Preoperative risk stratification models fail to predict hospital cost of cardiac surgery patients.

BACKGROUND: Preoperative risk stratification models have previously been suggested to predict cardiac surgery unit costs. However, there is a lack of consistency in their reliability in this field. In this study we aim to test the correlation between the values of six commonly known preoperative scoring systems and evaluate their reliability at predicting unit costs of cardiac surgery patients. METHODS: Over a period of 14 months all consecutive adult patients undergoing cardiac surgery on cardiopulmonary bypass were prospectively classified using six preoperative scoring models (EuroSCORE, Parsonnet, Ontario, French, Pons and CABDEAL). Transplantation patients were the only patients we excluded. Total hospital costs for each patient were calculated independently on a daily basis using the bottom up method. The full unit costs were calculated including preoperative diagnostic tests, operating room cost, disposable materials, drugs, blood components as well as costs for personnel and fixed hospital costs. The correlation between hospital cost and the six models was determined by linear regression analysis. Both Spearman's and Pearson's correlation coefficients were calculated from the regression lines. An analysis of residuals was performed to determine the quality of the regression. RESULTS: A total of 887 patients were operated on for CABG (n = 608), valve (n = 142), CABG plus valve (n = 100), thoracic aorta (n = 33) and ventricular assist devices (n = 4). Mean age of the patients was 68.3±9.9 years, 27.6% were female. 30-day mortality rate was 4.1%. Correlation between the six models and hospital cost was weak (Pearson's: r < 0.30; Spearman's: r < 0.40). CONCLUSION: The risk stratification models in this study are not reliable at predicting total costs of cardiac surgical patients. We therefore do not recommend their use for this purpose.

A comparative study of four intensive care outcome prediction models in cardiac surgery patients.

BACKGROUND: Outcome prediction scoring systems are increasingly used in intensive care medicine, but most were not developed for use in cardiac surgery patients. We compared the performance of four intensive care outcome prediction scoring systems (Acute Physiology and Chronic Health Evaluation II [APACHE II], Simplified Acute Physiology Score II [SAPS II], Sequential Organ Failure Assessment [SOFA], and Cardiac Surgery Score [CASUS]) in patients after open heart surgery. METHODS: We prospectively included all consecutive adult patients who underwent open heart surgery and were admitted to the intensive care unit (ICU) between January 1st 2007 and December 31st 2008. Scores were calculated daily from ICU admission until discharge. The outcome measure was ICU mortality. The performance of the four scores was assessed by calibration and discrimination statistics. Derived variables (Mean- and Max- scores) were also evaluated. RESULTS: During the study period, 2801 patients (29.6% female) were included. Mean age was 66.9 ± 10.7 years and the ICU mortality rate was 5.2%. Calibration tests for SOFA and CASUS were reliable throughout (p-value not < 0.05), but there were significant differences between predicted and observed outcome for SAPS II (days 1, 2, 3 and 5) and APACHE II (days 2 and 3). CASUS, and its mean- and maximum-derivatives, discriminated better between survivors and non-survivors than the other scores throughout the study (area under curve ≥ 0.90). In order of best discrimination, CASUS was followed by SOFA, then SAPS II, and finally APACHE II. SAPS II and APACHE II derivatives had discrimination results that were superior to those of the SOFA derivatives. CONCLUSIONS: CASUS and SOFA are reliable ICU mortality risk stratification models for cardiac surgery patients. SAPS II and APACHE II did not perform well in terms of calibration and discrimination statistics.