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Ann Thorac Surg 1997;64:410-413
© 1997 The Society of Thoracic Surgeons

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Original Articles: Cardiovascular

Risk Stratification for Open Heart Operations: Comparison of Centers Regardless of the Influence of the Surgical Team

Arturo Pinna Pintor Foundation–Torino and Cardiac Surgery Department, Spedali Civili–Brescia, Torino, Italy

Accepted for publication January 30, 1997.

	Abstract

Top Footnotes Abstract Introduction Material and Methods Results Comment Acknowledgments References

 
Background. Risk-adjusted mortality was previously used to compare institutions as a whole or surgeons. Because the same surgical team is working in two different hospitals, the aim of our study was to assess whether the institution can make a difference in surgical mortality.

Methods. Preoperative data of 554 patients in institution A and 500 in institution B were prospectively collected during the same period of time. All patients were operated on by the same surgeon with the same first assistant and anesthesiology staff in both institutions. Patient population was stratified according to Parsonnet's predictive model, in five risk groups, and mortality was adjusted by the direct standardization method.

Results. At institution A it was observed that in-hospital mortality was 2.3% (95% confidence interval, 1.3% to 4.0%), and in institution B 4.0% (95% confidence interval, 2.5% to 6.1%). The difference between the two mortality rates (1.7%; 95% confidence interval, -0.5% to 3.8%) is not statistically significant (p = 0.16), nor is the difference within each class. The standardized mortality ratio was 3.6% (95% confidence interval, 2.7% to 4.8%) and 5.8% (95% confidence interval, 4.6% to 7.2%), respectively. The difference of 2.2% (95% confidence interval, 0.5% to 3.8%) is statistically significant (p = 0.01).

Conclusions. The institution can affect mortality of patients undergoing open heart operations, regardless of the influence of the surgical team.

	Introduction

Top Footnotes Abstract Introduction Material and Methods Results Comment Acknowledgments References

 
There is increasing interest in the preoperative assessment of the operative risk of cardiac operations to predict the mortality rate for patients referred to cardiac operation and to make feasible and effective comparisons between individual surgeons, surgical teams, hospitals, and countries [1], as well as within the same surgical institution in the course of the years. During the past 15 years several predictive methods have been developed [2–10] to predict the risk of in-hospital mortality after coronary artery bypass grafting or cardiac operations. These methods have been used to compare the quality of care in different institutions or surgeons to allow patients, purchasers, or insurance companies to select hospitals not only on structural or process criteria, but on outcome assessment. Risk prediction is also useful to decide between different therapeutic strategies (medical, surgical, or angioplasty).

Surgical mortality is the most reliable outcome used to assess the quality of surgical institutions or individual surgeons. However, it is well known that the crude mortality rate does not take into account the severity of the patients' conditions and because of case-mix confounders it can be viewed as a misleading measure of quality of care. It is possible to make objective comparisons between different institutions only by stratifying patients into different risk groups. In previous studies risk-adjusted mortality was used to compare different institutions as a whole. The relation between hospital setting and hospital mortality also have been previously studied [11, 12]. Because our same surgical team is working in two different hospitals, the aim of our study was to assess whether the institution itself can make a difference in surgical mortality.

	Material and Methods

Top Footnotes Abstract Introduction Material and Methods Results Comment Acknowledgments References

 
According to the first version of the method of Parsonnet and colleagues [3], we stratified patients undergoing cardiac operations for 15 clinical and angiographic preoperative variables. This predictive model represents an easy, quick, and effective method of presurgical patient stratification, and it was demonstrated to be associated with 30-day mortality after cardiac operation.

From January 1992 to December 1993, we collected data on 554 patients in institution A and 500 in institution B. Institution A is a private hospital with 100 beds; as there is no emergency room, patients are operated on by reservation and they are admitted to the ward on the day before the operation. Institution B is a public hospital with 400 beds; most patients are referred from the cardiology department and few undergo operation on an emergency basis. In both institutions, all patients were operated on by the same surgeon with the same first assistant and anesthesiology staff. Patient population was stratified according to Parsonnet's predictive model, in five risk groups: good (predictive mortality 0% to 4%), fair (5% to 9%), poor (10% to 14%), high (15% to 19%), and extremely high risk (20%). Two physicians were trained to review patient charts and to collect information from the 15 predictive variables. Each one scored the information gathered in one institution, but in case of doubt the score was discussed between the two physicians and a third investigator. An effort was made to avoid missing data. In case of few variables (eg, diabetes), we assumed that the missing information on the chart was equivalent to absence of the variable. In-hospital mortality was defined as any death that occurred within 30 days of operation or during the same hospitalization, regardless of its cause.

All patients were treated by hypothermic cardiopulmonary bypass with a membrane oxygenator. Cardiac arrest was obtained by anterograde (up to 1993) and retrograde cold crystalloid cardioplegia (from 1993). Warm blood reperfusion was adopted in patients with a low ejection fraction.

Statistical Analysis
To adjust the overall mortality rate by patients' severity, we adopted the direct standardization method [13, 14]. This type of standardization process is commonly used to adjust for the demographic disparities of age and sex that may distort epidemiologic comparisons of mortality rate. The five risk class-specific death rates of both institutions were applied to the surgical population described by Parsonnet and the number of deaths that would be expected if each institution had the same risk class structure of the standard population were obtained. The total number of expected deaths for each institution was then divided by the total population to yield the standardized death rate. The comparative mortality ratio was calculated by dividing the standardized mortality rate of institution B by the one obtained in institution A. The between-group comparison of both crude and standardized mortality rates was done with the test for two proportions.

	Results

Top Footnotes Abstract Introduction Material and Methods Results Comment Acknowledgments References

 
Observed Mortality
Five hundred fifty-four patients were operated on at institution A and 500 at institution B. The principal characteristics of both populations are listed in Table 1. In institution A patients were older (60 ± 11 years versus 55 ± 13 years), fewer of them were women, they had a higher rate of diabetes and hypertension, and there were fewer valvular reoperations and shorter extracorporeal and ischemic times.

	Comment

Top Footnotes Abstract Introduction Material and Methods Results Comment Acknowledgments References

 
In-hospital mortality is one of the most important short-term outcome measures and is applied to evaluate the quality of care. However, the crude mortality rate is affected by case-mix severity and the risk-adjusted mortality rate must be used to compare individual surgeons or institutions. Statistical standardization is an appealing and powerful tool to compare results from different sources, but comparison remains a difficult challenge because of a less than perfect adjustment. Very recently preoperative risk stratification was also used in our institution to predict hospital charges of coronary artery bypass grafting [15].

In previous studies mortality was used to measure the quality of surgical institutions evaluated as a whole (surgeons, anesthesiologists, technicians, equipment, postoperative intensive and ward care). Because we had the opportunity to study two groups of patients operated on by the same surgical and anesthesiology staff in two different institutions, we decided to compare the importance of the institution without the influence of the surgical team, as a variable that can affect mortality. The crude mortality rate demonstrates a slightly higher death rate in the patients operated on in institution B (4.0%) than in institution A (2.3%), but the difference between them did not reach the value of statistical significance. When data were adjusted by the preoperative severity of conditions and compared with a population used as standard, the difference in mortality rate (5.8% versus 3.6%, respectively) was statistically significant. The higher difference after adjustment is attributable to the fact that the crude mortality rate in each risk class is higher in institution B than in institution A. This is a very interesting example of the role of adjustment that makes a real difference in the conclusion of the study. Our results became a challenge to evaluate the structure and procedure reasons why institution B performed worse and to elicit a discussion about the ways to increase quality of care.

Our study has three major limitations. First, we used Parsonnet's predictive model. We could have reached different results by adopting a different model to adjust the mortality rate in the two populations, because different variables and different cut-offs are taken into account. We are far from having a gold standard; the proliferation of presurgical predictive methods is an indication of the inadequacy of the available models. The first version of Parsonnet's method is based on a surgical population operated on about 15 years ago. The mortality observed in that data base is higher than the one usually obtained at present. The problem of the transferability of predictive models to other countries, other institutions, or other times must be taken into consideration.

Second, the two institutions have a different pattern of referred patients. In institution A elective patients are usually operated on, whereas in institution B emergency operations account for several patients. According to risk classification 56% of patients in institution A were in a low-risk class versus 29% in institution B. Recently, we demonstrated that three predictive scoring systems are less accurate for higher risk patients.

Third, even if the two investigators who collected the data were trained to categorize consistently the data from the charts, we cannot exclude a systematic bias that could have affected the estimate of preoperative patient severity of condition, particularly in the assessment of catastrophic conditions. For technical reasons it has not been possible to have the charts reviewed by both investigators. Most variables are strictly defined and they can be objectively categorized. However, in the first version of Parsonnet's model, catastrophic conditions can be freely estimated by the surgeon, and the estimates from two observers could differ from one to another.

In conclusion, by adjusting the mortality data of patients operated on in two institutions by the same surgical staff, we have been able to demonstrate that the institution can affect the mortality of patients undergoing open heart operations, regardless of the influence of the surgical team. Our results have become a good reason to reassess the procedures in institution B.

	Acknowledgments

Top Footnotes Abstract Introduction Material and Methods Results Comment Acknowledgments References

 
We thank the anesthetists, perfusionists, and nurses in the cardiac theaters for their help in carrying out this study. We also thank Mrs Piera Colonna and Maddalena Caviglia for their skillful secretarial assistance.

	Footnotes

Top Footnotes Abstract Introduction Material and Methods Results Comment Acknowledgments References

 
Address reprint requests to Dr Pinna-Pintor, Clinica Pinna Pintor, Via Vespucci 61, 10129 Torino, Italy.

	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): Luca Sandrelli Francesco Patané Ottavio Alfieri Permission Requests Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Pinna-Pintor, P. Articles by Alfieri, O. Search for Related Content PubMed PubMed Citation Articles by Pinna-Pintor, P. Articles by Alfieri, O.