The DOI matches its Crossref record; 27 of the 28 references it cites were found in a registry or an index.
Generic, Simple Risk Stratification Model for Heart Valve Surgery
Open accessAbstract
Background— Heart valve surgery has an associated in-hospital mortality rate of 4% to 8%. This study aims to develop a simple risk model to predict the risk of in-hospital mortality for patients undergoing heart valve surgery to provide information to patients and clinicians and to facilitate institutional comparisons. Methods and Results— Data on 32 839 patients were obtained from the Society of Cardiothoracic Surgeons of Great Britain and Ireland on patients who underwent heart valve surgery between April 1995 and March 2003. Data from the first 5 years (n=16 679) were used to develop the model; its performance was evaluated on the remaining data (n=16 160). The risk model presented here is based on the combined data. The overall in-hospital mortality was 6.4%. The risk model included, in order of importance (all P <0.01), operative priority, age, renal failure, operation sequence, ejection fraction, concomitant tricuspid valve surgery, type of valve operation, concomitant CABG surgery, body mass index, preoperative arrhythmias, diabetes, gender, and hypertension. The risk model exhibited good predictive ability (Hosmer-Lemeshow test, P =0.78) and discriminated between high- and low-risk patients reasonably well (receiver-operating characteristics curve area, 0.77). Conclusions— This is the first risk model that predicts in-hospital mortality for aortic and/or mitral heart valve patients with or without concomitant CABG. Based on a large national database of heart valve patients, this model has been evaluated successfully on patients who had valve surgery during a subsequent time period. It is simple to use, includes routinely collected variables, and provides a useful tool for patient advice and institutional comparisons.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Increasing age and comorbidities substantially increase both operative and long-term mortality after AVR217;218.”
From Calcific aortic stenosis (2016) · cites this paper as [217]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(1990) · Choice Reviews Online · doi:10.5860/choice.27-3338 · Cited by 35,305
Hosmer, D. W., & Lemeshow, S. (2000) · doi:10.1002/0471722146 · Cited by 20,302
Regression Modeling Strategies
Harrell, F. E. (2001) · Springer series in statistics · doi:10.1007/978-1-4757-3462-1 · Cited by 9,270
Harrell (2015) · Springer Series in Statistics · doi:10.1007/978-3-319-19425-7 · Cited by 6,878
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
ACC/AHA guidelines for coronary artery bypass graft surgery
Eagle, K. A., Guyton, R. A., Davidoff, R., Ewy, G. A., Fonger, J. D., Gardner, T. J., Gott, J. P., Herrmann, H. C., Marlow, R. A., Nugent, W. C., et al. (1999) · Journal of the American College of Cardiology · doi:10.1016/s0735-1097(99)00389-7 · Cited by 1,982
Parsonnet, V., DEAN, D. L., & Bernstein, A. D. (1989) · PubMed · Cited by 1,112
The use of fractional polynomials to model continuous risk variables in epidemiology
Royston, P., Ambler, G., & Sauerbrei, W. (1999) · International Journal of Epidemiology · doi:10.1093/ije/28.5.964 · Cited by 1,066
Self-citationThe society of thoracic surgeons: 30-day operative mortality and morbidity risk models
Shroyer, A. L., Coombs, L. P., Peterson, E. D., Eiken, M. C., DeLong, E. R., Chen, A., Ferguson, T. B., Grover, F. L., & Edwards, F. H. (2003) · The Annals of Thoracic Surgery · doi:10.1016/s0003-4975(03)00179-6 · Cited by 661
Prediction of operative mortality after valve replacement surgery
Edwards, F. H., Peterson, E. D., Coombs, L. P., DeLong, E. R., Jamieson, W. R. E., Shroyer, A. L., & Grover, F. L. (2001) · Journal of the American College of Cardiology · doi:10.1016/s0735-1097(00)01202-x · Cited by 422
Cited by
Sorted by citations
mice: Multivariate Imputation by Chained Equations in R
Buuren, S. V., & Groothuis-Oudshoorn, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i03 · Cited by 13,870
“…al. 2008; van den Hout et al. 2009), atherosclerosis (Tiemeier et al. 2004; van Oijen et al. 2007; McClelland et al. 2008), cardiovascular system (Ambler et al. 2005; van Buuren et al. 2006a; Chase et al. 2008; Byrne et al. 2009; Klein et al. 2009), cancer (Clark et al. 2001, 2003; Clark and…”
BackgroundACC/AHA 2006 Guidelines for the Management of Patients With Valvular Heart Disease
Bonow, R. O., Carabello, B. A., Chatterjee, K., de Leon, A. C., Faxon, D. P., Freed, M. D., Gaasch, W. H., Lytle, B. W., Nishimura, R. A., O’Gara, P. T., et al. (2006) · Circulation · doi:10.1161/circulationaha.106.176857 · Cited by 8,487
Moons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, G. S. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0698 · Cited by 5,147
Guidelines on the management of valvular heart disease
Vahanian, A. S., Baumgartner, H., Bax, J. J., Butchart, E., Dion, R., Filippatos, G. S., Flachskampf, F., Hall, R., Iung, B., Kasprzak, J. D., et al. (2007) · European Heart Journal · doi:10.1093/eurheartj/ehl428 · Cited by 3,579
Guidelines on the management of valvular heart disease (version 2012)
Authors/Task Force Members, Vahanian, A., Alfieri, O., Andreotti, F., Antunes, M. J., Barón-Esquivias, G., Baumgartner, H., Borger, M. A., Carrel, T. P., De Bonis, M., et al. (2012) · European Heart Journal · doi:10.1093/eurheartj/ehs109 · Cited by 3,559
Metadata
- Authors
- Ambler, G., Omar, R. Z., Royston, P., Kinsman, R., Keogh, B. E., & Taylor, K. M.
- Affiliations
- University College London Hospitals NHS Foundation Trust (GB)
- Venue
- Circulation
- Published
- 2005-07-12
- Volume
- 112
- Issue
- 2
- Pages
- 224-231
- ISSN
- 0009-7322 (ISSN-L)
- 1524-4539
- OpenAlex
- W2113394968
- Cited by
- 279
- References
- 28
- Citations by year
- 2025: 2 · 2024: 5 · 2023: 8 · 2022: 8 · 2021: 4 · 2020: 5 · 2019: 8 · 2018: 9 · 2017: 5 · 2016: 10 · 2015: 12 · 2014: 15 · 2013: 28 · 2012: 25
- FWCI
- 15.50
- Type
- journal article
- Language
- en
- Open access
- green
- discovery.ucl.ac.uk
- Licence
- public-domain
- Locations
- Circulation (publishedVersion, public-domain)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion, public-domain)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac Valve Diseases and Treatments
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac, Anesthesia and Surgical Outcomes
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Aortic Disease and Treatment Approaches
- Keywords
- heart valve surgery, in-hospital mortality, risk stratification, mortality prediction model, aortic valve surgery, mitral valve surgery, concomitant coronary artery bypass grafting, risk factors, comparative institutional analysis, patient counseling
- MeSH terms
- Cardiac Surgical Procedures, Heart Valves, Humans, Prognosis, Risk Factors, Models, Statistical, Databases, Factual, Hospital Mortality, Risk Assessment, Knowledge Bases
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
