Resource utilization in coronary artery bypass operation: does surgical risk predict cost?

Riordan, Christopher J.; Engoren, Milo; Zacharias, Anoar; Schwann, Thomas A.; Parenteau, Gary L.; Durham, Samuel J.; Habib, Robert H.

doi:10.1016/s0003-4975(99)01562-3

Cited by 35 publications

(30 citation statements)

References 5 publications

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“…After including postoperative complications, they were able to develop a better, but far from perfect, model. Similar results at a single institution were found using STS PROM [21]. …”

Section: Commentsupporting

confidence: 83%

Bundled Payments in Cardiac Surgery: Is Risk Adjustment Sufficient to Make It Feasible?

Yount

Isbell

Lichtendahl

et al. 2015

The Annals of Thoracic Surgery

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Background Policymakers have proposed risk-adjusted bundled payment as the single-most promising method of linking reimbursement to value rather than to quantity of service. Our objective was to assess the relationship between risk and cost to develop a model for forecasting cardiac surgery costs under a bundled payment scheme. Methods All patients undergoing adult cardiac surgery operations for which there was a Society of Thoracic Surgeons (STS) risk score over a 5-year period (2008–2013) at a tertiary care, university hospital were reviewed. Patients were stratified into 5 groups based on preoperative risk as a basis for negotiating risk-adjusted bundles. A multivariable regression model was developed to analyze the relationship between risk and log-transformed costs. Monte Carlo simulation was performed to validate the model by comparing predicted to actual FY2013 costs. Results Among the 2514 patients analyzed, preoperative risk was strongly correlated with costs (p<0.001) but was only able to explain 28% (R2=0.28) of the variation in costs between individual patients. Using bundling to diffuse and adjust for risk improved prediction to only 33% (R2=0.33). Actual costs in 2013 were $21.6M compared to predicted costs of $19.3M (±$350K), which is well outside the forecast’s 95% confidence interval. Conclusion Even among the most routine cardiac surgery operations using the most widely validated surgical risk score available, much of the variation in costs cannot be explained by preoperative risk or surgeon. Consequently, policymakers should re-examine whether individual practices or insurers are best suited to manage the residual financial risk.

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“…After including postoperative complications, they were able to develop a better, but far from perfect, model. Similar results at a single institution were found using STS PROM [21]. …”

Section: Commentsupporting

confidence: 83%

Bundled Payments in Cardiac Surgery: Is Risk Adjustment Sufficient to Make It Feasible?

Yount

Isbell

Lichtendahl

et al. 2015

The Annals of Thoracic Surgery

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“…All studies mentioned above [2-7,20,21] showed a good relationship between hospital costs and length of stay in the ICU, which was also confirmed by our study (r = 0.94, p = 0.0001, Figure 2). Time in the operating room (OR) is the most expensive part of hospitalization, however, OR time is relatively uniform between patients.…”

Section: Discussionsupporting

confidence: 90%

“…A similar finding was reported by Riordan et al [21], who demonstrated that the STS model is a poor predictor of costs on an individual patient level. The correlation coefficient in this study was r = 0.34.…”

Section: Discussionsupporting

confidence: 89%

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

Badreldin

Doerr

Kroener

et al. 2013

J Cardiothorac Surg

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BackgroundPreoperative 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.MethodsOver 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.ResultsA 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).ConclusionThe 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.

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“…Applying risk adjustment to resource utilization reduced the range of mean total charges and charges per day, but there was little change in the range when we applied risk adjustment to LOS, though relative hospital rankings were shuffled in the process. Though other studies have shown a positive correlation between surgical risk and resource consumption (Kurki et al, 2001;Nilsson et al, 2005;Pinna Pintor et al, 2003;Riordan et al, 2000), we reasoned that there are likely to be other factors that are not associated with increased risk to the patient but which are associated with variation in charges and LOS. This may be particularly true in Japan where LOS tends to be very long, and where patient and physician preference plays such a large role in determining discharge dates (Hirose et al, 2005).…”

Section: Discussionmentioning

confidence: 88%

Risk adjusted resource utilization for AMI patients treated in Japanese hospitals

et al. 2006

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Though risk adjustment is necessary in order to make equitable comparisons of resource utilization in the treatment of acute myocardial infarction patients, there is little in the literature that can be practically applied without access to clinical records or specialized registries. The aim of this study is to show that effective models of resource utilization can be developed based on administrative data, and to demonstrate a practical application of the same models by comparing the risk-adjusted performance of the hospitals in our dataset. The study sample included 1748 AMI cases discharged from 10 large, private teaching hospitals in Japan, between 10 April 2001 and 30 June 2004. Explanatory variables included procedures (CABG and PCI), length of stay, outcome, patient demographics, diagnosis and comorbidity status. Multiple linear regression models constructed for the study were able to account for 66.5, 27.7, and 58.4% of observed variation in total charges, length of stay and charges per day, respectively. The performance of models constructed for this study was comparable to or better than performance reported by other studies that made use of explanatory variables extracted from clinical data. The use of administrative data in risk adjustment makes broad scale application of risk adjustment feasible.

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Resource utilization in coronary artery bypass operation: does surgical risk predict cost?

Cited by 35 publications

References 5 publications

Bundled Payments in Cardiac Surgery: Is Risk Adjustment Sufficient to Make It Feasible?

Bundled Payments in Cardiac Surgery: Is Risk Adjustment Sufficient to Make It Feasible?

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

Risk adjusted resource utilization for AMI patients treated in Japanese hospitals

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