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Eur J Cardiothorac Surg 2004;26:12-17
© 2004 Elsevier Science NL

The RACHS-1 risk categories reflect mortality and length of hospital stay in a large German pediatric cardiac surgery population

^a Department of Pediatric Cardiology and Intensive Care, Hannover Medical University, Hannover, Germany
^b Department of Cardiology, Children's Hospital, Boston, MA, USA
^c Department of Biometry, Hannover Medical University, Hannover, Germany
^d Pediatric Cardiology Practice, Hannover, Germany
^e Department of Thoracic, Cardiac and Vascular Surgery, Hannover Medical University, Hannover, Germany

Received 9 February 2004; accepted 29 March 2004.

^* Corresponding author. Department for Pediatric Cardiology and Intensive Care, Hannover Medical University, D-30625 Hannover, Carl-Neuberg-Strasse 1, Germany. Tel.: +49-511-523-9424; fax: +49-511-532-8419
e-mail: boethig{at}thg.mh-hannover.de

	Abstract

Top Abstract 1. Introduction 2. Patients and methods 3. Results 4. Discussion 5. Conclusion References

 
Objectives: The Risk Adjusted classification for Congenital Heart Surgery (RACHS-1) was published in January 2002, based on 4370 operations registered by the Pediatric Cardiac Care Consortium. It is designed for being easily applicable also for retrospective analysis of hospital discharge data sets; the classification was not developed for patients with heart transplantations, ventricular assist devices or patients above 18 years. We apply this classification to our 2368 correspondent procedures that were performed consecutively on 2223 patients between June 1996 and October 2002 in Bad Oeynhausen and analyze its relation to mortality and length of hospital stay. Methods: The procedures were grouped by the 6 RACHS-1 categories. Groping criteria were mainly the performed procedures; for few procedures age or diagnoses are needed in addition. The classification process itself took less than 10 working hours. Risk group frequencies in our/ the PCCC population were 1: 368/964 (15.5%/22.0%), 2: 831/1433 (35.1%/33.1%), 3: 744/1523 (31.4%/34.7%), 4: 284/276 (12.0%/6.3%), 5: 4/4 (0.2%/0.1%), 6: 137/168 (5.3%/3.8%). 18.8%/19.2% were under 1 month, 37.5%/31.6% 1–12 months of age, respectively. Results: Hospital mortality (%) in our population/ the PCCC Group 1–6 was: 0.3/0.4, 4.0/3.8, 5.6/8.5, 9.9/19.4, 50.0/0, 40.1/47.7%. Geometric means of total (13.1, 19.6, 23.5, 29.1, 31.5, 52.6 days, respectively) and postoperative length of stay of survivors show significant differences between the single risk groups. The prediction capacity of the score as expressed by the area under the receiver–operator curve was nearly equal to the value found for the American hospital discharge data sets. Length of stay rises exponentially with the RACHS-1 category. However, the RACHS-1 category explains only 13.5% of the total and 16.8% of individual postoperative lengths of hospital stay in survivors. Conclusion: The RACHS-1 classification is applicable to European pediatric populations, too. Category Distribution, outcome class distinction capacity, distribution and mortality are similar. RACHS-1 is able to classify patients into significantly different groups concerning total and postoperative hospital stay duration, although there remains a large variability within the groups.

Key Words: Congenital cardiac surgery • Scoring system • Mortality • Risk adjustment

	1. Introduction

Top Abstract 1. Introduction 2. Patients and methods 3. Results 4. Discussion 5. Conclusion References

 
In January 2002, The Journal of Thoracic and Cardiovascular Surgery published an article entitled ‘Consensus based method for risk adjustment for surgery for congenital heart disease’ [1]. The expertise of a panel of 11 clinically and statistically experienced pediatric surgeons and cardiologists led to a risk classification for surgical correction of cardiac defects. The data used to develop the resulting score system Risk Adjustment in Congenital Heart Surgery (RACHS-1) were 4370 procedures performed in 1996 in 32 hospitals that belonged to the Pediatric cardiac care Consortium (PCCC [2]) and 3646 American hospital discharge data (HDD) sets of 1994 and 1995. Six mortality risk groups were differentiated, based on a few clearly defined criteria. Classification criteria were operative procedures and age; prematurity, major non-cardiac malformation and the distinction between single or combined procedure were found to be of minor importance for precise outcome prediction. We apply the classification to the Bad Oeynhausen pediatric cardiac surgery patients. They were grouped only by procedure and age. In the described PCCC or the validation dataset, the omission of those complementary criteria did not result in relevant loss of predictive information. Besides the direct outcome comparison, we looked for correlations between risk category and length of hospital stay.

	2. Patients and methods

Top Abstract 1. Introduction 2. Patients and methods 3. Results 4. Discussion 5. Conclusion References

 
From July 1996–October 2002, 3064 operative procedures (besides interventions) were performed in patients under 18 years at the Heart Center North-Rhine Westphalia in Bad Oeynhausen. 2368 of them fulfilled the criteria for inclusion in the RACHS-1 evaluation: secondary thorax closures, pacemaker implantations or revisions, rethoracotomies for (suspected or actual) bleeding, ventricular assist device procedures or heart and/or lung transplantations were excluded, as well as neonates under 30 days with patent ductus arteriosus as an isolated cardiac defect. Six hundred and seventy-nine procedures (29.1%) were combined procedures. The pediatric cardiac surgeon described the performed procedure, which was coded by an experienced surgeon. Both procedure description and code were recorded in the administrative database; the patients' official discharge status was recorded in the same place. 63.1% were performed by the first surgeon. The higher the risk class, the higher the proportion of procedures performed by the first surgeon, arriving at 86.1% in category 6 (Fig. 1) .

View larger version (17K): [in this window] [in a new window]   Fig. 1. The fraction of patients operated by the main surgeon is displayed groupwise, setting all patients of a certain risk group to 100%. The more complicated the procedure was, the higher the percentage of patients that was operated by the main surgeon.

The classification into the 6 RACHS-1 risk categories [1] was done by ordering the operations by procedure code, then by manual confirmation of each single procedure.

Data acquisition was performed without difficulty and in a timely fashion. If several procedure steps took place during one hospital stay (this was the case in 2.3%, 53 of 2368 procedures), 2/3 of the interim period were attributed to the previous and 1/3 to the following procedure. This took into account the fact that the vast majority of this small subgroup underwent planned reoperations, namely staged reconstructions aiming at a Fontan circulation. Such patients were occasionally not in a very stable state. Some patients preferred short discharge intervals between the Norwood operation and the upper cavopulmonary anastomosis, and they were frequently re-hospitalized for recompensation. Others—those who are the discussed group—preferred to stay in the hospital all the time, even if they might have been discharged during stable periods. We admit that the described division is not based on hard criteria, but the decisions that underly such a policy are not that clearly defined either. Finally, we decided to adopt this pragmatic way of handling the interim hospital stay durations because it did not really matter: the results did not change significantly since the number of patients or hospital days in question was quite small.

We assigned patients with combined procedures to the riskiest of the different procedures that were performed, as done in the PCCC group. The entire group of 2368 patients comprised 42 patients with atypical procedures; we considered them as combined procedures and grouped them with the riskiest part of the operation.

Statistical methods: SPSS 11.5.1 was used for the analyses.

The classification system's sensitivity and specificity to predict mortality was calculated and compared to the PCCC estimation with the area under the curve of the Receiver operating characteristic.

The Mann–Whitney U-test was used to compare the distribution of RACHS-1 risk classes in different patient populations.

For comparisons of lethality between groups we used ² tests.

Since the length of hospital stay (total and postoperative time) was not distributed normally, but had a skewed normal distribution, we did a log transformation before looking for means and confidence intervals. We then used ANOVA tests (Bonferroni adjusted) to look for significant differences between the log transformed RACHS-1 groups. To get a usual graph instead of a logarithmic scale, we transformed the obtained mean values and the diversity measures back to a linear scale. The relevant graphs therefore display retransformed 95% confidence intervals as diversity measures, and geometric instead of arithmetical means.

To see whether the RACHS-1 classification reflected mortality in our, as in the PCCC, patient group, we calculated the exponential regression R² value of weightened (groupwise) mortality risks: The geometric means of total and postoperative lengths of stay were fitted to an exponential regression curve against the risk category number. Individual instead of groupwise lengths of hospital stay were entered into different regression models in order to get a measure for the length of stay variability within the RACHS-1 groups. To illustrate the broad variability of hospital treatment times that patients even within the same RACHS-1 group do require, we calculated Kaplan Meier curves with death or discharge as the terminal event. The significance of differences between the risk categories was tested with pairwise log rank tests.

	3. Results

Top Abstract 1. Introduction 2. Patients and methods 3. Results 4. Discussion 5. Conclusion References

 
Risk category distribution is quite similar between the 3 groups (Bad Oeynhausen, PCCC, hospital discharge data, Fig. 2 ). However, looking at the fractions that the single risk groups occupy within the data sets it becomes evident that in Bad Oeynhausen low-risk patients are less frequent and high risk patients are more frequent.

View larger version (42K): [in this window] [in a new window]   Fig. 2. Distribution of procedure classes among Bad Oeynhausen (BO), the Pediatric Cardiac Care Consortium (PCCC) group and the Hospital Discharge (HD) data sets as described in [1]. The patient population of each data set was set to 100%. The distribution is basically similar, but the Bad Oeynhausen group has more patients in higher risk groups (P<0.01). In each population there are not enough patients in category 5 to make displaying them seem reasonable. Frequencies in categories 2 and 4 are roughly constant, while higher percentages in categories 4 and 6 are paralleled by lower fractions in category 1.

Mortality rates in the different risk groups were quite similar to the published ones: (Fig. 3) . Mortality risk grows according to the risk group—monotonically, but not linearly—as observed for the PCCC population [1]. A difference between the Bad Oeynhausen and the PCCC population is noted in the risk groups above 2: Bad Oeynhausen mortality rates are still growing, but the risk groups 2 and 3 lack a statistically significant difference. The other groups (except group 5 for low patient numbers) differed significantly (P<0.05) from each other.

View larger version (15K): [in this window] [in a new window]   Fig. 3. Mortality rates of the different populations, split by risk categories. The effect of the parallel rises in risk group and mortality that can be observed in the Pediatric Cardiac Care Consortium (PCCC) and the Hospital Discharge (HD) data sets is still present, but less marked in Bad Oeynhausen (BO).

The surgical team was already experienced when the described series began: The overall performance, expressed in saved lives [3] per 100 procedures, when compared to the PCCC standard, and stratified by year, remained constantly above the expectations. Between 1996 and 2002, the number of saved lives was 4.0, 0.8, 2.3, 3.9, 2.6, 2.0 and 2.2 per 100 procedures, respectively.

The Receiver Operating Characteristic (ROC) indicates sensitivity and specifity of a method. The area under the curve (AUC) is a combined measure of both. If the curve is rectangular, the AUC is 1 and the method has 100% sensitivity and specifity; if the curve is a diagonal line, the AUC is 0.5 and the method has no predictive value. The AUC for the model containing the risk category alone was 0.784 for the PCCC population and 0.749 for the hospital discharge data set [1]. For our patient group, the AUC was 0.755 (95% confidence interval=0.715–0.796, Fig. 6), indicating a very similar predictive value to the classification in the 3 populations.

View larger version (17K): [in this window] [in a new window]   Fig. 6. Medians and interquartile ranges of the length of stay of all patients, split by risk groups. The classification's ability to predict the individual length of stay is limited: in each risk group there are many patients with short treatment times and many who have to stay extraordinarily long in the hospital. The interquartile ranges overlap broadly.

View larger version (21K): [in this window] [in a new window]   Fig. 4. Length of stay (geometric mean and retransformed 95% confidence intervals of the log transformed stay times, see ‘patients and methods’) of the Bad Oeynhausen pediatric cardiac surgery population, stratified by risk categories and grouped by total, pre- and postoperative time. While the preoperative time varies less with the risk category, the mean total and postoperative length of stay rises continuously with increasing risk, with statistically relevant differences between each group.

The geometric means of total and postoperative stay times of survivors follow very exactly (R²=0.95 and 0.96) an exponential function (Fig. 5) .

View larger version (18K): [in this window] [in a new window]   Fig. 5. When classified by the RACHS-1 categories, the geometric means of total and postoperative hospital stays of survivors come very close to an exponential curve.

A different way to express this is the percentage of variance that is explained by the RACHS-1 group, as calculated from regression analysis: If the RACHS-1 class were the only predictor of postoperative stay time, the length of this time period would be explained completely (100%) by the RACHS-1 category, and the length of stay indicators of each patient would be found directly on the regression line. In reality, many other factors determine the procedure–discharge interval, resulting in a scattering of postoperative stay time over a broad range. Regression analysis shows that the RACHS-1 category explains only small percentages of the hospital stay time: 13.5% of total length of stay of all patients and 16.8% of the postoperative length of stay of survivors.

The RACHS-1 stratified Kaplan Meier graph on postoperative hospital stay duration of survivors (Fig. 7A) illustrates the conspicuous variability within the RACHS-1 groups. The frequency of medium and long hospital stays follows exactly the RACHS-1 category. The differences between all of the RACHS-1 classes (except 5) are statistically significant (P<0.001). More frequent early deaths in high risk classes confound the picture when the total hospital stay of all patients (including non-survivors) is considered (Fig. 7B).

View larger version (31K): [in this window] [in a new window]   Fig. 7. (A) Kaplan Meier curve of survivors, showing ‘freedom’ from hospital discharge, stratified by RACHS-1 group. Obviously, higher risk of a procedure increases the probability of a complicated postoperative course. (B) Kaplan Meier curve of survivors, displaying the duration of the entire hospital stay, regardless of the termination mode (discharge, death, next operation). The comparison with Fig. 7A shows that mainly early deaths in category 6 confound the otherwise quite regular picture.

	4. Discussion

Top Abstract 1. Introduction 2. Patients and methods 3. Results 4. Discussion 5. Conclusion References

In adult cardiac surgery, coronary artery bypass or heart valve operations are performed in large numbers with standardized methods. This has led to many attempts to produce scoring systems—but still no model has attained general acceptance. In pediatric cardiac surgery, dealing with congenital cardiovascular diseases, defects demonstrate a much larger variety, and patient numbers in individual centres and as a whole are much smaller. Moreover, one main influence factor, the individual surgeon, is rarely exposed for interinstitutional data collections. Some cofactors (such as patient age) are easy to address, others (such as gestational age or comorbidities) pose major problems for retrospective data analyses. The RACHS-1 approach is a very interesting solution proposal for this impending problem. RACHS-1 is a small, compact, easily applicable tool, requiring only very few data, at the expense of low individual predictive precision. However, two large North American populations have shown its ability to distinguish group-related mortality differences, and application to our study population also resulted in distinct groups: related to mortality, but also concerning length of hospital stay.

Risk adjustment is necessary. As shown in Fig. 2, there are marked differences in the percentage of malformation complexity among the pediatric cardiac surgery populations from different hospitals or hospital groups. This may be explained by the regional centre function of certain institutions. Smaller units take advantage of it, sending away the complicated cases and keeping the simpler ones. A scoring system is needed to investigate and compare work and performance.

Previous attempts in the field of pediatric cardiac surgery have been made: In 2000, Stark [6] looked at 1378 patients operated by 11 surgeons. He concluded that larger patient numbers were needed for valid information on the small subgroups. A year later, with 2718 patients included [7], his relevant statements were similar. A subgroup scoring system for neonates operated in deep circulatory arrest was published by Clancy [8]. A different classification for children under 3 months was created by Kirklin [9]. Currently, the Aristotle Complexity Score is being developed for complexity adjustation in pediatric cardiac surgery [10]. Relevant information was given at the EACTS meeting in Vienna in 2003. This scoring system includes a larger number of items and is supposed to increase the precision of individual predictions.

The RACHS-1 classification, based on the expertise of a large expert panel and a very large number of procedures, is here applied to the data set of one single institution. This might be seen as a limitation: results of a single unit, even a big one, may not be representative. In fact, the effect of accumulation of high-risk patients in the institution (Table 1) is further amplified because one of the surgeons operated most of the high risk patients (Table 2). The good results from our group in the risk categories 3 and 4 (Fig. 3) might be partly attributed to this training effect, but on the other hand the PCCC and Hospital Discharge Data are from 1995 and 1996, while our data collection starts in 1996 and extends to 2002. General progress in cardiac surgery has surely taken place since then, but has not resulted in a further improvement (concerning mortality) in overall performance in the special field of pediatric cardiac surgery in the observed unit. This might be different for the PCCC mortality. A year-wise stratification of saved lives—when compared to the expected (PCCC) lethality, and expressed in saved lives per 100 operations—shows that standardized results were somewhat oscillating, constantly above the PCCC average, but without substantial trend towards further improvement between 1996 and 2002.

The PCCC's simple and well-defined, mainly procedure-based criteria showed significant risk group differences in (geometric) mean total and postoperative duration of hospital stay for survivors, and a generally shorter length of stay for non-survivors.

In our population, the mean preoperative time was around one week for all groups except the minimum risk group, and the perioperative geometric mean total hospital stay was 19.8 days. Vricella [12] reported his experience with much shorter total in-hospital periods: a mean of 2.0 days from admission. His reported lengths of stay are extraordinarily short, and a risk-adjusted mortality comparison [3] would be of great interest.

We found the RACHS-1 classification able to differentiate very exactly the geometric means of total and postoperative lengths of stay. The marked intra-class scattering of length of stay times limits its usability for individual prediction. The groupwise mortality prediction ability—as judged from the receiver- operator curve—is astonishingly similar to the previously described populations. The lower overall mortality in our population decreased the significance of the inter-class differences, but this specific case does not reduce the value of the RACHS-1 classification system.

	5. Conclusion

Top Abstract 1. Introduction 2. Patients and methods 3. Results 4. Discussion 5. Conclusion References

 
The RACHS-1 classification is also applicable on this side of the Atlantic ocean. It yields very similar results concerning distribution and outcome prediction capabilities for American pediatric cardiac surgery units and a German patient population. A higher risk category coincides statistically significant with a longer hospital stay. Using this relatively simple classification tool, a conspicuous proportion of patients deviates by large from the mean group-specific length of stay. However, the RACHS-1 classification is able to form patient groups that differ significantly from each other in mortality and mean postoperative length of hospital stay. In other words: The RACHS-1 classification has a good groupwise, but a low individual predictive ability concerning length of stay and mortality in our population.

	Footnotes

 
¹ Formerly Department of Thoracic and Cardiovascular Surgery, Heart-Center North-Rhine Westphalia, Bad Oeynhausen, Germany

² Formerly Department of Pediatric Cardiology, Heart-Center North-Rhine Westphalia, Bad Oeynhausen, Germany

	References

Top Abstract 1. Introduction 2. Patients and methods 3. Results 4. Discussion 5. Conclusion References

 

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2. Moller H.J. Perspectives in pediatric cardiology, vol.6 Surgery of congenital heart disease. Pediatric Cardiac Care Consortium 1984–1995. New York: Futura Publishing, 1998.
3. Sergeant P., de Worm E., Meyns B., Wouters P. The challenge of departmental quality control in the reengineering towards off-pump coronary artery bypass grafting. Eur J Cardio-Thorac Surg 2001;20:538-543.[Abstract/Free Full Text]
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5. Finlayson E.V., Birkmeyer J.D., Stukel T.A., Siewers A.E., Lucas F.L., Wennberg D.E. Adjusting surgical mortality rates for patient comorbidities: more harm than good?. Surgery 2002;132(5):787-794.[CrossRef][Medline]
6. Stark J., Gallivan S., Lovegrove J., Hamilton J.R., Monro J.L., Pollock J.C., Watterson K.G. Mortality rates after surgery for congenital heart defects in children and surgeons' performance. Lancet 2000;355(9208):1004-1007.[CrossRef][Medline]
7. Stark J.F., Gallivan S., Davis K., Hamilton J.R., Monro J.L., Pollock J.C., Watterson K.G. Assessment of mortality rates for congenital heart defects and surgeons' performance. Ann Thorac Surg 2001;72(1):169-174.[Abstract/Free Full Text]
8. Clancy R.R., McGaurn S.A., Wernovsky G., Spray T.L., Norwood W.I., Jacobs M.L., Murphy J.D., Gaynor J.W., Goin J.E.J. Preoperative risk-of-death prediction model in heart surgery with deep hypothermic circulatory arrest in the neonate. J Thorac Cardiovasc Surg 2000;119(2):347-357.[Abstract/Free Full Text]
9. Kirklin J.K., Blackstone E.H., Kirklin J.W., McKay R., Pacifico A.D., Bargeron L.M., Jr Intracardiac surgery in infants under age 3 months: predictors of postoperative in-hospital cardiac death. Am J Cardiol 1981;48(3):507-512.[CrossRef][Medline]
10. Lacour-Gayet F. Risk stratification theme for congenital heart surgery. Semin Thorac Cardiovasc Surg Pediatr Card Surg Annu 2002;5:148-152.[CrossRef][Medline]
11. Brown K.L., Ridout D.A., Goldman A.P., Hoskote A., Penny D.J. Risk factors for long intensive care unit stay after cardiopulmonary bypass in children. Crit Care Med 2003;31(1):28-33.[CrossRef][Medline]
12. Vricella L.A., Dearani J.A., Gundry S.R., Razzouk A.J., Brauer S.D., Bailey L.L. Ultra fast track in elective congenital cardiac surgery. Ann Thorac Surg 2000;69(3):865-871.[Abstract/Free Full Text]

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This Article Abstract Full Text (PDF) 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 Boethig, D. Articles by Breymann, T. Search for Related Content PubMed PubMed Citation Articles by Boethig, D. Articles by Breymann, T. Related Collections Congenital - acyanotic Congenital - cyanotic