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Ann Thorac Surg 2007;83:921-929
© 2007 The Society of Thoracic Surgeons

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Original Articles: Cardiovascular

Risk Index for Predicting In-Hospital Mortality for Cardiac Valve Surgery

^a University at Albany, State University of New York, New York, New York
^b St. Peter’s Hospital, Albany, New York, New York
^c Mercy Hospital, Buffalo, New York, New York
^d New York University Medical Center, New York, New York
^e Medical University of Ohio, Toledo, Ohio
^f Rush University Medical Center, Chicago, Illinois
^g Columbia-Presbyterian Medical Center, New York, New York
^h Duke University Medical Center, Durham, North Carolina

Accepted for publication September 15, 2006.

^_* Address correspondence to Dr Hannan, State University of New York at Albany, Department of Health Policy, Management, and Behavior, One University Place, Rensselaer, NY 12144 (Email: elh03{at}health.state.ny.us).

	Abstract

Top Abstract Introduction Patients and Methods Results Comment Acknowledgments References

 
Background: Numerous studies have developed a "severity score" or "risk index" for short-term mortality associated with coronary artery bypass graft (CABG) surgery, but very few studies have developed risk indices derived from statistical models to predict outcomes for cardiac valve replacement patients.

Methods: Data from New York’s Cardiac Surgery Reporting System in 2001 to 2003 were used to develop statistical models that predict mortality for valve surgery and for valve/CABG surgery. These models were used to develop risk indices based on the type of valve surgery performed and several patient risk factors. The fit of each index was tested by examining the correspondence of expected and observed mortality rates for various risk score ranges using New York data between 1998 and 2000.

Results: There were a total of 11 risk factors for valve patients without CABG surgery and 12 risk factors for patients with both valve and CABG surgery. Risk factors represented measures of demographics, type of valve surgery, previous open heart surgery, ventricular function, hemodynamic state, and various comorbidities. Possible variable scores ranged from 0 to 7 in the isolated valve model and 0 to 5 in the valve/CABG model. The highest overall risk scores possible for the two models were 49 for isolated valve surgery and 35 for valve/CABG surgery, and the highest scores observed for any patient were 32 and 26, respectively.

Conclusions: These valve surgery risk indices will enable providers to estimate patients’ short-term mortality risk and allow for comparisons of valve surgery outcomes with other regions.

	Introduction

Top Abstract Introduction Patients and Methods Results Comment Acknowledgments References

 
Numerous studies have reported a "severity score" or "risk index" for short-term mortality associated with coronary artery bypass graft (CABG) surgery [1–12], and some of these studies also enable the user to calculate the risk for patients undergoing cardiac valve surgery [3, 5, 10]. Other studies have identified significant risk factors for short-term mortality among patients undergoing cardiac valve surgery [13–20]. However, very few studies have developed risk indices derived from statistical models to predict outcomes for cardiac valve replacement patients [13, 14].

The purpose of this study is to develop risk indices for two groups of valve patients: those with and without concomitant CABG surgery. For the remainder of this communication, the term "risk index" will be used to denote the entire formula used to predict mortality, with weights assigned to various risk factors. The term "risk score" will be used to denote the actual score associated with a given set of risk factors. The final product is a score that can be obtained for each patient along with a table that provides the expected in-hospital mortality rate associated with that score.

Prediction of operative risk helps patients and their physicians to select the most appropriate therapeutic option. Preoperative risk assessment also identifies those patients most likely to require intensive monitoring in the postoperative period. Furthermore, hospitals, surgeons, and health care monitoring entities can compare their observed outcomes with those expected using New York state data after adjusting for differences in patient risk factors.

	Patients and Methods

Top Abstract Introduction Patients and Methods Results Comment Acknowledgments References

 
Data
The data used for the study were taken from New York’s Cardiac Surgery Reporting System, which was created in late 1988 for the purpose of improving the quality of cardiac care in the state as well as to inform hospitals, surgeons and the public of patient outcomes and risk factors. Data in the system include patient demographics (age, sex, race, and so forth), numerous patient risk factors and comorbidities, patient disposition, complications of care, and hospital and surgeon identifiers (see Appendix for data definitions). The dependent variable in this study was in-hospital mortality, which was also used by most other studies on this topic.

Patients in the study consisted of all patients who underwent isolated cardiac valve surgery (valve surgery without CABG surgery) or cardiac valve surgery along with concomitant CABG surgery in New York in 2001 to 2003. Risk scores obtained from these patients were validated using 1998 to 2000 data from New York with the same case definitions.

Overview of Analysis Plan
For each of the two sets of analyses (valve only, and valve with CABG), continuous risk factors (age, ejection fraction) were first expressed as categorical variables by identifying ranges that were internally homogeneous with regard to mortality. Then ² tests were used to identify all significant bivariate relationships between each of the potential risk factors and in-hospital mortality (at the 0.05 level).

The data base was then split into a development and validation group with roughly equal mortality rates and prevalences for the risk factors significant in the bivariate analyses. The risk factors were then entered as candidate independent variables in a stepwise logistic regression model with p less than 0.10 in the development group with in-hospital mortality as the binary dependent variable. Significant variables in this model were then validated in a stepwise model on the validation group. Significant variables from the validation model were then used to develop a stepwise model with the entire data set. This model (with significance level 0.05) yielded the set of significant patient risk factors. The fit of the final logistic regression model was measured in terms of its discrimination (C statistic) [21] and calibration (Hosmer-Lemeshow statistic) [22].

Development of the Risk Index From the Logistic Regression Model
The only continuous risk factor, age, was split into different categories based on internal consistency of mortality. The youngest group was used as the reference or base category; other groups were represented by their midpoints (the last group was represented by the midpoint of its lowest value and the 99th percentile) [1, 23, 24]. For binary risk factors (eg, presence or absence of diabetes), the base category was chosen as the absence of the characteristic. For categorical variables with more than two categories, the base categories were chosen to be the lowest risk categories (eg, "no previous MI within 7 days prior to surgery" and "ejection fraction of at least 40%").

In each model, the constant corresponding to one point in the risk score was obtained by multiplying one half the length of each age range (5 years) by the age coefficient in that model (a reference of 45 years old was chosen based on inspecting a graph of age versus mortality rate). As an example of the process in developing the risk index for the valve/CABG model, 0.0555 (the coefficient of age) was multiplied by 5 to obtain 5 x 0.0555 = 0.2775 as the constant corresponding to one point in the risk score for the valve/CABG model. For all other risk factors, each of which was represented by one or more categories in the logistic regression model, the coefficient of the categorical variable was divided by 0.2775 and then rounded off to the nearest integer. For example, the risk factor "mitral valve replacement" in the valve/CABG model has a coefficient of 0.8534, and 0.8534/0.2775 = 3.08, which rounds to 3. The total risk score for a patient is then the sum of the risk scores for each of the risk factors the patient has. Total possible risk scores ranged from 0 to 49 for the valve-only model and 0 to 35 for the valve/CABG model.

Development of the Relationship Between Risk Scores and Predicted Mortality
A predicted mortality rate for each risk score was obtained by using the risk score in a logistic regression formula for predicted value of death using methods described by Sullivan and colleagues [23]. The accuracy of the risk index was tested by contrasting the predicted mortality values associated with each risk score with observed in-hospital mortality for patients in New York State with the same risk score in the period 1998 to 2000, and by examining the relative abilities of the risk score and the logistic regression model to predict observed mortality rates.

The fit of each the models used to develop the two risk indices was also assessed using 2001 to 2003 New York data by calculating the C statistic and the Hosmer-Lemeshow statistic. Because the mortality rates for each surgery group in 2001 to 2003 was lower than its rate in 1998 to 2000 (4.41% versus 5.22%, and 8.89% versus 9.32%, respectively), the 2001 to 2003 models were recalibrated for use on the 1998 to 2000 data. This was done by calculating a new mortality rate for each risk score by multiplying the old 2001 to 2003 rate by the ratio of the overall observed mortality rate in 1998 to 2000 divided by the overall rate predicted by the 2001 to 2003 model when it is applied to the 1998 to 2000 data. Again, the ability of the risk score to predict observed rates and the relative abilities of the risk score and the logistic regression model to predict observed rates were assessed.

The correspondence between the valve mortality risk indices and length of stay was then investigated. Patients who died were first included and then omitted from these analyses, and the results were similar with regard to the correspondence of length of stay with the risk index; consequently lengths of stay for all patients (including deaths) are presented. All statistical analyses were conducted in SAS version 9.1 (SAS Institute, Cary, North Carolina).

	Results

Top Abstract Introduction Patients and Methods Results Comment Acknowledgments References

 
A total of 10,702 patients in the study underwent isolated valve surgery, with an overall in-hospital mortality rate of 4.41%. Another 8,823 patients underwent concomitant valve and CABG surgery, with an in-hospital mortality rate of 8.89%. Table 1 contains the significant independent risk factors for mortality for the first group along with their logistic regression coefficients, odds ratios with 95% confidence intervals, and p values; and Table 2 presents the same information for the concomitant valve and CABG surgery group. Both statistical models had very good C statistics (0.794 and 0.750) and reasonably good Hosmer-Lemeshow statistics (p = 0.52 and p = 0.04). Although the latter p value is small, this frequently occurs for large samples.

View larger version (13K): [in this window] [in a new window]   Fig 1. (A) Observed (with 95% confidence interval [open bars]) and predicted risks of in-hospital mortality by total risk score for isolated valve surgery patients in New York State, 2001 to 2003 (n = 10,702). (Circles/dashed line = predicted by logistic model; diamonds/solid line = predicted by risk score.) (B) Observed (with 95% confidence interval [open bars]) and predicted risks of in-hospital mortality by total risk score for valve and coronary artery bypass graft surgery patients in New York State, 2001 to 2003 (n = 8,823). (Circles/dashed line = predicted by logistic model; diamonds/solid line = predicted by risk score.)

View larger version (14K): [in this window] [in a new window]   Fig 2. (A) Observed (with 95% confidence interval [open bars]) and rescaled predicted risks of in-hospital mortality by total risk score for isolated valve surgery patients in New York State, 1998 to 2000 (n = 9,662). (Circles/dashed line = predicted by logistic model; diamonds/solid line = predicted by risk score.) (B) Observed (with 95% confidence interval [open bars]) and rescaled predicted risks of in-hospital mortality by total risk score for valve and coronary artery bypass graft surgery patients in New York State, 1998 to 2000 (n = 8,463). (Circles/dashed line = predicted by logistic model; diamonds/solid line = predicted by risk score.)

View larger version (16K): [in this window] [in a new window]   Fig 3. (A) Length of stay by total risk score for isolated valve surgery patients in New York State, 1998 to 2000 (n = 9,662). (B) Length of stay by total risk score for valve and coronary artery bypass graft surgery patients in New York State, 1998 to 2000 (n = 8,463).

	Comment

Top Abstract Introduction Patients and Methods Results Comment Acknowledgments References

Although it may appear counterintuitive that the potential score is higher for valve/CABG than it is for isolated valve surgery patients, the indices are not comparable and should be used separately. The reason the indices were not combined into a single index was that the relative impact of various risk factors differed based on whether patients underwent concomitant CABG surgery along with valve surgery. The isolated valve risk score has a higher maximum value because the process consisted of having "1" be the lowest non-zero risk score for any risk factor, and two risk factors (shock and age 75+ years) had risks that were 7 times as high as the risk factor with a "1" (ages 46 to 54 years). In contrast, for valve/CABG, the ratio between the risk factor with a "1" (ages 61 to 69 years) and the risk factor with the highest risk (age 80+ years) was only 5.

For example, the risk score for an isolated mitral valve replacement patient who is a 70-year-old woman undergoing renal dialysis and who has had previous open heart surgery is 18, and the predicted mortality rate is 22.0%. An identical patient except the procedure performed is mitral valve replacement/CABG has a risk score of 14 and a predicted mortality rate of 36.7%.

For valve-only patients, a total of 92.2% of patients had a score of 14 or less and a predicted mortality rate of 10.1% or less. For valve/CABG patients, a total of 81.4% of patients had a score of 9 or less and a predicted mortality rate of 15% or less. The statistical models that are the basis of the risk indices were found to predict mortality quite well, with respective C-statistics of 0.794 and 0.750. Furthermore, after recalibration to account for different underlying mortality rates, these risk indices predicted mortality very well in another time period (1998 to 2000) in New York. For most risk score ranges, the predicted mortality rate fell within the confidence interval for the observed mortality rate.

Use of the risk indices is quite straightforward. For example, a 75-year-old man who requires a mitral valve replacement and CABG surgery, has an ejection fraction of 50%, and has diabetes but no other risk factors in Table 4 will have a total risk score of 3 + 3 + 1 = 7. Table 5 indicates that this patient’s predicted risk of in-hospital mortality in New York is 7.69%.

Our risk index is based on principles that are similar to, but not exactly the same as, other recent risk indices that have been developed for cardiac valve surgery. The European System for Cardiac Operative Risk Evaluation (EuroSCORE), which may be the best known of the cardiac surgery risk indices, was created using European data, and attempts to predict mortality for all isolated CABG, valve, and valve/CABG patients with a single index that has additional points associated with valve and valve/CABG surgery [4, 5]. Ambler and associates [14] used data from Great Britain and Ireland to develop an index that is limited to valve patients but accounts for concomitant CABG surgery by adding an additional two points within a single index. Nowicki and coworkers [13] created separate risk indices for aortic valve surgery and mitral valve surgery in which concomitant CABG surgery received an additional 1.5 points for aortic valve surgery but no additional points for mitral valve surgery.

Our method differs from EuroSCORE [4, 5] in that we have a separate index for valve surgery. We found that it is not wise to combine isolated valve and isolated CABG patients because the impact of some risk factors is quite different depending on the type of cardiac surgery they undergo [1]. As an example, we found in this study that endocarditis is an important risk factor for valve surgery with or without accompanying CABG surgery, whereas it was not even considered in our earlier study of isolated CABG patients [1]. Also, we found that ejection fraction was not a significant risk factor for patients undergoing valve surgery without CABG surgery, but it was significant for isolated CABG patients in our earlier study [1].

Our method differs from that of Ambler and colleagues [14] in that we developed separate indices for isolated valve and for valve/CABG surgery. We did this because we found that the relative impact of certain pairs of risk factors is not necessarily similar when patients have isolated valve surgery as when they have valve/CABG surgery. For example, based on the underlying logistic regression models that were developed in this study, we have assigned the same risk score (0) to mitral valve repair and aortic valve replacement when they are performed without CABG surgery, but we have assigned scores of 2 and 0 to these procedures when they are performed with CABG surgery.

Our method differs from Nowicki and associates [13] in that they have separate models for aortic and mitral valve surgery (we combine them but have different weights), and we have different models for valve procedures with and without CABG (they assign an additional weight of 1.5 for CABG with aortic valve surgery and 0 for CABG with mitral valve surgery). Thus, these two models are different approaches to account for the fact that the relative weights of various patient risk factors change depending on the nature of the surgery being performed (see Table 7).

It should be noted that the outcome measure that was used in the study is in-hospital mortality, rather than 30-day mortality or in-hospital/30-day mortality because deaths outside of the hospital were not obtainable for that time period. However, an advantage of using in-hospital mortality is that the predictive power of the index can be compared with other indices, most of which have used in-hospital mortality as the out come measure [4, 5, 13, 14]. Also, at least in New York State, and we suspect elsewhere, hospitals do not know whether most patients have died outside the hospital within 30 days, so it would be difficult to use an index based on 30-day mortality to assess hospital performance. New York is currently in the process of obtaining 30-day mortality using vital statistics data, and that will be used in future reports based on New York’s Cardiac Surgery Reporting System. At that time, a risk index can be developed from New York data based on in-hospital/30-day mortality for the minority of hospitals in the country that have that information.

We look forward to studies that will test the accuracy of our risk index when it is applied to other patient populations, and to attempts to compare its utility and accuracy with other valve surgery risk indices after appropriate recalibration.

	Appendix

 

	Acknowledgments

Top Abstract Introduction Patients and Methods Results Comment Acknowledgments References

 
The authors would like to thank Kenneth Shine, MD, the Chair of New York State’s Cardiac Advisory Committee (CAC), and the remainder of the CAC for their encouragement and support of this study; and Paula Waselauskas, Donna Doran, Kimberly Cozzens, Rosemary Lombardo, and the participating hospitals for their tireless efforts to ensure the timeliness, completeness, and accuracy of the registry data.

	References

Top Abstract Introduction Patients and Methods Results Comment Acknowledgments References

 

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This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Add to Personal Folders Download to citation manager Author home page(s): Edward V. Bennett Russell E. Carlson Alfred T. Culliford Jeffrey P. Gold Robert S.D. Higgins Craig R. Smith Robert H. Jones Permission Requests Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Hannan, E. L. Articles by Jones, R. H. Search for Related Content PubMed PubMed Citation Articles by Hannan, E. L. Articles by Jones, R. H. Related Collections Professional affairs Related Article