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Ann Thorac Surg 1996;61:1740-1745
© 1996 The Society of Thoracic Surgeons

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Original Article: Cardiovascular

Preoperative Prediction of Postoperative Morbidity in Coronary Artery Bypass Grafting

Heart Center, Deaconess Hospital, and National Public Health Institute, Helsinki, Finland

Accepted for publication February 6, 1996.

	Abstract

Top Footnotes Abstract Introduction Patients and Methods Results Comment Appendix 1. Acknowledgments References

 
Background. The risk factors of patients selected for coronary artery bypass grafting have increased in recent years because of the aging population. Prediction of postoperative complications is essential for optimal use of the available resources. The aim of this study was to develop a scoring method for prediction of postoperative morbidity of individual patients undergoing bypass grafting.

Methods. Data from 386 consecutive patients who underwent coronary artery bypass grafting in a single center were retrospectively collected. The relationship between the preoperative risk factors and the postoperative morbidity was analyzed by the Bayesian approach. Three risk indices (15-factor and seven-factor computed and seven-factor manual models) were developed for the prediction of morbidity. The criterion for morbidity was a prolonged hospital stay postoperatively (>12 days) because of adverse events.

Results. The best predictive preoperative factors for increased morbidity were emergency operation, diabetes, rhythm other than sinus rhythm on the electrocardiogram or recent myocardial infarction, low ejection fraction (<0.49), age greater than 70 years, decreased renal function, chronic pulmonary disease, cerebrovascular disease, and obesity. The sensitivity of the scoring methods ranged from 51% to 72% and the specificity, from 77% to 86%.

Conclusions. The results show that individual patients can be stratified according to postoperative risk for complications on the basis of preoperative information that is available for most patients.

	Introduction

Top Footnotes Abstract Introduction Patients and Methods Results Comment Appendix 1. Acknowledgments References

 
The increasing use of coronary artery bypass grafting (CABG) to treat coronary artery disease sets high demands on the healthcare system. The risk factors of patients selected for CABG have increased in recent years [1]. Prediction of risk of postoperative complications is necessary for optimal use of the available resources. For individual hospitals, knowledge of the risk of delayed postoperative recovery would aid in the planning of capacity, allocation of monitoring resources, and choice of patients. Definition of the risk factors also makes it possible to compare patient populations and postoperative morbidity as well as costs in different hospitals. Risk equation would also facilitate the use of historical controls in the evaluation of new therapeutic measures.

Most studies addressing the risk factors of CABG were prompted by the need of quality control and fair comparison of the results in different hospitals. Several studies [2–9] with mortality as the end point have demonstrated preoperative risk factors for CABG and valve operations. The Coronary Artery Surgery Study group has developed a logistic risk equation for quality assurance [10]. The value of mortality as the sole end point has been questioned in the evaluation of clinical trials. Instead, morbidity has been suggested as a valid end point [11]. The aim of this study was to develop a simple, easily used preoperative risk model and to find the preoperative risk factors that best predict postoperative morbidity for CABG with special reference to prolonged hospital stay.

	Patients and Methods

Top Footnotes Abstract Introduction Patients and Methods Results Comment Appendix 1. Acknowledgments References

 
Data Collection
The study protocol was approved by the institutional review board. Data from 386 consecutive patients who underwent CABG (in 1990 and 1991) in the Heart Center of Deaconess Hospital, Helsinki, were collected retrospectively. Files for 14 of the 400 patients were not available, and those patients were excluded from the study. The following preoperative data were collected: patient age (in years) and sex, body surface area and body mass index (weight in kilograms/square of the height in meters), New York Heart Association status and history of previous myocardial infarction or previous open heart operation, evaluation of preoperative electrocardiogram (rhythm and ST segment changes), priority of the operation (emergency or elective), and cardiac catheterization data: ejection fraction, left ventricular end-diastolic pressure, number of diseased vessels from one through three, occurrence of left main stenosis, degree of congestion, and size of the heart from the chest roentgenogram. Comorbidity factors were also collected: renal insufficiency, arterial hypertension, chronic pulmonary disease, cerebrovascular disease (with previous stroke), obesity, diabetes, and other endocrine disorders. The influence of the 21 preoperative variables on the probability of a postoperative short-term outcome were studied. There were a few missing observations: serum creatinine values for 20 patients, data about diabetes for 11 patients, type of operation (emergency versus elective) for 4 patients, presence of an abnormal electrocardiogram for 2 patients, and data on chronic pulmonary disease for 20 patients.

A complicated postoperative short-term outcome was defined as a prolonged hospital stay (>12 days) after operation because of adverse events, transfer to another hospital for treatment of complications, or death during the hospital stay. The following complications led to a prolonged hospitalization: central nervous system problems including stroke and hemiparesis, postoperative infection in the sternum or a leg wound, pulmonary infection, arrhythmia requiring pacing or cardioversion, renal failure postoperatively and need of dialysis, inotropic support, or intraaortic balloon pump, or death during the hospital stay.

Statistical Methods
Univariate analysis was first accomplished using a ² technique with 2 x 2 contingency tables to determine the relationship between the preoperative risk factors.

BAYESIAN ANALYSIS.
A stepwise Bayesian approach [3, 12, 13] was used to determine the posterior probabilities and likelihood ratios and to ascertain the sensitivity and the specificity of the rule. The Bayesian method is described in Appendix 1. A receiver operating characteristics (ROC) curve was also calculated. It gives a graphic representation of the relationship between true-positive and false-positive rates and can be used to study the effect of changing the decision rule. The area under the ROC curve is commonly used to measure the predictive power of a statistical model [14].

DERIVATION OF INDICES.
We have developed an optimization process to make the Bayesian analysis easier. The program goes through all the possible variables and selects first only one variable that will best predict the selected outcome (prolonged hospital stay in our study). Then the program goes through all the remaining variables and selects as a second variable the one that will best predict the outcome together with the first variable. Adding one variable after another to the model, the program goes through all the variables. The optimum is the number of variables that together give the smallest error rate (best sensitivity and specificity) in the study group. Usually the optimum does not include all the variables. At the end, the program provides the best combination of variables that will most accurately predict the outcome. It also gives the critical area for the total risk index, where the number of false-negative and false-positive results are minimal. The program also calculates the ROC curve. The program makes an assumption that the variables are independent of each other. The model ignores the interrisk factor correlations.

We used the Bayesian optimization process to find the combination of factors that will best predict morbidity. The program selected 15 of the 21 variables for the first model. Then we deleted one factor at a time from the model and tested the result. We ended up with a seven-factor short binary model. On the basis of the information in this short model, another model that needs no computing was developed. After weighing the factors for ``risk'' (1 to 2 points) and ``no risk'' (0 points) values, we ended up with a model that has a total score of 10 and an individual score for factors between 0 and 2.

	Results

Top Footnotes Abstract Introduction Patients and Methods Results Comment Appendix 1. Acknowledgments References

 
The hospital records of 386 patients were analyzed. Patient demographics and outcomes are shown in Table 1. Of the 386 patients, 89 were in the hospital for longer than 12 days postoperatively or were transported to another hospital. Arrhythmia (mainly atrial fibrillation) was the most common reason for a prolonged hospital stay (65/89 patients) and infections (sternum, 3 patients; leg wound, 8; and pulmonary, 2), the next most common. Hemiparesis occurred in 5 patients and postoperative disorientation in 3. Two patients had low cardiac output requiring inotropic support, and another required intraaortic balloon pump treatment. One patient needed dialysis because of renal failure postoperatively. The hospital (<30 days) mortality rate was 1.6% (6/386 patients).

The results of the shorter seven-factor model are presented in Table 3. This version gave 44 false-positive and 39 false-negative results compared with the observed results (Tables 4, 5). The sensitivity of the rule was 51% and the specificity, 87%. The efficiency for the correct prediction was 79% and the total error rate, 22%. The ROC curve gave an area under the curve value of 0.787.

View this table: [in this window] [in a new window]   Table 3. . Additive Model With Values, Weights, and 2 Values for the Seven Most Important Variablesa

As an example of the manual CABDEAL, 1 of our patients had a preoperative creatinine level of 150 µmol/L (1.7 mg/dL) (2 points). He was 75 years old (1 point), and the body mass index was 29 (1 point). He also had diabetes (2 points). The operation was elective (0 point). The patient had chronic atrial fibrillation (1 point) and chronic obstructive pulmonary disease with a forced expiratory volume in 1 second of less than 50% of normal (1 point). The total score for this patient was 8. He had a prolonged hospitalization because of a wound infection in the leg and disorientation postoperatively. The patient also returned to atrial fibrillation in the intensive care unit on the first postoperative day. He was discharged from the hospital on postoperative day 15.

	Comment

Top Footnotes Abstract Introduction Patients and Methods Results Comment Appendix 1. Acknowledgments References

 
Multifactorial risk indices have been developed to estimate the risk of cardiac complications in noncardiac surgical procedures [15–17]. The index of Goldman and colleagues [15] has been in clinical use for several years. Our results suggest that the short-term postoperative outcome (morbidity) for individual patients and patient groups can be predicted with reasonable accuracy by using preoperative information available for virtually all patients scheduled for CABG. The computed models predicted postoperative complications with 51% to 72% sensitivity and more than 80% specificity depending on the number of factors. In addition, we have developed a simple manual scoring method that had a sensitivity of 56% and a specificity of 77%. This index, CABDEAL, can be used to identify individual patients with a high risk of postoperative complications.

The information about risk can be used for several purposes. The observation of high risk in an individual patient can influence the timing of the operation, the planning of the surgical procedure, the type of anesthesia (fast-track versus non-fast-track), and the resource allocation for postoperative observation and treatment or hospital discharge plan (``7-day package'' versus longer stay). It may help in comparing the results over time to estimate the impact of changes in therapeutic procedures. The index can also be used to compare the immediate results of CABG in different institutions. This kind of comparison is not possible without normalizing the risk factors in the patient populations treated in individual hospitals. The risk index may also provide an opportunity to estimate the treatment costs of a patient population.

The validity of the present findings for CABG patients in other hospitals and countries remains to be tested. Our study population consisted of patients with a wide variety of factors that influence outcome. The risk factor profile of the patients was typical for patients referred for CABG. Patients were treated by a small, experienced team, and this diminished the variability resulting from perioperative factors. The influence of local factors on the results cannot be excluded, and therefore the index has to be validated in other patient populations.

Many previous studies [2–10, 18, 19] have focused on postoperative mortality, whereas only a few studies have focused on short-term morbidity and complications. One previous study [20] measured outcome in patients after a cardiac operation on the basis of length of stay in the intensive care unit. One recent review article [21] concluded that there are several indices available to predict mortality from different institutions. However, there is a lack of easy-to-use models predicting individual risk for complications (morbidity). Our study gives an option for individual risk stratification of morbidity. Table 7 compares the present results with those of four previous studies. All risk factors except the electrocardiographic abnormalities were found in at least two of the other studies. Emergency operation, renal dysfunction, diabetes, and pulmonary disease were identified as strong risk factors in at least three studies, including ours. Using univariate analysis and logistic regression analysis to relate risk factors for perioperative morbidity and mortality, Higgins and associates [22] studied a large CABG patient group. Their outcome measures were mortality and severe complications. Several of the risk factors were found in their study and our study: emergency procedure, elevated serum creatinine level, chronic pulmonary disease, and diabetes mellitus. Higgins and associates [22] also used an additive scoring system comprising 14 weighted factors that were graded from 1 through 6 giving a maximum total score of 33 points. Our manual scoring system had only seven factors that were graded 0 through 2 to give a maximum score of 10. The specificity and sensitivity of the two simplified risk scores were comparable. However, the CABDEAL model is easier to use at bedside for the evaluation of individual patients.

In conclusion, patients selected for CABG can be stratified according to their risk of postoperative complications. This knowledge can be used both for patient groups and for individual patients.

	Appendix 1.

Top Footnotes Abstract Introduction Patients and Methods Results Comment Appendix 1. Acknowledgments References

 
The Bayesian approach states how posterior probability P(D'x) is calculated using the prior probabilities P(D) and P(D`), which show how common the diagnosis D and its absence are in the population. The probabilities of symptoms given the diagnosis P(x'D) and P(x'D`) are commonly estimated by their frequencies in learning material [12, 13]. In the case of a two-valued outcome, the Bayesian approach is commonly developed to give a likelihood ratio. The likelihood ratio (L) of a set of observed symptoms of properties x_i is written as the product of the probability ratios of each x_i, also known as risk ratios in epidemiology:

L=i[P(xi'D)/P(xi'D`)]

Posterior probability = L(x)/[1+L(x)]

The likelihood ratios are expressed in the tables as 10 x 10 based logarithms of the original values. A constant 10 has been added: 10 x [1 + log (L)]. If any risk factor value is missing, an estimate of 10 has been used. That will equal a likelihood value of 1.0. Thus, a missing value is interpreted as ``no risk.'' Similarly, missing values were given a value of 0 in the manual CABDEAL model.

	Acknowledgments

Top Footnotes Abstract Introduction Patients and Methods Results Comment Appendix 1. Acknowledgments References

 
We thank the personnel of the Cardiac Intensive Care Unit, Helsinki Heart Center, for helping with data collection.

	Footnotes

Top Footnotes Abstract Introduction Patients and Methods Results Comment Appendix 1. Acknowledgments References

 
Address reprint requests to Dr Kurki, Lukupolku 19, Helsinki 00680, Finland.

	References

Top Footnotes Abstract Introduction Patients and Methods Results Comment Appendix 1. 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 Permission Requests Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Kurki, T. S. O. Articles by Kataja, M. Search for Related Content PubMed PubMed Citation Articles by Kurki, T. S. O. Articles by Kataja, M.