European Journal of Cardio-thoracic Surgery 16 (1999) 9±13European system for cardiac operative risk evaluation (EuroSCORE) q
S.A.M. Nashef*, F. Roques, P. Michel, E. Gauducheau, S. Lemeshow, R. Salamon,
the EuroSCORE study group
Papworth Hospital, Cambridge CB3 8RE, UK
Received 21 September 1998; accepted 29 March 1999

Abstract
Objective: To construct a scoring system for the prediction of early mortality in cardiac surgical patients in Europe on the basis ofobjective risk factors. Methods: The EuroSCORE database was divided into developmental and validation subsets. In the former, risk factors
deemed to be objective, credible, obtainable and dif®cult to falsify were weighted on the basis of regression analysis. An additive score ofpredicted mortality was constructed. Its calibration and discrimination characteristics were assessed in the validation dataset. Thresholds
were de®ned to distinguish low, moderate and high risk groups. Results: The developmental dataset had 13 302 patients, calibration by
Hosmer Lemeshow Chi square was 8  8:26 (P , 0:40) and discrimination by area under ROC curve was 0.79. The validation dataset had
1479 patients, calibration Chi square 10  7:5, P , 0:68 and the area under the ROC curve was 0.76. The scoring system identi®ed threegroups of risk factors with their weights (additive % predicted mortality) in brackets. Patient-related factors were age over 60 (one per 5 years
or part thereof), female (1), chronic pulmonary disease (1), extracardiac arteriopathy (2), neurological dysfunction (2), previous cardiac
surgery (3), serum creatinine .200 mmol/l (2), active endocarditis (3) and critical preoperative state (3). Cardiac factors were unstable angina
on intravenous nitrates (2), reduced left ventricular ejection fraction (30±50%: 1, ,30%: 3), recent (,90 days) myocardial infarction (2) andpulmonary systolic pressure .60 mmHg (2). Operation-related factors were emergency (2), other than isolated coronary surgery (2), thoracic
aorta surgery (3) and surgery for postinfarct septal rupture (4). The scoring system was then applied to three risk groups. The low risk group(EuroSCORE 1-2) had 4529 patients with 36 deaths (0.8%), 95% con®dence limits for observed mortality (0.56±1.10) and for expectedmortality (1.27±1.29). The medium risk group (EuroSCORE 3±5) had 5977 patients with 182 deaths (3%), observed mortality (2.62±3.51),
predicted (2.90±2.94). The high risk group (EuroSCORE 6 plus) had 4293 patients with 480 deaths (11.2%) observed mortality (10.25±
12.16), predicted (10.93±11.54). Overall, there were 698 deaths in 14 799 patients (4.7%), observed mortality (4.37±5.06), predicted (4.72±
4.95). Conclusion: EuroSCORE is a simple, objective and up-to-date system for assessing heart surgery, soundly based on one of the largest,
most complete and accurate databases in European cardiac surgical history. We recommend its widespread use. q 1999 Elsevier Science
B.V. All rights reserved.
Keywords: Cardiac surgery; Risk strati®cation; Mortality

1. Introduction
The purpose of this work was to use the EuroSCORE
project database to construct a risk strati®cation system tohelp in the assessment of the quality of cardiac surgical care.

2. Methods
The EuroSCORE project set-up, data collection and entryq
Presented at the 12th Annual Meeting of the European Association for
Cardio-thoracic Surgery, Brussels, Belgium, September 20±23, 1998.
* Corresponding author. Tel.: 144-1480-830541; EuroSCORE website:
euroscore.co.uk.
E-mail address: sam.nashef@papworth-tr.anglox.nhs.uk(S.A.M. Nashef)and quality checks have been described elsewhere andmultiple regression analysis had already identi®ed a numberof risk factors associated with postoperative mortality [1].
These factors were then evaluated by an international panelof cardiac surgeons with an interest in risk strati®cation in
the hope of identifying those risk factors most likely to be
useful in a risk model. The evaluation was on the basis ofobjectivity, credibility, availability and resistance to falsi®cation. Factors deemed to satisfy these criteria were used forthe construction of the model.
The database was randomly divided into two subsets: adevelopmental dataset which served for the construction of
the risk model, and a validation subset for testing and validating the model. In the developmental subset, variables
entered in the model were selected using bivariate tests,
chi square tests for categorical covariates and t-tests or

1010-7940/99/$ - see front matter q 1999 Elsevier Science B.V. All rights reserved.
PII: S 1010-794 0(99)00134-7

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S.A.M. Nashef et al. / European Journal of Cardio-thoracic Surgery 16 (1999) 9±13

Table 1SCORE datasets
Calibration model
DatasetPatients

Discrimination model
Chi square (Hosmer Lemeshow)

Area under ROC curve

Developmental
Validation

13302
1497

Chi2(8)  8.26, P , 0:40
Chi2(10)  7.5, P , 0:68

0.79
0.76

Wilcoxon rank sum tests for continuous covariates. All variables signi®cant at the P , 0:2 level were entered into themodel provided they were present in at least 2% of thesample. Non-signi®cant variables were eliminated fromthe model one at a time, beginning with the variable having
the highest P-value. Stability of the model was checked
every time a variable was eliminated. In the case of continuous variables where the relationship with outcome was not
linear, such as age and serum creatinine, we determined cutoff points using the fractional polynomials method. When
all statistically non-signi®cant variables had been eliminated from the model, goodness-of-®t testing (Hosmer
Lemeshow Chi square) was used to assess how well themodel was calibrated and the area under the receiver operating characteristic (ROC) curve was used to assess how
well the model could discriminate between patients who
lived and patients who died.
After initial assessment of the performance of the model,
variables whose elimination improved calibration while notsigni®cantly affecting discrimination were dropped from themodel one at a time. Although signi®cantly associated withoutcome, urgent operation and chronic congestive heart failure were eliminated because they were liable to distortion.
The ®nal step was to search for ®rst-degree interaction. The
criteria for including an interaction term were that it had tobe signi®cant at P , 0:05, 1% of the sample had to exhibitthat combination of factors and the combination had to beclinically relevant.
The weights attributed to each variable in the score wereobtained from the logistic regression b-coef®cients. The
calibration and discrimination power of the model werethen assessed in the validation dataset. The scoring system

Fig. 1. ROC curve graphs for the developmental dataset (n  13302).

was then used to de®ne three risk groups (low, medium andhigh risk). The thresholds were chosen so that the groups
would be of similar size.
3. Results
The statistical features of the developmental and validation datasets are in Table 1 and Figs. 1 and 2. The validation
analysis con®rmed that the model performed well both in its
calibration and discrimination characteristics. Seventeen
risk factors were weighted for the de®nitive scoring system.
There were nine patient-related factors, four factors were
derived from the preoperative cardiac status and four
depended on the timing and nature of the operation
performed. The risk factors, their de®nitions and the weights
allocated to them are detailed in Table 2. The system isadditive: to calculate the predicted risk for a patient, thescores for existing risk factors are added to give an approximate percentage predicted mortality ®gure. When the scoring system was applied in three different risk groups, there
was very good overlap between observed and expectedmortality in all three groups (Table 3).
4. Discussion
Those who provide, purchase and use health care recognise that the resources for such care are limited. It is now
established that the cost of treatment must be taken into
consideration in decisions about health care provision.

Fig. 2. ROC curve graphs for the validation dataset (n  1497). The irregular form of the curve is due to the smaller sample size in the validation
dataset.

S.A.M. Nashef et al. / European Journal of Cardio-thoracic Surgery 16 (1999) 9±13
Table 2
Risk factors, de®nitions and weights (score)

Patient-related factors
Age
Sex
Chronic pulmonary disease
Extracardiac arteriopathy

Neurological dysfunction
Previous cardiac surgery
Serum creatinine
Active endocarditis
Critical preoperative state

Cardiac-related factors
Unstable angina
LV dysfunction
Recent myocardial infarct
Pulmonary hypertension
Operation-related factors
Emergency
Other than isolated CABG
Surgery on thoracic aorta
Postinfarct septal ruptureDe®nition

Score

Per 5 years or part thereof over
60 years)
FemaleLongterm use of
bronchodilators or steroids forlung disease
Any one or more of thefollowing: claudication,
carotid occlusion or .50%
stenosis, previous or plannedintervention on the abdominal
aorta, limb arteries or carotids
Disease severely affecting
ambulation or day-to-day
functioning
Requiring opening of the
pericardium.200 mmol/l preoperatively
Patient still under antibiotic
treatment for endocarditis at
the time of surgery
Any one or more of thefollowing: ventriculartachycardia or ®brillation oraborted sudden death,
preoperative cardiac massage,
preoperative ventilation
before arrival in theanaesthetic room, preoperative
inotropic support, intraaortic
balloon counterpulsation or
preoperative acute renal
failure (anuria or oliguria , 10
ml/h)

1

Rest angina requiring i.v.
nitrates until arrival in theanaesthetic room
Moderate or LVEF 30±50%
Poor or LVEF , 30(,90 days)
Systolic PA pressure .60
mmHg
Carried out on referral before
the beginning of the next
working day
Major cardiac procedure otherthan or in addition to CABG
For disorder of ascending,arch or descending aorta

1
1
2

2
3
2
3
3

2
1
3
2
2
2
2
3
4

Future debate in this ®eld will focus on the quality of treatment and the measurement of this quality. In cardiac
surgery, it has long been accepted that operative or hospitalmortality is an indicator of quality of care. This is true to alarge extent: death following heart surgery is often due tofailure to achieve a satisfactory cardiac outcome, itself the11cause of major early morbidity as well as poor longterm
results. Crude operative mortality fails as a measure of quality only when there are major variations in casemix. For
operative mortality to remain a valid measure of qualityof care, it must be related to the risk pro®le of the patients
receiving surgery, hence the need for a reliable risk strati®cation model, already recognised by earlier workers in this®eld (see reference list of [1]).
There is another reason for the development and regularuse of risk strati®cation in the assessment of cardiac surgicalresults. Doctors and hospitals operate in an increasingly
open system where the availability of results and public
accountability may in¯uence decision-making. Without
risk strati®cation, surgeons and hospitals treating high-riskpatients will appear to have worse results than others. This
may prejudice referral patterns, affect the allocation of
resources and even discourage the treatment of high-riskpatients. This is especially undesirable in cardiac surgery
because it is precisely this group of patients which stands
to gain most from surgical treatment, in spite of theincreased risk [2]. Risk strati®cation helps eliminate thebias against high-risk patients [3].
An individual patient will either survive or die after
cardiac surgery. Clearly, no scoring system will predict
the speci®c outcome for every patient. Risk strati®cation,
however, will inform patients and clinicians of the likely
risk of death for a group of patients with a similar risk pro®leundergoing the proposed operation. This information isuseful, and should form part of the basis on which thepatient and surgeon decide whether to proceed.
The EuroSCORE database is large, up-to-date and unrivalled in completeness and accuracy. It is also derived froma cross-section of contemporary European cardiac surgery.
It is therefore an appropriate database for the construction ofa risk evaluation scoring system for use in Europe. The
limitations of the study are those which are due to thestudy design in the areas of centre recruitment and datacollection, and have already been addressed [1].
It can be argued that the transition from database to scoring system sacri®ces some precision for the sake of simplicity. There are two extremes in the selection of a risk
strati®cation system. Accuracy can be achieved by assessing
a large number of risk factors for an individual patient andcomparing the ®ndings with the results of a large databasesuch as EuroSCORE. Such a system should provide very
accurate risk assessment for small subgroups of patients.
This approach, however, would require the gathering oflarge amounts of patient data and complex statistical operations. It would be of limited use in the day-to-day world ofclinical surgery, and impossible to implement withoutsophisticated information technology which is not yet available to all hospitals. On the other hand, very simple models
relying on one or two risk factors (such as age and sex, forexample) are also possible. This approach would have someuse for the overall assessment of a hospital's performance,
but is unlikely to be useful for risk assessment for an indi-

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S.A.M. Nashef et al. / European Journal of Cardio-thoracic Surgery 16 (1999) 9±13

Table 3
Application of scoring system
95% con®dence limits for mortality
EuroSCORE

Patients

Died

Observed

Expected

0±2 (low risk)
3±5 (medium risk)
6 plus (high risk)
Total

4529
5977
4293
14799

36 (0.8%)
182 (3.0%)
480 (11.2%)
698 (4.7%)(0.56±1.10)(2.62±3.51)(10.25±12.16)(4.37±5.06)(1.27±1.29)(2.90±2.94)(10.93±11.54)(4.72±4.95)

vidual patient and is likely to perpetuate a reluctance tooperate on high-risk patients. A compromise must be
reached so that the system recognises common risk factors,
is able to provide some degree of risk prediction yet remains
simple enough to use at the point of delivery of care [4,5].
EuroSCORE satis®es these requirements. The existence ofthe scoring system does not preclude full use of the database, when resources permit, for more precise analysis.
It is essential that the risk strati®cation system is objective
and resistant to manipulation. This is achieved by the selection of real, measurable and easily available risk factors. In
addition, it is important that as few risk factors as possible
are determined by surgical decision-making. Most EuroSCORE risk factors are derived from the clinical status of
the patient. Only four risk factors are related to the operationand these are factors that are dif®cult to in¯uence throughsubtle variation in surgical decision-making.
EuroSCORE is sound in planning and derivation, easy to
use and applicable either as a paper system or through information technology. However, the true test of such a system
is in its widespread application in the ®eld. We invite andwelcome other workers to put it to the test in their hospitals,
overall and in individual patient and procedural subgroups,
in relation to operative mortality, to major morbidity and to
the use of resources. Quality monitoring is now one of therequirements of good surgical practice: EuroSCORE is atool by which this can be achieved.
References[1] Roques F, Nashef SAM, Michel P, Gauducheau E, de Vincentiis C,
Baudet E, Cortina J, David M, Faichney A, Gabrielle F, Gams E,
Harjula A, Jones MT, Pinna Pintor P, Salamon R, Thulin L. Risk
factors and outcome in European cardiac surgery: analysis of the EuroSCORE multinational database of 19 030 patients. Eur J Cardio-thorac
Surg 1999;15:816±823.
[2] Mark DB. Implications of cost in treatment selection for patients withcoronary disease. Ann Thorac Surg 1996;61:S12±S15.
[3] Hannan FL, Siu AL, Kumar D, Racz M, Pryor DB, Chassin MR.
Assessment of coronary artery bypass graft surgery performance in
New York. Is there a bias against taking high risk patients? Med
Care 1997;35:49±56.
[4] Tu JV, Sykora K, Naylor CD. Assessing the outcomes of coronary
artery bypass graft surgery: how many risk factors are enough? J Am
Coll Cardiol 1997;30:1317±1323.
[5] Jones RH, Hannan EL, Hammermeister KE, Delong ER, O'Connor
GT, Luepker RV, Parsonnet V, Pryor DB. Identi®cation of preopera-

tive variables needed for risk adjustment of short-term mortality after
coronary artery bypass graft surgery. J Am Coll Cardiol 1996;28:1478±
1487.

Appendix A. Conference discussion
Dr T. Aberg (Umea, Sweden): When you compare this outcome withother risk strati®cation systems, what do you feel is the main difference?
Dr Nashef: We have done this in our own database, but then of course
our risk strati®cation system is derived from that database and therefore it
would be expected that it would perform well in that database, and this is
one reason for my inviting other workers to take it on and compare it withother systems. We would very much like to see it tested.
Dr Aberg: I think this is one of the important papers of this meeting andyou have heard the speaker make a plea for using the system more or lessroutinely. We participated in the study and we de®nitely are going to use it
in conjunction with the usual Higgins and Parsonnet scores that we are
currently collecting.
Dr Murday: Can I press you a little further to give your opinion as towhat you see as being the advantages of your EuroSCORE system against,
for example, Parsonnet, which a lot of us use already?
Dr Nashef: My opinion is likely to be biased because this is my work,
but there are areas in which EuroSCORE is much more objective andParsonnet is subjective. Parsonnet has been shown to overestimate risk
and has had to be redone a number of times in order to correct for that.
This will not be a problem with Euroscore. There are some risk factors that
we simply have not found to be signi®cant in spite of what Parsonnet has
found. There are some risk factors that Parsonnet has excluded that all
cardiac surgeons know are a serious contribution to risk, such as, for example, the presence of extracardiac vascular disease, and we have managed toshow that impact. So we believe it is a signi®cant advance. It does relate toEuropean cardiac surgery because it is based on the European population.
Dr G. Rizzoli (Padua, Italy): I would like to know the composition ofyour database. How many patients were coronary bypass patients, howmany were valvular patients? I would also like to know if you tested theresults of the overall analysis on each subset of patients, the group withcoronary artery bypass and the group with valvular disease?
Dr Nashef: Yes, we have. In the development of the score we looked at
subgroups both as coronary patients and valve patients. As far as thecomposition is concerned and if I remember correctly, approximately
60% were coronary patients, about 30% valve patients and 10% other.
We looked at developing separate scores for each population and we looked
at developing an overall score, and we were successful in developing an
overall score.
Dr F. Grover (Denver, CO, USA): I have had considerable experience
working with our STS database in the United States and was curious about
whether you thought about taking odds ratios for the various risk factors andrather than adding them up as a score, developing very simple software thatyou could have in a handheld computer which would allow you to estimate
the risk of the patient and to separate into risk groups at 10% intervals or
subsets.

S.A.M. Nashef et al. / European Journal of Cardio-thoracic Surgery 16 (1999) 9±13
Dr Nashef: So you mean to use the database itself as a backbone forpredicting risk for individual patients?
Dr Grover: Yes.
Dr Nashef: Yes, that is certainly possible and we are looking at that in
the future.

13

Dr Aberg: The publication and dissemination of this data, what is thetime frame for you to do that? How do you plan to make these data known
in more detail?
Dr Nashef: Well, we have expected it will be published in the European
Journal of Cardio-thoracic Surgery.