Eur J Cardiothorac Surg 2001;20:1183-1187
© 2001 Elsevier Science NL
Evaluation of the relationship between preoperative risk scores, postoperative and total length of stays and hospital costs in coronary bypass surgery
T.S. Kurkia,
U. Häkkinenb,
J. Lauharantac,
J. Rämöd,
M. Leijalac
a HUCH, Department of Anaesthesia and Intensive Care Medicine, Meilahti Hospital, FIN-00029 HUS, Helsinki, Finland
b National Research and Development Centre for Welfare and Health, FIN-00530 Helsinki, Finland
c Helsinki University Central Hospital, FIN-00290 HUS, Helsinki, Finland
d HUCH, Department of Cardiothoracic Surgery, Meilahti Hospital, FIN-00029 HUS, Helsinki, Finland
Received 21 May 2001;
received in revised form 20 August 2001;
accepted 31 August 2001.
Corresponding author. Lukupolku 19, 00680 Helsinki, Finland. Tel.: +358-9-47161781
e-mail: tuula.kurki{at}hus.fi
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Abstract
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Objectives: Several risk indices have been developed for the prediction of postoperative mortality and morbidity in coronary artery bypass operations, in which the risk scores are currently recorded as routine praxis. The aim of the present study was to determine whether the risk scores can be used to predict the hospital (LOS) and postoperative (POS) lengths of stay and total costs among coronary artery bypass graft (CABG) patients. Methods: All first-time CABG patients (n=2104) treated at Helsinki University Central Hospital during 1997–1998 were preoperatively scored using the Cleveland Clinic preoperative model. A multivariate analysis was used to evaluate the effects of the risk scores on the LOS and POS and total costs. Results: The mean preoperative risk score for the patients was 1.69. The increase in preoperative risk score was associated with an increase in the LOS (0.8 days by point), and POS (with 0.55 days by point). An age over 74 years increased the LOS by an extra day. The mean total cost for the CABG procedure was 8750 euros (SD 4430 euros). The costs increased as the risk score increased. Compared with the zero risk score, a score value of 2 was associated with a 1300 euros increase in total cost and a score value of over 6 was associated with an over 7000 euros cost increase. On average, the costs increased by 6980 euros (80%) for one major complication and by 935 euros (10%) in the elderly (>74 years of age). Conclusions: The results show that increasing risk scores were associated with longer postoperative hospital lengths of stay (POS and LOS) and with increased total costs. An age over 74 years appears to be an independent risk factor in increased POS, LOS and total cost. These results may help to estimate the impact of the preoperative risk profile on the resource requirement in CABG surgery.
Key Words: Coronary bypass surgery • Costs • Length of hospital stay • Morbidity • Risk scores
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1. Introduction
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In Western societies, the costs of healthcare are increasing year by year [1]. One of the reasons for this growth is the fact that the population in Western countries is ageing [2]. Elderly patients (over 70 years of age) have a high incidence of chronic diseases such as hypertension, diabetes and chronic lung disease. Another factor contributing to increasing costs is the use of expensive technology such as coronary artery bypass graft (CABG) surgery. The third party (state, communities, insurance companies and health maintenance organizations) has taken an active part in controlling hospital costs. The requirement is to cut costs without diminishing the quality of care. One solution is to increase efficiency; hospitals need to plan their operations to use available resources in an optimal fashion. This is especially true for CABG surgery which is one of the most expensive elective procedures in a hospital. The cost of CABG surgery can vary enormously between patients with uncomplicated recovery and those who suffer from postoperative complications such as stroke, arrhythmia and infections. Prediction of postoperative morbidity would facilitate decisions to operate, allocate resources and estimate costs [1]. The purpose of the present investigation was to evaluate the relationship between preoperative risk scores (Cleveland model) [3] and hospital length of stay (LOS), postoperative length of stay (POS) as well as total hospital costs in Helsinki University Central Hospital.
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2. Material and methods
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We studied retrospectively 2104 first-time CABG patients operated on in Helsinki University Central Hospital during 1997–1998. The mean age of the CABG patients was 63.13 years (SD 9.4, range 32–88 years). There were 1560 men (74.1%) and 544 women (25.9%). The patients were preoperatively scored in the clinic according to the Cleveland Clinic preoperative model [3], which predicts both mortality and morbidity. The cutoff point for morbidity is a score level of 4 or more and for mortality a score level of 6 or more. The maximum possible score in this model is 32. The mean preoperative risk score was 1.69. The preoperative factors and score values for this model are listed in Table 1. This model was used because it is accepted as a standard risk stratification model for all cardiac surgery centres nationwide. Hospitals collect the preoperative risk score data for every patient; the data were included in the National Discharge Register.
The patient level and risk score data were collected from the National Discharge Register. The register also includes the following postoperative outcome measures: stroke, myocardial infarction, infection (mediastinitis, wound infection or pneumonia) and bleeding (causing resternotomy). Other complications include: renal failure requiring dialysis, use of the intra-aortic balloon pump, ventilator support over 48 h or a prolonged stay in the intensive care unit (ICU) for over 3 days. Death during the hospital stay was also recorded by the National Discharge Register.
In addition, patient-level cost data were linked using personal identification numbers. The costs are based on the hospital's patient-level accounting system. The patient-level costs include total costs (including depreciation) excluding user (inpatient) charges of 40 euros/day.
We calculated mean total costs, LOS and POS associated with different levels of risk scores. In addition we used multivariate regression analysis to evaluate the effect of risk scores on the three dependent variables. Thus, it is possible to evaluate the effects of risk scores after controlling for other relevant variables.
The multivariate regression analysis was performed by using three alternative specifications of independent variables. Firstly (model 1), the dependent variables were regressed against variables describing risk scores only. Secondly (model 2), a variable describing an age over 74 years was included in addition to risk scores in order to evaluate whether the age of the patient was adequately considered in the risk score. In the third model, variables describing postoperative complications (morbidity), death and the character of the operation were included in the analysis to evaluate the sensitivity of the effect of risk factors after controlling for other variables. All explanatory variables were dichotomous. Risk scores were divided into six dichotomous variables according to their distribution.
In the regression analysis the functional form and the nature of the dependent variables were considered. Cost per patient was usually analyzed using linear or log-linear functional form. By means of Box–Cox transformation and the likelihood ratio (LR) test it is possible to obtain guidance for the appropriate functional form. In this case both linearity and log-linearity of the independent variable were rejected. Instead, the use of a value of 0.7 for lambda gave the maximum value for log-likelihood.1
The number of hospital days (LOS or POS) can have only nonnegative integer values, which means that the use of ordinary least-squares regression is limited. The Poisson distribution is often used to describe a discrete variable and Poisson regression to explain its variation. In the present study, as is usual in this type of analysis, the data included over dispersion compared with the Poisson distribution. This problem can often be handled by negative binomial regression.
The results of multivariate analysis are illustrated by calculating the marginal effects of the explanatory variables on the dependent variables. Since all variables are dichotomous, the marginal effects indicate how change in the variable (from 0 to 1) changes the value of the dependent variable. In the case of risk score variables the reference value is a risk score value of zero, which means that the marginal effect compares the effects of different risk scores with the situation in which the risk score is zero.
2.1. Ethical issues
This study did not interfere with the treatment of patients, and the retrospective database was organized in a way that makes the identification of an individual patient impossible.
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3. Results
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The majority of the patients, 1926 (91.5%) out of 2104, had favourable, uncomplicated outcomes. Major postoperative complications were documented in 7.3% (173) of patients. A total of 25 patients (1.2%) died during their hospital stay. Cerebral complications (stroke) were the most frequent complications (46/2104, 2.2%). Postoperative excessive bleeding requiring a resternotomy occurred in 29 patients (1.4%). There were 29 postoperative infections (1.4%). Myocardial infarction was documented in 28 patients (1.3%) postoperatively. Other cardiac complications occurred in eight patients, and complications in other organ systems were documented in ten patients including renal complications in three patients. The patients showed a mean LOS value of 9.2 days (Table 2). With a risk score of zero, the mean LOS was 8.3 days, with risk scores of 4–6 the mean LOS was 10 days and with scores of 7–10 the LOS was 11.3 days. With a risk score over 10 the LOS increased up to 15 days (mean). The mean POS value was 7.7 days. In patients with no risk factors, the POS was 6.8 days and in patients with scores of >6, the POS was a mean of 9.8 days. The results of negative binomial regression confirmed the results given by means (Table 3). For example, the marginal effect of a risk score of 1 with one variable is 0.6, i.e. patients having a risk score of 1 will have about a 0.6 days longer length of stay compared with the reference value (risk score=0). The effect of risk scores will be rather stable even after controlling for age and complication variables. However, patients over 74 years of age tend to have about a 1.4 days longer length of stay compared with younger patients. The results of negative binomial regression (Table 4) of POS values were rather similar to those of LOS values.
The hospital costs were on average 8750 euros per patient, and they increased in relation to the risk score. According to the results of Box–Cox regression (Table 5), a patient with a risk score of 1 was associated with about a 350 euros higher cost compared with the reference value (risk score=0). With a risk score of 4, the costs were increased by 2500 euros compared with a zero risk score level. If the risk score was over 6, the additional cost was 7000 euros. The average additional cost for the elderly was 935 euros. Postoperative complications increased the total cost on average by 6700 euros per complication (range 4200–9700 euros). Reoperation due to excessive bleeding was the most expensive postoperative complication (an additional cost of 9760 euros). Infections and other complications increased the basic total cost by 6200 and 8400 euros, respectively.
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4. Discussion
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Several risk indexes have been developed for the prediction of postoperative mortality and morbidity in coronary bypass surgery [4–8]. Although the risk indexes can only give a rough estimate of the risk for an individual patient, they can be used for planning purposes at the population level. The results of the present study demonstrate that there is a close relationship between the preoperative risk scores as measured by the Cleveland model on one hand, and postoperative and total lengths of stay and total cost on the other hand. These results extend the previous findings that suggested a relationship between LOS and preoperative patient characteristics [9]. Risk stratification models will aid hospitals in predicting the need for resources.
In the present study we used the Cleveland model because it is routinely used nationwide and is collected from the National Discharge Register. Most risk factors in the Cleveland model are the same in the Parsonnet and EuroSCORE models (Table 6), although the weights may vary. These models are derived using logistic regression analysis. The Cleveland model predicts both morbidity and mortality, whereas the EuroSCORE and Parsonnet models have been validated only for mortality [10].
Our intention was to investigate whether the risk scores recorded routinely for other purposes could also be used to predict the LOS, POS and economic outcome. Our aim was neither to develop a new score for the economic outcome, nor investigate the impact of individual variables on costs.
The impact of the individual risk factors was not investigated since, with the exception of age, we did not have access to single risk factors. In elderly patients (over 75 years of age), the length of hospital stay was increased by an average of 1.4 days compared with younger patients. The total hospital cost was increased by 940 euros (mean) in the elderly, even after controlling for other risk factors.
This result is interesting, considering the fact that the population in Western countries is ageing. Furthermore, the epidemiology of coronary artery disease suggests that the disease will become symptomatic at a later age in the future. Advanced age is associated with general arteriosclerosis, affecting not only coronary arteries but also the renal and cerebral arteries. Elderly patients (over 70 years of age) also have a higher incidence of hypertension, diabetes and chronic lung disease. Nevertheless, it is surprising that age alone has such a major impact on costs in CABG surgery. Further prospective studies are needed to evaluate the effects of single preoperative comorbidity factors on LOS values and total costs since their relative impact on costs may be different from their weight in the established risk score. Other risk-scoring systems should also be tested to evaluate the real benefits of risk stratification for resource allocation and in long-term planning of hospital schedules in cardiac surgery.
In conclusion, modelling of hospital costs and prediction of length of stay is possible on the basis of preoperative risk scores. This also makes it possible to allocate resources and to plan weekly schedules for CABG operations. However, the most obvious use of the present results is in long-term planning. Risk stratification may help decision-makers to estimate the need for resources for these different patient groups in the future.
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Footnotes
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Presented at the Annual Meeting of the American Society of Anesthesiologists, San Francisco, CA, October 14–18, 2000.
1 We estimated the optimum value for lambda for each of three alternative models. In each case the value 0.7 gave the highest log-likelihood ratio. In practice this means that we performed the following transformation to the cost variable (c): (c0.7-1)/0.7. 
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Top
Abstract
1. Introduction
