Dynamic trends in cardiac surgery: why the logistic EuroSCORE is no longer suitable for contemporary cardiac surgery and implications for future risk models
Open accessAbstract
OBJECTIVES: Progressive loss of calibration of the original EuroSCORE models has necessitated the introduction of the EuroSCORE II model. Poor model calibration has important implications for clinical decision-making and risk adjustment of governance analyses. The objective of this study was to explore the reasons for the calibration drift of the logistic EuroSCORE. METHODS: Data from the Society for Cardiothoracic Surgery in Great Britain and Ireland database were analysed for procedures performed at all National Health Service and some private hospitals in England and Wales between April 2001 and March 2011. The primary outcome was in-hospital mortality. EuroSCORE risk factors, overall model calibration and discrimination were assessed over time. RESULTS: A total of 317 292 procedures were included. Over the study period, mean age at surgery increased from 64.6 to 67.2 years. The proportion of procedures that were isolated coronary artery bypass grafts decreased from 67.5 to 51.2%. In-hospital mortality fell from 4.1 to 2.8%, but the mean logistic EuroSCORE increased from 5.6 to 7.6%. The logistic EuroSCORE remained a good discriminant throughout the study period (area under the receiver-operating characteristic curve between 0.79 and 0.85), but calibration (observed-to-expected mortality ratio) fell from 0.76 to 0.37. Inadequate adjustment for decreasing baseline risk affected calibration considerably. DISCUSSIONS: Patient risk factors and case-mix in adult cardiac surgery change dynamically over time. Models like the EuroSCORE that are developed using a 'snapshot' of data in time do not account for this and can subsequently lose calibration. It is therefore important to regularly revalidate clinical prediction models.
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Data drift can cause models' performance to deteriorate or behave in unexpected ways.
“Data drift, defined as the systematic shift in the underlying distribution of input features 11 , can cause models' performance to deteriorate [11][12][13] or behave in unexpected ways, which can pose a threat to the safety of patients.”
From Empirical data drift detection experiments on real-world medical imaging data (2024) · cites this paper as [12]
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Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,706 · see citing works
DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988) · Biometrics · doi:10.2307/2531595 · Cited by 22,649 · see citing works
ggplot2: Elegant Graphics for Data Analysis
Wickham, H. (2009) · doi:10.1007/978-0-387-98141-3 · Cited by 14,447 · see citing works
Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
Cleveland, W. S., & Devlin, S. J. (1988) · Journal of the American Statistical Association · doi:10.1080/01621459.1988.10478639 · Cited by 5,740 · see citing works
Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures
Steyerberg, E. W., Vickers, A. J., Cook, N. R., Gerds, T., Gonen, M., Obuchowski, N., Pencina, M. J., & Kattan, M. W. (2010) · Epidemiology · doi:10.1097/ede.0b013e3181c30fb2 · Cited by 5,170 · see citing works
European system for cardiac operative risk evaluation (EuroSCORE)
Nashef, S. A. M., Roques, F., Michel, P., Gauducheau, E., Lemeshow, S., & Salamon, R. (1999) · European Journal of Cardio-Thoracic Surgery · doi:10.1016/s1010-7940(99)00134-7 · Cited by 3,362 · see citing works
Approximate is Better than “Exact” for Interval Estimation of Binomial Proportions
Agresti, A., & Coull, B. A. (1998) · The American Statistician · doi:10.1080/00031305.1998.10480550 · Cited by 3,223 · 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
Roques, F. (1999) · European Journal of Cardio-Thoracic Surgery · doi:10.1016/s1010-7940(99)00106-2 · Cited by 1,767 · see citing works
Roques, F. (2003) · European Heart Journal · doi:10.1016/s0195-668x(02)00799-6 · Cited by 1,144 · see citing works
Fung, C. H., Lim, Y.-W., Mattke, S., Damberg, C. L., & Shekelle, P. (2008) · Annals of Internal Medicine · doi:10.7326/0003-4819-148-2-200801150-00006 · Cited by 981 · see citing works
Nashef, S. A. M. (2002) · European Journal of Cardio-Thoracic Surgery · doi:10.1016/s1010-7940(02)00208-7 · Cited by 551 · see citing works
Khuri, S. F. (2002) · Archives of Surgery · doi:10.1001/archsurg.137.1.20 · Cited by 453 · see citing works
Risk stratification in heart surgery: comparison of six score systems
Geissler, H. J. (2000) · European Journal of Cardio-Thoracic Surgery · doi:10.1016/s1010-7940(00)00385-7 · Cited by 334 · see citing works
Logistic or additive EuroSCORE for high-risk patients?☆
Michel, P., Roques, F., & Nashef, S. A. M. (2003) · European Journal of Cardio-Thoracic Surgery · doi:10.1016/s1010-7940(03)00074-5 · Cited by 288 · see citing works
EuroSCORE: a systematic review of international performance
Gogbashian, A., Sedrakyan, A., & Treasure, T. (2004) · European Journal of Cardio-Thoracic Surgery · doi:10.1016/j.ejcts.2004.02.022 · Cited by 214 · see citing works
Performance of the original EuroSCORE
Siregar, S., Groenwold, R. H. H., de Heer, F., Bots, M. L., van der Graaf, Y., & van Herwerden, L. A. (2012) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezr285 · Cited by 132 · see citing works
Grant, S. W., Hickey, G. L., Dimarakis, I., Trivedi, U., Bryan, A. J., Treasure, T., Cooper, G. J., Pagano, D., Buchan, I. E., & Bridgewater, B. (2012) · Heart · doi:10.1136/heartjnl-2012-302483 · Cited by 107 · see citing works
Self-citationChlamydia pneumoniae IgA titres and coronary heart disease: prospective study and meta-analysis
Danesh, J. (2003) · European Heart Journal · doi:10.1016/s0195-668x(02)00801-1 · Cited by 70 · see citing works
Choong, C. K. C., Sergeant, P., Nashef, S. A. M., Smith, J. A., & Bridgewater, B. (2008) · European Journal of Cardio-Thoracic Surgery · doi:10.1016/j.ejcts.2008.10.009 · Cited by 60 · see citing works
Self-citationCardiac registers: the adult cardiac surgery register
Bridgewater, B. J. M., & On behalf of the Society for Cardiothoracic Surgery in GB and Ireland (2010) · Heart · doi:10.1136/hrt.2010.194019 · Cited by 56 · see citing works
Self-citationGrant, S. W., Grayson, A. D., Jackson, M., Au, J., Fabri, B. M., Grotte, G. J., Jones, M. T., & Bridgewater, B. (2007) · Heart · doi:10.1136/hrt.2006.110478 · Cited by 37 · see citing works
Self-citationSampling time error in EuroSCORE II
Poullis, M. P., Fabri, B. M., Pullan, M. D., & Chalmers, J. (2012) · Interactive Cardiovascular and Thoracic Surgery · doi:10.1093/icvts/ivs034 · Cited by 23 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [12]Bridgewater B, Keogh B, Kinsman R, Walton P. Henley-on-Thames: Dendrite Clinical Systems Ltd.; 2009. Sixth National Adult Cardiac Surgical Database Report. [Google Scholar]
- [19]Ludman PF. Leeds, UK: The NHS Information Centre; 2011. National Audit of Angioplasty Procedures 2010-Procedures Performed between 1st January and 31st December 2009. Report No. IC03020111. [Google Scholar]
Cited by
Sorted by citations
2014 ESC/EACTS Guidelines on myocardial revascularization
Authors Task Force Members, Windecker, S., Kolh, P., Alfonso, F., Collet, J., Cremer, J., Falk, V., Filippatos, G., Hamm, C. W., Head, S. J., et al. (2014) · European Heart Journal · doi:10.1093/eurheartj/ehu278 · Cited by 4,340 · see citing works
2014 ESC/EACTS Guidelines on myocardial revascularization
Kolh, P. H., Windecker, S., Alfonso, F., Collet, J., Cremer, J., Falk, V., Filippatos, G. S., Hamm, C. W., Head, S. J., Jüni, P., et al. (2014) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezu366 · Cited by 4,211 · see citing works
Evaluation of clinical prediction models (part 1): from development to external validation
Collins, G. S., Dhiman, P., Ma, J., Schlussel, M. M., Archer, L., Van Calster, B., Harrell, F. E., Martin, G. P., Moons, K. G. M., van Smeden, M., et al. (2024) · BMJ · doi:10.1136/bmj-2023-074819 · Cited by 798 · see citing works
2014 ESC/EACTS Guidelines on myocardial revascularization
Windecker, S., Kolh, P. H., Alfonso, F., Collet, J., Cremer, J., Falk, V., Filippatos, G. S., Hamm, C. W., Head, S. J., Jüni, P., et al. (2014) · Kardiologia Polska · doi:10.5603/kp.2014.0224 · Cited by 416 · see citing works
2014 ESC/EACTS Guidelines on myocardial revascularization
Windecker, S., Kolh, P. H., Alfonso, F., Collet, J., Cremer, J., Falk, V., Filippatos, G. S., Hamm, C. W., Head, S. J., Jüni, P., et al. (2015) · EuroIntervention · doi:10.4244/eijy14m09_01 · Cited by 332 · see citing works
Calibration drift in regression and machine learning models for acute kidney injury
Davis, S. E., Lasko, T. A., Chen, G., Siew, E. D., & Matheny, M. E. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx030 · Cited by 312 · see citing works
There is no such thing as a validated prediction model
Van Calster, B., Steyerberg, E. W., Wynants, L., & van Smeden, M. (2023) · BMC Medicine · doi:10.1186/s12916-023-02779-w · Cited by 286 · see citing works
Statistical Primer: developing and validating a risk prediction model†
Grant, S. W., Collins, P. G. S., & Nashef, S. A. M. (2018) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezy180 · Cited by 253 · see citing works
Self-citationDetection of calibration drift in clinical prediction models to inform model updating
Davis, S. E., Greevy, R. A., Lasko, T. A., Walsh, C. G., & Matheny, M. E. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103611 · Cited by 160 · see citing works
Coronary artery bypass grafting: Part 1--the evolution over the first 50 years
Head, S. J., Kieser, T. M., Falk, V., Huysmans, H. A., & Kappetein, A. P. (2013) · European Heart Journal · doi:10.1093/eurheartj/eht330 · Cited by 167 · 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
Self-citationJenkins, 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
Clinical registries: governance, management, analysis and applications
Hickey, G. L., Grant, S. W., Cosgriff, R., Dimarakis, I., Pagano, D., Kappetein, A. P., & Bridgewater, B. (2013) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezt018 · Cited by 125 · see citing works
Self-citationOsnabrugge, R. L. J., Speir, A. M., Head, S. J., Fonner, C. E., Fonner, E., Kappetein, A. P., & Rich, J. B. (2014) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezu033 · Cited by 126 · see citing works
Empirical data drift detection experiments on real-world medical imaging data
Kore, A., Abbasi Bavil, E., Subasri, V., Abdalla, M., Fine, B., Dolatabadi, E., & Abdalla, M. (2024) · Nature Communications · doi:10.1038/s41467-024-46142-w · Cited by 124 · see citing works
Predicting breast cancer 5-year survival using machine learning: A systematic review
Li, J., Zhou, Z., Dong, J., Fu, Y., Li, Y., Luan, Z., & Peng, X. (2021) · PLoS ONE · doi:10.1371/journal.pone.0250370 · Cited by 123 · see citing works
Targeted validation: validating clinical prediction models in their intended population and setting
Sperrin, M., Riley, R. D., Collins, P. G. S., & Martin, G. P. (2022) · Diagnostic and Prognostic Research · doi:10.1186/s41512-022-00136-8 · Cited by 115 · see citing works
A nonparametric updating method to correct clinical prediction model drift
Davis, S. E., Greevy, R., Fonnesbeck, C., Lasko, T., Walsh, C. G., & Matheny, M. E. (2019) · J. Am. Medical Informatics Assoc. · doi:10.1093/jamia/ocz127 · Cited by 112 · see citing works
Dynamic models to predict health outcomes: current status and methodological challenges
Jenkins, D. A., Sperrin, M., Martin, G., & Peek, N. (2018) · Diagnostic and Prognostic Research · doi:10.1186/s41512-018-0045-2 · Cited by 96 · see citing works
Combining Health Data Uses to Ignite Health System Learning
Buchan, I. E., & Ainsworth, J. D. (2015) · Methods of Information in Medicine · doi:10.3414/me15-01-0064 · Cited by 65 · see citing works
Self-citationImpact of sarcopenia on long-term mortality following endovascular aneurysm repair
Hale, A. L., Twomey, K., Ewing, J. A., Langan, E. M., Cull, D. L., & Gray, B. H. (2016) · Vascular Medicine · doi:10.1177/1358863x15624025 · Cited by 65 · see citing works
Howitt, S. H., Grant, S. W., Caiado, C., Carlson, E., Kwon, D., Dimarakis, I., Malagon, I. L., & McCollum, C. N. (2018) · BMC Nephrology · doi:10.1186/s12882-018-0946-x · Cited by 64 · see citing works
Self-citationThe Changing Face of Cardiac Surgery: Practice Patterns and Outcomes 2001-2010
Buth, K. J., Gainer, R. A., Légaré, J., & Hirsch, G. M. (2013) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2013.10.020 · Cited by 60 · see citing works
Stähli, B. E., Tasnady, H., Lüscher, T. F., Gebhard, C., Mikulicic, F., Erhart, L., Bühler, I., Landmesser, U., Altwegg, L. A., Wischnewsky, M. B., et al. (2013) · Cardiology · doi:10.1159/000351438 · Cited by 60 · see citing works
Dynamic Prediction Modeling Approaches for Cardiac Surgery
Hickey, G., Grant, S., Caiado, C., Kendall, S., Dunning, J., Poullis, M., Buchan, I. E., & Bridgewater, B. (2013) · Circulation. Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.111.000012 · Cited by 55 · see citing works
Self-citationNational risk prediction model for elective abdominal aortic aneurysm repair
Grant, S. W., Hickey, G. L., Grayson, A. D., Mitchell, D. C., & McCollum, C. N. (2013) · British journal of surgery · doi:10.1002/bjs.9047 · Cited by 58 · see citing works
Self-citationOhri, S. K., Benedetto, U., Luthra, S., Grant, S. W., Goodwin, A. T., Trivedi, U., Kendall, S. W. H., & Jenkins, D. P. (2021) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezab391 · Cited by 56 · see citing works
Self-citationDeveloping more generalizable prediction models from pooled studies and large clustered data sets
de Jong, V. M. T., Moons, K. G. M., Eijkemans, M. J. C., Riley, R. D., & Debray, T. P. A. (2021) · Statistics in Medicine · doi:10.1002/sim.8981 · Cited by 56 · see citing works
Sinning, J., Wollert, K. C., Sedaghat, A., Widera, C., Radermacher, M.-C., Descoups, C., Hammerstingl, C., Weber, M., Stundl, A., Ghanem, A., et al. (2015) · American Heart Journal · doi:10.1016/j.ahj.2015.07.003 · Cited by 54 · see citing works
Cronin, R. M., VanHouten, J. P., Siew, E. D., Eden, S., Fihn, S. D., Nielson, C. D., Peterson, J. F., Baker, C. R., Ikizler, T. A., Speroff, T., et al. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv051 · Cited by 53 · see citing works
Lopez-Sendon, J. L., González‐Juanatey, J. R., Pinto, F. J., Castillo, J. J. C., Badimón, L., Dalmau, R., Torrecilla, E. G., López-Mínguez, J. R., Maceira, A. M., Pascual-Figal, D. A., et al. (2015) · Revista Española de Cardiología · doi:10.1016/j.recesp.2015.07.005 · Cited by 52 · see citing works
Serum creatinine role in predicting outcome after cardiac surgery beyond acute kidney injury.
Najafi, M. (2014) · World Journal of Cardiology · doi:10.4330/wjc.v6.i9.1006 · Cited by 43 · see citing works
Evaluation of machine learning solutions in medicine
Antoniou, T., & Mamdani, M. (2021) · Canadian Medical Association Journal · doi:10.1503/cmaj.210036 · Cited by 53 · see citing works
Head, S. J., Osnabrugge, R. L. J., Howell, N. J., Freemantle, N., Bridgewater, B., Pagano, D., & Kappetein, A. P. (2013) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezt044 · Cited by 50 · see citing works
Self-citationCalibration Drift Among Regression and Machine Learning Models for Hospital Mortality.
Davis, S. E., Lasko, T. A., Chen, G., & Matheny, M. E. (2017) · PubMed Central · Cited by 50 · see citing works
Strobl, A. N., Vickers, A. J., CALSTER, B. V., Steyerberg, E. W., Leach, R. J., Thompson, I. M., & Ankerst, D. P. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.05.001 · Cited by 46 · see citing works
Davis, S. E., Dorn, C., Park, D. J., & Matheny, M. E. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf039 · Cited by 43 · see citing works
Chi, S., Tian, Y., Wang, F., Zhou, T., Jin, S., & Li, J. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102256 · Cited by 46 · see citing works
Incidence and outcomes of sepsis after cardiac surgery as defined by the Sepsis-3 guidelines
Howitt, S. H., Herring, M., Malagon, I. L., McCollum, C. N., & Grant, S. W. (2017) · British Journal of Anaesthesia · doi:10.1016/j.bja.2017.10.018 · Cited by 45 · see citing works
Self-citationMartin, G. P., Sperrin, M., Ludman, P., de Belder, M., Redwood, S. R., Townend, J., Gunning, M., Moat, N. E., BANNING, A. P., Buchan, I. E., et al. (2017) · Heart · doi:10.1136/heartjnl-2017-312489 · Cited by 43 · see citing works
Self-citationMontgomery, C., Stelfox, H. T., Norris, C. M., Rolfson, D. B., Meyer, S. R., Zibdawi, M., & Bagshaw, S. M. (2021) · CMAJ Open · doi:10.9778/cmajo.20200034 · Cited by 42 · see citing works
Hickey, G. L., Bridgewater, B., Grant, S. W., Deanfield, J. E., Parkinson, J., Bryan, A. J., Dalrymple-Hay, M. J. R., Moat, N. E., Buchan, I. E., & Dunning, J. (2016) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2016.6936 · Cited by 42 · see citing works
Self-citationMethods for updating a risk prediction model for cardiac surgery: a statistical primer.
Siregar, S., Nieboer, D., Versteegh, M., Steyerberg, E., & Takkenberg, J. (2019) · Interactive Cardiovascular and Thoracic Surgery · doi:10.1093/icvts/ivy338 · Cited by 32 · see citing works
A technical review of the United Kingdom National Adult Cardiac Surgery Governance Analysis 2008–11
Hickey, G. L., Cosgriff, R., Grant, S. W., Cooper, G. J., Deanfield, J. E., Roxburgh, J. C., & Bridgewater, B. (2013) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezt476 · Cited by 34 · see citing works
Self-citationOpen questions and research gaps for monitoring and updating AI-enabled tools in clinical settings
Davis, S. E., Walsh, C. G., & Matheny, M. E. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.958284 · Cited by 33 · see citing works
United States national trends in comorbidity and outcomes of adult cardiac surgery patients
Mullan, C. W., Mori, M., Pichert, M. D., Mahmood, S. U. B., Yousef, S., & Geirsson, A. (2020) · Journal of Cardiac Surgery · doi:10.1111/jocs.14764 · Cited by 32 · see citing works
Kieser, T. M., Rose, M. S., & Head, S. J. (2016) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezw072 · Cited by 32 · see citing works
Wolff, G., Shamekhi, J., Al‐Kassou, B., Tabata, N., Parco, C., Klein, K., Maier, O., Sedaghat, A., Polzin, A., Sugiura, A., et al. (2020) · Clinical Research in Cardiology · doi:10.1007/s00392-020-01731-9 · Cited by 29 · see citing works
Taylor, M., Szafron, B., Martin, G. P., Abah, U. D., Smith, M. O., Shackcloth, M. J., Granato, F., Shah, R. D., Grant, S. W., (NWTSC), T. N. W. T. S. C., et al. (2020) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezaa422 · Cited by 29 · see citing works
Self-citationPredicting 30-Day Hospital Readmission Risk in a National Cohort of Patients with Cirrhosis
Koola, J., Ho, S. B., Cao, A., Chen, G., Perkins, A. M., Davis, S. E., & Matheny, M. E. (2019) · Digestive Diseases and Sciences · doi:10.1007/s10620-019-05826-w · Cited by 31 · see citing works
Grant, S. W., Hickey, G. L., Carlson, E. D., & McCollum, C. N. (2014) · European Journal of Vascular and Endovascular Surgery · doi:10.1016/j.ejvs.2014.03.040 · Cited by 27 · see citing works
Self-citationImproved Prediction by Dynamic Modeling
Siregar, S., Nieboer, D., Vergouwe, Y., Versteegh, M. I. M., Noyez, L., Vonk, A. B. A., Steyerberg, E. W., & Takkenberg, J. J. M. (2016) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.114.001645 · Cited by 26 · see citing works
Lopez-Sendon, J. L., González‐Juanatey, J. R., Pinto, F. J., Castillo, J. J. C., Badimón, L., Dalmau, R., Torrecilla, E. G., López-Mínguez, J. R., Maceira, A. M., Pascual-Figal, D. A., et al. (2015) · Revista Española de Cardiología (English Edition) · doi:10.1016/j.rec.2015.07.003 · Cited by 24 · see citing works
Davis, S. E., Greevy, R. A., Lasko, T. A., Walsh, C. G., & Matheny, M. E. (2019) · PubMed Central · Cited by 24 · see citing works
A systematic review of risk prediction models for perioperative mortality after thoracic surgery
Taylor, M., Hashmi, S. F., Martin, G. P., Shackcloth, M. J., Shah, R. D., Booton, R., & Grant, S. W. (2020) · Interactive Cardiovascular and Thoracic Surgery · doi:10.1093/icvts/ivaa273 · Cited by 24 · see citing works
Self-citationHartmann, S., Dwyer, D., Cavve, B. S., Byrne, E. M., Scott, I., Gao, C. X., Wannan, C., Yuen, H. P., Hartmann, J., Lin, A. P., et al. (2024) · World Psychiatry · doi:10.1002/wps.21240 · Cited by 23 · see citing works
Mejía, O. A. V., Antunes, M. J., Goncharov, M., Dallan, L. R. P., Veronese, E. T., Lapenna, G. A., Lisboa, L. A. F., Dallan, L. A. O., Brandão, C. M. D. A., Zubelli, J. P., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0199277 · Cited by 23 · see citing works
Grant, S. W., Sperrin, M., Carlson, E., Chinai, N., Ntais, D., Hamilton, M. P., Dunn, G., Buchan, I. E., Davies, L. M., & McCollum, C. N. (2015) · Health Technology Assessment · doi:10.3310/hta19320 · Cited by 22 · see citing works
Self-citationBashir, M., Shaw, M., Grayson, A. D., Fok, M., Hickey, G. L., Grant, S. W., Bridgewater, B., & Oo, A. (2016) · The Annals of Thoracic Surgery · doi:10.1016/j.athoracsur.2015.10.067 · Cited by 21 · see citing works
Self-citationUpdating methods for artificial intelligence–based clinical prediction models: a scoping review
Meijerink, L., Dunias, Z. S., Leeuwenberg, A. M., de Hond, A., Jenkins, D. A., Martin, G. P., Sperrin, M., Peek, N. B., Spijker, R., Hooft, L., et al. (2024) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2024.111636 · Cited by 19 · see citing works
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Spann, A., Strauss, A. T., Davis, S. E., & Bhat, M. (2025) · Gastroenterology · doi:10.1053/j.gastro.2025.05.012 · Cited by 21 · see citing works
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D’Angelo, A. M., Nemeth, S. K., Wang, C. K., Kossar, A. P., Takeda, K., Takayama, H., Bapat, V., Naka, Y., Argenziano, M., Smith, C. R., et al. (2021) · Interactive Cardiovascular and Thoracic Surgery · doi:10.1093/icvts/ivab310 · Cited by 19 · see citing works
Grant, S. W., Hickey, G. L., Ludman, P., Moat, N. E., Cunningham, D., de Belder, M., Blackman, D. J., Hildick‐Smith, D., Uppal, R., Kendall, S. W. H., et al. (2015) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezv270 · Cited by 19 · see citing works
Self-citationA multiple‐model generalisation of updating clinical prediction models
Martin, G. P., Mamas, M. A., Peek, N. B., Buchan, I. E., & Sperrin, M. (2017) · Statistics in Medicine · doi:10.1002/sim.7586 · Cited by 19 · see citing works
Self-citationMejía, O. A. V., Borgomoni, G. B., Zubelli, J. P., Dallan, L. R. P., Pomerantzeff, P. M. A., Oliveira, M. A. P., Petrucci, O., Tiveron, M. G., Nakazone, M. A., Tineli, R. Â., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0238737 · Cited by 19 · see citing works
Abbasciano, R. G., Lai, F. Y., Roman, M., Rizzello, A., Pathak, S., Ramzi, J., Lucarelli, C., Layton, G. R., Kumar, T., Wozniak, M. J., et al. (2021) · British Journal of Anaesthesia · doi:10.1016/j.bja.2021.04.032 · Cited by 18 · see citing works
Self-citationBenedetto, U., Dimagli, A., Gibbison, B., Sinha, S., Pufulete, M., Fudulu, D. P., Cocomello, L., Bryan, A. J., Ohri, S. K., Caputo, M., et al. (2021) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2020.100003 · Cited by 18 · see citing works
Ensor, J., Snell, K. I. E., Debray, T. P. A., Lambert, P. C., Look, M. P., Mamas, M. A., Moons, K. G. M., & Riley, R. D. (2021) · Statistics in Medicine · doi:10.1002/sim.8959 · Cited by 17 · see citing works
Mejía, O. A. V., Borgomoni, G. B., Dallan, L. R. P., Mioto, B. M., Accorsi, T. A. D., Lima, E. G., Soeiro, A. D. M., Lima, F. G., Brandão, C. M. D. A., Pomerantzeff, P. M. A., et al. (2022) · International Journal of Surgery · doi:10.1016/j.ijsu.2022.106931 · Cited by 17 · see citing works
Maxwell, B. G., Wong, J., Miller, D. C., & Lobato, R. L. (2014) · Health Services Research · doi:10.1111/1475-6773.12174 · Cited by 17 · see citing works
Lin, Y., Shao, H., Shi, L., Anderson, A. H., & Fonseca, V. A. (2022) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14806 · Cited by 17 · see citing works
Dong, T., Sinha, S., Zhai, B., Fudulu, D. P., Chan, J., Narayan, P., Judge, A., Caputo, M., Dimagli, A., Benedetto, U., et al. (2023) · Digital Health · doi:10.1177/20552076231187605 · Cited by 16 · see citing works
Hickey, G. L., Grant, S. W., Freemantle, N., Cunningham, D., Munsch, C. M., Livesey, S. A., Roxburgh, J. C., Buchan, I. E., & Bridgewater, B. (2014) · Journal of the Royal Society of Medicine · doi:10.1177/0141076814538788 · Cited by 16 · see citing works
Self-citationMejía, O. A. V., Lisboa, L. A. F., Arita, E. C. T. C., Brandão, C. M. D. A., Dias, R. R., Costa, R., Jatene, M. B., Pomerantzeff, P. M. A., Dallan, L. A. O., & Jatene, F. B. (2020) · Arquivos Brasileiros de Cardiologia · doi:10.36660/abc.20190736 · Cited by 16 · see citing works
Performance of EuroSCORE II in Hungary: A Single-centre Validation Study
Koszta, G., Sira, G., Szatmári, K., Farkas, E., Szerafin, T., & Fülesdi, B. (2014) · Heart Lung and Circulation · doi:10.1016/j.hlc.2014.04.005 · Cited by 15 · see citing works
Hickey, G. L., & Blackstone, E. H. (2016) · Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2016.04.023 · Cited by 15 · see citing works
Self-citationPotger, K. C., McMillan, D., & Ambrose, M. (2013) · Journal of ExtraCorporeal Technology · doi:10.1051/ject/201345077 · Cited by 15 · see citing works
Creating transparency in UK adult cardiac surgery data
Grant, S. W., Hickey, G. L., Cosgriff, R., Cooper, G. J., Deanfield, J. E., Roxburgh, J. C., & Bridgewater, B. (2013) · Heart · doi:10.1136/heartjnl-2013-304068 · Cited by 15 · see citing works
Self-citationContinuous quality improvement programme in cardiovascular surgery: the Latin American perspective
Mejía, O. A. V., Lisboa, L. A. F., & Jatene, F. B. (2016) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezw087 · Cited by 15 · see citing works
Thoma, B., Spadafore, M., Sebok‐Syer, S. S., George, B. C., Chan, T. M., & Krumm, A. E. (2023) · Academic Medicine · doi:10.1097/acm.0000000000005605 · Cited by 14 · see citing works
Risk Prediction Models in Perioperative Medicine: Methodological Considerations
Collins, P. G. S., Ma, J., Gerry, S., Ohuma, E. O., Odondi, L., Trivella, M., de Beyer, J. A., & Vazquez-Montes, M. (2016) · Current anesthesiology reports · doi:10.1007/s40140-016-0171-8 · Cited by 14 · see citing works
Perioperative risk scores: prediction, pitfalls, and progress
Bedford, J., Redfern, O., O’Brien, B., & Watkinson, P. (2024) · Current Opinion in Anaesthesiology · doi:10.1097/aco.0000000000001445 · Cited by 18 · see citing works
The Effect of Low Preoperative Ejection Fraction on Mortality After Cardiac Surgery in Indonesia
Kurniawaty, J., Setianto, B. Y., Supomo, S., Widyastuti, Y., & Boom, C. E. (2022) · Vascular Health and Risk Management · doi:10.2147/vhrm.s350671 · Cited by 13 · see citing works
Caiado, C., McCollum, C. N., Goldstein, M., Malagon, I. L., Venkateswaran, R., Grant, S. W., & Howitt, S. H. (2018) · The Thoracic and Cardiovascular Surgeon · doi:10.1055/s-0037-1608897 · Cited by 12 · see citing works
Self-citationMost deaths in low-risk cardiac surgery could be avoidable
Mejía, O. A. V., Borgomoni, G. B., Lima, E. G., Guerreiro, G. P., Dallan, L. R., Silva, P. D. B. E., Nakazone, M. A., Petrucci, O., Gomes, W. J., Oliveira, M. A. P., et al. (2021) · Scientific Reports · doi:10.1038/s41598-020-80175-7 · Cited by 12 · see citing works
Abbasciano, R. G., Tomassini, S., Roman, M., Rizzello, A., Pathak, S., Ramzi, J., Lucarelli, C., Layton, G. R., Butt, A., Lai, F., et al. (2023) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd013584.pub2 · Cited by 11 · see citing works
Self-citationCastro, M. D. L., Alves, M., Martins, A. R., Papoila, A. L., Botelho, M. A., & Fragata, J. (2023) · Revista Portuguesa de Cardiologia · doi:10.1016/j.repc.2022.03.009 · Cited by 9 · see citing works
Murphy, G., Mumford, A., Rogers, C., Wordsworth, S., Stokes, E., Verheyden, V., Kumar, T., Harris, J., Clayton, G., Ellis, L., et al. (2017) · Programme Grants for Applied Research · doi:10.3310/pgfar05170 · Cited by 8 · see citing works
Self-citationAl‐Farra, H., de Mol, B. A. J. M., Ravelli, A. C. J., ter Burg, W. J. P. P., Houterman, S., Henriques, J. P. S., & Abu–Hanna, A. (2021) · IJC Heart & Vasculature · doi:10.1016/j.ijcha.2021.100716 · Cited by 9 · see citing works
Booth, S., Mozumder, S. I., Archer, L., Ensor, J., Riley, R. D., Lambert, P. C., & Rutherford, M. J. (2023) · Statistics in Medicine · doi:10.1002/sim.9898 · Cited by 8 · see citing works
van Dijk, W. B., Leeuwenberg, A. M., Grobbee, D. E., Siregar, S., Houterman, S., Daeter, E. J., de Vries, M. C., Groenwold, R. H. H., Schuit, E., Bramer, S., et al. (2023) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezad301 · Cited by 8 · see citing works
Janikowski, K., Morawiec, R., Jegier, B., Jaszewski, R., & Lelonek, M. (2015) · Kardiologia Polska · doi:10.5603/kp.a2015.0215 · Cited by 8 · see citing works
Davis, S. E., Brown, J. R., Dorn, C. A., Westerman, D. M., Solomon, R., & Matheny, M. E. (2022) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.121.008635 · Cited by 8 · see citing works
Is your clinical prediction model past its sell by date?
Reynard, C., Jenkins, D. A., Martin, G. P., Kontopantelis, E., & Body, R. M. (2022) · Emergency Medicine Journal · doi:10.1136/emermed-2021-212224 · Cited by 7 · see citing works
Risk Prediction Models for Long-Term Survival after Cardiac Surgery: A Systematic Review
Argus, L., Taylor, M., Ouzounian, M., Venkateswaran, R., & Grant, S. W. (2023) · The Thoracic and Cardiovascular Surgeon · doi:10.1055/s-0043-1760747 · Cited by 7 · see citing works
Self-citationKarthikesalingam, A., Holt, P., Loftus, I. M., & THOMPSON, M. M. (2015) · European Journal of Vascular and Endovascular Surgery · doi:10.1016/j.ejvs.2015.06.003 · Cited by 7 · see citing works
Howitt, S. H., Oakley, J., Caiado, C., Goldstein, M., Malagon, I. L., McCollum, C. N., & Grant, S. W. (2019) · Critical Care Medicine · doi:10.1097/ccm.0000000000004074 · Cited by 7 · see citing works
Self-citationFurusato, S., Kurogochi, K., Mizuno, M., Shinoda, S., Tanoshima, R., & Uechi, M. (2025) · Journal of Veterinary Internal Medicine · doi:10.1111/jvim.70152 · Cited by 6 · see citing works
External validation of a paediatric Smart triage model for use in resource limited facilities
Kigo, J., Kamau, S., Mawji, A., Mwaniki, P., Dunsmuir, D. T., Pillay, Y., Zhang, C., Pallot, K., Ogero, M. O., Kimutai, D. K., et al. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000293 · Cited by 6 · see citing works
Dynamic Updating of Psychosis Prediction Models in Individuals at Ultra-High Risk of Psychosis
Hartmann, S., Dwyer, D., Scott, I., Wannan, C., Nguyen, J., Lin, A. P., Middeldorp, C. M., Wood, S. J., Yung, A. R., McGorry, P. D., et al. (2025) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2025.03.006 · Cited by 6 · see citing works
- Authors
- Hickey, G. L., Grant, S. W., Murphy, G. J., Bhabra, M., Pagano, D., McAllister, K., Buchan, I., & Bridgewater, B.
- Affiliations
- Manchester Academic Health Science Centre (GB)
- Bristol Royal Infirmary (GB)
- New Cross Hospital (GB)
- University Hospitals Birmingham NHS Foundation Trust (GB)
- Corresponding author
- Bridgewater, B.
- Venue
- European Journal of Cardio-Thoracic Surgery
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-11-14
- Volume
- 43
- Issue
- 6
- Pages
- 1146-1152
- ISSN
- 1010-7940 (ISSN-L)
- 1873-734X
- PMC
- PMC3655624
- OpenAlex
- W2164862771
- Cited by
- 154
- References
- 23
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 11 · 2023: 14 · 2022: 11 · 2021: 18 · 2020: 11 · 2019: 6 · 2018: 10 · 2017: 9 · 2016: 10 · 2015: 14 · 2014: 11 · 2013: 12 · 2012: 1
- FWCI
- 7.82
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- European Journal of Cardio-Thoracic Surgery (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (submittedVersion)
- Funders
- Medical Research Council (MR/K006665/1, MC_PC_13042)
- 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
- logistic EuroSCORE, EuroSCORE II, in-hospital mortality, coronary artery bypass grafting, risk adjustment
- MeSH terms
- Calibration, Europe, Cardiac Surgical Procedures, Humans, Prospective Studies, Risk, Reproducibility of Results, Models, Statistical, Logistic Models, 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.
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