Cited by 130Cardiac Valve Diseases and TreatmentsCardiac, Anesthesia and Surgical OutcomesCardiac and Coronary Surgery Techniques

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 access

Ettema, Roelof G.A., Peelen, Linda M., Schuurmans, Marieke J., Nierich, Arno P., Kalkman, Cor J., Moons, Karel G.M.·2010·Circulation

DOI 10.1161/circulationaha.109.926808

Open this paper in CoChat — free account Open PDF

Abstract

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.

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

Crossref · 10.1161/circulationaha.109.926808

Cites

51 of 51 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856
Regression Modeling Strategies
Harrell, F. E. (2001) · Springer series in statistics · doi:10.1007/978-1-4757-3462-1 · Cited by 9,270
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-citation
Use 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
Risk factors and outcome in European cardiac surgery: analysis of the EuroSCORE multinational database of 19030 patients
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-citation
Assessing 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

Show all 52 references

Research works printed but not linked: 1

10.1161/circ.99.16.2098 · DOI

Sorted by citations

Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration
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-citation
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement
Collins, 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-citation
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement
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
Self-citation
Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist
Moons, 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-citation
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement
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
Self-citation
Risk prediction models: II. External validation, model updating, and impact assessment
Moons, K. G. M., Kengne, A. P., Grobbee, D. E., Royston, P., Vergouwe, Y., Altman, D. G., & Woodward, M. (2012) · Heart · doi:10.1136/heartjnl-2011-301247 · Cited by 1,195
Self-citation
Prediction models for cardiovascular disease risk in the general population: systematic review
Damen, J. A. A. G., Hooft, L., Schuit, E., Debray, T. P. A., Collins, G. S., Tzoulaki, I., Lassale, C. M., Siontis, G. C. M., Chiocchia, V., Roberts, C., et al. (2016) · BMJ · doi:10.1136/bmj.i2416 · Cited by 935
Self-citation
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD statement
Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · British Journal of Surgery · doi:10.1002/bjs.9736 · Cited by 889
Self-citation
Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement
Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · Circulation · doi:10.1161/circulationaha.114.014508 · Cited by 793
Self-citation
External validation of multivariable prediction models: a systematic review of methodological conduct and reporting
Collins, G. S., de Groot, J. A., Dutton, S., Omar, O., Shanyinde, M., Tajar, A., Voysey, M., Wharton, R., Yu, L.-M., Moons, K. G., et al. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-40 · Cited by 729
Self-citation
Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement
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
Self-citation
Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD statement
Collins, P. G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2014.11.010 · Cited by 336
Self-citation
Methodological standards for the development and evaluation of clinical prediction rules: a review of the literature
Cowley, L., Farewell, D. M., Maguire, S., & Kemp, A. (2019) · Diagnostic and Prognostic Research · doi:10.1186/s41512-019-0060-y · Cited by 271
Postoperative Delirium in a Substudy of Cardiothoracic Surgical Patients in the BAG-RECALL Clinical Trial
Whitlock, E. L., Torres, B. A., Lin, N., Helsten, D. L., Nadelson, M. R., Mashour, G. A., & Avidan, M. S. (2014) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000000028 · Cited by 193
Does EuroSCORE II perform better than its original versions? A multicentre validation study
Barili, F., Pacini, D., Capo, A., Rasovic, O., Grossi, C., Alamanni, F., Bartolomeo, R. D., & Parolari, A. (2012) · European Heart Journal · doi:10.1093/eurheartj/ehs342 · Cited by 185
Surveillance for ocular hypertension: an evidence synthesis and economic evaluation.
Burr, J., Botello-Pinzon, P., Takwoingi, Y., Hernández, R., Vazquez-Montes, M., Elders, A., Asaoka, R., Banister, K., van der Schoot, J., Fraser, C., et al. (2012) · Health Technology Assessment · doi:10.3310/hta16290 · Cited by 180
Vasoactive-inotropic score as a predictor of morbidity and mortality in adults after cardiac surgery with cardiopulmonary bypass
Yamazaki, Y., Oba, K., Matsui, Y., & Morimoto, Y. (2018) · Journal of Anesthesia · doi:10.1007/s00540-018-2447-2 · Cited by 137
Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis ( TRIPOD ): the TRIPOD statement
Collins, P. G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.13244 · Cited by 145
Self-citation
Deep Learning for Improved Risk Prediction in Surgical Outcomes
Jalali, A., Lonsdale, H., Do, N., Peck, J., Gupta‐Malhotra, M., Kutty, S., Ghazarian, S. R., Jacobs, J. P., Rehman, M., & Ahumada, L. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-62971-3 · Cited by 78
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
Self-citation
Prediction of Patient Length of Stay on the Intensive Care Unit Following Cardiac Surgery: A Logistic Regression Analysis Based on the Cardiac Operative Mortality Risk Calculator, EuroSCORE
Meadows, K., Gibbens, R., Gerrard, C., & Vuylsteke, A. (2018) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2018.03.007 · Cited by 68
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis ( TRIPOD ): the TRIPOD S tatement
Collins, P. G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · European Journal of Clinical Investigation · doi:10.1111/eci.12376 · Cited by 62
Self-citation
Evaluation of Serial Arterial Lactate Levels as a Predictor of Hospital and Long-Term Mortality in Patients After Cardiac Surgery
Delgado, J. C. L., Esteve, F., Javierre, C. F., Torrado, H., Rodríguez-Castro, D., Carrió, M. L., Farrero, E., Skaltsa, K., Máñez, R., & Ventura, J. L. (2015) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2015.04.024 · Cited by 59
Current trends in preoperative, intraoperative, and postoperative care of the adult cardiac surgery patient
Szelkowski, L. A., Puri, N. K., Singh, R., & Massimiano, P. S. (2014) · Current Problems in Surgery · doi:10.1067/j.cpsurg.2014.10.001 · Cited by 57
Fracture Risk Assessment: State of the Art, Methodologically Unsound, or Poorly Reported?
Collins, G. S., & Michaëlsson, K. (2012) · Current Osteoporosis Reports · doi:10.1007/s11914-012-0108-1 · Cited by 51
Fast-track practice in cardiac surgery: results and predictors of outcome
Haanschoten, M. C., van Straten, A. H. M., Woorst, J. T., Stepaniak, P. S., van der Meer, A. D., van Zundert, A. A. J., & Hamad, M. A. S. (2012) · Interactive Cardiovascular and Thoracic Surgery · doi:10.1093/icvts/ivs393 · Cited by 51
Preoperative Endogenous Ouabain Predicts Acute Kidney Injury in Cardiac Surgery Patients*
Bignami, E. G., Casamassima, N., Frati, E., Lanzani, C. L., Corno, L., Alfieri, O., Gottlieb, S. S., Simonini, M., Shah, K. B., Mizzi, A., et al. (2013) · Critical Care Medicine · doi:10.1097/ccm.0b013e3182741599 · Cited by 50
Comparison of contemporary risk scores for predicting outcomes after surgery for active infective endocarditis
Wang, T. K. M., Oh, T., Voss, J., Gamble, G. D., Kang, N., & Pemberton, J. (2014) · Heart and Vessels · doi:10.1007/s00380-014-0472-0 · Cited by 49
Predicting length of stay in hospitals intensive care unit using general admission features
Abd-Elrazek, M. A., Eltahawi, A. A., Elaziz, M., & Abd-Elwhab, M. N. (2021) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2021.02.018 · Cited by 52
Participatory design of probability-based decision support tools for in-hospital nurses
Jeffery, A. D., Novak, L. L., Kennedy, B., Dietrich, M. S., & Mion, L. C. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx060 · Cited by 46
Predicting Prolonged Length of ICU Stay through Machine Learning
Wu, J., Lin, Y., Li, P., Hu, Y., Zhang, L., & Kong, G. (2021) · Diagnostics · doi:10.3390/diagnostics11122242 · Cited by 47
Clinical Outcomes in Patients With Prolonged Intensive Care Unit Length of Stay After Cardiac Surgical Procedures
Hassan, A., Anderson, C. A., Kypson, A. P., Kindell, L. C., Ferguson, T. B., Chitwood, W. R., & Rodríguez, E. (2011) · The Annals of Thoracic Surgery · doi:10.1016/j.athoracsur.2011.10.024 · Cited by 42
Prediction of Prolonged Intensive Care Unit Length of Stay Following Cardiac Surgery
Rotar, E. P., Beller, J. P., Smolkin, M. E., Chancellor, W. Z., Ailawadi, G., Yarboro, L. T., Hulse, M., Ratcliffe, S. J., & Teman, N. R. (2021) · Seminars in Thoracic and Cardiovascular Surgery · doi:10.1053/j.semtcvs.2021.02.021 · Cited by 41
Length of intensive care unit stay following cardiac surgery: is it impossible to find a universal prediction model?
Widyastuti, Y., Stenseth, R., Wahba, A., Pleym, H., & Videm, V. (2012) · Interactive Cardiovascular and Thoracic Surgery · doi:10.1093/icvts/ivs302 · Cited by 41
Prediction Models and Their External Validation Studies for Mortality of Patients with Acute Kidney Injury: A Systematic Review
Ohnuma, T., & Uchino, S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0169341 · Cited by 38
Computerized prediction of intensive care unit discharge after cardiac surgery: development and validation of a Gaussian processes model
Meyfroidt, G., Güiza, F., Cottem, D., Becker, W. D., Loon, K. V., Aerts, J., Berckmans, D., Ramon, J., Bruynooghe, M., & den Berghe, G. V. (2011) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-11-64 · Cited by 38
Composite Outcomes in Clinical Prediction Modeling: Are We Trying to Predict Apples and Oranges?
Dash, K., Goodacre, S. W., & Sutton, L. (2022) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2022.01.046 · Cited by 37
Transfusion of 1 and 2 units of red blood cells does not increase mortality and organ failure in patients undergoing isolated coronary artery bypass grafting
Koster, A., Zittermann, A., Börgermann, J., Knabbe, C., Diekmann, J., Schirmer, U. B., & Gummert, J. F. (2015) · European Journal of Cardio-Thoracic Surgery · doi:10.1093/ejcts/ezv252 · Cited by 34
EM for regularized zero‐inflated regression models with applications to postoperative morbidity after cardiac surgery in children
Wang, Z., Ma, S., Wang, C., Zappitelli, M., Devarajan, P., & Parikh, C. R. (2014) · Statistics in Medicine · doi:10.1002/sim.6314 · Cited by 34
Very Prolonged Stay in the Intensive Care Unit After Cardiac Operations: Early Results and Late Survival
Silberman, S., Bitran, D., Fink, D. L., Tauber, R., & Merin, O. (2013) · The Annals of Thoracic Surgery · doi:10.1016/j.athoracsur.2013.01.103 · Cited by 32
Determinants of prolonged intensive care unit stay after cardiac surgery in the elderly
Cacciatore, F., Anello, C. B., Ferrara, N., Mazzella, F., Manzi, M., Angelis, U. D., & Abete, P. (2012) · Aging Clinical and Experimental Research · doi:10.1007/bf03654834 · Cited by 29
A score to estimate 30-day mortality after intensive care admission after cardiac surgery
Lamarche, Y., Elmi-Sarabi, M., Ding, L., Abel, J. G., Sirounis, D., & Denault, A. Y. (2016) · Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2016.11.039 · Cited by 29
Predicting Intensive Care Unit Length of Stay After Acute Type A Aortic Dissection Surgery Using Machine Learning
Chen, Q., Zhang, B., Yang, J., Mo, X., Zhang, L., Li, M., Chen, Z., Jin, F., Wang, F., Huang, W., et al. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.675431 · Cited by 29
Fast-Track Failure After Cardiac Surgery
Lee, A. S. Y., Zhu, F., Underwood, M. J., & Gomersall, C. D. (2013) · Critical Care Medicine · doi:10.1097/ccm.0b013e31827711ad · Cited by 29
Predictors of Prolonged Stay in the Intensive Care Unit following Cardiac Surgery
Eltheni, R., Giakoumidakis, K., Brokalaki, H., Galanis, P., Nenekidis, I., & Fildissis, G. (2012) · ISRN Nursing · doi:10.5402/2012/691561 · Cited by 28
Prolonged ventilation post cardiac surgery - tips and pitfalls of the prediction game
Knapik, P., Cieśla, D., Borowik, D., Czempik, P. F., & Knapik, T. (2011) · Journal of Cardiothoracic Surgery · doi:10.1186/1749-8090-6-158 · Cited by 28
An original model to predict Intensive Care Unit length-of stay after cardiac surgery in a competing risk framework
Barili, F., Barzaghi, N., Cheema, F. H., Capo, A., Jiang, J., Ardemagni, E., Argenziano, M., & Grossi, C. (2012) · International Journal of Cardiology · doi:10.1016/j.ijcard.2012.09.091 · Cited by 27
Outcomes of neonates requiring prolonged stay in the intensive care unit after surgical repair of congenital heart disease
Mori, M., McCracken, C., Maher, K. O., Kogon, B. E., Mahle, W. T., Kanter, K. R., & Alsoufi, B. (2016) · Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2016.04.040 · Cited by 26
Critical care after lung resection: CALoR 1, a single‐centre pilot study
McCall, P. J., Macfie, A., Kinsella, J., & Shelley, B. (2015) · Anaesthesia · doi:10.1111/anae.13267 · Cited by 25
Impaired right ventricular ejection fraction after cardiac surgery is associated with a complicated ICU stay
Bootsma, I. T., Scheeren, T., de Lange, F., Haenen, J., Boonstra, P. W., & Boerma, E. C. (2018) · Journal of Intensive Care · doi:10.1186/s40560-018-0351-3 · Cited by 25
Benefits of ultra-fast-track anesthesia in left ventricular assist device implantation: a retrospective, propensity score matched cohort study of a four-year single center experience
Zayat, R., Menon, A. K., Goetzenich, A., Schaelte, G., Autschbach, R., Stoppe, C., Simon, T.-P., Tewarie, L., & Moza, A. K. (2017) · Journal of Cardiothoracic Surgery · doi:10.1186/s13019-017-0573-9 · Cited by 24
The Need to Prioritize Model-Updating Processes in Clinical Artificial Intelligence (AI) Models: Protocol for a Scoping Review
Otokiti, A. U., Ozoude, M. M., Williams, K. S., Sadiq-Onilenla, R. A., Ojo, S. A., Warsame, L., Walsh, S., & Edomwande, M. (2022) · JMIR Research Protocols · doi:10.2196/37685 · Cited by 26
Predictors of Length of Stay in Intensive Care Unit after Coronary Artery Bypass Grafting: Development a Risk Scoring System
Zarrizi, M., Paryad, E., Khanghah, A. G., Leili, E. K., & Faghani, H. (2020) · Brazilian Journal of Cardiovascular Surgery · doi:10.21470/1678-9741-2019-0405 · Cited by 22
Risk factors associated with longer stays in cardiovascular surgical intensive care unit after CABG
Kao, K.-D., Lee, S. K. C., Liu, C., & Chou, N. (2021) · Journal of the Formosan Medical Association · doi:10.1016/j.jfma.2021.04.020 · Cited by 28
Development of a nursing intervention to prepare frail older patients for cardiac surgery (the PREDOCS programme), following phase one of the guidelines of the Medical Research Council
Ettema, R. G., Hoogendoorn, M. E., Kalkman, C. J., & Schuurmans, M. J. (2014) · European Journal of Cardiovascular Nursing · doi:10.1177/1474515113511715 · Cited by 21
Self-citation
Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis ( TRIPOD ): the TRIPOD Statement
Collins, P. G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · Diabetic Medicine · doi:10.1111/dme.12654 · Cited by 20
Self-citation
Long-term survival and resource use in critically ill cardiac surgery patients: a population-based study
McIsaac, D. I., McDonald, B. J., Wong, C. A., & van Walraven, C. (2018) · Canadian Journal of Anesthesia/Journal canadien d anesthésie · doi:10.1007/s12630-018-1159-2 · Cited by 20
Surgical scheduling via optimization and machine learning with long-tailed data
Shi, Y., Mahdian, S., Blanchet, J., Glynn, P. W., Shin, A. Y., & Scheinker, D. (2023) · Health Care Management Science · doi:10.1007/s10729-023-09649-0 · Cited by 19
Derivation and Validation of a Nomogram to Predict In‐Hospital Complications in Children with Tetralogy of Fallot Repaired at an Older Age
Liu, H., Zheng, S., Li, X., Zeng, Z., Zhong, J., Chen, J.-Q., Chen, T., Liu, Z., Liu, X., & Shao, Y. (2019) · Journal of the American Heart Association · doi:10.1161/jaha.119.013388 · Cited by 21
Derivation and Validation of a Clinical Model to Predict Intensive Care Unit Length of Stay After Cardiac Surgery
Sun, L. Y., Eddeen, A. B., Ruel, M., MacPhee, E., & Mesana, T. G. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.120.017847 · Cited by 19
Comparison of Risk Scores for Prediction of Complications following Aortic Valve Replacement
Wang, T. K. M., Choi, D. H.-M., Haydock, D. A., Gamble, G. D., Stewart, R., & Ruygrok, P. (2014) · Heart Lung and Circulation · doi:10.1016/j.hlc.2014.11.021 · Cited by 18
Critical care management of patients following transcatheter aortic valve replacement
Raiten, J. M., Gutsche, J. T., Horák, J., & Augoustides, J. G. (2013) · F1000Research · doi:10.12688/f1000research.2-62.v1 · Cited by 17
Applying a BP Neural Network Model to Predict the Length of Hospital Stay
Li, J., Tian, Y., Liu, Y., Shu, T., & Liang, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37899-7_2 · Cited by 15
Predicting prolonged intensive care unit stays in older cardiac surgery patients: a validation study
Ettema, R. G. A., Peelen, L. M., Kalkman, C. J., Nierich, A. P., Moons, K. G. M., & Schuurmans, M. J. (2011) · Intensive Care Medicine · doi:10.1007/s00134-011-2314-1 · Cited by 15
Self-citation
A Method to Account for Variation in Congenital Heart Surgery Length of Stay*
Brennan, A., Gauvreau, K., Connor, J. A., Almodovar, M. C., DiNardo, J. A., Banka, P., Nathan, M., Mathieu, D., Kaza, A. K., Mayer, J. E., et al. (2017) · Pediatric Critical Care Medicine · doi:10.1097/pcc.0000000000001168 · Cited by 15
Prolonged intensive care treatment of octogenarians after cardiac surgery: a reasonable economic burden?†
Deschka, H., Schreier, R., El-Ayoubi, L., Erler, S., Müller, D., Alken, A., & Wimmer‐Greinecker, G. (2013) · Interactive Cardiovascular and Thoracic Surgery · doi:10.1093/icvts/ivt229 · Cited by 14
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
Acute risk change (ARC) identifies outlier institutions in perioperative cardiac surgical care when the standardized mortality ratio cannot
Coulson, T. G., Bailey, M., Reid, C. M., Tran, T., Mullany, D. V., Parker, J., Hicks, P. R., & Pilcher, D. V. (2016) · British Journal of Anaesthesia · doi:10.1093/bja/aew180 · Cited by 14
A straightforward approach to designing a scoring system for predicting length-of-stay of cardiac surgery patients
Barbini, P., Barbini, E., Furini, S., & Cevenini, G. (2014) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-14-89 · Cited by 14
Can soluble urokinase plasminogen receptor predict outcomes after cardiac surgery?
Schultz‐Swarthfigure, C. T., McCall, P. J., Docking, R. I., Galley, H. F., & Shelley, B. (2020) · Interactive Cardiovascular and Thoracic Surgery · doi:10.1093/icvts/ivaa239 · Cited by 13
Independent Risk Factors for Cardiac Operations in Adults With Congenital Heart Disease: A Retrospective Study of 543 Operations for 500 Patients
Vogt, M. O., Hörer, J., Grünewald, S., Otto, D., Kaemmerer, H. M., Schreiber, C., & Hess, J. (2011) · Pediatric Cardiology · doi:10.1007/s00246-011-0093-7 · Cited by 13
Selective Prediction-Set Models with Coverage Rate Guarantees
Feng, J., Sondhi, A., Perry, J., & Simon, N. (2021) · Biometrics · doi:10.1111/biom.13612 · Cited by 31
Association between Preoperative Plasma sRAGE Levels and Recovery from Cardiac Surgery
Creagh-Brown, B. C., Quinlan, G. J., Hector, L. R., Evans, T. W., & Burke-Gaffney, A. C. (2013) · Mediators of Inflammation · doi:10.1155/2013/496031 · Cited by 12
Factors Associated with a Short (<2 Days) or Long (>10 Days) Length of Stay after Colectomy: A Multivariate Analysis of over 400 Patients
Cologne, K. G., Byers, S., Rosen, D. R., Hwang, G. S., Ortega, A. E., Ault, G. T., & Lee, S. W. (2016) · The American Surgeon · doi:10.1177/000313481608201022 · Cited by 12
Externally validated model predicting gait independence after stroke showed fair performance and improved after updating
Langerak, A. J., McCambridge, A. B., Stubbs, P. W., Fabricius, J., Rogers, K. D., de Oliveira, C. Q., Nielsen, J. F., & Verhagen, A. P. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.03.022 · Cited by 12
Customized Prediction of Short Length of Stay Following Elective Cardiac Surgery in Elderly Patients Using a Genetic Algorithm
Lee, J., Govindan, S., Celi, L. A., Khabbaz, K. R., & Subramaniam, B. (2013) · World Journal of Cardiovascular Surgery · doi:10.4236/wjcs.2013.35034 · Cited by 11
Development of an Open-Heart Intraoperative Risk Scoring Model for Predicting a Prolonged Intensive Care Unit Stay
Tribuddharat, S., Sathitkarnmanee, T., Ngamsaengsirisup, K., Charuluxananan, S., Hurst, C. P., Silarat, S., & Lertmemongkolchai, G. (2014) · BioMed Research International · doi:10.1155/2014/158051 · Cited by 11
Updating EuroSCORE by including emotional, behavioural, social and functional factors to the risk assessment of patients undergoing cardiac surgery: a study protocol
Cromhout, P. F., Berg, S. K., Moons, P., Damgaard, S., Nashef, S. A. M., & Thygesen, L. C. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-026745 · Cited by 10
Mitral Regurgitation and Coronary Artery Bypass Surgery: Comparison of Mitral Valve Repair and Replacement
Ljubačev, A., Medved, I., Ostric, M., Žuvić-Butorac, M., & Sokolici, J. (2013) · Acta chirurgica Belgica · doi:10.1080/00015458.2013.11680909 · Cited by 10
Multiple arterial conduits for multi-vessel coronary artery bypass grafting in patients with mild to moderate left ventricular systolic dysfunction: a multicenter retrospective study
Zhang, H., Chen, W., Zhao, Y., Guan, L., Yu, M., Wang, R., & Chen, X. (2021) · Journal of Cardiothoracic Surgery · doi:10.1186/s13019-021-01463-5 · Cited by 9
Unidad de cuidados intermedios tras la cirugía cardiaca: impacto en la estancia media y la evolución clínica
Labata, C., Oliveras, T., Berastegui, E., Ruyra, X., Romero, B., Cámara, M.-L., Just, M.-S., Serra, J., Rueda, F., Ferrer, M., et al. (2018) · Revista Española de Cardiología · doi:10.1016/j.recesp.2017.10.018 · Cited by 9
Factors Associated with the Need for, and the Impact of, Extracorporeal Membrane Oxygenation in Children with Congenital Heart Disease during Admissions for Cardiac Surgery
Aiello, S., & Loomba, R. S. (2017) · Children · doi:10.3390/children4110101 · Cited by 9
Accurate Prediction of Congenital Heart Surgical Length of Stay Incorporating a Procedure-Based Categorical Variable*
Alexander, P. M. A., DiOrio, M., Andren, K. G., Gauvreau, K., Mistry, K. P., Mathieu, D., Wright, J., Allan, C. K., Nathan, M., Mayer, J. E., et al. (2018) · Pediatric Critical Care Medicine · doi:10.1097/pcc.0000000000001668 · Cited by 9
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration. Translation in to Russian
Moons, K. G. M., Altman, D. G., Reitsma, J. B., loannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, P. G. S. (2022) · Digital Diagnostics · doi:10.17816/dd110794 · Cited by 10
Self-citation
Prediction models for the risk of postoperative nausea and vomiting
Pace, N. L., Carlisle, J. B., Eberhart, L. H. J., Kranke, P., Trivella, M., Lee, A. S. Y., & Bennett, M. H. (2014) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd011318 · Cited by 8
Prognosis of Prolonged Intensive Care Unit Stay After Aortic Valve Replacement for Severe Aortic Stenosis in Octogenarians
Augustin, P., Tanaka, S., Chhor, V., Provenchère, S., Arnaudovski, D., Ibrahim, H., Dilly, M., Allou, N., Montravers, P., & Philip, I. (2016) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2016.07.029 · Cited by 7
Preoperative Red Cell Distribution Width to Lymphocyte Ratio As Biomarkers for Prolonged Intensive Care Unit Stay Among Older Patients Undergoing Cardiac Surgery: A Retrospective Longitudinal Study
Katipoğlu, B., Aydınlı, B., Demir, A., & Özmen, H. (2022) · Biomarkers in Medicine · doi:10.2217/bmm-2022-0341 · Cited by 7
Prognostic Value of GDF-15 in Predicting Prolonged Intensive Care Stay following Cardiac Surgery: A Pilot Study
Barton, H., Zechendorf, E., Ostareck, D. H., Ostareck‐Lederer, A., Stoppe, C., Zayat, R., Simon, T. P., Marx, G., & Bickenbach, J. (2021) · Disease Markers · doi:10.1155/2021/5564334 · Cited by 7
Predictors of Prolonged Intensive Care Unit Stay and In-Hospital Mortality Following Cardiac Surgery: An Integrated Analysis from the PROCARD-ATI Study
Hădăreanu, C.-D., Hădăreanu, D.-R., Stoiculescu, F.-M., Berceanu, M. C., Donoiu, I., Istrătoaie, O., Florescu, C., Novac, M.-B., & Raicea, V.-C. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14082747 · Cited by 6
Outcomes After Prolonged ICU Stays in Postoperative Cardiac Surgery Patients
Curran, T. F. (2022) · Federal Practitioner · doi:10.12788/fp.0300 · Cited by 6
The reporting of prognostic prediction models for obstetric care was poor: a cross-sectional survey of 10-year publications
Liu, C., Qi, Y., Liu, X., Chen, M., Xiong, Y., Huang, S., Zou, K., Tan, J., & Sun, X. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-023-01832-9 · Cited by 6
Prediction of in-hospital death following acute type A aortic dissection
Chen, J., Bai, Y., Liu, H., Qin, M., & Guo, Z. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1143160 · Cited by 6
Algengi og áhættuþættir lengdrar dvalar á gjörgæsludeild eftir kransæðahjáveituaðgerð
Gunnarsdóttir, E. L. T., Gunnarsdóttir, S. L. X., Heimisdóttir, A. A., Heiðarsdóttir, S. R., Helgadóttir, S., Guðbjartsson, T., Sigurdsson, M. I., & Sigurðsson, M. I. (2020) · Læknablaðið · doi:10.17992/lbl.2020.03.471 · Cited by 6
Risk prediction models: are they really necessary?
Braile, D. M., Monteiro, R., Brandau, R., & Jatene, F. B. (2010) · PubMed · Cited by 6
Intermediate Care Unit After Cardiac Surgery: Impact on Length of Stay and Outcomes
Labata, C., Oliveras, T., Berastegui, E., Ruyra, X., Romero, B., Cámara, M.-L., Just, M.-S., Serra, J., Rueda, F., Ferrer, M., et al. (2017) · Revista Española de Cardiología (English Edition) · doi:10.1016/j.rec.2017.10.018 · Cited by 5
Prolonged QTc affects short-term and long-term outcomes in patients with normal left ventricular function undergoing cardiac surgery
Anantasit, N., Boyd, J. H., Russell, J. A., Fjell, C. D., Lichtenstein, S. V., & Walley, K. R. (2014) · Journal of Thoracic and Cardiovascular Surgery · doi:10.1016/j.jtcvs.2013.11.043 · Cited by 5
Development and external validation of a prediction model for prolonged intensive care unit stay in heart failure patients
Yang, E., He, X., Li, L., Cui, Y., Yang, H., & Wang, Q. (2025) · European Journal of Cardiovascular Nursing · doi:10.1093/eurjcn/zvaf075 · Cited by 4
Supplementing prediction by EuroSCORE with social and patient‐reported measures among patients undergoing cardiac surgery
Cromhout, P. F., Thygesen, L. C., Moons, P., Nashef, S. A. M., Damgaard, S., Christensen, A. V., Rasmussen, T. B., Borregaard, B., Thrysoee, L., Thorup, C. B., et al. (2020) · Journal of Cardiac Surgery · doi:10.1111/jocs.15227 · Cited by 4
Multivariable Risk Prediction Models
Collins, P. G. S., & Manach, Y. L. (2013) · Anesthesiology · doi:10.1097/aln.0b013e31828e13e9 · Cited by 4
Invasive Mediastinal Staging in Lung Cancer. Use a Prediction Model or Just Do It?
Tanner, N. T., & Gould, M. K. (2017) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201702-0397ed · Cited by 3

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: Ettema, Roelof G.A.
Peelen, Linda M.
Schuurmans, Marieke J.
Nierich, Arno P.
Kalkman, Cor J.
Moons, Karel G.M.
Venue: Circulation
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 2010-08-17
Volume: 122
Issue: 7
Pages: 682-689
ISSN: 0009-7322 (ISSN-L)
1524-4539
DOI: 10.1161/circulationaha.109.926808
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

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

Prediction Models for Prolonged Intensive Care Unit Stay After Cardiac Surgery: Systematic Review and Validation Study

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks