improving surgical quality and outcomes.10 NSQIP data collection includes demographic characteristics, presurgical comorbidities, intraoperative variables, and 30-day postoperative morbidity and mortality data. All NSQIP data are carefully reviewed by each site’s surgical clinical reviewer, and centers not meeting specific criteria for quality are removed from the dataset. The systematic sampling process and criteria for maintaining the high quality of the NSQIP dataset have been described.11

Hospitals have the option to report only general and vascular surgery cases or cases from multiple specialties, including general, gynecologic, neurologic, orthopedic, otolaryngologic, plastic, cardiac, thoracic, urologic, and vascular,12 although the dataset consists primarily of data from general (71.8%) and vascular (11.6%) surgery.

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Patient Selection

We obtained the ACS-NSQIP participant use data files for the years 2005–2010, consisting of 1,334,886 records. Clinical Classifications Software for Services and Procedures (CCS-SP; Agency for Healthcare Research and Quality, Rockville, MD)b was used to classify procedures based on the primary Current Procedural Terminology (CPT; American Medical Association, Chicago, IL) code. The CCS-SP classifies CPT codes into 244 clinically meaningful categories that are mutually exclusive and can represent unique procedures or, for relatively infrequent procedures, classifications according to the body system involved in the procedure. Fifteen CCS-SP categories representing intraabdominal general surgery procedures were identified (Appendix 1), resulting in a sample of 630,542 records. Most categories refer to a specific type of procedure, but of the body system categories, other operating room (OR) gastrointestinal (GI) procedures consist mainly of hepatobiliary and pancreatic procedures, whereas other OR lower GI therapeutic procedures consist of intestinal and colorectal cases that do not fit a specific procedure category.

Because outpatient procedures had a low rate of AKI (0.05%), only inpatient procedures were included, reducing the sample to 466,177 records. Finally, patients were excluded if they had preoperative acute renal failure, defined by the dataset as “the clinical condition associated with rapid, steadily increasing azotemia (increase in BUN) and a rising creatinine of above 3 mg/dl … within 24 hours prior to surgery,” or dialysis, defined as “acute or chronic renal failure requiring treatment with peritoneal dialysis, hemodialysis, hemofiltration, hemodiafiltration, or ultrafiltration within 2 weeks prior to surgery,”12 resulting in a final sample of 457,656 records.

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Baseline Demographic and Operative Variables

Patient baseline demographic and operative variables were collected directly from the ACS-NSQIP dataset. The most recent creatinine (mg/dL) and hematocrit (%) values before surgery were obtained. Age was obtained, but for privacy concerns, the dataset reports patients who are ≥90 years old as being 90 years old. Race/ethnicity was categorized as Caucasian versus non-Caucasian. Body mass index (BMI) was calculated from height and weight data. The estimated glomerular filtration rate (eGFR, mL/min/1.73 m2)2 was calculated using the Modification of Diet in Renal Disease formula incorporating creatinine, sex, age, and race13 and categorized into 6 groups corresponding to the stages of chronic kidney disease (CKD)14: <15, 15–30, 30–60, 60–90, >90, or missing. The missing category for preoperative eGFR was included to account for the fact that not all patients require a full laboratory workup before surgery and that the choice to proceed with surgery without a creatinine measurement may be an important prognostic indicator in predicting their risk of developing AKI and 30-day mortality. The patient was identified as having a history of cancer if positive on at least 1 of the following criteria: 1) a history of disseminated cancer, 2) chemotherapy for malignancy within 30 days before procedure, 3) radiotherapy of malignancy within 90 days before procedure, or 4) a final International Classification of Diseases, Ninth Revision (ICD-9, Centers for Disease Control and Prevention, Hyattsville, MD) diagnosis code for neoplasm (ICD-9 range: 140–239). Data on other comorbidities and patient characteristics were collected directly from the dataset.

Intraoperative and postoperative variables were also collected from the dataset. The laparoscopic nature of the procedure was determined from the primary CPT code. Total operative time was obtained. The ACS-NSQIP changed its reporting for transfusion variables in 2010. Before 2010, the dataset reported separately the number of red blood cell units transfused intraoperatively and whether the patient received >4 units of red blood cells in the first 72 hours of the postoperative period. Beginning in 2010, transfusion of 1 or more units in the intraoperative period or the first 72 hours of the postoperative period is reported. Patients were identified as having had an intra/postoperative transfusion if any intra- or postoperative transfusion was reported in the dataset. A consequence of this change is that a patient who received no intraoperative blood transfusion but received 1–4 units in the first 72 hours after surgery will be coded as having had a perioperative transfusion in 2010 but coded as having received no perioperative transfusion in 2005–2009.

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Clinical End Points

Our outcome of interest was the development of AKI, defined by having 1 of the 2 renal end points identified in the dataset: 1) progressive renal insufficiency, defined as an increase in the creatinine level >2 mg/dL above the preoperative value; and/or 2) the need for dialysis in a patient who did not require dialysis before the operation, both occurring within 30 days of the operation. Postoperative creatinine and urine output data are not available, precluding the use of RIFLE15 or Acute Kidney Injury Network16 criteria to evaluate AKI. In addition, we noted whether the patient died within 30 days of his or her procedure.

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Statistical Analysis

The differences in preoperative patient characteristics and comorbidities among procedure categories were compared with one-way analysis of variance for continuous variables and the χ2 test for categorical variables. To calculate risk ratios (RRs) and 95% confidence intervals (CIs), relative risk regression was performed for all analyses using a modified Poisson model with robust error variance.17,18 When procedure type was used as a categorical variable in regression models, colorectal resection was used as the reference category because it contained the largest number of cases (n = 102,503).

First, we modeled the effects of the type of procedure on the risk of perioperative AKI, including clinically significant covariates that were identified in either prior studies3,4 or our own analyses. Of note, our model for AKI included only those variables known preoperatively, so operative time and perioperative transfusion were not included. However, we did include the laparoscopic variable in this model because the patient is usually aware if the laparoscopic approach will be used before the procedure, and conversions from open to laparoscopic procedures generally do not occur.

We then modeled the effects of the type of procedure and AKI on the risk of 30-day mortality using clinically important variables identified in our analyses. We first modeled AKI as the only postoperative complication and subsequently included other complications, including postoperative sepsis, myocardial infarction, cardiac arrest, mechanical ventilation, and pneumonia. To calculate an adjusted RR (aRR) for AKI on 30-day mortality for each procedure, multivariable analyses were performed and stratified by the procedure type. For each procedure category, we also calculated the predicted marginal risk of AKI and the predicted marginal risks of 30-day mortality with and without AKI. The predicted marginal risk corresponds to the predicted risk for an “average” patient at the mean value of each covariate in the model. The final multivariable regression models for AKI and 30-day mortality resulted in 4.5%–4.7% of the observations missing 1 or more covariates in the models.

The continuous variables age, BMI, hematocrit, and operative time were assessed to determine how they would optimally be entered into the regression models for each outcome, with a separate analysis for each variable–outcome pair. The variables were divided into deciles and plotted against the log risk of outcome to visualize the relationships. For both outcomes, age was modeled as a linear variable while hematocrit was categorized as ≤34, 34–44, >44, or missing. For the outcome of AKI, BMI was categorized as <29, 29–39, or >39. For the outcome of 30-day mortality, BMI and operative time were entered as linear variables.

The effects of missing data were assessed with multiple imputation using a sequential regression strategy19 with IVEware software (University of Michigan, Ann Arbor, MI). Complete case analyses were also performed. Statistical analyses were performed using SAS software version 9.4 (SAS Institute, Cary, NC). In all analyses, statistical significance was determined with a P < 0.05.

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Risk Factors for AKI Vary by Type of Procedure

The overall rate of AKI among the 457,656 cases of inpatient intraabdominal general surgery was 1.1%. Of those developing AKI, the number of cases diagnosed peaked at postoperative day 2 (12.1%) and gradually decreased over the next 28 days (Fig. 1). Among the 15 CCS-SP categories examined, the rate of AKI ranged from 0.2% in appendectomy to 3.5% in exploratory laparotomy (Table 1). AKI risk factors were generally more prevalent in the procedures with the highest risks of AKI, including older age and higher rates of emergency procedures, congestive heart failure, and ascites. Full details on comorbidities and demographic characteristics are available in Appendix 2.

Figure 1

Figure 1

Table 1

Table 1

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The Risk of AKI Varies by Type of Procedure

After accounting for procedure and comorbidities, the relative risks for developing AKI were determined for the CCS-SP categories (Table 2). Using colorectal resection as the reference category, the aRR for AKI ranged from 0.23 in appendectomy to 1.34 in ileostomy, reflecting a nearly 6-fold difference in the adjusted risk of AKI in the highest risk category compared with the lowest risk category. The strongest predictor of AKI was the severity of preoperative renal dysfunction: compared with those with normal renal function (eGFR >90), the aRRs (95% CI) for those with stage 5 (eGFR <15), stage 4 (eGFR 15–30), stage 3 (eGFR 30–60), and stage 2 (eGFR 60–90) CKD were 7.80 (6.35–9.57), 5.10 (4.56–5.69), 2.54 (2.33–2.78), and 1.24 (1.14–1.35), respectively. There was no significant difference in AKI risk comparing those with missing creatinine data to those with normal renal function (aRR 0.97 [0.80–1.18]), indicating that, on average, those with missing creatinine data likely had normal renal function or only mild renal dysfunction. Indeed, with multiple imputation20 of those with missing values, 37% were imputed to eGFR > 90 and 29% to eGFR 60–90. Multiple imputation analysis and complete case analysis did not result in any clinically meaningful changes in the interpretation of our results (Supplemental Digital Content,

Table 2

Table 2

Other strong predictors of AKI were hypertension (aRR 1.50 [1.40–1.61]), BMI (aRR 1.97 [1.80–2.15] in BMI > 39 versus BMI < 29; aRR 1.26 [1.19–1.35] in BMI 29–39 versus BMI < 29), ascites (aRR 1.50 [1.35–1.66]), and preoperative sepsis (aRR 1.52 [1.40–1.64]). Female sex and laparoscopic procedure were associated with a reduced risk of AKI with aRRs of 0.54 (0.51–0.57) and 0.52 (0.47–0.58), respectively. Additional significant predictors of AKI were emergency surgery, functional dependence, ventilator dependence, dyspnea, chronic obstructive pulmonary disease, current smoking, diabetes, congestive heart failure, myocardial infarction, bleeding disorders, hematocrit, chronic steroid use, and cancer (aRR < 1.50; not shown).

The predicted marginal risk for AKI varied tremendously among procedure categories, ranging from 0.1% in appendectomy and 0.3% in gastric bypass to 1.5% in both ileostomy and small bowel resection (Table 2).

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The Effect of AKI on 30-Day Mortality

In our cohort, 9988 (2.2%) subjects died within 30 days of their procedure, with a mortality of 1.9% in patients who did not develop AKI compared with 31% in those who developed AKI (Table 3). The lowest mortality was observed in patients undergoing appendectomy and gastric bypass (0.2%) and the highest mortality in patients undergoing exploratory laparotomy (12.4%). In adjusted analysis, there was a 5-fold difference in the risk of mortality between the highest and lowest risk procedures.

Table 3

Table 3

After adjusting for procedure, comorbidities, and intraoperative factors, the aRR (95% CI) for 30-day mortality with AKI in the overall sample was 3.51 (3.29–3.74), indicating that AKI was a strong predictor of 30-day mortality in this cohort. Other important predictors of mortality were age (aRR 1.044 [1.042–1.046] for each additional year), history of cancer (aRR 2.30 [2.18–2.42]), functional dependence (aRR 2.18 [2.07–2.30]), preoperative sepsis (aRR 1.86 [1.77–1.97]), ascites (aRR 1.74 [1.63–1.85]), emergency (aRR 1.56 [1.49–1.64]), and perioperative transfusion (aRR 1.53 [1.46–1.61]). In addition, dyspnea, ventilator dependence, chronic obstructive pulmonary disease, current smoking, peripheral vascular disease, stroke, bleeding disorders, chronic steroid use, eGFR, and preoperative hematocrit were also significantly associated with 30-day mortality (aRR < 1.50; not shown). Multiple imputation analysis and complete case analysis did not result in any clinically meaningful changes in the interpretation of our results (not shown).

After stratification based on procedure type, there was significant variation in the aRR for 30-day mortality with AKI, ranging from 1.87 (1.62–2.17) in exploratory laparotomy to 31.6 (17.9–55.9) in gastric bypass (Table 3). When further adjusted for other postoperative complications, the aRR for 30-day mortality in the overall sample was 2.05 (1.90–2.22). In addition, the variation in aRR for 30-day mortality with AKI was reduced, ranging from 1.49 (1.00–2.20) in excision and lysis of peritoneal adhesions to 7.02 (3.25–15.2) in appendectomy.

Based on our multivariable model for 30-day mortality without other postoperative complications, the predicted marginal risk of death without AKI ranged from <0.01% (gastric bypass) to 5.5% (exploratory laparotomy) (Table 4). With AKI, the predicted marginal risk of death ranged from 6.2% (other OR lower GI therapeutic procedures) to 29.5% (exploratory laparotomy). When other postoperative complications were accounted for in the analysis, the predicted marginal risk of death without AKI did not materially change. However, the predicted marginal risk of death with AKI changed in many procedures, such as a reduction from 8.0% to 0.8% in gastric bypass. There was a negligible change in predicted marginal risk in a few procedure categories, such as exploratory laparotomy and other OR lower GI therapeutic procedures.

Table 4

Table 4

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Using a large high-quality national dataset of surgical outcomes, we assessed the procedure-specific risk of perioperative AKI for patients undergoing intraabdominal general surgery, a group that is at high risk for developing AKI.4 Consistent with prior published results, the overall rate of perioperative AKI in our cohort was 1.1%.3,4 However, this rate varied tremendously among the 15 categories of surgical procedures analyzed, ranging from 0.2% in patients undergoing appendectomy to 3.5% in patients undergoing exploratory laparotomy, with a 6-fold difference in the adjusted risk of AKI between the highest and lowest risk procedures. The predicted marginal risk of AKI varied from 0.1% in appendectomy to 1.5% in ileostomy. Only information known before the surgery was used in this analysis, so this model is appropriate for preoperatively assessing AKI risk.

We also evaluated the effect of AKI on 30-day mortality, and after adjusting for procedure type and other factors, AKI was associated with a 3.5-fold increase in the risk of 30-day mortality. However, there was tremendous variability in the adjusted risk of AKI on 30-day mortality within the procedure categories, with the aRR ranging from 1.80 in exploratory laparotomy to 31.6 in gastric bypass. The predicted marginal risk of 30-day mortality with AKI ranged from 6.2% in other OR lower GI therapeutic procedures to 29.5% in exploratory laparotomy. These estimates of the aRRs and predicted marginal risks likely represent the upper bounds of the effects of AKI on mortality because the estimates were reduced when accounting for other adverse outcomes. Taken together, our results demonstrate that intraabdominal general surgery cannot be treated as a single category, and the specific procedure must be accounted for when evaluating the risk of AKI in noncardiac general surgery procedures.

Although intraperitoneal surgery is identified as being high risk for AKI3,4 and other complications,21 this group consists of a diverse collection of procedures, and considerable variation in the risk of complications should be expected. The highest risk of AKI was observed in small intestinal procedures (ileostomy and small bowel resection), colorectal procedures (colorectal resection), hepatobiliary and pancreatic procedures (other OR GI procedures), splenic procedures, and exploratory laparotomy (Table 2). Exploratory laparotomies are typically emergent cases (44% in our sample) in acute settings with high rates of associated morbidity. A high proportion of small bowel resections (40%) were also emergent, likely reflecting acute disease states such as small bowel obstruction22 or acute mesenteric ischemia.23 In addition, preclinical studies suggest that intestinal ischemia–reperfusion injury leads to multiorgan dysfunction and systemic inflammation24 and that the gut is the “motor” of systemic inflammation.25 Manipulation and resection of the small intestine may result in an inflammatory response that leads to AKI. Patients undergoing colorectal resection (22% of total sample) often undergo mechanical bowel preparation despite its questionable clinical utility,26 and this practice may have adverse physiologic consequences affecting their risk of AKI.27

Procedures with the lowest adjusted risk of AKI include cholecystectomy, gastric bypass, and appendectomy. Despite the high rate of emergency procedures with acute inflammation (appendicitis and cholecystitis) in this category, these procedures are usually performed on younger, healthier patients, and it is not surprising that they are associated with the lowest risk of AKI.

In addition to the specific procedure, the strongest predictor of perioperative AKI in our study was preexisting renal dysfunction. Although we excluded patients with preoperative acute renal failure or dialysis, we retained those who had CKD without acute changes in renal function. As expected, stage 5 (eGFR <15) and stage 4 (eGFR 15–30) CKD were associated with an 8-fold and 5-fold increase in AKI risk, respectively, compared with those with normal renal function (eGFR >90). Stage 3 CKD (eGFR 30–60) was associated with a 2.5-fold in the risk of AKI, and even stage 2 CKD (eGFR 60–90) was associated with a significant increase in the risk of AKI (1.24-fold). It should be noted that 58% of patients in the sample had stage 2 CKD or worse, indicating that the majority of patients presenting for inpatient intraabdominal general surgery had at least 1 significant risk factor for the development of AKI.

The known risk factors for AKI were consistent in their associations with increased AKI across the different procedures in stratified analyses (not shown). Impaired preoperative renal function (eGFR), sepsis, hypertension, and ascites were all associated with an increased risk of AKI in the majority of procedures. However, there were some exceptions. For instance, there was no association between emergency status and AKI in appendectomy (>77% emergent) and gastric bypass (<1% emergent), both procedures with low risks of AKI. These findings underscore the need to evaluate risk factors in the context of the patient population being examined.

AKI appears to have the greatest effect on 30-day mortality in procedures where the rates of AKI and mortality are relatively low, such as gastric bypass and appendectomy. It is clear that the effect of AKI on mortality varies dramatically based on the underlying patient population and characteristics of each procedure, and the reasons for these differential effects will need to be investigated. In addition, AKI may be a spectrum of diseases with different causes and consequences that depend on the clinical context in which it occurs. For instance, AKI associated with gastric bypass (generally younger patients with fewer comorbidities) may have different etiologies and mechanisms of illness than AKI associated with small bowel resection, an older patient population with multiple risk factors and comorbidities.

Initially, AKI was seen simply as a marker of illness and not a cause,28 but mounting epidemiologic evidence suggests that AKI contributes directly to mortality, at least in patients with severe AKI requiring renal replacement therapy.29 Potential mechanisms for AKI-associated morbidity include fluid overload, acid–base disturbances, inflammation and multiorgan dysfunction, as well as inadequate antimicrobial therapy.29 However, the specific mechanisms involved remain unclear, and despite all of the available research, no known therapies reduce the incidence of perioperative AKI.30 AKI may contribute to mortality alone, but more likely, it is part of a cascade of events leading to systemic inflammation and multiorgan dysfunction.31 Indeed, our analysis demonstrates that other postoperative complications, such as sepsis and myocardial infarction, at least partially explain the apparent associations between AKI and mortality.

As with any observational study of large datasets,32 this study is subject to some limitations. The study of AKI has historically been limited by varying definitions of renal failure,33 and ACS-NSQIP only identifies patients with severe changes in creatinine (>2 mg/dL above baseline) or dialysis as having developed AKI. Creatinine changes as small as 0.3 mg/dL may constitute clinically significant AKI,8,16 and the ACS-NSQIP definition of AKI may thus underestimate the overall incidence of AKI in the NSQIP cohort. Although we account for only the most severe cases of AKI, the large sample size and quality of the ACS-NSQIP data provide the opportunity to obtain valuable insights on variations in the risk of AKI among the different categories of intraabdominal surgery. Factors such as intraoperative fluid management34,35 and the use of vasopressors and diuretics3 may affect AKI rates, but the dataset does not report this information.

Categorization of individual procedures into larger groups may lead to biased parameter estimates for the other independent variables.36 However, when we incorporated the individual procedure in our models, the parameter estimates did not change in a meaningful way (Supplemental Digital Content,, and our findings were robust to the classification of procedures.c Additionally, modeling the actual procedure instead of the scheduled procedure may lead to misclassification bias.36 Unfortunately, we do not have data on the scheduled procedure, and although we do not expect significant miscategorization of procedures, this is a limitation of our analysis. Despite these limitations, our analysis serves to highlight important aspects of the epidemiology of perioperative AKI.

In conclusion, we have demonstrated that among patients undergoing intraabdominal general surgery procedures, the risk of AKI and 30-day mortality varies considerably depending on the specific procedure. Prior studies identify intraabdominal surgery as a risk factor for adverse outcomes, but it is clear that the risk of adverse outcomes is not uniformly distributed among this group. These results highlight the importance of preoperative risk stratification and identify procedure type as a significant risk factor for AKI and 30-day mortality.

Appendix 1

Appendix 1

Appendix 2

Appendix 2

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Name: Minjae Kim, MD, MS.

Contribution: This author was involved in study design, conduct of the study, data analysis, and manuscript preparation.

Attestation: Minjae Kim approved the final manuscript and attests to the integrity of the original data and analysis reported in this manuscript. Dr. Kim is the archival author.

Name: Joanne E. Brady, PhD.

Contribution: This author was involved in study design, data analysis, and manuscript preparation.

Attestation: Joanne E. Brady approved the final manuscript and attests to the integrity of the original data and analysis reported in this manuscript.

Name: Guohua Li, MD, DrPH.

Contribution: This author was involved in study design, data analysis, and manuscript preparation.

Attestation: Guohua Li approved the final manuscript and attests to the integrity of the original data and analysis reported in this manuscript.

This manuscript was handled by: Avery Tung, MD.

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a The American College of Surgeons National Surgical Quality Improvement Program and the hospitals participating in the ACS-NSQIP are the source of the data used herein; they have not verified and are not responsible for the statistical validity of the data analysis or the conclusions derived by the authors.
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b Available at: Accessed June 15, 2012.
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c We used a random effects (random intercept) logistic regression model, first with the CCS category of procedure as the random effect and then with the individual CPT codes as the random effect. The variances (on the logit scale) were 0.6608 (SE: 0.2527) by CCS code and 0.7158 (SE: 0.0907) by CPT. Absence of significant differences in the variances demonstrates that our findings are robust to the classification of procedures.
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1. Kumar AB, Suneja M. Cardiopulmonary bypass-associated acute kidney injury. Anesthesiology. 2011;114:964–70
2. Wald R, Waikar SS, Liangos O, Pereira BJG, Chertow GM, Jaber BL. Acute renal failure after endovascular vs open repair of abdominal aortic aneurysm. J Vasc Surg. 2006;43:460–6
3. Kheterpal S, Tremper KK, Englesbe MJ, O’Reilly M, Shanks AM, Fetterman DM, Rosenberg AL, Swartz RD. Predictors of postoperative acute renal failure after noncardiac surgery in patients with previously normal renal function. Anesthesiology. 2007;107:892–902
4. Kheterpal S, Tremper KK, Heung M, Rosenberg AL, Englesbe M, Shanks AM, Campbell DA Jr.. Development and validation of an acute kidney injury risk index for patients undergoing general surgery: results from a national data set. Anesthesiology. 2009;110:505–15
5. Goldman L, Caldera DL, Nussbaum SR, Southwick FS, Krogstad D, Murray B, Burke DS, O’Malley TA, Goroll AH, Caplan CH, Nolan J, Carabello B, Slater EE. Multifactorial index of cardiac risk in noncardiac surgical procedures. N Engl J Med. 1977;297:845–50
6. Rahbari NN, Knebel P, Diener MK, Seidlmayer C, Ridwelski K, Stöltzing H, Seiler CM. Current practice of abdominal wall closure in elective surgery: is there any consensus? BMC Surg. 2009;9:8
7. Borthwick E, Ferguson A. Perioperative acute kidney injury: risk factors, recognition, management, and outcomes. BMJ. 2010;341:c3365
8. Bihorac A, Yavas S, Subbiah S, Hobson CE, Schold JD, Gabrielli A, Layon AJ, Segal MS. Long-term risk of mortality and acute kidney injury during hospitalization after major surgery. Ann Surg. 2009;249:851–8
9. Chertow GM, Burdick E, Honour M, Bonventre JV, Bates DW. Acute kidney injury, mortality, length of stay, and costs in hospitalized patients. J Am Soc Nephrol. 2005;16:3365–70
10. Fink AS, Campbell DA Jr, Mentzer RM Jr, Henderson WG, Daley J, Bannister J, Hur K, Khuri SF. The National Surgical Quality Improvement Program in non-Veterans Administration hospitals: initial demonstration of feasibility. Ann Surg. 2002;236:344–53
11. Hua M, Brady J, Li G. The epidemiology of upper airway injury in patients undergoing major surgical procedures. Anesth Analg. 2012;114:148–51
12. ACS-NSQIP. . User Guide for the 2010 Participant Use Data File. 2011 Available at: Accessed October 5, 2011
13. Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen S, Kusek JW, Van Lente FChronic Kidney Disease Epidemiology Collaboration. . Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med. 2006;145:247–54
14. National Kidney Foundation. . K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis. 2002;39:S1–266
15. Bellomo R, Ronco C, Kellum JA, Mehta RL, Palevsky P. Acute renal failure—definition, outcome measures, animal models, fluid therapy and information technology needs: the Second International Consensus Conference of the Acute Dialysis Quality Initiative (ADQI) Group. Crit Care. 2004;8:R204–12
16. Mehta RL, Kellum JA, Shah SV, Molitoris BA, Ronco C, Warnock DG, Levin A. Acute Kidney Injury Network: report of an initiative to improve outcomes in acute kidney injury. Crit Care. 2007;11:R31
17. Spiegelman D, Hertzmark E. Easy SAS calculations for risk or prevalence ratios and differences. Am J Epidemiol. 2005;162:199–200
18. Zou G. A modified Poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159:702–6
19. Raghunathan TE, Lepkowski JM, Van Hoewyk J, Solenberger P. A multivariate technique for multiply imputing missing values using a sequence of regression models. Surv Methodol. 2001;27:85–95
20. van der Heijden GJ, Donders AR, Stijnen T, Moons KG. Imputation of missing values is superior to complete case analysis and the missing-indicator method in multivariable diagnostic research: a clinical example. J Clin Epidemiol. 2006;59:1102–9
21. Lee TH, Marcantonio ER, Mangione CM, Thomas EJ, Polanczyk CA, Cook EF, Sugarbaker DJ, Donaldson MC, Poss R, Ho KK, Ludwig LE, Pedan A, Goldman L. Derivation and prospective validation of a simple index for prediction of cardiac risk of major noncardiac surgery. Circulation. 1999;100:1043–9
22. Oyasiji T, Angelo S, Kyriakides TC, Helton SW. Small bowel obstruction: outcome and cost implications of admitting service. Am Surg. 2010;76:687–91
23. Acosta-Merida MA, Marchena-Gomez J, Hemmersbach-Miller M, Roque-Castellano C, Hernandez-Romero JM. Identification of risk factors for perioperative mortality in acute mesenteric ischemia. World J Surg. 2006;30:1579–85
24. Kim M, Park SW, Kim M, D’Agati VD, Lee HT. Isoflurane post-conditioning protects against intestinal ischemia-reperfusion injury and multiorgan dysfunction via transforming growth factor-β1 generation. Ann Surg. 2012;255:492–503
25. Clark JA, Coopersmith CM. Intestinal crosstalk: a new paradigm for understanding the gut as the “motor” of critical illness. Shock. 2007;28:384–93
26. Wille-Jørgensen P, Guenaga KF, Matos D, Castro AA. Pre-operative mechanical bowel cleansing or not? An updated meta-analysis. Colorectal Dis. 2005;7:304–10
27. Holte K, Nielsen KG, Madsen JL, Kehlet H. Physiologic effects of bowel preparation. Dis Colon Rectum. 2004;47:1397–402
28. Kellum JA, Angus DC. Patients are dying of acute renal failure. Crit Care Med. 2002;30:2156–7
29. Hoste EA, De Corte W. Clinical consequences of acute kidney injury. Contrib Nephrol. 2011;174:56–64
30. Zacharias M, Mugawar M, Herbison GP, Walker RJ, Hovhannisyan K, Sivalingam P, Conlon NP. Interventions for protecting renal function in the perioperative period. Cochrane Database Syst Rev. 2013;9:CD003590
31. Faubel S. Acute kidney injury and multiple organ dysfunction syndrome. Minerva Urol Nefrol. 2009;61:171–88
32. Ward RA, Brier ME. Retrospective analyses of large medical databases: what do they tell us? J Am Soc Nephrol. 1999;10:429–32
33. Josephs SA, Thakar CV. Perioperative risk assessment, prevention, and treatment of acute kidney injury. Int Anesthesiol Clin. 2009;47:89–105
34. Nisanevich V, Felsenstein I, Almogy G, Weissman C, Einav S, Matot I. Effect of intraoperative fluid management on outcome after intraabdominal surgery. Anesthesiology. 2005;103:25–32
35. Kambhampati G, Ross EA, Alsabbagh MM, Asmar A, Pakkivenkata U, Ejaz NI, Arif AA, Ejaz AA. Perioperative fluid balance and acute kidney injury. Clin Exp Nephrol. 2012;16:730–8
36. Dexter F, Dexter EU, Ledolter J. Statistical grand rounds: importance of appropriately modeling procedure and duration in logistic regression studies of perioperative morbidity and mortality. Anesth Analg. 2011;113:1197–201

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