Skip to main content
Top
Published in: BMC Medical Informatics and Decision Making 1/2014

Open Access 01-12-2014 | Correspondence

A straightforward approach to designing a scoring system for predicting length-of-stay of cardiac surgery patients

Authors: Paolo Barbini, Emanuela Barbini, Simone Furini, Gabriele Cevenini

Published in: BMC Medical Informatics and Decision Making | Issue 1/2014

Login to get access

Abstract

Background

Length-of-stay prediction for cardiac surgery patients is a key point for medical management issues, such as optimization of resources in intensive care units and operating room scheduling. Scoring systems are a very attractive family of predictive models, but their retraining and updating are generally critical. The present approach to designing a scoring system for predicting length of stay in intensive care aims to overcome these difficulties, so that a model designed in a given scenario can easily be adjusted over time or for internal purposes.

Methods

A naïve Bayes approach was used to develop a simple scoring system. A set of 36 preoperative, intraoperative and postoperative variables collected in a sample of 3256 consecutive adult patients undergoing heart surgery were considered as likely risk predictors. The number of variables was reduced by selecting an optimal subset of features. Scoring system performance was assessed by cross-validation.

Results

After the selection process, seven variables were entered in the prediction model, which showed excellent discrimination, good generalization power and suitable sensitivity and specificity. No significant difference was found between AUC of the training and testing sets. The 95% confidence interval for AUC estimated by the BCa bootstrap method was [0.841, 0.883] and [0.837, 0.880] in the training and testing sets, respectively. Chronic dialysis, low postoperative cardiac output and acute myocardial infarction proved to be the major risk factors.

Conclusions

The proposed approach produced a simple and trustworthy scoring system, which is easy to update regularly and to customize for other centers. This is a crucial point when scoring systems are used as predictive models in clinical practice.
Literature
1.
go back to reference Teasdale G, Jennett B: Assessment of coma and impaired consciousness. A practical scale. Lancet. 1974, 2: 81-84.CrossRefPubMed Teasdale G, Jennett B: Assessment of coma and impaired consciousness. A practical scale. Lancet. 1974, 2: 81-84.CrossRefPubMed
2.
go back to reference Knaus WA, Wagner DP, Draper EA, Zimmerman JE, Bergner M, Bastos PG, Sirio CA, Murphy DJ, Lotring T, Damiano A: The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest. 1991, 100: 1619-1636. 10.1378/chest.100.6.1619.CrossRefPubMed Knaus WA, Wagner DP, Draper EA, Zimmerman JE, Bergner M, Bastos PG, Sirio CA, Murphy DJ, Lotring T, Damiano A: The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest. 1991, 100: 1619-1636. 10.1378/chest.100.6.1619.CrossRefPubMed
3.
go back to reference Higgins TL, Estafanous FG, Loop FD, Beck GJ, Lee JC, Starr NJ, Knaus WA, Cosgrove DM: ICU admission score for predicting morbidity and mortality risk after coronary artery bypass grafting. Ann Thorac Surg. 1997, 64: 1050-1058. 10.1016/S0003-4975(97)00553-5.CrossRefPubMed Higgins TL, Estafanous FG, Loop FD, Beck GJ, Lee JC, Starr NJ, Knaus WA, Cosgrove DM: ICU admission score for predicting morbidity and mortality risk after coronary artery bypass grafting. Ann Thorac Surg. 1997, 64: 1050-1058. 10.1016/S0003-4975(97)00553-5.CrossRefPubMed
4.
go back to reference Metnitz PGH, Moreno RP, Almeida E, Jordan B, Bauer P, Campos RA, Iapichino G, Edbrooke D, Capuzzo M, Le Gall JR, SAPS 3 Investigators: SAPS 3 – From evaluation of the patient to evaluation of the intensive care unit. Part 1: Objectives, methods and cohort description. Intensive Care Med. 2005, 31: 1336-1344. 10.1007/s00134-005-2762-6.CrossRefPubMedPubMedCentral Metnitz PGH, Moreno RP, Almeida E, Jordan B, Bauer P, Campos RA, Iapichino G, Edbrooke D, Capuzzo M, Le Gall JR, SAPS 3 Investigators: SAPS 3 – From evaluation of the patient to evaluation of the intensive care unit. Part 1: Objectives, methods and cohort description. Intensive Care Med. 2005, 31: 1336-1344. 10.1007/s00134-005-2762-6.CrossRefPubMedPubMedCentral
5.
go back to reference Moreno RP, Metnitz PGH, Almeida E, Jordan B, Bauer P, Campos RA, Iapichino G, Edbrooke D, Capuzzo M, Le Gall JR, SAPS 3 Investigators: SAPS 3 – From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission. Intensive Care Med. 2005, 31: 1345-1355. 10.1007/s00134-005-2763-5.CrossRefPubMedPubMedCentral Moreno RP, Metnitz PGH, Almeida E, Jordan B, Bauer P, Campos RA, Iapichino G, Edbrooke D, Capuzzo M, Le Gall JR, SAPS 3 Investigators: SAPS 3 – From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission. Intensive Care Med. 2005, 31: 1345-1355. 10.1007/s00134-005-2763-5.CrossRefPubMedPubMedCentral
6.
go back to reference Barbini E, Cevenini G, Scolletta S, Biagioli B, Giomarelli P, Barbini P: A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part I: model planning. BMC Med Informat Decis Making. 2007, 7: 35-10.1186/1472-6947-7-35.CrossRef Barbini E, Cevenini G, Scolletta S, Biagioli B, Giomarelli P, Barbini P: A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part I: model planning. BMC Med Informat Decis Making. 2007, 7: 35-10.1186/1472-6947-7-35.CrossRef
7.
go back to reference Higgins TL: Quantifying risk and benchmarking performance in the adult intensive care unit. J Intensive Care Med. 2007, 22: 141-156. 10.1177/0885066607299520.CrossRefPubMed Higgins TL: Quantifying risk and benchmarking performance in the adult intensive care unit. J Intensive Care Med. 2007, 22: 141-156. 10.1177/0885066607299520.CrossRefPubMed
8.
9.
go back to reference Murphy-Filkins R, Teres D, Lemeshow S, Hosmer DW: Effect of changing patient mix on the performance of an intensive care unit severity-of-illness model: how to distinguish a general from a specialty intensive care unit. Crit Care Med. 1996, 24: 1968-1973. 10.1097/00003246-199612000-00007.CrossRefPubMed Murphy-Filkins R, Teres D, Lemeshow S, Hosmer DW: Effect of changing patient mix on the performance of an intensive care unit severity-of-illness model: how to distinguish a general from a specialty intensive care unit. Crit Care Med. 1996, 24: 1968-1973. 10.1097/00003246-199612000-00007.CrossRefPubMed
10.
go back to reference Schaufer JH, Maurer A, Jochimsen F, Emde C, Wegscheider K, Arntz HR, Heitz J, Krell-Schroeder B, Distler A: Outcome prediction models on admission in a medical intensive care unit: do they predict individual outcome?. Crit Care Med. 1990, 18: 1111-1117. 10.1097/00003246-199010000-00012.CrossRef Schaufer JH, Maurer A, Jochimsen F, Emde C, Wegscheider K, Arntz HR, Heitz J, Krell-Schroeder B, Distler A: Outcome prediction models on admission in a medical intensive care unit: do they predict individual outcome?. Crit Care Med. 1990, 18: 1111-1117. 10.1097/00003246-199010000-00012.CrossRef
11.
go back to reference Ryan TA, Rady MY, Bashour CA, Leventhal M, Lytle M, Starr NJ: Predictors of outcome in cardiac surgical patients with prolonged intensive care stay. Chest. 1997, 112: 1035-1042. 10.1378/chest.112.4.1035.CrossRefPubMed Ryan TA, Rady MY, Bashour CA, Leventhal M, Lytle M, Starr NJ: Predictors of outcome in cardiac surgical patients with prolonged intensive care stay. Chest. 1997, 112: 1035-1042. 10.1378/chest.112.4.1035.CrossRefPubMed
12.
go back to reference Biagioli B, Scolletta S, Cevenini G, Barbini E, Giomarelli P, Barbini P: A multivariate Bayesian model for assessing morbidity after coronary artery surgery. Crit Care. 2006, 10: R94-10.1186/cc4951.CrossRefPubMedPubMedCentral Biagioli B, Scolletta S, Cevenini G, Barbini E, Giomarelli P, Barbini P: A multivariate Bayesian model for assessing morbidity after coronary artery surgery. Crit Care. 2006, 10: R94-10.1186/cc4951.CrossRefPubMedPubMedCentral
13.
go back to reference Cevenini G, Barbini E, Scolletta S, Biagioli B, Giomarelli P, Barbini P: A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part II: an illustrative example. BMC Med Informat Decis Making. 2007, 7: 36-10.1186/1472-6947-7-36.CrossRef Cevenini G, Barbini E, Scolletta S, Biagioli B, Giomarelli P, Barbini P: A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part II: an illustrative example. BMC Med Informat Decis Making. 2007, 7: 36-10.1186/1472-6947-7-36.CrossRef
14.
go back to reference Barbini P, Cevenini G, Furini S, Barbini E: A naïve approach for deriving scoring systems to support clinical decision making. J Eval Clin Pract. 2014, 20: 1-6. 10.1111/jep.12064.CrossRefPubMed Barbini P, Cevenini G, Furini S, Barbini E: A naïve approach for deriving scoring systems to support clinical decision making. J Eval Clin Pract. 2014, 20: 1-6. 10.1111/jep.12064.CrossRefPubMed
15.
go back to reference Mitchell TM: Machine Learning. 1997, New York: McGraw Hill Mitchell TM: Machine Learning. 1997, New York: McGraw Hill
16.
go back to reference Domingos P, Pazzani M: On the optimality of the simple Bayesian classifier under zero–one loss. Mach Learn. 1997, 29: 103-130. 10.1023/A:1007413511361.CrossRef Domingos P, Pazzani M: On the optimality of the simple Bayesian classifier under zero–one loss. Mach Learn. 1997, 29: 103-130. 10.1023/A:1007413511361.CrossRef
17.
go back to reference Lavrac N: Intelligent data analysis for medical diagnosis: using machine learning and temporal abstraction. AI Comm. 1998, 11: 191-218. Lavrac N: Intelligent data analysis for medical diagnosis: using machine learning and temporal abstraction. AI Comm. 1998, 11: 191-218.
18.
go back to reference Demichelis F, Magni P, Piergiorgi P, Rubin MA, Bellazzi R: A hierarchical Naïve Bayes Model for handling sample heterogeneity in classification problems: an application to tissue microarrays. BMC Bioinformatics. 2006, 7: 514-10.1186/1471-2105-7-514.CrossRefPubMedPubMedCentral Demichelis F, Magni P, Piergiorgi P, Rubin MA, Bellazzi R: A hierarchical Naïve Bayes Model for handling sample heterogeneity in classification problems: an application to tissue microarrays. BMC Bioinformatics. 2006, 7: 514-10.1186/1471-2105-7-514.CrossRefPubMedPubMedCentral
19.
go back to reference DiCiccio TJ, Efron B: Bootstrap confidence intervals. Stat Sci. 1996, 11: 189-228.CrossRef DiCiccio TJ, Efron B: Bootstrap confidence intervals. Stat Sci. 1996, 11: 189-228.CrossRef
20.
go back to reference Armitage P, Berry G, Matthews JNS: Statistical Methods in Medical Research. 2002, Oxford: Blackwell Science Ltd., 4CrossRef Armitage P, Berry G, Matthews JNS: Statistical Methods in Medical Research. 2002, Oxford: Blackwell Science Ltd., 4CrossRef
21.
go back to reference Lasko TA, Bhagwat JG, Zou KH, Ohno-Machado L: The use of receiver operating characteristic curves in biomedical informatics. J Biomed Inform. 2005, 38: 404-415. 10.1016/j.jbi.2005.02.008.CrossRefPubMed Lasko TA, Bhagwat JG, Zou KH, Ohno-Machado L: The use of receiver operating characteristic curves in biomedical informatics. J Biomed Inform. 2005, 38: 404-415. 10.1016/j.jbi.2005.02.008.CrossRefPubMed
22.
go back to reference Langley P, Sage S: Induction of Selective Bayesian Classifiers. Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence. Edited by: de Mantaras RL, Pool D. 1994, San Francisco: Morgan Kaufmann, 399-406. Langley P, Sage S: Induction of Selective Bayesian Classifiers. Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence. Edited by: de Mantaras RL, Pool D. 1994, San Francisco: Morgan Kaufmann, 399-406.
23.
go back to reference Boullé M: An Enhanced Selective Naïve Bayes Method with Optimal Discretization. Feature Extraction – Foundations and Applications. Edited by: Guyon I, Gunn S, Nikravesh M, Zadeh LA. 2006, Berlin: Springer-Verlag, 499-507.CrossRef Boullé M: An Enhanced Selective Naïve Bayes Method with Optimal Discretization. Feature Extraction – Foundations and Applications. Edited by: Guyon I, Gunn S, Nikravesh M, Zadeh LA. 2006, Berlin: Springer-Verlag, 499-507.CrossRef
24.
go back to reference Hosmer DW, Lemeshow S: Applied Logistic Regression. 2000, New York: Wiley, 2CrossRef Hosmer DW, Lemeshow S: Applied Logistic Regression. 2000, New York: Wiley, 2CrossRef
25.
go back to reference Kuzniewicz MW, Vasilevskis EE, Lane R, Dean ML, Trivedi NG, Rennie DJ, Clay T, Kotler PL, Dudley RA: Variation in ICU risk-adjusted mortality: impact of methods of assessment and potential confounders. Chest. 2008, 133: 1319-1327. 10.1378/chest.07-3061.CrossRefPubMed Kuzniewicz MW, Vasilevskis EE, Lane R, Dean ML, Trivedi NG, Rennie DJ, Clay T, Kotler PL, Dudley RA: Variation in ICU risk-adjusted mortality: impact of methods of assessment and potential confounders. Chest. 2008, 133: 1319-1327. 10.1378/chest.07-3061.CrossRefPubMed
27.
go back to reference Ettema RGA, Peelen LM, Schuurmans MJ, Nierich AP, Kalkman CJ, Moons KGM: Prediction models for prolonged intensive care unit stay after cardiac surgery: systematic review and validation study. Circulation. 2010, 122: 682-689. 10.1161/CIRCULATIONAHA.109.926808.CrossRefPubMed Ettema RGA, Peelen LM, Schuurmans MJ, Nierich AP, Kalkman CJ, Moons KGM: Prediction models for prolonged intensive care unit stay after cardiac surgery: systematic review and validation study. Circulation. 2010, 122: 682-689. 10.1161/CIRCULATIONAHA.109.926808.CrossRefPubMed
Metadata
Title
A straightforward approach to designing a scoring system for predicting length-of-stay of cardiac surgery patients
Authors
Paolo Barbini
Emanuela Barbini
Simone Furini
Gabriele Cevenini
Publication date
01-12-2014
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2014
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/1472-6947-14-89

Other articles of this Issue 1/2014

BMC Medical Informatics and Decision Making 1/2014 Go to the issue