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(Chest. 2000;118:736-743.)
© 2000 American College of Chest Physicians

Perioperative Predictors of ICU and Hospital Costs in Coronary Artery Bypass Graft Surgery*

Lynn V. Doering, RN, DNSc; Fardad Esmailian, MD, FCCP and Hillel Laks, MD, FCCP

* From the School of Nursing (Dr. Doering), and School of Medicine (Drs. Esmailian and Laks), University of California, Los Angeles, Los Angeles, CA.

Correspondence to: Lynn V. Doering, RN, DNSc, Assistant Professor, Acute Care, Factor Building 4–250, PO Box 956918, Los Angeles, CA 90095-6918; e-mail: ldoering{at}sonnet.ucla.edu


   Abstract
TOP
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
 References
 
Study objective: Economic forces have precipitated intense interest in cost-saving practices for patients undergoing coronary artery bypass grafting (CABG). While several preoperative variables have been implicated in higher costs, few studies have included perioperative factors. This study evaluated the predictive power of a preoperative mortality risk measurement (Parsonnet score) and of early extubation (<= 6 h from ICU admission) in determining ICU and hospital costs.

Design: Multivariate correlational design.

Setting: University hospital in a large metropolitan area.

Patients: All patients (n = 116) undergoing isolated CABG during a 6-month period were studied after the introduction of a clinical pathway.

Measurements and results: Clinical data were collected. Costs data were obtained retrospectively from the institutional data system and were derived from individual patient charges by application of department-specific cost-to-charge ratios. In multivariate logistic regression, Parsonnet score (per point odds ratio [OR], 1.09; confidence interval [CI], 1.03 to 1.17), in-hospital coronary angiography (OR, 3.51; CI, 1.23 to 10.01), delayed extubation (OR, 4.59; CI, 1.29 to 16.29), and presence of arrhythmia (OR, 3.50; CI, 1.15 to 10.64) were independent predictors of ICU costs. Only Parsonnet score (OR, 1.09; CI, 1.03 to 1.15) and cardiopulmonary bypass time (OR, 1.01; CI, 1.00 to 1.02) were independent predictors of hospital costs.

Conclusions: The Parsonnet score is a useful indicator of both ICU and hospital costs. Early extubation is associated with decreased ICU costs, but is not independently predictive of hospital costs.

Key Words: coronary artery bypass graft • costs • extubation • mortality risk


   Introduction
TOP
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 Conclusion
 References
 
Coronary artery bypass grafting (CABG) is one of the most common procedures performed in the United States. Annually, it accounts for an estimated 607,000 operations, with an average cost of $44,820.1 Changes in health-care financing have resulted in an emphasis on systematizing clinical practices in the care of CABG patients. As a means to reduce costs, the development and institution of clinical pathways has become commonplace.2 3 4 At the same time, the rapid advancement of invasive cardiology techniques has led to a dramatic increase in the preoperative risk profile of CABG patients.5 To build and maintain cardiac surgery programs, clinicians are faced not only with maximizing clinical efficiency, but also with critically assessing individual patient risk in relation to costs and finding ways to more effectively manage high-risk patients.5

Early extubation has been a focus of study as a means to streamline clinical practices.2 3 4 Despite the intense interest in potentially cost-saving clinical practices, there have been few reports regarding the effects of early extubation on ICU and hospital costs.6 7 The relationship of early extubation to ICU and hospital costs has not been considered in a multivariate analysis. Therefore, it is unclear whether the cost-saving benefit of early extubation is retained when other clinical variables are taken into account.

As a means of assessing patient risk in relation to costs, investigators have considered a wide range of clinical and nonclinical variables.8 9 10 11 12 13 14 Preoperative clinical variables reported to be associated with costs include age, sex, prior CABG, diabetes, congestive heart failure/ejection fraction, and angina.8 9 10 11 12 13 14 Other variables cited less frequently as being associated with increased costs include urgency of operation, creatinine level, and prior myocardial infarction.9 11 12 13 To our knowledge, only one study has considered postoperative variables; significant correlates of costs included ARDS, septicemia, pneumonia, intra-aortic balloon pump (IABP), surgical reexploration for bleeding, fluid overload, neurologic events, and major arrhythmia.10 Few investigators have considered intraoperative factors. Nonclinical variables associated with increased cost include assignment to a teaching service, lower socioeconomic status, living alone, restricted preoperative activity, and surgeon.11 14 Of the preoperative clinical variables most frequently associated with increased cost, all except angina are included in the Parsonnet score, an established mortality risk measure highly predictive of 30-day operative mortality, and closely related to overall complication rate and duration of postoperative hospitalization.15

While it may seem intuitive that preoperative comorbidities are associated with increased costs, the degree to which increased preoperative risk accounts for increased cost is not clear. It seems likely that an additive-risk algorithm, such as the Parsonnet score, may be useful to clinicians as a means of projecting the increase in cost associated with an increase in risk.16 The usefulness of the Parsonnet score in predicting ICU and hospital costs has not been reported.

The purpose of the present study was (1) to determine if a preoperative mortality risk assessment score, the Parsonnet score, was useful in predicting postoperative costs in CABG patients; and (2) to evaluate the predictive power of early extubation (<= 6 h after cardiothoracic ICU admission) on both ICU and total hospital costs in a multivariate model.


   Materials and Methods
TOP
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 Conclusion
 References
 
Sample and Setting
The study was conducted 6 months after the introduction of a clinical pathway at an urban university medical center. Permission to conduct the study was obtained from the Institutional Review Board. All patients undergoing CABG during a 6-month period were included in the study. All patients were admitted to a 12-bed dedicated cardiothoracic ICU and transferred to a 32-bed observation unit. The clinical pathway included projected extubation in the ICU within 6 h after admission from the operating room and transfer to the observational unit the morning following surgery. Patients were excluded from study if they were undergoing CABG in conjunction with another procedure.

To assess preoperative risk of mortality, a composite score weighing the contribution of age, sex, and comorbid conditions was calculated using the method of Parsonnet and colleagues.15 The Parsonnet score was selected because it is easily calculated, clinically useful, widely accepted, and validated in varied settings.5 17 18 19 20 The Parsonnet mortality risk assessment score was calculated using assigned weights described in Table 1 .


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  		Table 1.. Variable Weights for Parsonnet Risk Assessment Score*

 
Procedure
Anesthesia for all patients was accomplished by following a standard protocol that included use of end-tidal concentrations of isoflurane, 0.2 to 1.5%; midazolam, total doses of 10 to 30 mg; fentanyl, total doses of 15 to 40 µg/kg; and morphine, total doses of 10 to 15 mg. A median sternotomy approach was used for all patients. All had mild hypothermia (28 to 32°C) during cardiopulmonary bypass (CPB) with a single aortic cross-clamp technique. Neuromuscular blockade was used at the discretion of the anesthesiologist and was not reversed in the operating room prior to transfer to the ICU.

Postoperative analgesia was provided in one of three ways: via a patient-controlled analgesia device, an epidural catheter, or intermittent IV administration. All patients were included in a clinical pathway that called for extubation in <= 6 h after arrival in the ICU. A standard weaning and extubation protocol was used (Table 2 ).21 The weaning protocol was initiated promptly on arrival in the ICU and after achievement of hemodynamic stability. The clinical nurse specialist assigned to the unit verified prompt initiation of the clinical pathway. For patients who remained intubated after 6 h, the clinical nurse specialist recorded clinical factors that contributed to prolonged intubation.


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  		Table 2.. Weaning and Extubation Protocol*

 
Cost data were obtained after patient discharge from the institutional data system. In the institutional data system, costs were derived from individual patient charges with the application of department-specific cost-to-charge ratios used for Medicare reporting.13 22 Professional fees were excluded. Direct and total costs were defined according to the institutional data system format. Specifically, direct costs were defined as all patient service costs, including nursing/technical personnel and supplies. In the ICU, variable nursing care charges (and thus costs) were assessed daily according to level of care provided (1:1 or 1:2 nurse-to-patient ratios). Total costs were defined as direct costs plus indirect variable support costs, such as dietary, housekeeping, laundry services, and indirect fixed administrative costs, such as computing services and building costs. In the institutional data system, the allocation of indirect costs was made based on the usage of nursing departments, with administrative costs allocated as a department-specific percentage of costs. Because institutional practices dictated the inclusion of some costs that vary with patient volume, and which could therefore be influenced by changes in clinical practices, as indirect costs, total costs figures were used for analysis, rather than direct-only cost figures. This approach was also selected because direct costs have been reported to account for only 57% of total hospital costs.23 Additionally, this approach is consistent with other reports.13 22

Both ICU and hospital costs were adjusted to discount costs incurred prior to surgery, so that reported costs include only those associated with the surgery and its postoperative course.

Data Analysis
Descriptive statistics were used to determine measures of central tendency. Independent t tests were used to compare total ICU and total hospital lengths of stay and costs in early (<= 6 h) and delayed (> 6 h) extubation groups. For interval level data, a correlation matrix was constructed to identify variables correlated with both total ICU and total hospital costs.

High- and low-costs groups were created for both total ICU costs and total hospital costs by use of median splits. Using the high- and low-costs groups, univariate and multivariate logistic regression analyses were used to identify predictors of ICU and hospital costs greater than the medians of $3,840 and $20,768, respectively. Variables that were significant at the 0.10 level by univariate analyses were included in the multivariable analyses. For both ICU and total hospital costs, multivariate logistic regression was conducted in a stepwise fashion. In addition, variables were entered into the equation in three blocks, with preoperative variables entered first, followed by intraoperative and then postoperative variables. The Hosmer-Lemeshow goodness of fit test was used to evaluate the degree to which the data fit the proposed model. For multivariate analyses, significance was set at 0.05. As determined by power analysis, the sample size was sufficient to consider up to six predictor variables in a multivariate model with a power of 0.86, given moderate effect sizes of 0.15 and a significance level of 0.05.24 Therefore, only the six variables most significantly associated with ICU and total hospital costs, respectively, were included in the multivariate analyses.


   Results
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 Conclusion
 References
 
The sample included 116 patients, 90 of whom (78%) were male. The mean age was 67.9 ± 9.9 years. Mean Parsonnet score was 12.5 ± 8.2. Fifty-seven patients (49%) underwent elective surgery, while urgent or emergency surgery was necessary in 45 patients (39%) and 14 patients (12%), respectively. Mean CPB time was 129.5 ± 40.8 min. The average number of grafts per patient was 4.2 ± 1.1. Mean postoperative intubation time was 20.4 ± 51.7 h; median postoperative intubation time was 10.3 h (6.8 to 15.8 h). Mean postoperative ICU and hospital costs were $7,184 ± $12,181 and $25,860 ± $17,690, respectively. Median ICU and hospital costs were $3,893 ($3,678 to $5,294) and $20,768 ($17,405 to $26,222), respectively. ICU costs averaged 35 ± 19% of total hospital costs. Clinical variables are presented in Table 3 .


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  		Table 3.. Clinical Characteristics (n = 116)*

 
Characteristics of patients in whom extubation was delayed (n = 89) and those who achieved early extubation (n = 27) are compared in Table 4 . The delayed extubation group was older (69.3 ± 9.2 years vs 63.5 ± 11.2 years; p < 0.01) and had a greater proportion of patients with high (Parsonnet score, 15 to 19) or extremely high (Parsonnet score >= 20) preoperative risk scores (39 [43.8%] vs 5 [18.5%]; p = 0.04). Also, the delayed extubation group had a higher proportion of patients with early hemodynamic instability, as previously reported.21 The delayed extubation group had longer ICU stays (1.9 ± 0.70 days vs 4.9 ± 9.1 days; p = 0.003) and total hospital stays (5.0 ± 1.4 days vs 8.3 ± 9.2 days; p = 0.002), compared to the early extubation group. Similarly, the delayed extubation group had greater postoperative ICU costs ($3,454 ± 1,219 vs $8,316 ±13,708; p = 0.001) and greater postoperative total hospital costs ($18,303 ± $4,381 vs $28,153 ± $19,504; p < 0.001).


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  		Table 4.. Characteristics of Early and Delayed Extubation Groups

 
A correlation matrix indicating bivariate correlations of clinical variables with total ICU and total hospital costs is presented in Table 5 . When costs alone were considered, ICU costs were correlated with total hospital costs (r = 0.87; p 0 < 0.01). As might be expected, patients who incurred higher ICU costs were more likely to also incur greater total hospital costs (odds ratio [OR], 6.80; confidence interval [CI], 2.88 to 16.05; p < 0.0001).


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  		Table 5.. Correlations of Clinical Variables With Total ICU and Total Hospital Costs

 
Table 6 includes the clinical variables that were significantly associated with higher median ICU and total hospital costs by univariate analysis. Although some variables are clinically related to each other (eg, hospital admission prior to the day of surgery and in-hospital coronary angiography, and postoperative use of IABP and postoperative use of vasoactive drips), none were sufficiently related by Spearman correlation coefficients to be excluded as candidates for multivariate analysis.


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  		Table 6.. Variables Associated With Costs Greater Than the Median by Univariate Analysis

 
ICU costs, Parsonnet score, hospital admission prior to day of surgery, in-hospital coronary angiography, delayed extubation, postoperative IABP use, and CPB time were entered in the multivariate model. When these variables were considered together, Parsonnet score, hospital admission prior to the day of surgery, and delayed extubation were independent predictors of greater ICU costs. For total hospital costs, five variables were entered into the multivariate model: Parsonnet score, hospital admission prior to the day of surgery, in-hospital coronary angiography, CPB time, and postoperative use of IABP. Only two independent predictors of total hospital costs, Parsonnet score and CPB time, were identified. Adjusted ORs and 95% CIs for multivariate analyses are presented in Table 7 . In both models, the Parsonnet score was an independent predictor of costs. For every unit increase in Parsonnet score, there was a 9% increased risk of both higher ICU costs and higher hospital costs. Delayed extubation was predictive of higher ICU costs, but not higher total hospital costs.


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  		Table 7.. Variables Independently Associated With ICU and Hospital Costs Greater Than the Median

 

   Discussion
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
Conclusion
 References
 
Parsonnet Score
These findings indicate that the Parsonnet score is a useful instrument for predicting both ICU and hospital total costs when considered with other variables. This study builds on the work of previous investigators in that it uses an additive-risk algorithm, the Parsonnet score, to consider cumulatively preoperative variables shown by others to contribute individually to increased costs. Factors previously shown to be associated with increased costs, such as age, sex, congestive heart failure/ejection fraction, repeat CABG, diabetes, and urgency of operation are all included in the Parsonnet algorithm.8 9 10 11 12 13 14 One study used the Parsonnet score to evaluate marginal costs and marginal risks of CABG by demonstrating a correlation between the Parsonnet score and length of stay, which was in turn correlated with costs.15 The current finding extends those results by looking at the Parsonnet score directly in relation to cost.

The power of the Parsonnet score to predict higher costs may be explained by the findings of other investigators that the effect of any single preoperative variable on cost is relatively small.9 12 22 The use of a cumulative indicator, such as the Parsonnet score, may provide a stronger costing variable. The predictive ability of the Parsonnet score may also be related to its power as a predictor of mortality. Investigators have reported that operative death is the most costly outcome.10 12

Other investigators have found that the total amount of variance in costs explained by preoperative variables is modest, ranging from 8 to 19%, and they suggest that other variables must contribute to costs.9 10 22 The current findings support this contention. Indeed, intraoperative predictors of higher hospital costs (CPB time) and postoperative predictors of higher ICU costs (delayed extubation and occurrence of arrhythmias) were identified in the current study. In addition, a preoperative procedural variable, in-hospital coronary angiography, was identified as highly predictive of higher ICU costs (OR, 3.51). In contrast, a 1-point increase in cumulative risk using the Parsonnet score accounted for a 9% increase in the risk of higher ICU and hospital costs (OR, 1.09). While this may represent a substantial increase in the risk of higher than median costs associated with preoperative variables, it is still less than the risk associated other factors, such as delayed extubation and in-hospital catheterization.

Delayed Extubation
Delayed extubation yielded a very high OR regarding ICU costs. Patients extubated > 6 h after ICU admission were 4.59 times more likely to incur higher ICU costs than patients extubated in <= 6 h. However, when total hospital costs were considered in multivariate analysis, the independent predictive power of delayed extubation was lost. These findings differ from those of other investigators, who have reported a post-ICU cost advantage with early extubation.6 7 In the current analysis, only Parsonnet score (preoperative mortality risk) and CPB time were independent predictors of total hospital costs. Our findings could differ from those of previous investigators because we used a blocking method to remove the portion of explained variance related to preoperative and intraoperative variables before evaluating the effect of early vs delayed extubation on costs. Previous investigators used group comparisons, so that the influence of preoperative and intraoperative variables may not have been controlled.

The finding that delayed extubation is not associated with greater hospital costs in a multivariate analysis suggests that other factors may influence cost during the post-ICU hospitalization. Other clinical variables, such as ARDS and surgical reexploration for bleeding, have been associated with higher costs,10 but these events are more likely to occur during early (ie, ICU) recovery and may not account for the unexplained variance in hospital costs. In fact, in the current study, these variables were not associated with greater hospital costs. If deaths occur late in the hospitalization, they could account for an increase in post-ICU costs, but in the current study, mortality increased both ICU and hospital costs. Therefore, mortality did not account for our findings. While we did not study landmarks such as the ability to ambulate independently and to carry out activities of daily living, these sorts of factors are likely to contribute to post-ICU costs and may account for some of the unexplained variance in hospital costs. As the time for hospital discharge approaches, patients who are not quite ready to go home may be kept in the hospital longer while appropriate institutional placements are sought and alternative arrangements are made. In the current study, discharge to a skilled nursing facility or a rehabilitation center was associated with greater hospital costs.

Additional nonclinical factors that may contribute to hospital costs have been suggested by Denton and colleagues,11 who reported that patients assigned to a teaching service, living alone, with restricted preoperative activity, and with lower socioeconomic status were more likely to incur greater costs. Other nonclinical factors that could contribute to greater hospital costs may include systems issues, such as decreased weekend discharges. In the current study, all patients were on a teaching service, so that type of medical coverage could not be a factor. We did not have access to socioeconomic data and did not evaluate day of discharge to determine its influence on costs. Further study is needed to identify both clinical and nonclinical predictors of post-ICU and total hospital costs.

To further explain our finding that delayed extubation was associated with higher ICU costs but not higher hospital costs, we considered the possibility that comorbidity and early extubation are overlapping constructs that cannot be separated clinically. In examining our clinical practice, we observed that the presence of comorbidities, such as those measured by the Parsonnet score (Table 1) , may indeed suggest a cautious approach to early extubation for individual patients. However, we have found that in the face of favorable postoperative factors, such as adequate gas exchange and hemodynamic stability, both of which are included in the Weaning and Extubation Protocol (Table 2) , early extubation is achievable in patients with comorbidities. In fact, this flexible approach has been the basis for many successful early extubation protocols.25 26 Our finding that advanced age and the presence of early hemodynamic instability, but not number of comorbidities, predict delayed extubation support our belief that the constructs of comorbidity and early extubation can be separated clinically.21

Limitations
This study has several limitations. Because patients were not randomized to early or late extubation, it is possible that some other covariate accounts for our findings regarding the association of extubation and costs. The relatively small sample size prohibited the inclusion of a greater number of predictor variables, which may have improved the fit of the model or resulted in the identification of other variables independently associated with costs. However, the inclusion of a limited number of predictor variables increased the power of each multivariate model to detect significant predictors when they were present and reduced the likelihood of a type II error. Because costs data were obtained from the institutional data system, the validity of the findings is dependent on the accuracy of data collection in that system. Also, the generalizability of these findings is limited in that these data were obtained at a university medical center in a large metropolitan area, in which costs have been traditionally higher. Therefore, these findings may not apply to patients at community hospitals or those in less urban settings.


   Conclusion
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
References
 
In the current, highly competitive health-care marketplace, providers are continually alert to practice issues that influence costs. These findings indicate that a cumulative mortality risk score, the Parsonnet score, is an independent predictor of both ICU and total hospital costs. In addition to its use as a clinical indicator of mortality, the predictive power of the Parsonnet score regarding costs may be useful to clinicians and administrators as a means of anticipating resource utilization and as a means of cost adjustment when considering patient mix. Furthermore, these findings indicate that the popular practice of early extubation after CABG, while clinically effective, has an independent association with ICU costs only and may not be an independent cost saver in relation to overall hospital costs. Together, these findings reinforce the contention that some patients incur greater costs when undergoing CABG than others. The ability to demonstrate that higher costs are associated with patient mix, even when other factors are considered, may provide administrators with important ammunition to negotiate more realistic and specific service agreements with contracting agencies and third-party payers.


   Footnotes
 
Abbreviations: CABG = coronary artery bypass grafting; CI = confidence interval; CPB = cardiopulmonary bypass; IABP = intra-aortic balloon pump; OR = odds ratio

Received for publication August 18, 1999. Accepted for publication May 11, 2000.


   References
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
 References
 

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