OBJECTIVE: The aim of this study is to determine if a preoperative risk stratification model can identify different surgical costs. METHODS: Four hundred and eighty-eight patients undergoing open heart surgery between March 2000 and March 2001 were classified with the EuroSCORE model. Direct variable costs were prospectively collected, surgical team costs excluded. The multivariate analysis was used to find variables independently associated with costs. RESULTS: Of the 488 patients enrolled 342 (70%) were males, mean age 65+/-10 years, 57 (12%) had myocardial infarction, 20 (4%) had ejection fraction <30%, 56 (11%) were operated in emergency, 26 (5%) had a re-operation. 113 (23.2%) were operated for valvular disease, 30 (6.1%) were operated for thoracic aortic surgery, one (0.2%) was operated for interatrial septal defect, 79 (16.2%) were operated for other intervention in addition to coronary bypass and 265 (54.3%) for isolated coronary bypass. The mean intensive care unit length of stay (ICU-LOS) was 2.3+/-4.1 days and the postoperative LOS was 8.2+/-5.3 days. According to EuroSCORE, 117 patients (24%) were at low, 187 (38%) at medium, and 184 (38%) at high risk. Costs were significantly and directly correlated with preoperative risk model with a correlation coefficient of 0.47 and an increase of costs of 3.5% (95% CI 2.3-4.7, P<0.0001) for each single rise of risk score. The relationship EuroSCORE vs. direct costs is, respectively: EuroSCORE 0-2 ==> 6863+/-861 Euro; 3-4 ==> 8292+/-3714 Euro; 5-6 ==> 8908+/-3480 Euro; 7-8 ==> 10,462+/-6123 Euro; 9-10 ==> 13,711+/-12,634 Euro; >10 ==> 21,353+/-18,507 Euro. Excluding EuroSCORE from the preoperative logistic model, age, preoperative creatinine, critical condition, ejection fraction, re-operation and sex were independently correlated with costs. CONCLUSIONS: From our data the EuroSCORE model developed to predict (30-day postoperative) hospital mortality could be used to predict direct operative costs and identify patients with different levels of resource consumption.
OBJECTIVE: The aim of this study is to determine if a preoperative risk stratification model can identify different surgical costs. METHODS: Four hundred and eighty-eight patients undergoing open heart surgery between March 2000 and March 2001 were classified with the EuroSCORE model. Direct variable costs were prospectively collected, surgical team costs excluded. The multivariate analysis was used to find variables independently associated with costs. RESULTS: Of the 488 patients enrolled 342 (70%) were males, mean age 65+/-10 years, 57 (12%) had myocardial infarction, 20 (4%) had ejection fraction <30%, 56 (11%) were operated in emergency, 26 (5%) had a re-operation. 113 (23.2%) were operated for valvular disease, 30 (6.1%) were operated for thoracic aortic surgery, one (0.2%) was operated for interatrial septal defect, 79 (16.2%) were operated for other intervention in addition to coronary bypass and 265 (54.3%) for isolated coronary bypass. The mean intensive care unit length of stay (ICU-LOS) was 2.3+/-4.1 days and the postoperative LOS was 8.2+/-5.3 days. According to EuroSCORE, 117 patients (24%) were at low, 187 (38%) at medium, and 184 (38%) at high risk. Costs were significantly and directly correlated with preoperative risk model with a correlation coefficient of 0.47 and an increase of costs of 3.5% (95% CI 2.3-4.7, P<0.0001) for each single rise of risk score. The relationship EuroSCORE vs. direct costs is, respectively: EuroSCORE 0-2 ==> 6863+/-861 Euro; 3-4 ==> 8292+/-3714 Euro; 5-6 ==> 8908+/-3480 Euro; 7-8 ==> 10,462+/-6123 Euro; 9-10 ==> 13,711+/-12,634 Euro; >10 ==> 21,353+/-18,507 Euro. Excluding EuroSCORE from the preoperative logistic model, age, preoperative creatinine, critical condition, ejection fraction, re-operation and sex were independently correlated with costs. CONCLUSIONS: From our data the EuroSCORE model developed to predict (30-day postoperative) hospital mortality could be used to predict direct operative costs and identify patients with different levels of resource consumption.
Authors: Roelof G A Ettema; Linda M Peelen; Cor J Kalkman; Arno P Nierich; Karel G M Moons; Marieke J Schuurmans Journal: Intensive Care Med Date: 2011-07-30 Impact factor: 17.440
Authors: David R Lawrence; Rajael Somaskanthan; Matthew J Barnard; Miles Curtis; Bruce E Keogh Journal: Ann R Coll Surg Engl Date: 2009-04-02 Impact factor: 1.891
Authors: Geert Meyfroidt; Fabian Güiza; Dominiek Cottem; Wilfried De Becker; Kristien Van Loon; Jean-Marie Aerts; Daniël Berckmans; Jan Ramon; Maurice Bruynooghe; Greet Van den Berghe Journal: BMC Med Inform Decis Mak Date: 2011-10-25 Impact factor: 2.796
Authors: Ricardo Casalino; Flávio Tarasoutchi; Guilherme Spina; Marcelo Katz; Antonio Bacelar; Roney Sampaio; Otavio T Ranzani; Pablo M Pomerantzeff; Max Grinberg Journal: PLoS One Date: 2015-02-25 Impact factor: 3.240
Authors: Jessica G Y Luc; Michelle M Graham; Colleen M Norris; Sadek Al Shouli; Yugmel S Nijjar; Steven R Meyer Journal: BMC Cardiovasc Disord Date: 2017-11-02 Impact factor: 2.298