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Original Contribution |

Derivation and Validation of a Simplified Predictive Index for Renal Replacement Therapy After Cardiac Surgery FREE

Duminda N. Wijeysundera, MD; Keyvan Karkouti, MD, MSc; Jean-Yves Dupuis, MD; Vivek Rao, MD, PhD; Christopher T. Chan, MD; John T. Granton, MD; W. Scott Beattie, MD, PhD
[+] Author Affiliations

Author Affiliations: Department of Anesthesia, Toronto General Hospital and University of Toronto, Toronto, Ontario (Drs Wijeysundera, Karkouti, and Beattie); Department of Health Policy Management and Evaluation, University of Toronto (Drs Wijeysundera and Karkouti); Department of Anesthesia, University of Ottawa Heart Institute, Ottawa, Ontario (Dr Dupuis); Division of Cardiac Surgery, Toronto General Hospital and University of Toronto (Dr Rao); Division of Nephrology, University Health Network and University of Toronto, Toronto (Dr Chan); Division of Respirology and Critical Care Medicine, University Health Network and University of Toronto (Dr Granton).

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JAMA. 2007;297(16):1801-1809. doi:10.1001/jama.297.16.1801.
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Context A predictive index for renal replacement therapy (RRT; hemodialysis or continuous venovenous hemodiafiltration) after cardiac surgery may improve clinical decision making and research design.

Objectives To develop a predictive index for RRT using preoperative information.

Design, Setting, and Participants Retrospective cohort of 20 131 cardiac surgery patients at 2 hospitals in Ontario, Canada. The derivation cohort consisted of 10 751 patients at Toronto General Hospital (1999-2004). The validation cohorts consisted of 2566 patients at Toronto General Hospital (2004-2005) and 6814 patients at Ottawa Heart Institute (1999-2003).

Main Outcome Measure Postoperative RRT.

Results RRT rates in the derivation, Toronto validation, and Ottawa validation cohorts were 1.3%, 1.8%, and 2.2%, respectively. Multivariable predictors of RRT were preoperative estimated glomerular filtration rate, diabetes mellitus requiring medication, left ventricular ejection fraction, previous cardiac surgery, procedure, urgency of surgery, and preoperative intra-aortic balloon pump. The predictive index was scored from 0 to 8 points. An estimated glomerular filtration rate less than or equal to 30 mL/min was assigned 2 points; other components were assigned 1 point each: estimated glomerular filtration rate 31 to 60 mL/min, diabetes mellitus, ejection fraction less than or equal to 40%, previous cardiac surgery, procedure other than coronary artery bypass grafting, intra-aortic balloon pump, and nonelective case. Among the 53% of patients with low risk scores (≤1), the risk of RRT was 0.4%; by comparison, this risk was 10% among the 6% of patients with high-risk scores (≥4). The predictive index had areas under the receiver operating characteristic curve in the derivation, Toronto validation, and Ottawa validation cohorts of 0.81, 0.78, and 0.78, respectively. When these cohorts were stratified based on index scores, likelihood ratios for RRT were more concordant than observed RRT rates.

Conclusions RRT after cardiac surgery is predicted by readily available preoperative information. A simple predictive index based on this information discriminated well between low- and high-risk patients in derivation and validation cohorts. The index had improved generalizability when used to predict likelihood ratios for RRT.

Figures in this Article

Cardiac surgery remains a common surgical procedure, with approximately 646 000 open-heart operations performed annually in the United States alone.1 One of its most serious complications is acute renal failure. When severe enough to necessitate renal replacement therapy (RRT) with hemodialysis or continuous venovenous hemodiafiltration, postoperative acute renal failure is independently associated with mortality.2 Most clinicians believe that rates of RRT are likely to increase because patients undergoing cardiac surgery are increasingly older and have more comorbidity.3 Strategies that improve management of perioperative acute renal failure should, therefore, confer important benefits.

A simple risk index that uses readily available preoperative data to accurately predict RRT will improve clinical management and research design in this area of perioperative medicine. First, preoperative estimates of risk might help tailor use of existing, albeit limited, renal-protective strategies. Second, patients would receive accurate estimates of risk during preoperative counseling. Third, identification of high-risk patients could guide intensive care units in allocating resources for postoperative care. Although they comprise less than 2% of patients undergoing cardiac surgery, patients needing postoperative RRT use 12% of intensive care unit resources, as measured by length of stay.4 Finally, researchers might use this index to select intermediate- to high-risk participants for clinical trials of renal-protective therapies, and to facilitate risk adjustment with multivariable regression or propensity score techniques.

To achieve widespread use, a predictive index for RRT after cardiac surgery should have specific characteristics. It should provide accurate prognostic information based on readily available, clinically sensible preoperative data. This prognostic information should be generalizable; specifically, predictive accuracy should remain stable when the index is externally validated in different data sets, geographic locales, and time periods.5 Where possible, the index should also be simple and easily scored. Predictive indices for perioperative cardiac risk demonstrate the feasibility of improving ease of use while preserving accuracy. Compared with older indices, the Revised Cardiac Risk Index provides comparably accurate prognostic information with a reduced number of equally weighted component variables.6

There have been few previous studies of predictive indices for postoperative RRT, all of which had important limitations.2,4,7 Aside from a clinical prediction rule by Chertow et al,2 these previous indices had relatively complex scoring schemes and underwent only limited external validation. The Chertow et al2 prediction rule, which was derived in a predominantly male (99%) cohort, demonstrated only fair discrimination when externally validated in a more diverse population.8 In view of these limitations, we undertook a retrospective cohort study to derive and validate a simple, accurate predictive index for RRT after cardiac surgery at 2 tertiary care hospitals in Ontario, Canada.

Data Sources

After institutional research ethics approval, preoperative, intraoperative, and postoperative data on individuals undergoing cardiac surgery at the Toronto General Hospital (Toronto, Ontario) and University of Ottawa Heart Institute (Ottawa, Ontario) were prospectively collected in distinct hospital-specific registries. These databases have been previously described.9,10 Medical personnel (anesthesiologists, surgeons, perfusionists) collected preoperative and intraoperative data. Research personnel adjudicated all outcomes from medical records. Database accuracy at the Toronto General Hospital exceeded 95% when compared with 200 randomly selected medical records that had been reabstracted.11 Based on reabstraction of 175 randomly selected medical records, database accuracy at the Ottawa Heart Institute was 98%.9

Patient Samples

The study sample consisted of consecutive adults (≥18 years) who underwent cardiac surgery under cardiopulmonary bypass. Exclusion criteria included severe preoperative renal dysfunction (preoperative RRT dependence or creatinine concentration >3.4 mg/dL [300 μmol/L]) and infrequent procedures (eg, heart transplantation, ventricular assist device insertion). The derivation cohort consisted of eligible cases at the Toronto General Hospital between May 1999 and July 2004. This cohort was estimated at 10 000 patients, thereby permitting unbiased fitting of 10 or fewer predictor variables in multiple logistic regression (estimated 1% rate of postoperative RRT).12 We specified 2 distinct validation cohorts. One validation cohort consisted of eligible cases at the Toronto General Hospital between August 2004 and December 2005; this phase of external validation evaluated the generalizability of prognostic information when applied to a different time period.5 The other validation cohort consisted of eligible cases at the Ottawa Heart Institute between January 1999 and December 2003; this validation process evaluated generalizability based on a differing geographic locale and data collection method.5

Variable Definitions

Potential predictor variables were selected based on clinical sensibility, literature review, and availability before surgery. These variables included demographic characteristics (age, sex, weight), preoperative renal reserve, comorbid disease (left ventricular ejection fraction, hypertension, cerebrovascular disease, peripheral vascular disease, diabetes mellitus requiring medication, chronic obstructive pulmonary disease), operative characteristics (procedure type, previous cardiac surgery, preoperative intra-aortic balloon pump use), and urgency of surgery.4,7,11,1316

Given that creatinine concentration has important limitations as a measure of preoperative renal function, we measured renal reserve using estimated glomerular filtration rate (GFR), based on the Cockcroft-Gault equation.11,1720 New York Heart Association functional class was not included among potential predictor variables because its poor interrater reliability might adversely affect the reproducibility of the prediction index.21,22 Cardiopulmonary bypass time was not considered a potential predictor variable. Cardiopulmonary bypass time is associated with RRT, but it is not readily estimated before surgery.7,14,15 We calculated estimated GFR using serum creatinine concentrations and weights that are routinely measured within 30 days before surgery. If multiple measurements were present, the value closest to the date of surgery was used. All other preoperative variables were measured on the date of surgery.

The primary outcome was postoperative RRT (intermittent hemodialysis or continuous venovenous hemodiafiltration). Consulting nephrologists made decisions about implementing RRT. Indications for RRT were metabolic abnormalities (acidosis, hyperkalemia), anuria, and fluid overload.

Analysis

The bivariate associations between potential predictor variables and RRT were initially described using appropriate tests (unpaired t test, Mann-Whitney U test, χ2 test, Fisher exact test). These bivariate relationships guided any further simplification of categorical variables. Continuous variables were divided into clinically sensible categories based on their association with RRT in restricted cubic spline plots.23

All potential predictor variables were initially fitted into a multivariable logistic regression model that predicted postoperative RRT.24 This full model was subsequently reduced to include only variables with clinically sensible directions for their predictive effects.25 We assessed the discrimination of the reduced model using the area under the receiver operating characteristic (ROC) curve, and its calibration using an observed vs predicted plot and the Hosmer-Lemeshow goodness-of-fit statistic.24

The regression β coefficients of variables in the reduced model were then used to develop component scores for a simplified renal index to predict RRT.26 Where possible, we tried to assign equal weights to each included component variable. The discrimination of the simplified renal index was described using the area under the ROC curve, while its calibration was assessed using an observed vs predicted plot and the Hosmer-Lemeshow statistic. We internally validated the index by computing bias-corrected estimates of discrimination and calibration by bootstrap resampling (n = 200).5,23,27

The simplified renal index was externally validated in the Ottawa and Toronto validation cohorts. Index discrimination was measured in each validation cohort using the area under the ROC curve. We assessed calibration by stratifying observed RRT rates by simplified renal index score and graphically comparing rates across cohorts.28 Given that RRT is associated with intraoperative and postoperative factors that are not included in a preoperative index, we repeated the calibration analysis using likelihood ratios as opposed to observed rates of RRT.7,10,14,15,2931

We also compared the performance of the simplified renal index, as measured by the area under the ROC curve, against several alternative predictive indices in the validation cohorts. These alternative indices included: a nomogram tool by Mehta et al; a risk score by Thakar et al; a prediction rule by Chertow et al; and a modification of the simplified renal index that used exact weights from the logistic regression model.4,7,13 In the latter case, we calculated exact weights by multiplying the regression β coefficient of each component variable by 10.

General Analysis Issues

Missing data were present in less than 0.5% of records. These missing data were imputed. An unknown left ventricular ejection fraction was considered equal to a normal value (>60%).32 Missing values for dichotomous variables were assigned the most frequent sex-specific value, whereas continuous variables were assigned the sex-specific median value. Our results were unchanged when the analyses were repeated after excluding records with missing data. Statistical analyses were performed using R 2.3.1 (R Foundation for Statistical Computing, Vienna, Austria) and the Design Library of Biostatistical Modeling Functions.27 All P values were 2-tailed, with statistical significance defined by P≤.05.

The derivation cohort consisted of 10 751 patients, while the Toronto validation and Ottawa validation cohorts consisted of 2566 and 6814 patients, respectively (N = 20 131). The rates of RRT in the derivation cohort were 1.3% (n = 139), while rates in the Toronto validation and Ottawa validation cohorts were 1.8% (n = 45) and 2.2% (n = 152), respectively.

Bivariate and Multivariable Analyses

The perioperative characteristics of the derivation cohort are presented in Table 1. The bivariate associations of these potential predictor variables with postoperative RRT are also presented in Table 1. These bivariate associations guided further categorization of age (≤60 years, 61-80 years, >80 years); estimated GFR (>60 mL/min, 31-60 mL/min, ≤30 mL/min); weight (≤111 lb [<50 kg], 112-178 lb [51-80 kg], >178 lb [>80 kg]); left ventricular ejection fraction (>40%, ≤40%); operative procedure (coronary artery bypass graft or atrial septal defect repair, other); and urgency of procedure (elective, nonelective).

Table Graphic Jump LocationTable 1. Characteristics of the Derivation Cohort, and Their Bivariate Association With Renal Replacement Therapy*

While reducing the full multivariable regression model, we removed certain potential predictor variables (eg, age, weight) that were statistically significant in bivariate analyses. These eliminated variables had neither clinically sensible directions for their predictive effects nor statistically significant associations with RRT in the fitted regression model. The final multivariable model for predicting postoperative RRT included 8 predictor variables (Table 2). This model had good discrimination (area under ROC curve 0.83) and calibration (Hosmer-Lemeshow statistic P = .48).

Table Graphic Jump LocationTable 2. Multivariable Predictors of Postoperative Renal Replacement Therapy in the Derivation Cohort
Predictive Index Derivation

A simplified renal index (Table 3) was then developed using the regression coefficients of the multivariable model (Table 2). This simple predictive index contained 8 components, each of which was assigned 1 point, with the exception of estimated GFR of 30 mL/min or less (2 points). The proportions of the derivation cohort with scores of 0, 1, 2, 3, 4, and 5 or greater were 16% (n = 1714), 38% (n = 4097), 28% (n = 3035), 13% (n = 1343), 4% (n = 449), and 1% (n = 113), respectively.

Table Graphic Jump LocationTable 3. Simplified Renal Index Scoring Scheme for Estimating Risk of Postoperative Renal Replacement Therapy

The simplified renal index had excellent discrimination for postoperative RRT with an area under the ROC curve of 0.81 (bias-corrected 0.81; 95% confidence interval [CI], 0.78-0.84). The predictive index also had good calibration, based on its observed vs predicted plot and Hosmer-Lemeshow statistic (P = .27).

External Validation

The perioperative characteristics of the derivation, Toronto validation, and Ottawa validation cohorts are presented in Table 4. The cohorts differed with respect to several characteristics including estimated GFR, chronic obstructive pulmonary disease, cerebrovascular disease, left ventricular ejection fraction, operative procedure, and cardiopulmonary bypass time (Table 4).

Table Graphic Jump LocationTable 4. Perioperative Characteristics of the Derivation and Validation Cohorts*

The simplified renal index retained good discrimination in the Toronto and Ottawa validation cohorts. The area under the ROC curve in the Toronto cohort was 0.78 (95% CI, 0.72-0.84) and the area in the Ottawa cohort was 0.78 (95% CI, 0.74-0.81).

Nonetheless, when the simplified renal index score was used to estimate RRT rates, calibration varied among the cohorts (Figure 1). Specifically, patients' scores on the simplified renal index could discriminate between high- and low-risk individuals within the same cohort; however, when patients with the same score were compared between cohorts, observed RRT rates varied (Figure 1). Especially in risk strata defined by index scores of 2 or 3, the observed RRT rates were qualitatively different in the derivation and validation cohorts (Figure 2).

Figure 1. Proportions of Patients Requiring Renal Replacement Therapy Within Strata of the Simplified Renal Index Score
Graphic Jump Location

The circles denote percentages in each cohort that required renal replacement therapy. Error bars are 95% confidence intervals. Few patients in the Ottawa (n = 6) and Toronto (n = 9) validation cohorts had scores of 6 or greater; hence, these strata are not presented.

Figure 2. Likelihood Ratios of Patients Requiring Renal Replacement Therapy Within Strata of the Simplified Renal index Score
Graphic Jump Location

The circles denote likelihood ratios for renal replacement therapy in each cohort. Error bars are 95% confidence intervals.30 Few patients in the Ottawa (n = 6) and Toronto (n = 9) validation cohorts had scores of 6 or greater; hence, these strata are not presented.

In contrast, the agreement between derivation and validation cohorts for likelihood ratios of postoperative RRT was better (Figure 2). These ratios described the probability of RRT relative to the mean risk in the cohort. Ratios greater than 1 represent increases in risk compared with the mean event rate in the specific cohort; values less than 1 represent decreases in risk compared with the mean event rate. The overlap in 95% CIs for likelihood ratios was consistent across all risk strata.30

Comparison With Alternative Predictive Indices

The simplified renal index, its variant with exact weighting, the Thakar et al7 risk score, and the Mehta et al4 nomogram tool had qualitatively similar discrimination and widely overlapping 95% CIs (Table 5). In contrast, the Chertow et al13 prediction rule displayed only fair discrimination with an area under the ROC curve of 0.68 to 0.70 (Table 5).

Table Graphic Jump LocationTable 5. Comparison Between the Simplified Renal Index and Alternative Predictive Indices

Our study found that a predictive index with a simplified scoring system could use readily available, clinically sensible preoperative information to provide accurate prognostic information on RRT after cardiac surgery. The simplified renal index compares favorably with other perioperative risk indices with regard to discriminating between high- and low-risk patients.6,33,34

Comparison With Previous Indices

The simplified renal index warrants comparison against the few previous predictive indices for RRT after cardiac surgery. Compared with the more complex Thakar et al and Mehta et al predictive indices, our index demonstrated improved ease of use, because of a fewer number of component variables and simpler scoring system, with similar prognostic accuracy.4,7 In this respect, the simplified renal index mirrors the evolution of preoperative cardiac risk indices, where the newer Revised Cardiac Risk Index has comparable accuracy, but improved usability, compared with the older Detsky and Goldman indices.6,35,36

Potential explanations for the somewhat inferior performance of the Chertow et al prediction rule include its derivation cohort and its underlying statistical methods. As previously described, this prediction rule was derived in a predominantly male (99%) cohort.13 Its discrimination may, therefore, have deteriorated when externally validated in more diverse populations, as occurred in both a previous study by Fortescue et al (ROC area 0.72) and our present study (ROC area 0.68-0.70).8 In addition, Chertow et al derived their prediction rule using recursive partitioning. The latter statistical technique may have inferior discrimination compared with logistic regression, which was used to derive the other predictive indices.37

Strengths

The principal strength of the simplified renal index is its combination of simplicity and accuracy. It used only 8 clinically sensible components, all of which were equally weighted, to accurately predict a clinically relevant outcome, namely RRT.2 This simplicity will help promote its use in clinical care and research settings. It also differs from recent comparable indices by measuring preoperative renal function using estimated GFR.4,7 Perioperative studies have shown GFR to be superior to serum creatinine as a predictor of clinical outcomes, including mortality and postoperative renal function.19,20 Furthermore, guidelines recommend estimated GFR, as opposed to serum creatinine concentration, as the best overall estimate of kidney function.18

Despite its relative simplicity, the simplified renal index demonstrated good discrimination in derivation and validation cohorts. Furthermore, our external validation process evaluated performance in varying time periods, geographic locales, and data sets. It is important to emphasize that the Ottawa Heart Institute and Toronto General Hospital maintain distinct databases and data collection methods. The simplified renal index, therefore, likely has generalizable discrimination in varied settings.5

Our external validation process also demonstrated that calibration of the simplified risk index varied when used to predict RRT rates in different cohorts. There are several possible explanations for this observation. First, postoperative RRT is associated with intra- and post-operative factors that are not included in a preoperative risk index: cardiopulmonary bypass time, intraoperative hematocrit, low-cardiac output syndrome, postoperative glycemic control, available RRT resources, and thresholds for initiating RRT.7,10,14,15,31 Previous studies have demonstrated this combination of preserved discrimination but diminished calibration, with predictive indices used in similar contexts, namely other preoperative risk indices and critical care admission risk indices.3840 Second, inter-laboratory variation in serum creatinine assays might have affected both the prevalence of diminished estimated GFR and its association with RRT.41 Finally, the calibration of the simplified renal index may have been influenced by unmeasured preoperative variables.

Interpretation of a predictive index using likelihood ratios may correct, in part, for influential factors that occur after index measurement. Likelihood ratios measure the probability of RRT relative to the average risk in the cohort. Our results suggest that likelihood ratios, by accounting for mean risk within a cohort, help preserve the calibration of a preoperative risk index in external validation.

Implications

As a simple and accurate predictive index, the simplified renal index will improve preoperative risk stratification for RRT. Our index offers several options for risk stratification when prospectively applied in a new setting. One might simply extrapolate RRT rates observed in our derivation sample to the new cohort. In doing so, the simplified renal index will likely discriminate well between low- and high-risk patients; however, observed RRT rates may vary from predicted rates, especially if historical RRT rates in the new setting differ greatly from our derivation sample. This variation between observed and predicted RRT rates can be corrected, in part, by recalibrating the simplified renal index.42 This process of statistical recalibration entails both a preexisting large data set of cardiac surgery patients at the new setting and adequate statistical support.

In the absence of these resources, clinicians and researchers might instead use our index to estimate likelihood ratios for RRT, which appear to be relatively stable when extrapolated to a new setting. If the historical RRT rate in the new setting is known, these likelihood ratios can, in turn, be used to calculate predicted RRT rates. Alternatively, clinicians and researchers can directly interpret the likelihood ratios stratify patients by risk. For example, patients with risk scores of 0 and 1 have risks for RRT that are only one tenth and one third, respectively, that for an average patient in the new setting. In contrast, patients with risk scores of 4 and 5 have risks of RRT that are 6-fold and 12-fold higher, respectively, than the average patient. Despite not providing absolute risks for RRT, this information is clinically useful. As has been observed with preoperative cardiac risk indices, clinicians often do not estimate absolute risks for adverse events; instead, they simply classify patients as low-, intermediate-, or high-risk.43 Given that likelihood ratios less than 0.2 represent at least moderate reductions in risk while values greater than 5 represent at least moderate increases in risk, patients might be conveniently classified into low-risk (≤1 point), intermediate-risk (2-3 points), and high-risk (≥4 points) categories based on their simplified renal index score.29

Accurate preoperative risk stratification for RRT after cardiac surgery will, in turn, inform clinical decision making and research design. First, clinicians can tailor perioperative management based on a patient's predicted risk for RRT. Low-risk individuals, who constitute approximately 55% of cardiac surgery patients, would require no specific modifications in usual perioperative management. Intermediate-risk individuals might benefit from limited use of potential renal-protective interventions (eg, strict control of intraoperative hematocrit, off-pump coronary artery surgery, fenoldopam) and close postoperative surveillance with biomarkers of early renal injury (eg, urinary neutrophil gelatinase–associated lipocalin).10,4446 High-risk individuals may benefit from more aggressive use of potential renal-protective interventions. Second, preoperative identification of high-risk patients will assist intensive care units in planning allocation of postoperative RRT resources. Third, patients would receive accurate estimates of risk during preoperative counseling. Finally, clinical trials might improve selection of intermediate- to high-risk participants by incorporating the simplified renal index into their inclusion criteria. Recent investigations of perioperative renal-protective therapies have specifically called for research to improve identification of patients at risk for renal events after cardiac surgery.47

Limitations

There are several limitations to be considered when interpreting our results. The association between estimated GFR and RRT observed in our present study should not be extrapolated to alternative prediction equations, such as the Modification of Diet in Renal Disease formula or cystatin-C-based equations.48 We did not apply the Modification of Diet in Renal Disease formula, which may have improved the accuracy of estimated GFR, because our prospective clinical registries did not capture required ethnicity data. Nonetheless, current guidelines consider the Cockcroft-Gault equation to be an acceptable method for determining estimated GFR in adults.18 Calculation of estimated GFR also introduces more complexity to the preoperative assessment process than measuring serum creatinine concentration alone. Nonetheless, the improved estimation of true renal reserve more than compensates for the slight additional complexity entailed by estimated GFR.1820 Furthermore, estimated GFR calculation may be readily facilitated using existing computer or personal digital assistant software. In addition, our data were limited to in-hospital outcomes, therefore precluding any conclusions on long-term implications of postoperative RRT. Given that the initiation of RRT is based on clinical judgment, consulting nephrologists might have also modified their threshold for initiating in-hospital RRT based on patients' preoperative characteristics. Finally, our data were derived from 2 tertiary-care hospitals in Ontario, Canada. Multisite external validation of the simplified renal index in other geographic regions remains needed to further characterize its generalizability.5

In summary, we found that a simple predictive index can use readily available preoperative information to accurately predict RRT after cardiac surgery. The calibration of this index across cohorts improved when used to estimate likelihood ratios for RRT as opposed to predicted event rates. This simple predictive index may facilitate preoperative risk stratification for RRT, and thereby improve clinical decision making, communication of perioperative risk, resource allocation, and research design.

Corresponding Author: Duminda N. Wijeysundera, MD, FRCPC, Department of Anesthesia, Toronto General Hospital and University of Toronto, EN 3-450 200 Elizabeth St, Toronto, Ontario, Canada M5G 2C4 (duminda.wijeysundera@uhn.on.ca).

Author Contributions: Dr Wijeysundera had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Wijeysundera, Karkouti, Rao, Chan.

Acquisition of data: Wijeysundera, Karkouti, Dupuis, Beattie.

Analysis and interpretation of data: Wijeysundera, Dupuis, Rao, Granton, Beattie.

Drafting of the manuscript: Wijeysundera.

Critical revision of the manuscript for important intellectual content: Wijeysundera, Karkouti, Dupuis, Rao, Chan, Granton, Beattie.

Statistical analysis: Wijeysundera.

Obtained funding: Dupuis, Chan.

Administrative, technical, or material support: Wijeysundera, Karkouti, Dupuis, Chan, Beattie.

Study supervision: Rao.

Financial Disclosures: None reported.

Funding/Support: Dr Wijeysundera is supported by a Clinician Scientist Award from the Canadian Institutes of Health Research (CIHR). Dr Karkouti is supported by a New Investigator Award from CIHR and the Canadian Blood Services. Dr Rao is supported by a New Investigator Award from CIHR. Dr Rao is the Alfredo and Teresa DeGasperis Chair in Heart Failure Surgery at the University Health Network. Dr Beattie is the Fraser Elliot Chair of Cardiovascular Anesthesiology at the University Health Network. The Cardiac Anesthesia Division of the University of Ottawa Heart Institute funded data collection at the Ottawa Heart Institute.

Role of the Sponsors: These sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, or approval of the manuscript.

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Moons KG, Harrell FE, Steyerberg EW. Should scoring rules be based on odds ratios or regression coefficients?  J Clin Epidemiol. 2002;55:1054-1055
PubMed   |  Link to Article
Harrell FE Jr. Design: S functions for biostatistical/epidemiologic modeling, testing, estimation, validation, graphics, and prediction. Vanderbilt University Web site Department of Biostatistics. http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/Design. Accessed November 28, 2006
Lee DS, Austin PC, Rouleau JL, Liu PP, Naimark D, Tu JV. Predicting mortality among patients hospitalized for heart failure: derivation and validation of a clinical model.  JAMA. 2003;290:2581-2587
PubMed   |  Link to Article
Jaeschke R, Guyatt G, Sackett DL. Users' guides to the medical literature III, how to use an article about a diagnostic test; A: are the results of the study valid? Evidence-Based Medicine Working Group.  JAMA. 1994;271:389-391
PubMed   |  Link to Article
Simel DL, Samsa GP, Matchar DB. Likelihood ratios with confidence: sample size estimation for diagnostic test studies.  J Clin Epidemiol. 1991;44:763-770
PubMed   |  Link to Article
van den Berghe G, Wouters P, Weekers F.  et al.  Intensive insulin therapy in the critically ill patients.  N Engl J Med. 2001;345:1359-1367
PubMed   |  Link to Article
Pierpont GL, Kruse M, Ewald S, Weir EK. Practical problems in assessing risk for coronary artery bypass grafting.  J Thorac Cardiovasc Surg. 1985;89:673-682
PubMed
Arozullah AM, Khuri SF, Henderson WG, Daley J. Development and validation of a multifactorial risk index for predicting postoperative pneumonia after major noncardiac surgery.  Ann Intern Med. 2001;135:847-857
PubMed   |  Link to Article
Mathew JP, Fontes ML, Tudor IC.  et al.  A multicenter risk index for atrial fibrillation after cardiac surgery.  JAMA. 2004;291:1720-1729
PubMed   |  Link to Article
Detsky AS, Abrams HB, McLaughlin JR.  et al.  Predicting cardiac complications in patients undergoing non-cardiac surgery.  J Gen Intern Med. 1986;1:211-219
PubMed   |  Link to Article
Goldman L, Caldera DL, Nussbaum SR.  et al.  Multifactorial index of cardiac risk in noncardiac surgical procedures.  N Engl J Med. 1977;297:845-850
PubMed   |  Link to Article
Austin PC. A comparison of regression trees, logistic regression, generalized additive models, and multivariate adaptive regression splines for predicting AMI mortality.  Stat Med2006 [Epub ahead of print]
PubMed
Harrison DA, Brady AR, Parry GJ, Carpenter JR, Rowan K. Recalibration of risk prediction models in a large multicenter cohort of admissions to adult, general critical care units in the United Kingdom.  Crit Care Med. 2006;34:1378-1388
PubMed   |  Link to Article
Ivanov J, Tu JV, Naylor CD. Ready-made, recalibrated, or remodeled? issues in the use of risk indexes for assessing mortality after coronary artery bypass graft surgery.  Circulation. 1999;99:2098-2104
PubMed   |  Link to Article
Yap CH, Reid C, Yii M.  et al.  Validation of the EuroSCORE model in Australia.  Eur J Cardiothorac Surg. 2006;29:441-446
PubMed   |  Link to Article
Coresh J, Eknoyan G, Levey AS. Estimating the prevalence of low glomerular filtration rate requires attention to the creatinine assay calibration.  J Am Soc Nephrol. 2002;13:2811-2812
PubMed   |  Link to Article
DeLong ER, Peterson ED, DeLong DM, Muhlbaier LH, Hackett S, Mark DB. Comparing risk-adjustment methods for provider profiling.  Stat Med. 1997;16:2645-2664
PubMed   |  Link to Article
Taher T, Khan NA, Devereaux PJ, Fisher BW, Ghali WA, McAlister FA. Assessment and reporting of perioperative cardiac risk by Canadian general internists: art or science?  J Gen Intern Med. 2002;17:933-936
PubMed   |  Link to Article
Hix JK, Thakar CV, Katz EM, Yared JP, Sabik J, Paganini EP. Effect of off-pump coronary artery bypass graft surgery on postoperative acute kidney injury and mortality.  Crit Care Med. 2006;34:2979-2983
PubMed
Landoni G, Biondi-Zoccai GG, Tumlin JA.  et al.  Beneficial impact of fenoldopam in critically ill patients with or at risk for acute renal failure: a meta-analysis of randomized clinical trials.  Am J Kidney Dis. 2007;49:56-68
PubMed   |  Link to Article
Wagener G, Jan M, Kim M.  et al.  Association between increases in urinary neutrophil gelatinase-associated lipocalin and acute renal dysfunction after cardiac surgery.  Anesthesiology. 2006;105:485-491
PubMed   |  Link to Article
Burns KE, Chu MW, Novick RJ.  et al.  Perioperative N-acetylcysteine to prevent renal dysfunction in high-risk patients undergoing CABG surgery: a randomized controlled trial.  JAMA. 2005;294:342-350
PubMed   |  Link to Article
Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation, Modification of Diet in Renal Disease Study Group.  Ann Intern Med. 1999;130:461-470
PubMed   |  Link to Article

Figures

Figure 2. Likelihood Ratios of Patients Requiring Renal Replacement Therapy Within Strata of the Simplified Renal index Score
Graphic Jump Location

The circles denote likelihood ratios for renal replacement therapy in each cohort. Error bars are 95% confidence intervals.30 Few patients in the Ottawa (n = 6) and Toronto (n = 9) validation cohorts had scores of 6 or greater; hence, these strata are not presented.

Figure 1. Proportions of Patients Requiring Renal Replacement Therapy Within Strata of the Simplified Renal Index Score
Graphic Jump Location

The circles denote percentages in each cohort that required renal replacement therapy. Error bars are 95% confidence intervals. Few patients in the Ottawa (n = 6) and Toronto (n = 9) validation cohorts had scores of 6 or greater; hence, these strata are not presented.

Tables

Table Graphic Jump LocationTable 5. Comparison Between the Simplified Renal Index and Alternative Predictive Indices
Table Graphic Jump LocationTable 4. Perioperative Characteristics of the Derivation and Validation Cohorts*
Table Graphic Jump LocationTable 1. Characteristics of the Derivation Cohort, and Their Bivariate Association With Renal Replacement Therapy*
Table Graphic Jump LocationTable 3. Simplified Renal Index Scoring Scheme for Estimating Risk of Postoperative Renal Replacement Therapy
Table Graphic Jump LocationTable 2. Multivariable Predictors of Postoperative Renal Replacement Therapy in the Derivation Cohort

References

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Chertow GM, Levy EM, Hammermeister KE, Grover F, Daley J. Independent association between acute renal failure and mortality following cardiac surgery.  Am J Med. 1998;104:343-348
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Ferguson TB Jr, Hammill BG, Peterson ED, DeLong ER, Grover FL.Society of Thoracic Surgeons National Database Committee.  A decade of change—risk profiles and outcomes for isolated coronary artery bypass grafting procedures, 1990-1999: a report from the STS National Database Committee and the Duke Clinical Research Institute.  Ann Thorac Surg. 2002;73:480-489
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Mehta RH, Grab JD, O'Brien SM.  et al.  Bedside tool for predicting the risk of postoperative dialysis in patients undergoing cardiac surgery.  Circulation. 2006;114:2208-2216
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Lee TH, Marcantonio ER, Mangione CM.  et al.  Derivation and prospective validation of a simple index for prediction of cardiac risk of major noncardiac surgery.  Circulation. 1999;100:1043-1049
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Thakar CV, Arrigain S, Worley S, Yared JP, Paganini EP. A clinical score to predict acute renal failure after cardiac surgery.  J Am Soc Nephrol. 2005;16:162-168
PubMed   |  Link to Article
Fortescue EB, Bates DW, Chertow GM. Predicting acute renal failure after coronary bypass surgery: cross-validation of two risk-stratification algorithms.  Kidney Int. 2000;57:2594-2602
PubMed   |  Link to Article
Dupuis JY, Wang F, Nathan H, Lam M, Grimes S, Bourke M. The cardiac anesthesia risk evaluation score: a clinically useful predictor of mortality and morbidity after cardiac surgery.  Anesthesiology. 2001;94:194-204
PubMed   |  Link to Article
Karkouti K, Beattie WS, Wijeysundera DN.  et al.  Hemodilution during cardiopulmonary bypass is an independent risk factor for acute renal failure in adult cardiac surgery.  J Thorac Cardiovasc Surg. 2005;129:391-400
PubMed   |  Link to Article
Wijeysundera DN, Karkouti K, Beattie WS, Rao V, Ivanov J. Improving the identification of patients at risk of postoperative renal failure after cardiac surgery.  Anesthesiology. 2006;104:65-72
PubMed   |  Link to Article
Concato J, Feinstein AR, Holford TR. The risk of determining risk with multivariable models.  Ann Intern Med. 1993;118:201-210
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Chertow GM, Lazarus JM, Christiansen CL.  et al.  Preoperative renal risk stratification.  Circulation. 1997;95:878-884
PubMed   |  Link to Article
Conlon PJ, Stafford-Smith M, White WD.  et al.  Acute renal failure following cardiac surgery.  Nephrol Dial Transplant. 1999;14:1158-1162
PubMed   |  Link to Article
Mangano CM, Diamondstone LS, Ramsay JG, Aggarwal A, Herskowitz A, Mangano DT. Renal dysfunction after myocardial revascularization: risk factors, adverse outcomes, and hospital resource utilization: The Multicenter Study of Perioperative Ischemia Research Group.  Ann Intern Med. 1998;128:194-203
PubMed   |  Link to Article
Thakar CV, Liangos O, Yared JP.  et al.  ARF after open-heart surgery: influence of gender and race.  Am J Kidney Dis. 2003;41:742-751
PubMed   |  Link to Article
Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine.  Nephron. 1976;16:31-41
PubMed   |  Link to Article
National Kidney Foundation.  K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification.  Am J Kidney Dis. 2002;39:(suppl 1)  S1-S266
PubMed   |  Link to Article
Noyez L, Plesiewicz I, Verheugt FW. Estimated creatinine clearance instead of plasma creatinine level as prognostic test for postoperative renal function in patients undergoing coronary artery bypass surgery.  Eur J Cardiothorac Surg. 2006;29:461-465
PubMed   |  Link to Article
Wang F, Dupuis JY, Nathan H, Williams K. An analysis of the association between preoperative renal dysfunction and outcome in cardiac surgery: estimated creatinine clearance or plasma creatinine level as measures of renal function.  Chest. 2003;124:1852-1862
PubMed   |  Link to Article
Goldman L, Hashimoto B, Cook EF, Loscalzo A. Comparative reproducibility and validity of systems for assessing cardiovascular functional class: advantages of a new specific activity scale.  Circulation. 1981;64:1227-1234
PubMed   |  Link to Article
Laupacis A, Sekar N, Stiell IG. Clinical prediction rules: a review and suggested modifications of methodological standards.  JAMA. 1997;277:488-494
PubMed   |  Link to Article
Harrell FE Jr. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. New York, NY: Springer-Verlag; 2001
Hosmer DW, Lemeshow S. Applied Logistic Regression. New York, NY: John Wiley & Sons; 2000
Steyerberg EW, Eijkemans MJ, Harrell FE Jr, Habbema JD. Prognostic modeling with logistic regression analysis: in search of a sensible strategy in small data sets.  Med Decis Making. 2001;21:45-56
PubMed   |  Link to Article
Moons KG, Harrell FE, Steyerberg EW. Should scoring rules be based on odds ratios or regression coefficients?  J Clin Epidemiol. 2002;55:1054-1055
PubMed   |  Link to Article
Harrell FE Jr. Design: S functions for biostatistical/epidemiologic modeling, testing, estimation, validation, graphics, and prediction. Vanderbilt University Web site Department of Biostatistics. http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/Design. Accessed November 28, 2006
Lee DS, Austin PC, Rouleau JL, Liu PP, Naimark D, Tu JV. Predicting mortality among patients hospitalized for heart failure: derivation and validation of a clinical model.  JAMA. 2003;290:2581-2587
PubMed   |  Link to Article
Jaeschke R, Guyatt G, Sackett DL. Users' guides to the medical literature III, how to use an article about a diagnostic test; A: are the results of the study valid? Evidence-Based Medicine Working Group.  JAMA. 1994;271:389-391
PubMed   |  Link to Article
Simel DL, Samsa GP, Matchar DB. Likelihood ratios with confidence: sample size estimation for diagnostic test studies.  J Clin Epidemiol. 1991;44:763-770
PubMed   |  Link to Article
van den Berghe G, Wouters P, Weekers F.  et al.  Intensive insulin therapy in the critically ill patients.  N Engl J Med. 2001;345:1359-1367
PubMed   |  Link to Article
Pierpont GL, Kruse M, Ewald S, Weir EK. Practical problems in assessing risk for coronary artery bypass grafting.  J Thorac Cardiovasc Surg. 1985;89:673-682
PubMed
Arozullah AM, Khuri SF, Henderson WG, Daley J. Development and validation of a multifactorial risk index for predicting postoperative pneumonia after major noncardiac surgery.  Ann Intern Med. 2001;135:847-857
PubMed   |  Link to Article
Mathew JP, Fontes ML, Tudor IC.  et al.  A multicenter risk index for atrial fibrillation after cardiac surgery.  JAMA. 2004;291:1720-1729
PubMed   |  Link to Article
Detsky AS, Abrams HB, McLaughlin JR.  et al.  Predicting cardiac complications in patients undergoing non-cardiac surgery.  J Gen Intern Med. 1986;1:211-219
PubMed   |  Link to Article
Goldman L, Caldera DL, Nussbaum SR.  et al.  Multifactorial index of cardiac risk in noncardiac surgical procedures.  N Engl J Med. 1977;297:845-850
PubMed   |  Link to Article
Austin PC. A comparison of regression trees, logistic regression, generalized additive models, and multivariate adaptive regression splines for predicting AMI mortality.  Stat Med2006 [Epub ahead of print]
PubMed
Harrison DA, Brady AR, Parry GJ, Carpenter JR, Rowan K. Recalibration of risk prediction models in a large multicenter cohort of admissions to adult, general critical care units in the United Kingdom.  Crit Care Med. 2006;34:1378-1388
PubMed   |  Link to Article
Ivanov J, Tu JV, Naylor CD. Ready-made, recalibrated, or remodeled? issues in the use of risk indexes for assessing mortality after coronary artery bypass graft surgery.  Circulation. 1999;99:2098-2104
PubMed   |  Link to Article
Yap CH, Reid C, Yii M.  et al.  Validation of the EuroSCORE model in Australia.  Eur J Cardiothorac Surg. 2006;29:441-446
PubMed   |  Link to Article
Coresh J, Eknoyan G, Levey AS. Estimating the prevalence of low glomerular filtration rate requires attention to the creatinine assay calibration.  J Am Soc Nephrol. 2002;13:2811-2812
PubMed   |  Link to Article
DeLong ER, Peterson ED, DeLong DM, Muhlbaier LH, Hackett S, Mark DB. Comparing risk-adjustment methods for provider profiling.  Stat Med. 1997;16:2645-2664
PubMed   |  Link to Article
Taher T, Khan NA, Devereaux PJ, Fisher BW, Ghali WA, McAlister FA. Assessment and reporting of perioperative cardiac risk by Canadian general internists: art or science?  J Gen Intern Med. 2002;17:933-936
PubMed   |  Link to Article
Hix JK, Thakar CV, Katz EM, Yared JP, Sabik J, Paganini EP. Effect of off-pump coronary artery bypass graft surgery on postoperative acute kidney injury and mortality.  Crit Care Med. 2006;34:2979-2983
PubMed
Landoni G, Biondi-Zoccai GG, Tumlin JA.  et al.  Beneficial impact of fenoldopam in critically ill patients with or at risk for acute renal failure: a meta-analysis of randomized clinical trials.  Am J Kidney Dis. 2007;49:56-68
PubMed   |  Link to Article
Wagener G, Jan M, Kim M.  et al.  Association between increases in urinary neutrophil gelatinase-associated lipocalin and acute renal dysfunction after cardiac surgery.  Anesthesiology. 2006;105:485-491
PubMed   |  Link to Article
Burns KE, Chu MW, Novick RJ.  et al.  Perioperative N-acetylcysteine to prevent renal dysfunction in high-risk patients undergoing CABG surgery: a randomized controlled trial.  JAMA. 2005;294:342-350
PubMed   |  Link to Article
Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation, Modification of Diet in Renal Disease Study Group.  Ann Intern Med. 1999;130:461-470
PubMed   |  Link to Article
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