European system for cardiac operative risk evaluation (EuroSCORE)
Abstract
OBJECTIVE: To construct a scoring system for the prediction of early mortality in cardiac surgical patients in Europe on the basis of objective 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 difficult to falsify were weighted on the basis of regression analysis. An additive score of predicted mortality was constructed. Its calibration and discrimination characteristics were assessed in the validation dataset. Thresholds were defined 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 identified three groups 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 micromol/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) and pulmonary 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% confidence limits for observed mortality (0.56-1.10) and for expected mortality (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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
EuroSCORE is a model developed in 1999 for predicting mortality in hospital for patients undergoing cardiac surgery.
“For example, EuroSCORE is a model developed in 1999 for predicting mortality in hospital for patients undergoing cardiac surgery.99”
From Developing clinical prediction models: a step-by-step guide (2024) · cites this paper as [99]
Cites
Roques, F. (1999) · European Journal of Cardio-Thoracic Surgery · doi:10.1016/s1010-7940(99)00106-2 · Cited by 1,767 · see citing works
Self-citationJones, R. H., Hannan, E. L., Hammermeister, K. E., DeLong, E. R., O’Connor, G. T., Luepker, R. V., Parsonnet, V., & Pryor, D. B. (1996) · Journal of the American College of Cardiology · doi:10.1016/s0735-1097(96)00359-2 · Cited by 304 · see citing works
Tu, J. V., Sykora, K., & Naylor, C. D. (1997) · Journal of the American College of Cardiology · doi:10.1016/s0735-1097(97)00295-7 · Cited by 135 · see citing works
Assessment of Coronary Artery Bypass Graft Surgery Performance in New York
Hannan, E. L., Siu, A. L., Kumar, D., et al. (1997) · Medical Care · doi:10.1097/00005650-199701000-00004 · Cited by 70 · see citing works
Implications of cost in treatment selection for patients with coronary heart disease
Mark, D. B. (1996) · The Annals of Thoracic Surgery · doi:10.1016/0003-4975(95)01077-7 · Cited by 26 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Dindo, D., Demartines, N., & Clavien, P.-A. (2004) · Annals of Surgery · doi:10.1097/01.sla.0000133083.54934.ae · Cited by 31,378 · see citing works
Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · BMJ · doi:10.1136/bmj.g7594 · Cited by 4,388 · see citing works
Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0697 · Cited by 3,267 · see citing works
Nashef, S. A. M., Roques, F., Sharples, L. D., Nilsson, J., Smith, C., Goldstone, A. R., & Lockowandt, U. (2012) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezs043 · Cited by 2,971 · see citing works
Self-citationMoons, K. G. M., de Groot, J. A. H., Bouwmeester, W., Vergouwe, Y., Mallett, S., Altman, D. G., Reitsma, J. B., & Collins, G. S. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001744 · Cited by 2,018 · see citing works
Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. (2015) · BMC Medicine · doi:10.1186/s12916-014-0241-z · Cited by 1,927 · see citing works
Prognosis and prognostic research: validating a prognostic model
Altman, D. G., Vergouwe, Y., Royston, P., & Moons, K. G. M. (2009) · BMJ · doi:10.1136/bmj.b605 · Cited by 1,436 · see citing works
Moons, K. G. M., Kengne, A. P., Woodward, M., Royston, P., Vergouwe, Y., Altman, D. G., & Grobbee, D. E. (2012) · Heart · doi:10.1136/heartjnl-2011-301246 · Cited by 889 · see citing works
Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · Circulation · doi:10.1161/circulationaha.114.014508 · Cited by 793 · see citing works
Developing clinical prediction models: a step-by-step guide
Efthimiou, O., Seo, M., Chalkou, K., Debray, T., Egger, M., & Salanti, G. (2024) · BMJ · doi:10.1136/bmj-2023-078276 · Cited by 544 · see citing works
Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · European Urology · doi:10.1016/j.eururo.2014.11.025 · Cited by 439 · see citing works
Low-Cardiac-Output Syndrome After Cardiac Surgery
Lomivorotov, V. V., Efremov, S. M., Kirov, M. Y., Fominskiy, E. V., & Karaskov, A. M. (2017) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2016.05.029 · Cited by 315 · see citing works
Aortic Valve Replacement for Low-Flow/Low-Gradient Aortic Stenosis
Levy, F., Laurent, M., Monin, J. L., Maillet, J. M., Pasquet, A., Le Tourneau, T., Petit-Eisenmann, H., Gori, M., Jobic, Y., Bauer, F., et al. (2008) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2007.10.067 · Cited by 279 · see citing works
Generic, Simple Risk Stratification Model for Heart Valve Surgery
Ambler, G., Omar, R. Z., Royston, P., Kinsman, R., Keogh, B. E., & Taylor, K. M. (2005) · Circulation · doi:10.1161/circulationaha.104.515049 · Cited by 279 · see citing works
Rief, W., Shedden-Mora, M. C., Laferton, J. A. C., Auer, C., Petrie, K. J., Salzmann, S., Schedlowski, M., & Moosdorf, R. (2017) · BMC Medicine · doi:10.1186/s12916-016-0767-3 · Cited by 261 · see citing works
Ambler, G., Omar, R. Z., & Royston, P. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280206074466 · Cited by 233 · see citing works
Stability of clinical prediction models developed using statistical or machine learning methods
Riley, R. D., & Collins, G. S. (2023) · Biometrical Journal · doi:10.1002/bimj.202200302 · Cited by 211 · see citing works
Kusunose, K., Goodman, A., Parikh, R., Barr, T., Agarwal, S., Popovic, Z. B., Grimm, R. A., Griffin, B. P., & Desai, M. Y. (2014) · Circulation: Cardiovascular Imaging · doi:10.1161/circimaging.114.002041 · Cited by 183 · see citing works
A review of statistical updating methods for clinical prediction models
Su, T.-L., Jaki, T., Hickey, G. L., Buchan, I., & Sperrin, M. (2018) · Statistical Methods in Medical Research · doi:10.1177/0962280215626466 · Cited by 147 · see citing works
Hickey, G. L., Grant, S. W., Murphy, G. J., Bhabra, M., Pagano, D., McAllister, K., Buchan, I., & Bridgewater, B. (2013) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezs584 · Cited by 154 · see citing works
Jenkins, D. A., Martin, G. P., Sperrin, M., Riley, R. D., Debray, T. P. A., Collins, G. S., & Peek, N. (2021) · Diagnostic and Prognostic Research · doi:10.1186/s41512-020-00090-3 · Cited by 131 · see citing works
Ettema, R. G. A., Peelen, L. M., Schuurmans, M. J., Nierich, A. P., Kalkman, C. J., & Moons, K. G. M. (2010) · Circulation · doi:10.1161/circulationaha.109.926808 · Cited by 130 · see citing works
Guida, P., Mastro, F., Scrascia, G., Whitlock, R., & Paparella, D. (2014) · The Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2014.07.039 · Cited by 126 · see citing works
van Loon (2016) · doi:10.1097/md.0000000000003428 · Cited by 124 · see citing works
Meta‐analysis and aggregation of multiple published prediction models
Debray, T. P. A., Koffijberg, H., Nieboer, D., Vergouwe, Y., Steyerberg, E. W., & Moons, K. G. M. (2014) · Statistics in Medicine · doi:10.1002/sim.6080 · Cited by 73 · see citing works
Sy, R. W., Bannon, P. G., Bayfield, M. S., Brown, C., & Kritharides, L. (2009) · Circulation: Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.109.857938 · Cited by 71 · see citing works
Acute Mesenteric Ischemia Among Postcardiac Surgery Patients Presenting with Multiple Organ Failure
Guillaume, A., Pili-Floury, S., Chocron, S., Delabrousse, E., De Parseval, B., Koch, S., Samain, E., Capellier, G., & Piton, G. (2017) · Shock · doi:10.1097/shk.0000000000000720 · Cited by 68 · see citing works
Systematic Review and Validation of Prognostic Models in Liver Transplantation
Jacob, M., Lewsey, J. D., Sharpin, C., Gimson, A., Rela, M., & van der Meulen, J. H. P. (2005) · Liver Transplantation · doi:10.1002/lt.20456 · Cited by 68 · see citing works
Intestinal ischemia after cardiac surgery: analysis of a large registry
Nilsson, J., Hansson, E., & Andersson, B. (2013) · Journal of Cardiothoracic Surgery · doi:10.1186/1749-8090-8-156 · Cited by 60 · see citing works
Ågren, S., Berg, S., Svedjeholm, R., & Strömberg, A. (2015) · Intensive and Critical Care Nursing · doi:10.1016/j.iccn.2014.04.005 · Cited by 33 · see citing works
Kidd, T., Poole, L., Ronaldson, A., Leigh, E., Jahangiri, M., & Steptoe, A. (2016) · British Journal of Health Psychology · doi:10.1111/bjhp.12191 · Cited by 30 · see citing works
Agrifoglio, M., Trezzi, M., Barili, F., Dainese, L., Cheema, F. H., Topkara, V. K., Ghislandi, C., Parolari, A., Polvani, G., Alamanni, F., et al. (2008) · Journal of Cardiothoracic Surgery · doi:10.1186/1749-8090-3-35 · Cited by 24 · see citing works
Levy, T. J., Coppa, K., Cang, J., Barnaby, D. P., Paradis, M. D., Cohen, S. L., Makhnevich, A., van Klaveren, D., Kent, D. M., Davidson, K. W., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34646-2 · Cited by 14 · see citing works
“For example, EuroSCORE was initially developed to predict mortality after heartsurgery 20 , and it wasupdated twelve years later 14 .”
Method
- Authors
- Nashef, S. A. M., Roques, F., Michel, P., Gauducheau, E., Lemeshow, S., & Salamon, R.
- Affiliations
- Royal Papworth Hospital (GB)
- Corresponding author
- Nashef, S.A.M.
- Venue
- European Journal of Cardio-Thoracic Surgery
- Publisher
- Oxford University Press (OUP)
- Published
- 1999-07
- Volume
- 16
- Issue
- 1
- Pages
- 9-13
- ISSN
- 1010-7940 (ISSN-L)
- 1873-734X
- OpenAlex
- W2156202966
- Cited by
- 3,362
- References
- 5
- Citations by year
- 2026: 40 · 2025: 56 · 2024: 53 · 2023: 91 · 2022: 97 · 2021: 152 · 2020: 109 · 2019: 112 · 2018: 126 · 2017: 139 · 2016: 144 · 2015: 209 · 2014: 200 · 2013: 232 · 2012: 223
- FWCI
- 15.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- European Journal of Cardio-Thoracic Surgery (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac Valve Diseases and Treatments
- Health Sciences › Medicine › Surgery › Cardiac and Coronary Surgery Techniques
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac, Anesthesia and Surgical Outcomes
- Keywords
- EuroSCORE, cardiac surgery, mortality prediction, risk scoring, preoperative risk factors, postoperative mortality, risk stratification, discrimination and calibration, thoracic aortic surgery, coronary artery bypass grafting, emergency surgery, left ventricular ejection fraction, serum creatinine, endocarditis, pulmonary hypertension, previous cardiac surgery, age and sex risk factors
- MeSH terms
- Europe, Evaluation Studies as Topic, Cardiac Surgical Procedures, Humans, Quality of Health Care, Regression Analysis, Risk Factors, ROC Curve, Logistic Models, Survival Analysis, Risk Assessment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
