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Ann Thorac Surg 2006;81:1385-1392
© 2006 The Society of Thoracic Surgeons

Original article: Cardiovascular

Predicting Survival in Patients Requiring Renal Replacement Therapy After Cardiac Surgery

Marzia Leacche, MD a , Wolfgang C. Winkelmayer, MD, ScD b , c , Subroto Paul, MD a , Julie Lin, MD c , Daniel Unic, MD a , James D. Rawn, MD a , Lawrence H. Cohn, MD a , John G. Byrne, MD a , *

a Division of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
b Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
c Renal Division, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts

Accepted for publication October 10, 2005.

* Address correspondence to Dr Byrne, Department of Cardiac Surgery, Vanderbilt University Medical Center, Nashville, TN 37232-8815 (Email: john.byrne{at}vanderbilt.edu).


   Abstract
Top
 Abstract
Introduction
 Material and Methods
 Results
 Comment
 Footnotes
 Acknowledgments
 References
 
BACKGROUND: We sought to develop and internally validate a prediction score for all-cause in-hospital mortality in patients who have acute renal failure and require renal replacement therapy after cardiac surgery.

METHODS: From January 1992 to July 2001, 136 of 14,000 patients (0.9%) had acute renal failure requiring renal replacement therapy after cardiac surgery. Multivariate logistic regression analysis, based on pre–renal replacement therapy variables, was used to construct a predictive score for all causes of in-hospital mortality. Subsequently, the score was validated in 27 patients who underwent surgery between August 2001 and March 2003.

RESULTS: In-hospital mortality was 58% (79 of 136). From the logistic regression model, we assigned a score (range, 0 to 6) based on the presence of independent predictors of operative mortality (preoperative creatinine ≤ 1.5 mg/dL [odds ratio (OR) = 5.0], hypertension [OR = 4.4], predialysis coma [OR = 9.6], sepsis [OR = 6.4], and total bilirubin ≥ 2 mg/dL [OR = 5.6]). Higher scores strongly predicted mortality: patients who scored 3 or higher before the initiation dialysis (n = 54), had a mortality rate of 94% (51 of 54). In contrast, patients who scored 1 or less on this scale (n = 36), had a mortality of 16% (6 of 36). In the validation cohort, the sensitivity of the new score at the cutoff of 2 or fewer points versus 3 or more points was 0.71, the specificity was 0.90, the positive predictive value was 0.92, and the negative predictive value was 0.64.

CONCLUSIONS: The prediction score represents a simple and accurate tool for predicting in-hospital mortality associated with renal replacement therapy for cardiac surgery patients before the institution of this resource-intensive treatment.


   Introduction
Top
 Abstract
 Introduction
Material and Methods
 Results
 Comment
 Footnotes
 Acknowledgments
 References
 
Despite improvements in surgical techniques, anesthesia, cardiopulmonary bypass and management of postoperative care, acute renal failure (ARF) after cardiac surgery necessitating renal replacement therapy still occurs in 1% to 5% of patients [1]. The mortality rate in this group remains impressively high, between 40% and 90% [2]. General outcome prediction models are used in the intensive care unit [3–5], but limited information is available about the application of these models in the setting of ARF. Specific ARF-prediction models have been developed for subsets, such as patients in whom ARF is caused exclusively by acute tubular necrosis [6, 7], or in medical intensive care unit patients requiring dialysis [8]. The applicability of these models to the cardiac surgery intensive care unit population, however, is unknown.

The literature on specific ARF model development in cardiac surgery patients is limited. Of the available models, one is applicable only to patients with ARF after aortic aneurysm surgery [9], one pertains to patients undergoing continuous renal replacement therapy [2], and the rest are not sustained by the statistical evidence because of the limited number of patients [10–12]. We sought to develop a clinically useful score that reliably predicts all-cause in-hospital mortality before implementation of resource-intensive renal replacement therapy in patients with new-onset ARF after cardiac surgery, and to validate this score in an independent cohort of patients at the same institution.


   Material and Methods
Top
 Abstract
 Introduction
 Material and Methods
Results
 Comment
 Footnotes
 Acknowledgments
 References
 
After approval by our Institutional Review Board in March 2003, we retrospectively identified all adult patients who underwent cardiac surgery between January 1992 and July 2001 (n = 14,000). Patients who were dialysis-dependent before surgery or had undergone renal transplant had been excluded from the study. Of these, 136 (0.9%) had ARF requiring renal replacement therapy. From the medical records, we abstracted a large number of patient characteristics (preoperative, perioperative, and postoperative) that were present before the implementation of renal replacement therapy (Tables 1 to 4). Go Go Go Definitions of risk factor variables used in this study are summarized in the Appendix.


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  		Table 1. Preoperative Patients' Characteristics and Preoperative Laboratory Data
 

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  		Table 2. Operative Data
 

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  		Table 3. Hemodynamic and Laboratory Data Starting Before Renal Replacement Therapy
 

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  		Table 4. Postoperative Complications Before Starting Renal Replacement Therapy
 
Patient Characteristics
There were 81 men (59%), and the mean age of the study population was 67 ± 12 years. Median New York Heart Association (NYHA) functional classification was 3 (range, 1 to 4). The mean ejection fraction was 42% ± 15%. Sixty-seven patients (49%) underwent operation on an urgent basis, and 19 patients (14%) on an emergent basis. The mean preoperative creatinine level was 1.6 ± 0.8 mg/dL (range, 0.6 to 5 mg/dL), with an estimated {epsilon} creatinine clearance of 53 ± 26 mL/min. Fifty-five patients (40%) underwent CABG/valve surgery, 39 (29%) underwent isolated CABG, 21 (15%) had isolated valve surgery, 12 (9%) had heart transplantation, and 9 (6%) had other procedures.

Renal Replacement Therapy
Intermittent hemodialysis usually consisted of a 3- to 4-hour treatment, 3 or 4 times a week, combining diffuse and convective clearance of solutes. Continuous arteriovenous hemofiltration involved using the patient's femoral artery and vein for vascular access. Continuous venovenous hemofiltration involved venous access only and an external pump to drive blood flow through the filter. Continuous arteriovenous or venovenous diafiltration refers to the addition of countercurrent dialysate to the filter system that resulted in diffusive clearance of solutes as well as convective clearance achieved by hemofiltration.

Model Development
We sought to develop a score useful for the routine care of cardiac surgery intensive care unit patients. Simple and multivariate logistic regression analysis was used to model mortality on renal replacement therapy. An observation was dropped from analysis if one or more variables used in the respective model were missing from that person. We performed a manual backward selection procedure with the goal of predicting mortality with a parsimonious multivariate logistic regression model. We only considered those variables that were available for 90% or more of patients. Once a limited multivariate model was found, including only those variables with an independent p value less than 0.05, we reintroduced each variable to test for significance until a final model was established. The coefficients from that model were then used to construct a simple prediction score for mortality. The points assigned for each variable were approximately proportional to the effect estimate (odds ratio) of the respective variable. Only integer points were allowed.

After the model was constructed, a score was calculated for each patient in the cohort. We also calculated other scores for our data using methods described in the literature, including the general intensive care unit models (Acute Physiology and Chronic Health Evaluation [APACHE] II, Sequential Organ Failure Assessment [SOFA], Simplified Acute Physiology Score [SAPS II]) [3–5], one ARF-specific model (Liano) [6], and a model for cardiac surgery patients (Bent) [2].

Validation
A separate cohort of patients requiring renal replacement therapy after cardiac surgery between August 2001 and March 2003 was identified. The same data elements collected for the study cohort were collected for the validation cohort. The new and previously described scores for each patient were then calculated. The performance of all scores were then analyzed in this independent sample of patients.

The validation cohort included 14 men (52%). The mean age was 69 ± 12 years. Median NYHA classification was 3 (range, 1 to 4); mean ejection fraction was 42% ± 5%; and mean preoperative creatinine was 1.8 ± 0.9 mg/dL (range, 0.7 to 4 mg/dL), with an estimated creatinine clearance of 59 ± 40 mL/min. Eleven patients (41%) underwent isolated valve surgery, 8 (29%) had CABG/valve, 6 (22%) had CABG, and 2 (7%) had other procedures. Urgent surgery was performed in 13 patients (48%) and emergent in 3 (11%).

Data are expressed as median or a percentage. For continuous variables, a Mann-Whitney test was used. The two-tailed Fisher's exact test was used for categorical variables. Data analysis and statistics were performed with STATA 7.0 for Windows (Stata Corp, College Station, Texas) and SAS for Windows (release 8.2; SAS Institute, Cary, North Carolina).


   Results
Top
 Abstract
 Introduction
 Material and Methods
 Results
Comment
 Footnotes
 Acknowledgments
 References
 
Of the 136 patients in the original cohort, 57 patients (42%) died on renal replacement therapy after cardiac surgery. Forty-three percent (59 patients) received continuous treatment with either continuous venovenous hemofiltration/diafiltration (47%, n = 64) or continuous arteriovenous hemofiltration/diafiltration (29%, n = 21); 31% (n = 43) received intermittent treatment, and 24% (n = 33) received a combination of both continuous and intermittent dialysis. The major indications for renal replacement were oliguria (48%, n = 65), fluid overload (83%, n = 112), and azotemia (84%, n = 113). From univariate analyses, we found that several characteristics differed significantly between survivors and nonsurvivors (Tables 1 to 4). Table 5 summarizes the difference between survivors and nonsurvivors with respect to type and mode of renal replacement therapy. For the purpose of this study, however, no variables regarding renal replacement therapy were entered into the logistic model.


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  		Table 5. Renal Replacement Therapy Data
 
Scoring System and Model Performance
From the variables analyzed for model selection, we arrived at a final logistic regression model consisting of five variables. The c-statistic for this five-variable model was 0.87, and the model goodness of fit not rejected range (p = 0.19). In tabulating the final score, 2 points were assigned for patients in a comatose state, and 1 point each was assigned for the other four conditions (Table 6). Using the calculated score rather than the actual model parameters in the logistic regression model yielded a c-statistic of 0.85. The predictive power of the other scores reported in the literature was also assessed using the c-statistic. The performance of these scores varied greatly: APACHE II (c = 0.72), Liano (c = 0.71), SAPS II (c = 0.68), SOFA (c = 0.65), and Bent (c = 0.64). The mean values of scores using other modeling systems were as follows: APACHE II 18 ± 6, Liano 0.6 ± 0.17, SAPS II 43 ± 12, SOFA 9 ± 3, and Bent 0.35 ± 0.35.


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  		Table 6. The Scoring System
 
We dichotomized the newly developed scores at a value of 2 or fewer points versus 3 or more points. Among the 79 patients who died, 51 had a score of 3 or more (sensitivity = 0.65). Conversely, among the 57 patients who survived, 54 scored 2 or less (specificity = 0.95). The positive and negative predictive values using this score cutoff were 0.94 and 0.67, respectively (Table 7).


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  		Table 7. Distribution of Risk Scores Assigned to Patients in Study Group
 
Model Validation
Fifteen of the 27 patients (55%) in the validation cohort received intermittent continuous venovenous hemofiltration/continuous venovenous hemodiafiltration, 6 (30%) received intermittent hemodialysis, and 4 (15%) received a combination of both. The in-hospital mortality among these patients was 63% (n = 17). In this independent sample, the c-statistic of the new score was 0.83, and the c-statistics for the other instruments were as follows: SAPS II (c = 0.85), SOFA (c = 0.80), Liano (c = 0.72) Bent (c = 0.65), and APACHE II (c = 0.56). Mean values of the scores were as follows: APACHE II 21 ± 5, SAPS II 47 ± 11, SOFA 9 ± 3, Liano 0.61 ± 18, and Bent 0.38 ± 0.35. The sensitivity of the new score at the cutoff of 2 or less versus 3 or more points was 0.71, and the specificity was 0.90. The positive and negative predictive valves using this cutoff score were 0.92 and 0.64, respectively (Table 8).


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  		Table 8. Distribution of Risk Scores Assigned to Patients in the Validation Cohort
 

   Comment
Top
 Abstract
 Introduction
 Material and Methods
 Results
 Comment
Footnotes
 Acknowledgments
 References
 
The principle finding in this study is that a score of 3 or higher was associated with a very high likelihood of hospital mortality whereas a score of 2 or less was associated with a more reasonable hospital survival rate. This scoring system may help to identify patients in whom resource intensive renal replacement therapy is likely to be futile versus those in whom it is more appropriate.

The overall survival rate of 42% in this study is consistent with previous reports [2, 10, 11, 13]. It is difficult to compare survival rates across the literature because of the wide variation in mode of renal replacement therapy, timing, frequency, intensity, membrane choice, and delivered dose. Coma (Glasgow Coma Scale score < 15), sepsis, jaundice, preoperative hypertension, and serum creatinine less than 1.5 mg/dL were independent risk factors for in-hospital mortality. The probability of survival in the 54 patients scoring 3 or higher was only 6%.

Coma, sepsis, and jaundice are obvious predictors of mortality. However, it is more difficult to explain how normal preoperative renal function and preoperative hypertension predicted mortality. A preoperative creatinine level less than 1.5 mg/dL was likely a surrogate for a major adverse intraoperative or postoperative event that eventually leads to the need for renal replacement therapy. These adverse events are probably varied. However, it is likely that the common final pathway was low cardiac output syndrome and renal hypoperfusion. Although clearance estimation is regarded as a more accurate assessment of renal function [14], in our logistic model, however, clearance of creatinine was not a predictor of death. Preoperative hypertension, as a predictor of mortality, could be explained by the presence of renal microvascular disease or glomerulosclerosis that may render them more susceptible to renal injury after cardiopulmonary bypass.

This prediction score should help in the daily management of postoperative cardiac surgery patients to help identify patients for whom dialysis is probably futile versus patients for whom it is more helpful.


   Appendix
 
Potential Risk Factor Variable Definitions
In-hospital mortality: death for any reason occurring within 30 days after the surgery or after 30 days occurring during the same hospitalization.

Endocarditis: active if patients did not complete a course of antibiotics (usually 5 to 6 weeks) and had fever or peripheral events requiring urgent valve surgery.

Hypertension: if there was a documented history of hypertension treated by antihypertensive medication before surgery.

Congestive heart failure: presence within 2 weeks before procedure of paroxysmal nocturnal dyspnea or dyspnea on exertion because of heart failure or chest x-ray film showing pulmonary congestion.

Cardiogenic shock: if intravenous inotropes or IABP was necessary to maintain a systolic blood pressure greater than 80 mm Hg or a cardiac index greater than 1.8 L · min–1 · m–1.

Myocardial infarction (MI): acute if present 7 or fewer days from the last documented MI or evolving, if, at the time of surgery, Q-waves or ST changes were present along with a CK-MB greater than 5% of total CPK.

Urgent surgery: procedure required during the same hospitalization in order to minimize chance of further clinical deterioration.

Emergent: if ischemic dysfunction (ongoing ischemia despite maximal medical treatment or IABP, acute/evolving MI, pulmonary edema requiring intubation) or shock. The interval between the onset of ARF (postoperative creatinine level of 2.0 mg/dL or greater, or an increment of 0.5 mg/dL or more, compared with the preoperative baseline value) and the initiation of RRT estimates the time of the beginning of renal treatment.

Acidosis: a pH of 7.25 or less on an arterial blood gas (ABG) sample.

Oliguria: a 24-hour urine output of 400 mL or less.

Low cardiac output syndrome: a cardiac index of 2.0 L · min–1 · m– 2 or less, requiring triple inotropic support to maintain a systolic pressure greater than 90 mm Hg for at least 30 minutes, or placement of intra-aortic balloon pump (IABP) or ventricular assist device (VAD).

Perioperative myocardial infarction: appearance of new Q waves or a CPK-MB fraction of 100 IU/L or more.

Bleeding: necessity of reexploration of the thorax for suspected bleeding during the postoperative period.

Acute respiratory distress syndrome: presence of bilateral pulmonary infiltrates on chest radiograph, impaired oxygenation resulting in a PaO2 to fraction of inspired oxygen (FIO2) ratio of less than 200, and absence of elevated pulmonary arterial occlusion pressure (PAOP) or left atrial pressure.

Stroke: evidence in the postoperative period of a new central neurologic deficit persisting for more than 72 hours.

Transient ischemic attack: if the neurologic deficit resolved in 72 hours.

Coma: new postoperative coma for at least 24 hours graded as a Glasgow Coma Scale score of 14 or less; if the patient was sedated, the Glascow score was ascertained by reviewing the patient's medical record before sedation.

Gastrointestinal complication: included diagnosis of upper and lower gastrointestinal hemorrhage, acute cholecystitis, pancreatitis, and mesenteric ischemia.

Pancreatitis: defined as a fivefold increase with amylase or lipase.

Sepsis: defined as presence of (1) temperative greater than 38C° or less than 36C° or (2) a white blood cell count greater than 14,000/ mm3 or less than 4,000/mm3 or the presence of more than 10% immature neutrophils, and (3) documented infection source with positive blood culture or strongly suspected.

Septic shock: defined as sepsis with hypotension (systolic blood pressure < 90 mm Hg), despite adequate fluid resuscitation, along with the presence of perfusion abnormalities.

Vascular complications: included any complication producing limb ischemia.

Disseminated intravascular coagulation: defined as a platelet count less than 100.000 and a prothrombin time greater than 16 with fibrin split products.


   Acknowledgments
Top
 Abstract
 Introduction
 Material and Methods
 Results
 Comment
 Footnotes
 Acknowledgments
References
 
We thank the Biostatistics Consulting Service, Center for Clinical Investigation, Brigham and Women's Hospital, for assistance in statistical analysis.


   Footnotes
Top
 Abstract
 Introduction
 Material and Methods
 Results
 Comment
 Footnotes
Acknowledgments
 References
 
{epsilon} Cockcroft and Gault formula. Back


   References
Top
 Abstract
 Introduction
 Material and Methods
 Results
 Comment
 Footnotes
 Acknowledgments
 References
 

  1. Chertow GM, Christiansen CL, Cleary PD, et al. Prognostic stratification in critically ill patients with acute renal failure requiring dialysis Arch Intern Med 1995;155:1505-1511.[Abstract/Free Full Text]
  2. Bent P, Tan HK, Bellomo R, et al. Early and intensive continuous hemofiltration for severe renal failure after cardiac surgery Ann Thorac Surg 2001;71:832-837.[Abstract/Free Full Text]
  3. Vincent JL, de Mendonca A, Cantraine F, et al. Working group on "sepsis-related problems" of the European Society of Intensive Care Medicine Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care unitsresults of a multicenter, prospective study. Crit Care Med 1998;26:1793-1800.[Medline]
  4. Knaus WA, Draper EA, Wagner DP, et al. APACHE IIa severity of disease classification system. Crit Care Med 1985;13:818-829.[Medline]
  5. Le Gall JR, Lemeshow S, Saulnier F. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study JAMA 1993;270:2957-2963.[Abstract/Free Full Text]
  6. Liano F, Garcia-Martin F, Gallego A, et al. Easy and early prognosis in acute tubular necrosisa forward analysis of 228 cases. Nephron 1989;51:307-313.[Medline]
  7. Rasmussen HH, Pitt EA, Ibels LS, et al. Prediction of outcome in acute renal failure by discriminant analysis of clinical variables Arch Intern Med 1985;145:2015-2018.[Abstract/Free Full Text]
  8. Schaefer JH, Jochimsen F, Keller F, et al. Outcome prediction of acute renal failure in medical intensive care Intensive Care Med 1991;17:19-24.[Medline]
  9. Berisa F, Beaman M, Adu D, et al. Prognostic factors in acute renal failure following aortic aneurysm surgery Q J Med 1990;76:689-698.[Medline]
  10. Tsang GM, Khan I, Dar M, et al. Hemofiltration in a cardiac intensive care unittime for a rational approach. ASAIO J 1996;42:M710-M713.[Medline]
  11. Lange HW, Aeppli DM, Brown DC. Survival of patients with acute renal failure requiring dialysis after open heart surgeryearly prognostic indicators. Am Heart J 1987;113:1138-1143.[Medline]
  12. Baudouin SV, Wiggins J, Keogh BF, et al. Continuous veno-venous haemofiltration following cardio-pulmonary bypass. Indications and outcome in 35 patients Intensive Care Med 1993;19:290-293.[Medline]
  13. Ostermann ME, Taube D, Morgan CJ, et al. Acute renal failure following cardiopulmonary bypassa changing picture. Intensive Care Med 2000;26:565-571.[Medline]
  14. Wang F, Dupuis JY, Nathan H, et al. An analysis of the association between preoperative renal dysfunction and outcome in cardiac surgeryestimated creatinine clearance or plasma creatinine level as measures of renal function. Chest 2003;124:1852-1862.[Abstract/Free Full Text]



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