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Ann Thorac Surg 2006;81:537-540
© 2006 The Society of Thoracic Surgeons

Original article: Cardiovascular

Validation of the 2000 Bernstein-Parsonnet Score Versus the EuroSCORE as a Prognostic Tool in Cardiac Surgery

Marius Berman, MD * , Alon Stamler, MD, Gideon Sahar, MD, Georgios P. Georghiou, MD, Erez Sharoni, MD, Ron Brauner, MD, Benjamin Medalion, MD, Bernardo A. Vidne, MD, Alexander Kogan, MD

Department of Cardiothoracic Surgery, Rabin Medical Center, Tel Aviv University, Petach Tikva, Israel

Accepted for publication August 18, 2005.

* Address correspondence to Dr Berman, Department of Cardiothoracic Surgery, Beilinson Campus, Rabin Medical Center, 49100 Petach Tikva, Israel (Email: berman_marius{at}yahoo.com).


   Abstract
Top
 Abstract
Introduction
 Patients and Methods
 Results
 Acknowledgments
 References
 
BACKGROUND: Intradepartmental and interdepartmental benchmarking requires scoring systems with reliability (calibration) and stability over the complete spectrum of periprocedural risk. The aim of this single-center study was to assess the performance of the 2000 Bernstein-Parsonnet risk stratification model in cardiac surgery, by itself and against the EuroSCORE.

METHODS: A prospective observational design was used. The study group consisted of 1,639 consecutive patients of mean age 64.6 ± 12.04 years who underwent elective or emergency cardiac surgery from January 2003 to June 2004. The probabilities of hospital death were estimated with the 2000 Bernstein-Parsonnet and EuroSCORE algorithms. The correlation of predicted and observed mortality was compared between the two models, and score validity was assessed by calculating the area under the receiver operating characteristic (ROC) curve.

RESULTS: The patients were stratified into five risk groups according to their scores in the two models. For the 2000 Bernstein-Parsonnet model, findings were as follows: score 0–10: predicted mortality 0%–2.2%, observed mortality 0.6%; score 10.5–20: predicted 2.3%–4.7%, observed 2.3%; score 20.5–30: predicted 4.8%–10%, observed 6.7%; score 30.5–40: predicted 10.1%–23%, observed 11.5%; and score greater than 40: predicted 23.1%–80%, observed 29.9%. For the EuroSCORE, findings were as follows: score 0%–2%: predicted mortality 1.1%, observed mortality 0.6%; score 3%–5%: predicted 2.1%, observed 3.0%; score 6%–8%: predicted 4.1%, observed 3.5%; score 9–11: predicted 7.6%, observed 6.6.%; and score greater than 12: predicted 13.8%, observed 14.0%. There was good agreement between the observed and expected number of deaths, with both models. The area under the ROC curve was higher for the Bernstein-Parsonnet model (0.83, odds ratio [OR] 2.01, 95% confidence interval [CI] 1.75–2.31, p < 0.0001) than for the EuroSCORE (0.73, OR 1.05, 95% CI 1.04–1.07, p < 0.001).

CONCLUSIONS: The 2000 Bernstein-Parsonnet model is a simple, objective system for the estimation of hospital mortality in patients undergoing cardiac surgery, with slightly higher calibration and discrimination than the EuroSCORE additive model.


   Introduction
Top
 Abstract
 Introduction
Patients and Methods
 Results
 Acknowledgments
 References
 
The growing interest in risk-adjusted analysis of outcome in cardiac surgery has led to the development and validation of several predictive models for postoperative mortality, morbidity, and prolonged hospital stay [1–10]. Most of the models are multifactorial risk indexes based on findings of multiple regression analysis. However, despite their potential usefulness for quality assurance and perioperative care planning, multifactorial risk indexes remain poorly integrated into clinical practice, probably because of their complexity of use, inaccuracy in predicting outcome for individual patients, and dependence on clinical variables that are not always available. The validity of the model may be compromised by the inclusion of covariates based even partially on subjective judgment, such as unstable angina, diffuseness of disease, and chronic obstructive pulmonary disease, which introduces uncertainty. The incorporation of generalized, extremely subjective risk factors, such as operative priority, contributes not only uncertainty but also colinearity if those factors are represented implicitly elsewhere in the model. One of the first and longest standing scores is the comprehensive, multifactorial risk estimate formulated by Parsonnet and colleagues in 1989 [1, 11, 12]. The 2000 Bernstein-Parsonnet (BP) estimation score [13], is a simplified bedside paper-and-pencil version of the original for the estimation of surgical mortality risk faced by the individual patient. It was developed to aid decision making by patients and physicians contemplating cardiac surgery.

The aim of the present study was to assess the accuracy of the BP bedside estimation score, by itself and against the European System for Cardiac Operative Risk Evaluation (EuroSCORE) [7], for predicting operative mortality in patients undergoing cardiac and thoracic aortic surgery.


   Patients and Methods
Top
 Abstract
 Introduction
 Patients and Methods
Results
 Acknowledgments
 References
 
The study was conducted in the cardiac surgical department of a tertiary referral center. The sample included 1,639 consecutive patients who underwent elective or emergency cardiac surgery from January 2003 to June 2004. The distribution of operations performed is shown in Table 1. The clinical and physiologic data for the BP and EuroSCORE were prospectively collected according to the criteria and definitions stipulated by their developers [7, 13] and entered into a computerized database. For the BP procedures the items of the bedside form proposed by Bernstein and Parsonnet [13] were listed on the front and back sides of a card and completed by hand. For the EuroSCORE, we downloaded the Internet version. The main outcome measure of the study was hospital mortality, defined as death occurring before hospital discharge. Hospital mortality rates were predicted according to the BP and EuroSCORE models, to 2 significant digits. Expected or predicted mortality for individual patients was calculated using the algorithms of the two systems, arranged sequentially in order of predicted score. The population was divided into five clinically relevant risk categories according to the range of the predicted mortality rates, and expected mortality was compared with observed or actual mortality for each category. Individual mortality was defined as death from any cause within 30 days of operation or within the same hospital admission.


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  		Table 1. Distribution of Cardiac Surgical Procedures and In-Hospital Mortality
 
Statistical Analysis
The receiver operating characteristic (ROC) curves were plotted for each model, and the area under the ROC curve was calculated as an index of the predictive value of the model [14, 15]. Continuous data were expressed as mean ± standard deviation, and categorical variables were expressed as percentages. A p value of less than 0.05 was considered significant. Statistical analyses were conducted using BMDP software version 7.1 (BMDP Statistical Software Inc, Los Angeles, CA).


   Results
Top
 Abstract
 Introduction
 Patients and Methods
 Results
Acknowledgments
 References
 
Overall hospital mortality was 4.82% (79 of 1,639 patients). The mortality rates by type of operative procedure are presented in Table 1. The predicted versus the observed mortality, stratified by risk group, according to the BP score is shown in Table 2. The paper-and-pencil application of the bedside BP model was simple to use, allowing for a rapid accomplished, risk estimate. The computerized EuroSCORE was quickly established as well (Table 3).


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  		Table 2. Predicted Versus Observed Mortality According to the Modified 2000 Parsonnet-Bernstein Score Assessment of Cardiac Surgery Risk Stratification
 

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  		Table 3. Predicted Versus Observed Mortality According to the EuroSCORE Assessment of Cardiac Surgery Risk Stratification
 
Both models showed good ability to predict mortality, though findings for the BP were slightly better than for the EuroSCORE (Fig 1). The area under the ROC curve was 0.83 for the BP (odds ratio [OR] 2.01, 95% confidence interval [CI] 1.75–2.31, p < 0.0001) and 0.73 for the EuroSCORE (OR 1.05, 95% CI 1.04–1.07, p < 0.001) (Fig 1). Analysis of the sample by type of surgery yielded good discriminative values for the BP for the procedures shown in Table 4.


Figure 1
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  		Fig 1. Receiver operating characteristic (ROC) curves of the Bernstein-Parsonnet (BP) score and EuroSCORE for the prediction of mortality. ({blacktriangleup} = EuroSCORE; {blacksquare} = Bernstein-Parsonnet.)

 

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  		Table 4. Score Validation (Areas Under the ROC Curve) by Type of Surgical Procedure
 
Comment
Risk stratification and objective risk-adjusted prediction of mortality in cardiac surgery are becoming increasingly important to health authorities, hospitals, medical practitioners, and patients. Risk stratification models make it possible to compare objectively surgical performance between institutions or individual surgeons, and to detect and quantify differences and changes in the risk profiles of surgical candidates. Furthermore, risk prediction allows for a more objective assessment of the indications for surgery in individual patients, by facilitating a more accurate balance of potential risks and benefits.

Although most groups agree on the need for cardiac surgery risk stratification to evaluate individual patients, explain the risks of surgery, and prepare postoperative facilities, this agreement ends when the time comes to decide on the best instruments. Studies have shown that risk stratification models may have reduced applicability when used in patient populations different from the ones on which they were formulated. For example, models developed in the US may not predict, satisfactorily, clinical outcome in European patient populations [[16].

There are currently more than 15 different scoring systems for risk assessment in cardiac surgery. These include the Parsonnet score [1], the 2000 Bernstein-Parsonnet score [13], the American Society of Thoracic Surgeons risk program [14], the Ontario Province risk score [3], the Newark Beth Israel Medical Center model [[12], the American Collage of Cardiology/American Heart Association (ACC/AHA) model [17], the UK Society of Cardiothoracic Surgeons risk algorithm [18], the EuroSCORE [7], the Higgins score [19], the French score [8], the North West Regional Cardiac Surgery Audit Steering Group score [20], the Cleveland Clinic score [21], the Pons model [22], the Working Group Panel on the Cooperative CABG Database Project [23], the Acute Physiology and Chronic Health Evaluation (APACHE) [9], the Simplified Acute Physiology Score (SAPS) [9], the Mortality Probability Models (MPM) [9], the Edinburgh Cardiac Surgery Score survival prediction [24], and the Cardiac Anesthesia Risk Evaluation (CARE) score [10]; and this long list continues to increase.

The Parsonnet additive model is a useful tool for quality monitoring in surgical practice. Parsonnet and colleagues [1] developed the original score on the basis of 3,500 consecutive open heart operations and validated it from 1,332 cases. The vast majority of procedures were coronary artery bypass grafting (CABG). The modified Bernstein-Parsonnet algorithm [13] showed a good correlation between predicted and observed mortality in patients undergoing CABG (on and off pump) or valve surgery. Its predictive ability was low for thoracic aortic surgery, surgery involving mechanical complications, and combined carotid-CABG procedures. The EuroSCORE similarly predicted mortality in the same patient population, but its discriminating power was lower than for the BP score (ROC 0.73 vs 0.83). When we assessed the population by risk stratification, the BP model was more accurate than the EuroSCORE.

The question of which score to use to evaluate patients was particularly interesting in Israel, which has a very heterogeneous population originating from North and South America, Eastern and Western Europe, Northern and Southern parts of Africa, the Far East and, of course, the Middle East. We opted for the BP score for several reasons: (1) the use of logistic regression with translation of the regression model into a simplified, paper-and-pencil form offers a potentially valuable resource for decision-making in cardiac surgery; (2) two-thirds of the patients who undergo cardiac surgery have no more than four risk factors and this method is both convenient and rapid; (3) the BP score is an impartial and objective method of predicting postoperative complications and intensive care unit (ICU) stay of far less than 24 hours [[25]; (4) it is also a useful indicator of ICU and hospital costs [26]; (5) several studies [9] reported the good predictive performance of the Parsonnet score in cardiac surgery patients and its high calibration and discrimination value. It has been criticized by teams from the UK and France as not reflecting adequately the risk stratification of their respective populations.

We have not found, in the literature, another assessment of the use of the modified BP system against the increasingly popular EuroSCORE. We mention that the original Parsonnet model was previously assessed against the EuroSCORE [27], but not the modified 2000 version.

This study has several limitations. First, although we used a prospective designed study, the data were collected from a single-center database. Therefore, the results may not be generalized for the whole population. Second, we examined all-cause in-hospital mortality, but we were unable to determine the cause of death (cardiac or noncardiac). Third, follow-up data on reinterventions were lacking. This is a valuable parameter and affects long-term outcome. Fourth, both systems failed to predict the outcome of specific patient groups, such as thoracic aorta surgery, acute mechanical complications, Ross procedures, assist devices, and heart transplantation patients, probably because of the scarcity of data.

In conclusion, the 2000 Bernstein-Parsonnet score, modified 5 years ago, is a simple, objective system for the estimation of hospital mortality in patients undergoing cardiac surgery, with slightly higher calibration and discrimination than the EuroSCORE additive model.


   Acknowledgments
Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Acknowledgments
References
 
We thank Pnina Lilus for the statistical analysis and Gloria Ginzach for her editorial assistance.


   References
Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Acknowledgments
 References
 

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