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Year : 2010
| Volume
: 13 | Issue : 3 | Page
: 241-245 |
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Is EuroSCORE applicable to Indian patients undergoing cardiac surgery? |
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Madhur Malik1, Sandeep Chauhan1, Vishwas Malik1, Parag Gharde1, Usha Kiran1, RM Pandey2
1 Department of Cardiac Anaesthesia, All India Institute of Medical Sciences, New Delhi - 110 029, India 2 Department of Biostatistics, All India Institute of Medical Sciences, New Delhi - 110 029, India
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Date of Submission | 04-Feb-2010 |
Date of Acceptance | 27-Jun-2010 |
Date of Web Publication | 6-Sep-2010 |
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Abstract | | |
Indian patients undergoing cardiac surgery have different demographics, clinical profile as well as risk profile, compared to the western population. The purpose of this study was to validate the European System for Cardiac Operative Risk Evaluation (EuroSCORE) risk stratification model in Indian patients undergoing cardiac surgery in a single cardiac center. Data from 1000 consecutive adult patients undergoing cardiac surgery (coronary artery bypass grafting or valve surgery) were prospectively collected as per the EuroSCORE model. The model's validity was assessed on the basis of its calibration power (Hosmer-Lemeshow test) and discriminatory power [area under receiver operating characteristic curve]. The patients were divided into three risk groups on the basis of their EuroSCORE. The Hosmer-Lemeshow test revealed a good calibration power (P = 0.73) and the area under the ROC curve was 0.8278, suggesting a good discriminative power. The predicted mortality was similar to observed mortality in low- and moderate-risk patients but the observed mortality in high-risk patients (15.6%) was double that of predicted mortality (7.5%). The risk factors prevalent in European population were not observed in Indian population. EuroSCORE accurately predicts mortality in low and moderate-risk Indian patients undergoing cardiac surgery but is less predictive for high-risk Indian patients. Updating and improvisation of EuroSCORE by incorporation of risk factors associated with rheumatic valvular heart disease which is more prevalent in India, may enable it to accurately predict mortality in high-risk patients also. Keywords: Risk stratification, EuroSCORE, cardiac surgery
How to cite this article: Malik M, Chauhan S, Malik V, Gharde P, Kiran U, Pandey R M. Is EuroSCORE applicable to Indian patients undergoing cardiac surgery?. Ann Card Anaesth 2010;13:241-5 |
How to cite this URL: Malik M, Chauhan S, Malik V, Gharde P, Kiran U, Pandey R M. Is EuroSCORE applicable to Indian patients undergoing cardiac surgery?. Ann Card Anaesth [serial online] 2010 [cited 2021 Jan 28];13:241-5. Available from: https://www.annals.in/text.asp?2010/13/3/241/69082 |
Introduction | |  |
Risk stratification in cardiac surgery has assumed special importance in this modern era as it allows scientific analysis of various treatment modalities, helps in improving quality of care and in optimal allocation and utilization of resources in a result-oriented and cost-effective manner. However, the search for an ideal model of risk assessment and stratification for cardiac surgery has constantly eluded us. The current practice is to develop a risk stratification model on a particular population and then validate it on the same as well as other populations. Difficulty in validation of a particular model on a different population indicates requirement of changes and updating of scoring system. This may be because of different patient demographics, treatment modalities, techniques and practice of medicine in different countries.
European System for Cardiac Operative Risk Evaluation (EuroSCORE) [1] was developed across 128 centers in eight European states after multiple regression analysis of risk factors associated with postoperative mortality in 19,030 patients undergoing cardiac surgery. The scoring system identifies three groups of risk factors with their assigned weights - patient-related risk factors, cardiac-related risk factors, surgery-related risk factors. The sum of the scores of risk factors gives the additive EuroSCORE which is equivalent to risk of mortality for that patient.
The EuroSCORE model has been developed on European population and has been validated on Japanese, [2] Spanish [3] and the North American [4] population. The model has not been validated till date in India, which is essential because of the different patient demographics, the usual delayed clinical presentation and the different treatment patterns as compared to the western population. Validation of the model on Indian patients will help in expanding its scope for Indian patients as well as comparing the surgical results of Indian institutions performing cardiac surgery with those of western countries.
Materials and Methods | |  |
After approval from the institution ethical committee, this study was conducted on 1000 consecutive consenting patients, above 18 years of age, undergoing cardiac surgery [coronary artery bypass grafting (CABG) or valve surgery]. The study involved assessment of 17 risk factors identified in EuroSCORE model through a thorough history, physical examination, investigations and review of the medical records in the preoperative period. Based upon these data, scores were given to each risk factor as guided by the EuroSCORE model [Table 1] and the sum of the 17 risk factors was calculated and recorded as EuroSCORE for that patient.
Based on the EuroSCORE, the patients were categorized as patients with low risk (EuroSCORE 0-2), moderate risk (EuroSCORE 3-5) and high risk (EuroSCORE >6) of in-hospital mortality following cardiac surgery. The patient was followed up in the postoperative period in the intensive care unit (ICU) wards till discharge, and a record of ICU stay and duration of hospital stay following cardiac surgery was made. The primary end point of the study was to record in-hospital mortality after cardiac surgery.
Statistical analysis
Statistical analysis was performed using STATA; V.11 statistical analysis package for windows 98. The model's validation was performed by assessing its calibration power and discriminatory power. Calibration power was assessed by goodness of fit testing using Hosmer-Lemeshow test. [5],[6] A P value >0.05 indicates that the model fits the data well and therefore accurately predicts mortality. Discriminatory power of a model pertains to its ability to discriminate between patients who died during hospitalization from those who did not and was assessed by calculating the area under receiver operating characteristic (ROC) curve. A value of 0.5 indicates that the model is equivalent to pure chance and a value of 1 indicates perfect discrimination.
Results | |  |
A total of 1000 adult patients undergoing cardiac surgery between October 2008 and July 2009 were included in the study [Table 2]. The data were collected as per the EuroSCORE model as given in the website www.euroscore.org [Table 1]. To validate the model on the Indian population, its calibration power and discriminatory power were assessed. Using Hosmer-Lemeshow test, C-statistic of 4.41 with P = 0.73 was obtained indicating satisfactory model fit. Area under ROC was calculated to be 0.8278, suggesting a good discriminatory power [Figure 1]. The prevalence of various risk factors in Indian population is shown in [Table 3] and is compared with the European population. | Table 3: Comparison of prevalence of risk factors in Indian and European population
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The observed mortality in low and moderate-risk patients is similar to predicted mortality as is evident from [Table 4]. However, in high-risk patients, the observed mortality is 15.62% as against a predicted mortality of 7.5%. Overall, the total mortality stands at 3.3% (33 patients out of 1000 patients) against a predicted mortality of 2.85%. | Table 4: Comparison of predicted and observed mortality in Indian population
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Discussion | |  |
Additive EuroSCORE model is a simple and easily applicable risk assessment tool to predict mortality in cardiac surgical patients. The model was constructed from analysis of 19,030 patients undergoing cardiac surgery across Europe and was validated in 1999. [1] Since then, the model has been validated on different populations like Japanese, [2] Spanish, [3] North American, [4] etc. Yap et al. could not validate the model in Australia because of different patient demographics and clinical profile in terms of higher prevalence of risk factors. [7] They reported a significantly low mortality as compared with predicted mortality using EuroSCORE. Of late it has been realized that the additive EuroSCORE model overpredicts mortality in low-risk patients and under predicts mortality in high-risk patients. [8],[9],[10],[11] This essentially translates into a scenario where the subset of patients who are likely to benefit the most from surgery are denied the chance of being operated upon and vice versa. To overcome this problem, a logistic model of EuroSCORE was developed and reported to be superior to additive EuroSCORE for high-risk patients. [6] This model has not been as extensively used as compared to the additive EuroSCORE model, because of its dependence on internet based calculator.
The aim of this study was to validate the additive EuroSCORE model in Indian patients undergoing cardiac surgery and this was done by testing its calibration power and discrimination power. Calibration power was tested by assessing goodness of fit by Hosmer-Lemeshow test which gave a P value of 0.73 indicating a satisfactory model fit. Discrimination power was assessed by calculating area under ROC curve which was 0.8278. This indicates an ability of the model to differentiate between patients who died during hospitalization from those who did not.
The Indian population is different from western population from which most of the current risk-stratification models including EuroSCORE have been developed. The difference lies in patient demographics (malnourished, small stature), delayed clinical presentation due to socioeconomic, cultural and geographic reasons, inequitable distribution of medical facilities, and a high endemicity of subclinical inflammation, infection and rheumatic heart disease. The Indian population is per se at increased risk of coronary artery disease due to visceral obesity, food habits and an increased prevalence of dysmetabolic syndrome. [12]
The same is evident in our study wherein the mean age of patients undergoing surgery was 45 years as against 62 years in European population. About 82% of patients in our study were less than 60 years of age compared to only 33.2% patients with age less than 60 years in European population. This difference can be attributed to more patients undergoing valvular heart surgery (60%) in India as against only 30% in European population. Age-related degenerative valvular heart disease is more common than rheumatic valvular heart disease in Europeans. [13] In India, rheumatic heart disease is still the leading cause of valvular heart disease [14] and the patients usually present at a young age. Since the patients undergoing cardiac surgery are young, they are less likely to have the risk factors mentioned in the EuroSCORE model. This is evident from a decreased prevalence of risk factors like chronic obstructive pulmonary disease, extracardiac arteriopathy, elevated serum creatinine, critical preoperative state, recent myocardial infarction, and unstable angina in our patients. The incidence of these risk factors increases with increasing age, association with diabetes mellitus and hypertension which by themselves are risk factors for coronary artery disease. [15] The prevalence of previous cardiac surgery, left ventricular dysfunction as evident by decrease in ejection fraction and neurological dysfunction is similar in the two populations. Pulmonary artery hypertension (PAH), described as systolic pulmonary artery pressures > 60 mm Hg, was observed in 21.93% of Indian patients as against only 2% of patients in Europe. This again can be attributed to valvular heart disease and usual delayed presentation of patients in India. PAH alone is a significant cause of mortality in valvular heart disease. [16],[17]
Despite the difference in prevalence of risk factors in Indian population, the predicted mortality and observed mortality were almost similar in patients with low risk (EuroSCORE 0-2) and moderate risk (EuroSCORE 3-5). In patients with high risk (EuroSCORE > 6), the observed mortality was significantly high (15.62%, i.e., 15 out of 96 patients) against a predicted mortality of 7.5%. This is in accordance with previous studies wherein it has been reported that additive EuroSCORE overpredicts mortality in low- and moderate-risk patients and underpredicts mortality in high-risk patients. [8],[9],[10],[11] This discrepancy increases with increasing additive EuroSCORE and is particularly evident at EuroSCORE > 10, as has been reported by Gobashian et al., [11] after a systematic review of international performance of EuroSCORE. In our study, only 1% (10 out of 1000 patients) patients had EuroSCORE above 10. An analysis of the high-risk group data (96 patients) revealed that out of the 15 patients who died during their hospital stay, 11 (73.3%) underwent surgery for valvular heart disease amongst which 8 had severe PAH suggesting a delayed clinical presentation. PAH is one of the factors for calculation of the EuroSCORE, but the prevalence of PAH in European population is quite less [Table 3]. This may be responsible for the poor predictability of EuroScore in Indian population with high EuroSCORE. Five patients underwent a repeat cardiac surgery, having undergone a closed mitral commissurotomy or open mitral valvulotomy earlier. Thus, the discrepancy between predicted and observed mortality in high-risk group can be explained by a higher prevalence of rheumatic valvular heart disease with severe PAH and a history of previous cardiac surgery.
The developmental dataset of EuroSCORE additive model had only 30% of patients with valvular heart disease. [1] Therefore, significant issues in patients with valvular heart disease such as congestive heart failure, comorbidities like hepatic or renal dysfunction, age, among others may have been missed during development of this model. Congestive heart failure is a significant risk factor in patients with valvular heart disease but is not categorized in the EuroSCORE model. This particular risk factor was lost during statistical analysis while this model was developed. Similarly, rheumatic heart disease patients usually present in fourth to fifth decades of life with the disease at an advanced stage. [17] Despite being sicker at a younger age, they are at low risk as per the EuroSCORE model wherein age > 60 years is a risk factor.
There is thus definitely a need to improvise the current EuroSCORE model for application to the Indian patient population by analysis of risk factors associated with valvular heart disease which is more prevalent in India. The additive EuroSCORE can be satisfactorily applied to low- and moderate-risk Indian patients but is less accurate for high-risk Indian patients undergoing cardiac surgery.
References | |  |
1. | Roques F, Nashef SA, Michel P, Gauducheau E, de Vincentiis C, Baudet E, et al. Risk factors and outcome in European cardiac surgery: Analysis of the EuroSCORE multinational database of 19030 patients. Eur J Cardiothorac Surg 1999;15:816-22. |
2. | Kawachi Y, Nakashima A, Toshima Y, Arinaga K, Kawano H. Risk stratification analysis of operative mortality in heart and thoracic aorta surgery: Comparison between Parsonnet and EuroSCORE additive model. Eur J Cardiothorac Surg 2001;20:961-6. |
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4. | Nashef SA, Roques F, Hammill BG, Peterson ED, Michel P, Grover FL, et al. Validation of European system for cardiac operative risk evaluation (Euro-SCORE) in North American cardiac surgery. Eur J Cardiothorac Surg 2002;22:101-5. |
5. | Lemeshow S, Hosmer DW Jr. A review of goodness of fit statistic for use in the development of logistic regresiσn models. Am J Epidemiol 1982;115:92-106. |
6. | Hosmer DW, Lemeshow S. Applied logistic regression. 2 nd ed. New York: John Wiley and Sons; 2000. |
7. | Yap CH, Reid C, Yii M, Rowland MA, Mohajeri M, Skillington PD, et al. Validation of the EuroSCORE model in Australia. Eur J Cardiothorac Surg 2006;29:441-6. |
8. | Michel P, Roques F, Nashef SA, The EuroSCORE project group. Logistic or additive EuroSCORE for high-risk patients? Eur J Cardiothorac Surg 2003;23:684-7. |
9. | Bridgewater B, Grayson AD, Jackson M, Brooks N, Grotte GJ, Keenan DJ, et al. Surgeon specific mortality in adult cardiac surgery: Comparison between crude and risk stratified data. BMJ 2003;327:13-7. |
10. | Karthik S, Srinivasan AK, Grayson AD, Jackson M, Sharpe DA, Keenan DJ, et al. Limitations of additive EuroSCORE for measuring risk stratified mortality in combined coronary and valve surgery. Eur J Cardiothorac Surg 2004;26:318-22. |
11. | Gogbashian A, Sedrakyan A, Treasure T. EuroSCORE: A systematic review of international performance. Eur J Cardiothorac Surg 2004;25:695-700. |
12. | Shaukat N, deBono DP. Are Indo-origin people especially susceptible to coronary artery disease? Postgrad Med J 1994;70:315-8. |
13. | Rose AG. Etiology of valvular heart disease. Curr Opin Cardiol 1996;11;98-113. |
14. | Supino P, Borer J, Preibisz J, Bornstein A. The epidemiology of valvular heart disease: A growing public health problem. Heart Fail Clin 2006;2:379-93. |
15. | Turner RC, Millns H, Neil HA, Stratton IM, Manley SE, Matthews DR, et al. Risk factors for coronary artery disease in non-insulin dependent diabetes mellitus: United Kingdom prospective diabetes study (UKPDS:23). BMJ 1998;316:823-8. |
16. | Malouf JF, Enriquez-Sarano M, Pellikka PA, Oh JK, Bailey KR, Chandrasekaran K, et al. Severe pulmonary artery hypertension in patients with severe aortic stenosis: Clinical profile and prognostic implications. J Am Coll Cardiol 2002:40;789-95. |
17. | Harikrishnan S, Kartha CC. Pulmonary hypertension in rheumatic heart disease. PVRI Rev 2009;1:13-9 |

Correspondence Address: Madhur Malik Department of Cardiac Anaesthesia, 7th Floor, C N Centre, All India Institute of Medical Sciences, New Delhi - 110 029 India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/0971-9784.69082

[Figure 1]
[Table 1], [Table 2], [Table 3], [Table 4] |
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