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Ann Thorac Surg 2004;78:1528-1534
© 2004 The Society of Thoracic Surgeons

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Original article: cardiovascular

EuroSCORE Predicts Intensive Care Unit Stay and Costs of Open Heart Surgery

^a Department of Cardiothoracic Surgery, Lund, Sweden
^b Department of Cardiothoracic Anesthesiology, Heart and Lung Center, University Hospital, Lund, Sweden
^c Department of Clinical Pharmacology, University Hospital, Lund, Sweden

Accepted for publication April 20, 2004.

^* Address reprint requests to Dr Nilsson, Department of Cardiothoracic Surgery, Heart and Lung Center, University Hospital, SE 221 85 Lund, Sweden
johan.nilsson{at}thorax.lu.se

	Abstract

Top Abstract Introduction Material and Methods Results Comment References

 
BACKGROUND: This study aimed to determine whether the preoperative risk stratification model EuroSCORE predicts the different components of resource utilization in open heart surgery.

METHODS: Data for all adult patients undergoing heart surgery at the University Hospital of Lund, Sweden, between 1999 and 2002 were prospectively collected. Costs were calculated for the surgery and intensive care and ward stay for each patient (excluding transplant cases and patients who died intraoperatively). Regression analysis was applied to evaluate the correlation between EuroSCORE and costs. The predictive accuracy for prolonged postoperative intensive care unit (ICU) stay was assessed by the Hosmer-Lemeshow goodness-of-fit test. The discriminatory power was evaluated by calculating the areas under receiver operating characteristics curves.

RESULTS: The study included 3,404 patients. The mean cost for the surgery was $7,300, in the ICU $3,746, and in the ward $3,500. Total cost was significantly correlated with EuroSCORE, with a correlation coefficient of 0.47 (p < 0.0001); the correlation coefficient was 0.31 for the surgery cost, 0.46 for the ICU cost, and 0.11 for the ward cost. The Hosmer-Lemeshow p value for EuroSCORE prediction of more than 2 days' stay in the ICU was 0.40, indicating good accuracy. The area under the receiver operating characteristics curve was 0.78. The probability of an ICU stay exceeding 2 days was more than 50% at a EuroSCORE of 14 or more.

CONCLUSIONS: In this single-institution study, the additive EuroSCORE algorithm could be used to predict ICU cost and also an ICU stay of more than 2 days after open heart surgery.

	Introduction

Top Abstract Introduction Material and Methods Results Comment References

 
Quality control is an established feature of contemporary medicine, and required as an instrument for improving the standard of care. It is known that variations in outcome may be influenced by excess in workload [1]. As a result of an increasing cost awareness, and a relative scarcity of resources, it has become important to optimize and quality-assure medical interventions.

Operative mortality is widely used as an indicator of the quality of cardiac surgery. To make an accurate comparison between different institutions or surgeons, mortality data must be adjusted to the risk profiles of the patients. During the last decades several models to calculate mortality risk before surgery have been developed. However, a preoperative risk algorithm specifically designed to predict the need for hospital care resources in cardiac surgery is lacking. As a result, a number of authors have evaluated different risk algorithms designed to prognosticate outcomes such as morbidity and mortality, to predict the need of resources after heart surgery. Most studies have focused on the total resource requirement for coronary artery bypass grafting only patients [2, 3]. The predictive value of risk scoring on the different components of resource utilization in heart surgery (surgery, intensive care unit [ICU], and ward) has previously not been studied.

The purpose of the present study was to evaluate whether the preoperative risk stratification model European System for Cardiac Operative Risk Evaluation (EuroSCORE) [4] predicts the different components of resource utilization in cardiac surgery, by applying this algorithm to a local, large Swedish adult cardiac surgery database.

	Material and Methods

Top Abstract Introduction Material and Methods Results Comment References

 
The study was approved by the Ethical Committee of the Medical Faculty, Lund University.

Data
Risk factors for all adult patients undergoing heart surgery at the University Hospital of Lund between October 1,1999, and December 31, 2002, were prospectively collected when the patients were admitted to the Department of Cardiothoracic Surgery. The patient record form contained a total of 248 variables (preoperative, intraoperative, and postoperative) based on the Higgins, Parsonnet, The Society of Thoracic Surgeons, and EuroSCORE patient record forms. The data were stored in a local adult cardiac surgery database.

Eighteen variables (Table 1) were imported into the statistical software package Intercooled Stata (version 8.2, 2003; Stata Corporation, USA). The duration of anesthesia (minutes), the Lund ICU workload score, the length of stay (LOS) in the ICU and in the ward, and the total in-hospital stay were collected. Patients who underwent transplantation, died intraoperatively, or in whom any of the preoperative, intraoperative, or postoperative data were missing were excluded from the study.

EuroSCORE is an additive risk algorithm developed on patients operated on in 128 surgical centers in eight European countries in 1995. Risk factors (97 variables) and mortality data from 19,030 consecutive adult patients undergoing all types of cardiac surgery were collected and stored in the EuroSCORE multinational database [6]. The risk algorithm was constructed by using logistic regression analysis. The included variables and their scores are listed in Table 1.

In the present study, the total risk score for every patient was calculated according to the EuroSCORE additive algorithm [4] (http://www.euroscore.org), and the individual cost was calculated according to a formula used by the hospital accounting system. Principles for calculations of cost of care are shown in Table 2. The hospital economy department established all starting costs and constants yearly.

Statistics
Values are given as mean ± standard deviation, median, and range. Univariate linear regression analysis was used to test the correlation between the EuroSCORE and either cost or LOS. Multivariate linear regression analysis was used to test which combination of the individual risk factors in the EuroSCORE model were significantly correlated to total cost. The cost and LOS were normalized by logarithmically transforming the data [7]. Analyses were performed using both individual patient data and patients grouped into six risk cohorts (Table 3) [3, 7].

The discriminatory power of the logistic regression model was evaluated by calculating the areas under the receiver operating characteristics (ROC) curves [9, 10]. These areas are presented with 95% confidence intervals. An area of 1.0 under the ROC curve indicates perfect discrimination, whereas an area of 0.50 indicates complete absence of discrimination. Any intermediate value is a quantitative measure of the ability of the risk predictor model to distinguish between a shorter and longer LOS at the ICU.

One-way analysis of variance was used to compare the difference between predicted and observed number of patients with an ICU stay of more than 2 days for each risk cohort.

Graphs and statistical analyses were performed with Intercooled Stata.

	Results

Top Abstract Introduction Material and Methods Results Comment References

 
Between October 1, 1999, and December 31, 2002, 3,819 patients underwent cardiac surgery at our institution. Patients who underwent transplantation (n = 66), died intraoperatively (n = 37), or in whom any of the preoperative, intraoperative, or postoperative data were missing (n = 312) were excluded from the study. Thus, 3,404 patients, undergoing 3,413 operations, were included in the analysis. There was accurate documentation of data including 30-day mortality in all cases, and no patient was lost to follow-up.

Preoperative characteristics are listed in Table 1. The average age was 67.5 ± 10.5 years (range, 18 to 89 years). The majority of patients were men (72%). A coronary artery bypass grafting only operation was performed in 2,487 cases (73%), 710 (21%) cases had a valve procedure with or without coronary artery bypass grafting surgery, and 216 (6%) were miscellaneous procedures (postinfarction septal rupture, aortic aneurysm or dissection, and so forth).

The actual 30-day postoperative mortality was 2.5% (95% confidence interval, 2.0% to 3.0%). The mean cost for the surgery was $7,300 ± $2,120 (median, $6,613; range, $2,563 to $25,988), in the ICU $3,746 ± $6,032 (median, $2,182; range, $632 to $134,263), and in the ward $3,500 ± $2,605 (median, $2,999; range, $0 to $41,626), and the mean total cost was $14,546 ± $7,658 (median, $12,546; range, $6,995 to $157,912). The mean costs (± standard deviation) were calculated for the EuroSCORE risk groups 0 to 24 for the surgery, the ICU, and the ward, with the results shown in Figure 1. The log-transformed cost for the individual patients was significantly correlated to EuroSCORE. The strongest correlation was between the EuroSCORE and log-transformed ICU costs, with a correlation coefficient (r) of 0.46 (p < 0.0001; Table 4).

View larger version (27K): [in this window] [in a new window]   Fig 1. Graph of costs (mean ± standard deviation) for each risk score. (USD = US dollars).

View larger version (12K): [in this window] [in a new window]   Fig 2. Linear regression analysis of mean EuroSCORE versus log-transformed mean costs in each cohort for surgery (circle), in the intensive care unit (square), in the ward (cross), and total (diamond). Cohorts are defined in Table 3. (USD = US dollars.)

View larger version (17K): [in this window] [in a new window]   Fig 3. The receiver operating characteristic curves for sensitivity and 1 minus specificity for the EuroSCORE prediction of an intensive care unit stay of more than 1 day (diamonds) and an intensive care unit stay of more than 2 days (circles). The solid line represents no discrimination.

View larger version (22K): [in this window] [in a new window]   Fig 4. Percentage of patients with an intensive care unit (ICU) stay more than 2 days (left y axis) for each EuroSCORE risk group (x axis). Predicted intensive care unit stay more than 2 days (dotted line) with 95% confidence interval (shaded area); observed intensive care unit stay more than 2 days (diamond) with 95% confidence interval (bars). The histogram shows the number of patients (right y axis) in each risk group.

	Comment

Top Abstract Introduction Material and Methods Results Comment References

 
The purpose of the present study was to evaluate whether the preoperative risk stratification model EuroSCORE predicts the different cost components in open heart surgery. The results show that the EuroSCORE algorithm correlates to both costs and length of ICU stay. Fifteen of the 18 variables included in the risk algorithm were significantly correlated to the total cost of open heart surgery.

The closest correlation was between EuroSCORE and cost at the ICU. The EuroSCORE algorithm also showed a good predictive power (accuracy) of an ICU stay more than 1 day, and even better for an ICU stay more than 2 days. In our experience, patients staying in the ICU more than 2 days are likely to remain there for prolonged periods. As other authors [11–13], we therefore chose to focus the additional analyses on a more than 2-day ICU stay as being clinically more important. The results from our study also suggest that accuracy and discrimination will be better for a stay of more than 2 days.

At higher risk scores the EuroSCORE predictive performance was less than at lower scores, as previously reported in mortality studies [14]. The lower numbers of patients in these high-risk score groups might contribute to this finding, but it may also reflect a weakness of the risk score algorithm. Indeed, the EuroSCORE was developed to predict mortality and not cost or duration of ICU stay. In our study, as in others [3, 15], the predictive value was limited for individual patients, but an excellent correlation was seen if the patients were grouped in risk cohorts. The predictive accuracy of the total number of patients with an ICU stay more than 2 days was independent of which risk cohort the patients belonged to. This indicates that preoperative prediction of ICU stay using EuroSCORE is possible for groups of patients operated on during a certain period of time (e.g., 1 week), and may therefore be used in clinical practice.

In the present study the strongest correlation was between EuroSCORE and resource requirements in the ICU. A patient with a high preoperative risk score is expected to need more ICU care and support; therefore, the cost of this may be better measured by total ICU workload than by LOS alone. The resource utilization in the ICU is primarily dependent on the patient's medical condition, whereas numerous other factors may influence the time to discharge from the regular ward.

Earlier studies have indicated that preoperative risk variables can be used to predict costs of cardiac surgery [16, 17]. An increasing Cleveland risk score has been shown to be associated with an increase in total cost and longer postoperative LOS [2], and similar results have been shown with the CABDEAL risk algorithm [18]. Riordan and colleagues [3] found that grouping patients in risk cohorts resulted in a correlation between The Society of Thoracic Surgeons' risk algorithm and total cost, but for individual patients the prediction was poor. In these studies the relationships found were between the risk algorithm and total cost for coronary artery bypass grafting only patients. In a study of the different components of heart surgery cost, Ferraris and associates [19] showed that the greatest expenses were generated by anesthesia and the surgery, but any relationship between the risk and cost was not evaluated.

The additive EuroSCORE model has been shown to work well to predict 30-day mortality in many European countries [20] and in the United States [21], and compares favorably with The Society of Thoracic Surgeons' risk stratification algorithm [22]. Use of the EuroSCORE risk algorithm to predict the need for different resources therefore appears logical. Pintor and coworkers [7] and Sokolovic and associates [23] recently demonstrated a correlation between EuroSCORE and cost for open heart surgery. Both these studies were rather small (488 and 201 patients, respectively), and their focus was on total cost and not on the different components of heart surgery resource utilization.

Several studies have tried to identify preoperative risk variables that predict LOS in the ICU after cardiac surgery. The Parsonnet risk algorithm seems able to preoperatively identify patients who are likely to spend more than 24 hours in the ICU [24] with an ROC area of 0.70, which is somewhat less than the ROC area for an ICU stay of more than 1 day in the present study (0.76). A strength of the EuroSCORE is that it has been developed fairly recently (1995) on a large multiinstitutional patient material. The Parsonnet score, on the other hand, is developed on a single-institution material collected between 1982 and 1987.

Three additional studies [11–13], comparing different risk algorithms, have found a correlation between EuroSCORE and ICU stay of more than 2 days. Tu and Guerriere [25] used a neural network as a predictive instrument, finding both advantages and disadvantages as compared with other statistical techniques.

Our study is based on patients treated in a single, European institution, but the EuroSCORE and the evaluation were applied to a relatively large patient material, in which the data were prospectively collected on a regular basis in the daily clinical work. We chose to exclude cases dying intraoperatively as they did not require any postoperative resources. The exclusion of these mainly high-risk patients probably reduces the actual predictive power of the analysis, but the difference will be minor because the number of patients who died intraoperatively was small (1.1% during the period in question).

Improved preoperative prediction of care requirements in cardiac surgery could give patients better access to treatment, and could also guide the selection of patients for surgery versus alternative, nonsurgical therapies. A preoperative risk algorithm specifically designed to predict the need for hospital care resources in cardiac surgery could help in decision-making to realistically estimate the need for resources and plan the care for high-risk patients more efficiently. By making a weekly operation schedule that is adjusted for expected resource utilization, the workload on the ICU could be more equal over time, which may improve patient management. This could also have positive effects on the outcome of surgery [1]. The EuroSCORE algorithm appears valuable in this process.

	References

Top Abstract Introduction Material and Methods Results Comment References

 

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This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Add to Personal Folders Download to citation manager Author home page(s): Johan Nilsson Johan Brandt Permission Requests Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Nilsson, J. Articles by Brandt, J. Search for Related Content PubMed PubMed Citation Articles by Nilsson, J. Articles by Brandt, J. Related Collections Professional affairs