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Ann Thorac Surg 1996;61:17-20
© 1996 The Society of Thoracic Surgeons

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Original Articles: Cardiovascular

No Continuous Relationship Between Veterans Affairs Hospital Coronary Artery Bypass Grafting Surgical Volume and Operative Mortality

Denver Veterans Affairs Medical Center, Divisions of Cardiothoracic Surgery, Internal Medicine, and Cardiology, University of Colorado School of Medicine, Denver, Colorado, and Department of Statistics, Catholic University of Chile, Santiago, Chile

Accepted for publication August 23, 1995.

	Abstract

Top Footnotes Abstract Introduction Material and Methods Results Comment Acknowledgments References

 
Background. The purpose of this study was to determine whether risk-adjusted coronary artery bypass grafting mortality rates are significantly related to coronary artery bypass grafting surgical procedure volume within the Department of Veterans Affairs hospital system.

Methods. From April 1987 to September 1992, expected mortality rates were calculated for 23,986 coronary artery bypass grafting procedures performed at 44 different Veterans Affairs hospitals.

Results. This study found a statistically significant relationship between annual hospital coronary artery bypass grafting volume and observed mortality rates (p < 0.02). However, no statistically significant relationship between coronary artery bypass grafting volume and risk-adjusted operative mortality was found (p = 0.10). Using analysis of variance on hospital-level data, hospitals with 100 or less cases per year have higher observed to expected mortality ratios than hospitals performing more than 100 cases per year (p = 0.03). Using Poisson regression models, however, a volume threshold could not be found.

Conclusions. These findings are consistent with the current Veterans Affairs policy requirements to periodically review quality at low-volume hospitals.

	Introduction

Top Footnotes Abstract Introduction Material and Methods Results Comment Acknowledgments References

 
The processes and structures of cardiac surgical care that directly affect patient care outcomes associated with coronary artery bypass grafting (CABG) procedures are largely undocumented [1, 2]. Procedure volume may be an indirect measure of the processes or the structures of care. Previous research has demonstrated a strong inverse curvilinear relationship between CABG procedure volume and observed mortality rates [3–11]. High

volume hospitals have been reported to have better surgical outcomes than low-volume hospitals.

Recent studies by Hannan and colleagues [12, 13] indicated that hospital volume is an independent predictor of cardiac surgical mortality, even after adjustment for patient clinical risk factors using logistic regression analysis. The policy implications of these findings suggest that either CABG program regionalization or selective referral of CABG procedures to low-mortality hospitals may be used as strategies to improve the quality of patient care for cardiac surgery patients.

See also pages 12 and 21.

To explore the potential policy implications of these previous research findings within the Department of Veterans Affairs (VA), this study was designed to determine whether risk-adjusted CABG mortality rates for patients operated in VA medical centers are significantly related to CABG surgical procedure volume.

	Material and Methods

Top Footnotes Abstract Introduction Material and Methods Results Comment Acknowledgments References

 
Patient data for clinical risk factors and 30-day operative mortality were obtained from the VA Continuous Improvement in Cardiac Surgery Study for 23,986 CABG patient records. These CABG procedures were performed at 44 different VA hospitals from April 1987 to September 1992. Data collection methods and the logistic regression analysis techniques used to calculate patient-specific risk estimates for operative mortality have been published previously [14–16]. The seven risk variables extracted from the Continuous Improvement in Cardiac Surgery Study for this analysis were chosen based on the most recently developed CABG predictive model. The patient risk factors contained in the stepwise logistic regression model developed for the prediction of CABG operative mortality during this period (where their p values were less than 0.0001) are as follows:

• Previous heart operation
  
• Surgical priority
  
• New York Heart Association functional class
  
• Age
  
• Pulmonary rales
  
• Peripheral vascular disease
  
• Cerebral vascular disease
  

As logistic regression requires that complete data be available for every variable used in the analysis, 1,965 records (6.8%) were dropped from the multivariate analysis performed. In general, this represents a fairly high completeness rate of data (93.2%) across the seven key fields. Thus, data imputation techniques to adjust for missing data were not used in this analysis.

The hospital's annual CABG volume was added to the CABG predictive model as an independent variable to determine whether a relationship between hospital volume and CABG risk-adjusted mortality existed at the patient-level unit of analysis. The purpose of this special analysis was to determine in a multivariate context whether volume was a statistically significant predictor of outcome. Also, the relationship between unadjusted mortality and age-adjusted mortality rates and hospital volume was explored. The c-index, a performance measure of a risk model's predictive power, was calculated [17]. The c-index represents the area under the receiver operating characteristic curve (which reflects the relative specificity and sensitivity of the model in predicting risk of operative death). Theoretically, the c-index may range from 0 to 1. The actual range is between 0.5 and 1.0, where a model with a c-index of 0.5 is useless for prediction purposes and a model with a c-index of 1.0 provides a perfect prediction.

Finally, patient data were aggregated to the hospital unit of analysis. The observed to expected mortality ratio (O/E) was calculated for each hospital for each year. Hospitals were classified according to their CABG annual procedure volume. A threshold analysis was performed to determine whether a volume O/E breakpoint could be identified [18]. The threshold analysis searched for the optimal volume breakpoint that maximized the difference in risk-adjusted mortality rates (O/E ratio) between low- and high-volume hospitals using a sequential analysis of variance technique. Analysis of variance is a classic method to compare differences in the means (in this case O/E ratio means) across multiple samples (in this case high/low volume groups of hospitals).

Poisson additive models were used to model the functional relationship between O/E ratio and hospital volume for each of the 5 years included in this study [19]. This analysis has several advantages compared with classic regression techniques such as linear regression models. Classic regression models assume that the observations have constant variance and all relevant tests associated with classic models assume that the response variable, in this case the O/E ratio, has a normal distribution. In examining the distribution of O/E ratio versus hospital volume, both assumptions are clearly violated, invalidating the posterior conclusions of such an analysis.

The Poisson additive models assume that the response variable is count data and the variance changes proportionally to the mean response. The observed number of deaths were modeled as count data using volume as a covariate, and the expected number of deaths were accounted in the model as an offset term on the logarithmic scale of the Poisson model.

Another advantage of the Poisson additive model is that flexible curves may be used to fit the data, rather than forcing linear or exponential decay relationships to risk-adjusted morality rates and volume data. The Poisson additive models will be more sensitive to a gradual and smooth change in the relationship between risk-adjusted mortality rates and volume, whereas the threshold analysis requires a sharp distinct change in this relationship.

Poisson additive models were fitted considering volume as the only covariable for each of the 5 years of the study. Initially, models were fit by forcing a linear and logarithmic effect of volume. In addition, models were fitted using two linear lines that allowed a break in the slope to determine whether a volume threshold existed.

	Results

Top Footnotes Abstract Introduction Material and Methods Results Comment Acknowledgments References

 
Patient Unit of Analysis
With no adjustment for patient clinical risk factors or with age adjustment only, a significant relationship between hospital volume and mortality rates was found (p < 0.02). The c-index for the models with no risk adjustment and age adjustment only was 0.521 and 0.590, respectively. However, the hospital's annual CABG volume was not a statistically significant predictor of operative mortality when added to the multivariate risk model (p = 0.10). The c-index of the multivariate model was 0.722.

Hospital Unit of Analysis
The threshold analysis using sequential analysis of variance indicates that hospitals with CABG volume of 100 cases or less per year have significantly higher O/E ratios (mean O/E ratio, 1.26) in comparison with hospitals with more than 100 cases per year (mean O/E ratio, 0.95) (p = 0.03). Although other breakpoints were explored, classifying hospitals by CABG volume at the breakpoint of 100 patients per year exhibited the maximal statistical significance between groups.

The prevalence of the risk factors predictive of operative death were compared between the two hospital volume groups (Table 1). A greater frequency of occurrence for all patient risk characteristics associated with higher risk of operative death was observed in the low-volume hospital group. The largest difference was observed for the urgent/emergent patient risk characteristic. These summary data, therefore, indicate that the low-volume hospitals perform CABG procedures on generally higher risk patients as well as exhibit generally higher operative mortality rates.

	Comment

Top Footnotes Abstract Introduction Material and Methods Results Comment Acknowledgments References

 
To optimize cardiac surgical program patient care outcomes, it is important to identify if a volume/outcome relationship exists. Using observed mortality rates, which are not adjusted for patient risk factors, may assess provider performance inaccurately. Assessing the impact of volume on provider performance should be done within the context of a multivariate analysis to examine if a potential bias exists in the distribution of patient risk characteristics across hospital-volume categories.

This study found a statistically significant relationship between both unadjusted mortality rates and age-only adjusted mortality rates with annual CABG volume. However, this study did not identify a statistically significant relationship between risk-adjusted mortality rates and hospital volume. As part of a search to determine a threshold for quality review, a break point of 100 cases per hospital per year was found using analysis of variance. However, alternative analytic techniques using Poisson regression analysis did not support this finding.

In examining Figure 1, it is evident that several low-volume hospitals performed equivalent to or better than the high-volume hospitals based on their annual O/E ratios. In contrast, some low-volume hospitals performed worse than the high-volume hospitals. This inconsistency in performance among the group of low-volume hospitals warrants continued monitoring to assure acceptable quality of cardiac surgical care. Hence, the existing VA policies established by the Cardiac Surgery Consultants Committee (where cardiac surgical programs with less than 100 cardiac surgical cases per year for a consecutive 2-year period may be subject to a site visit to assess their quality performance) appear appropriate.

View larger version (12K): [in this window] [in a new window]   Fig 1. . Risk-adjusted mortality rates by hospital volume category for Veterans Affairs coronary artery bypass grafting patient records from April 1987 to March 1992. (O/E = observed to expected.)

The findings of this study imply that different degrees of risk adjustment may potentially lead to divergent conclusions regarding the volume-outcome relationship for CABG. This study, however, has several important limitations that may restrict the generalizability of these findings. A very small percentage of VA hospitals perform less than 100 cases per year due to existing quality assurance review policies. In addition, the volume of CABG procedures performed at a VA hospital may reflect only a portion of the cardiac surgeon's (ie, faculty's and resident's) workload as surgeons usually operate at the affiliated university center. Finally, we have not collected physician-specific identifier information and cannot assess whether physician-volume and risk-adjusted mortality are related.

Given the increased raw and risk-adjusted operative mortality rates at VA centers performing 100 or less CABG procedures per year, the existing VA criteria and standards (related to the minimum volume requirement of 100 cases/year) appear to be appropriate. Given the conflicting results of the different analytic methods used to determine whether a volume threshold exists, caution should be used in the application of minimum volume requirements as a measure to assure quality of care. We believe that volume criteria are more appropriately used as a screening measure to initiate a more careful, in-depth review of provider quality.

	Acknowledgments

Top Footnotes Abstract Introduction Material and Methods Results Comment Acknowledgments References

 
This work was supported by both the Office of Quality Management and the Health Services Research and Development Service, Department of Veterans Affairs.

	Footnotes

Top Footnotes Abstract Introduction Material and Methods Results Comment Acknowledgments References

 
Address reprint requests to Dr Shroyer, Cardiac Research (151K), Denver VA Medical Center, 1055 Clermont St, Denver, CO 80220.

	References

Top Footnotes Abstract Introduction Material and Methods Results Comment Acknowledgments References

 

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This Article Abstract 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): Frederick L. Grover Karl E. Hammermeister Permission Requests Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Shroyer, A. L. W. Articles by Hammermeister, K. E. Search for Related Content PubMed PubMed Citation Articles by Shroyer, A. L. W. Articles by Hammermeister, K. E. Related Collections Related Articles