The DOI matches its Crossref record; 51 of the 52 references it cites were found in a registry or an index.
Prediction Models for Prolonged Intensive Care Unit Stay After Cardiac Surgery: Systematic Review and Validation Study
Open accessAbstract
Background— Several models have been developed to predict prolonged stay in the intensive care unit (ICU) after cardiac surgery. However, no extensive quantitative validation of these models has yet been conducted. This study sought to identify and validate existing prediction models for prolonged ICU length of stay after cardiac surgery. Methods and Results— After a systematic review of the literature, the identified models were applied on a large registry database comprising 11 395 cardiac surgical interventions. The probabilities of prolonged ICU length of stay based on the models were compared with the actual outcome to assess the discrimination and calibration performance of the models. Literature review identified 20 models, of which 14 could be included. Of the 6 models for the general cardiac surgery population, the Parsonnet model showed the best discrimination (area under the receiver operating characteristic curve=0.75 [95% confidence interval, 0.73 to 0.76]), followed by the European system for cardiac operative risk evaluation (EuroSCORE) (0.71 [0.70 to 0.72]) and a model by Huijskes and colleagues (0.71 [0.70 to 0.73]). Most of the models showed good calibration. Conclusions— In this validation of prediction models for prolonged ICU length of stay, 2 widely implemented models (Parsonnet, EuroSCORE), although originally designed for prediction of mortality, were superior in identifying patients with prolonged ICU length of stay.
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“Many reviews have showed that the quality of reporting in published articles describing the development or validation of multivariable prediction models in medicine is poor [23–27,29].”
From Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement (2015) · cites this paper as [29]
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“However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor.”
From Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement (2015) · cites this paper as [29]
In the citing paper
“Many reviews have showed that the quality of reporting in published articles describing the development or validation of multivariable prediction models in medicine is poor.23–27,29”
From Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement (2015) · cites this paper as [29]
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Cites
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
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Regression Modeling Strategies
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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
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
Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating
Steyerberg, E. W. (2019) · Statistics for Biology and Health · doi:10.1007/978-3-030-16399-0 · Cited by 3,281
Review: a gentle introduction to imputation of missing values.
Donders, A. R. T., van der Heijden, G. J. M. G., Stijnen, T., & Moons, K. G. M. (2006) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2006.01.014 · Cited by 2,582
Self-citationUse and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction
Cook, N. R. (2007) · Circulation · doi:10.1161/circulationaha.106.672402 · Cited by 2,156
Roques, F. (1999) · European Journal of Cardio-Thoracic Surgery · doi:10.1016/s1010-7940(99)00106-2 · Cited by 1,749
Prognosis and prognostic research: what, why, and how?
Moons, K. G. M., Royston, P., Vergouwe, Y., Grobbee, D. E., & Altman, D. G. (2009) · BMJ · doi:10.1136/bmj.b375 · Cited by 1,337
Self-citationAssessing the Generalizability of Prognostic Information
Justice, A. C., Covinsky, K. E., & Berlin, J. A. (1999) · Annals of Internal Medicine · doi:10.7326/0003-4819-130-6-199903160-00016 · Cited by 1,160
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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
Self-citationCollins, P. G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · British Journal of Cancer · doi:10.1038/bjc.2014.639 · Cited by 3,319
Self-citationCollins, 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
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
Self-citationCollins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. (2015) · BMC Medicine · doi:10.1186/s12916-014-0241-z · Cited by 1,927
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Metadata
- Authors
- Ettema, R. G. A., Peelen, L. M., Schuurmans, M. J., Nierich, A. P., Kalkman, C. J., & Moons, K. G. M.
- Affiliations
- University of Applied Sciences Utrecht (NL)
- ORCID
- Venue
- Circulation
- Published
- 2010-08-17
- Volume
- 122
- Issue
- 7
- Pages
- 682-689
- ISSN
- 0009-7322 (ISSN-L)
- 1524-4539
- OpenAlex
- W1998435896
- Cited by
- 130
- References
- 52
- Citations by year
- 2026: 2 · 2025: 6 · 2023: 9 · 2022: 6 · 2021: 11 · 2020: 6 · 2019: 4 · 2018: 8 · 2017: 7 · 2016: 8 · 2015: 14 · 2014: 15 · 2013: 15 · 2012: 9
- FWCI
- 7.38
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Circulation (publishedVersion)
- PubMed (publishedVersion)
- 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 › Surgery › Cardiac and Coronary Surgery Techniques
- Keywords
- cardiac surgery, prediction model, model validation, EuroSCORE, model discrimination, model calibration, postoperative outcomes
- MeSH terms
- Aged, Female, Cardiac Surgical Procedures, Humans, Intensive Care Units, Length of Stay, Male, Middle Aged, Postoperative Period, Predictive Value of Tests, Risk Factors, Time Factors, Models, Statistical, Treatment Outcome
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