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Eur J Cardiothorac Surg 2004;26:3-11
© 2004 Elsevier Science NL

Risk stratification in paediatric open-heart surgery

^a Department of Cardiothoracic Surgery, Great Ormond Street Hospital for Children, London, UK
^b Centre for Paediatric Epidemiology and Biostatistics, Institute of Child Health, London, UK

Received 21 October 2003; received in revised form 11 February 2004; accepted 29 March 2004.

^* Corresponding author. Tel.: +44-20-781-38159; fax: +44-50-7432-1281
e-mail: tsangv{at}gosh.nhs.uk

	Abstract

Top Abstract 1. Introduction 2. Materials and methods 3. Results 4. Discussion Appendix A. Conference... Appendix B. RACHS-1... appendix c. American Society... References

 
Objective: The aims of this study were to identify independent risk factors for mortality following paediatric open-heart surgery and to develop risk models for use in clinical audit based on identified risk factors. The study also tests the validity of the recently proposed Risk Adjustment in Congenital Heart Surgery (RACHS-1) method of risk stratification [J Thorac Cardiovasc Surg 123 (2002) 110] as applied to open-heart operations. Methods: A multiple logistic regression analysis was performed on all patients less than 18 years of age undergoing open-heart surgery at a single institution over a 3-year period. Preoperative and operative variables included for analysis were age at operation, weight, sex, American Society of Anaesthesiology (ASA) grade, RACHS-1 risk category, preoperative haemoglobin, bypass time, temperature, cross-clamp time, circulatory arrest time, blood transfusion on bypass and surgeon. The outcome measure was in-hospital death. Results: 1085 consecutive open-heart cases were identified. There were 51 in-hospital deaths (4.7%). Variables identified as being independently significant risk factors for in-hospital death were age (P=0.0002), RACHS-1 risk category (P<0.0001) and bypass time (P<0.0001). Based on these three variables, a risk model was constructed to predict mortality. The area under the receiver-operating-characteristic (ROC) curve for this model was 0.86. A second model was constructed ignoring bypass time. In this model, the significance of the ‘preoperative’ risk factors was P=0.0003 for age and P<0.0001 for RACHS-1 risk category. The area under the ROC curve was 0.81 for the second model. Conclusions: This study identifies age at operation, RACHS-1 risk category and bypass time as highly significant risk factors for mortality after paediatric open-heart surgery. It validates the RACHS-1 risk stratification method as applied to the subset of open-heart surgery, whilst accepting the limitations of such a system. The risk models formulated permit risk prediction and allow for analysis of surgical results. Such risk-adjustment is important when assessing performance and comparing outcomes amongst individuals or institutions.

Key Words: Mortality • Risk factors

	1. Introduction

Top Abstract 1. Introduction 2. Materials and methods 3. Results 4. Discussion Appendix A. Conference... Appendix B. RACHS-1... appendix c. American Society... References

 
Risk stratification in paediatric cardiac surgery is problematic owing to wide variations in casemix. Previous studies have documented differences in outcome between open and closed cases, and amongst different age groups [2,3]. Other research has suggested factors such as institutional caseload and individual surgeon volume may affect results [4,5]. Studies in neonates have demonstrated that preoperative risk factors such as age at admission, genetic diagnosis, Apgar scores and ventricular morphology may be important, as well as duration of circulatory arrest [6]. Insurance patterns and access to medical care have also been shown to influence outcome [7,8].

Other efforts have been made to refine risk models by grouping many different congenital heart operations into a small number of categories ranked in order of perceived increasing risk [1,9,10]. Jenkins et al. have recently proposed a ‘Risk Adjustment In Congenital Heart Surgery’ classification (RACHS-1), which has been shown to predict hospital mortality [1,11]. This method, which is utilised in this study, places 79 individual open and closed operations into 6 risk categories (see Appendix B). The authors demonstrated that this grading system alone could be used for risk stratification, but that it could also be refined further by including age group, prematurity and major non-cardiac anomaly into the risk modelling.

The aim of this study was to perform a risk factor analysis for hospital mortality following paediatric open-heart surgery, and to test the validity of the RACHS-1 method as applied to open-heart cases. Risk models were then constructed based on identified risk factors to develop a risk-stratification method that could be applied for use in clinical audit.

	2. Materials and methods

Top Abstract 1. Introduction 2. Materials and methods 3. Results 4. Discussion Appendix A. Conference... Appendix B. RACHS-1... appendix c. American Society... References

 
A 3-year retrospective cohort study (April 2000–March 2003) of paediatric open-heart surgery at a single institution was conducted. Prospectively collected data were retrieved from the hospital's computerised cardiac surgery database. Data quality is validated by participation in the Central Cardiac Audit Database Project (CCAD), which is a national database funded by the Department of Health, overseen by paediatric cardiac surgeons and cardiologists and managed by the National Health Service Information Authority in the United Kingdom.

The dataset was checked for errors and omissions by cross-referencing with another similar but separate computerised hospital operating theatre database, and also by manual reference to perfusionists' records, operation reports and departmental discharge summaries. RACHS-1 risk categories were assigned to each case retrospectively by strict adherence to the classification scheme as published in January 2002 [1].

2.1. Patients and variables
1085 open-heart cases in patients less than 18 years of age were identified. Open-heart surgery was defined as any operation employing cardiopulmonary bypass [3]. The peri-operative variables examined were age at operation, weight, sex, American Society of Anaesthesiology (ASA) grade (see Appendix C), RACHS-1 risk category, preoperative haemoglobin, total bypass time, lowest core temperature on bypass, cross-clamp time, circulatory arrest time, total red cell transfusion during bypass and surgeon. The outcome variable was in-hospital death. In cases where a death occurred following more than one open-heart operation during the same hospital admission, the death was only counted once and attributed to the primary operation.

2.2. Statistical analysis
Multiple logistic regression analysis was used to develop two separate prediction equations for in-hospital death, one restricted to factors available pre-operatively (the pre-operative model) and the other using all available data (the peri-operative model). Fractional polynomials of weight and age were used to optimise their association with the outcome (with 1 day added to age). A substantial minority (8%) of cases were operations not defined by the RACHS-1 grading system (see Table 3). The mortality of this group of patients approximated that of RACHS-1 risk category 3. The predictive equations were developed with (a) the cases omitted, and (b) the cases treated as RACHS-1 risk category 3.

There were no deaths among RACHS-1 risk category 1 patients, which meant that separate estimates of mortality by RACHS-1 risk category could not be estimated (logistic regression requires group mean mortality rates to exclude 0% and 100%). Instead the RACHS-1 risk category was treated as a continuous variable in the analysis, assuming a linear relationship between risk category and mortality (on the log odds scale). This implied a small but non-zero mortality rate for risk category 1.

For sensitivity analyses, weight was expressed as a z-score using post-natal age and infants under 3 months with a z-score below –2 were rated as premature. This variable was added to models containing weight and/or age to see how the results were affected. The analyses were repeated with different values for the age cut-off (default 3 months) and z-score (default –2) cut-off.

	3. Results

Top Abstract 1. Introduction 2. Materials and methods 3. Results 4. Discussion Appendix A. Conference... Appendix B. RACHS-1... appendix c. American Society... References

 
Of the 1085 cases, 51 died in hospital (4.7%). Table 1 summarises the univariate analysis of the risk factors studied, showing that several of the variables were significantly related to mortality. Table 2 summarises the results of the multiple logistic regression analysis, listing the independently significant risk factors for both the ‘preoperative’ and ‘peri-operative’ models.

Adding prematurity to the models, in addition to age and/or weight, made no appreciable difference to the results, and in particular it did not alter the conclusion that age was more important than weight.

In addition to age, the RACHS-1 risk category was the only other significant variable in the pre-operative model (odds ratio 1.89, 95% confidence interval 1.4–2.5, P<0.0001). This indicates that the odds of death increased by 1.89 times per category. Fig. 1 shows the linear trend in mortality versus RACHS-1 risk category (on a log odds scale). Note that the zero mortality for risk category 1 is at minus infinity on the log odds scale. Fig. 2 shows the non-linear effect of age after adjusting for RACHS-1 risk category.

View larger version (11K): [in this window] [in a new window]   Fig. 1. RACHS-1 risk category versus log odds of death. Note that there were no cases performed in risk category 5 and that there were no deaths in risk category 1 (log odds –).

View larger version (16K): [in this window] [in a new window]   Fig. 2. (A) Age versus predicted mortality (stratified by RACHS-1 risk category). Note that predicted mortality rises as age falls in all RACHS-1 groups. (B) Same data as (A) but focusing on age less than 60 days.

View larger version (12K): [in this window] [in a new window]   Fig. 3. Bypass time versus predicted mortality. A linear effect is observed, however an even more significant threshold effect is seen at 200 min.

The area under the ROC curve (AUC) for the pre-operative model using the risk factors age and RACHS-1 risk category was 0.81. For the peri-operative model with the three factors age, RACHS-1 risk category and total bypass time, the AUC was 0.86.

	4. Discussion

Top Abstract 1. Introduction 2. Materials and methods 3. Results 4. Discussion Appendix A. Conference... Appendix B. RACHS-1... appendix c. American Society... References

 
Risk models for adult cardiac surgery have been well established and refined over the last 15 years [12,13]. However congenital heart surgery has lagged behind in the development of systems of risk stratification. As public awareness and government scrutiny of health care outcomes has escalated, the need for validated risk models is becoming paramount in the interpretation of surgical results [14–16].

The RACHS-1 method represents one such means of risk stratification that might potentially be useful for comparing inter-institutional outcomes, and its recent publication [1] was the impetus for initiating our study. It is a procedure-based risk scheme, rather than a diagnosis-based method. This study in part examines its validity and attempts to identify its strengths and weaknesses. Another such consensus-based model currently being developed by the European Association of Cardiothoracic Surgery, the Society for Thoracic Surgeons, the Congenital Heart Surgeons Society and the European Congenital Heart Surgeons Foundation is the so-called ‘Aristotle score’ [10]. This differs significantly from the RACHS-1 method in being a complexity score, rather than a risk grade. The scoring system gives weight to a number of factors for any individual case, including estimated risk of mortality and morbidity, relative technical difficulty, diagnosis-dependent factors (such as presence of an intramural coronary artery in transposition) and diagnosis-independent factors (such as weight and age).

Our study identified three independent risk factors for paediatric open-heart surgery: age at operation, RACHS-1 risk category and total bypass time. The study therefore validates the RACHS-1 method as it applies to the subset of open-heart surgery, and demonstrates an extremely strong relationship between RACHS-1 risk category and risk of postoperative death. Moreover, the relationship was shown to be linear (on the log odds scale) in this study, a point which was not observed in the paper published by Jenkins et al. [1] but which adds strength to their proposed grading system.

However we noted a number of deficiencies in the RACHS-1 method. Firstly, a small but important minority of cases (8%) in our series could not be classified using the RACHS-1 system (see Table 3). Jenkins et al. also noted that between 2 and 11% of cases were unable to be classified with their method [1]. It is clearly relevant for institutions such as ours to employ a risk-stratification scheme that includes transplantation, as well as recognising other high-risk surgery such as tracheal reconstruction or pulmonary vein repair (often referred from other institutions following previous failed repair). Secondly, none of our 1085 cases fell into RACHS-1 risk category 5 (see Appendix B), rendering it meaningless, as noted by Jenkins et al. themselves. We suggest that this category be revised and if necessary removed altogether. Thirdly, some of the procedures listed are rather vague or ambiguous (e.g. ‘repair of unspecified septal defect’) or open to variable interpretation (e.g. ‘repair of double outlet right ventricle’). This therefore leaves the system open to errors in coding owing to inadequate definition of nomenclature. We also have concerns about the classification of combination procedures, which are assigned according to the category of highest risk. For example, we are unsure how to classify repair of multiple VSD's. Finally we needed to overcome the ‘problem’ in our series of a zero mortality in RACHS-1 risk category 1 by making certain statistical assumptions (as detailed in Section 2).

Our evaluation of the RACHS-1 method is that it is statistically useful when applied to clinical audit of large numbers of patients. By its nature however, it lacks precision when estimating risk for individual patients and should not be used for this purpose (as its authors correctly indicate). This is because many different and varied operations are lumped together in each group. Even for the same operation, different anatomical features may imply greater operative risk. Clinicians understand that, for example, the presence of an intramural coronary artery in an arterial switch operation, or a Fontan operation in a patient with elevated pulmonary vascular resistance, represents an increased risk. The advantage of the RACHS-1 method is in overcoming the lack of statistical power when trying to demonstrate differences amongst patients with so many different variables. The disadvantage is that it fails to account for subtle but important clinical characteristics and is therefore a rather blunt tool in assigning risk. It awaits to be seen whether a complexity-based scoring system such as the Aristotle score [10] can better allow estimation of risk in individual cases as well as patient populations.

We should point out that in our evaluation of the RACHS-1 method, we have excluded all ‘closed heart’ (non-bypass) cases. This is because our study was primarily aimed at identifying risk factors related to open-heart surgery (such as bypass time, cross clamp time, etc.). Therefore we are unable to pass judgement on the entire RACHS-1 classification system. Furthermore, the survival rates achieved by our institution for the various RACHS-1 categories are not directly comparable to those quoted by others because of these exclusions.

Although bypass time had a significant effect on mortality in our risk model, it may in fact be acting as a surrogate of outcome. In other words, in cases that carry a high mortality, long bypass time may simply be reflecting difficulties during surgery (e.g. inability to wean from bypass, multiple bypass runs to revise the repair). This became evident statistically when observing the threshold effect of 200 min, beyond which the odds of death increased almost 15-fold. It might therefore be too simplistic to state that minimising time on bypass can reduce the risk of death. For this reason, different models were produced either including or excluding bypass time (see Table 2). Both models have significant values for area under the ROC curve, i.e. they are both highly accurate in their prediction of outcome.

The association between age and risk of mortality is also extremely strong, but non-linear. This is because in our study, the risk only starts to rise importantly once age falls into the neonatal period. Beyond the neonatal period, risk becomes almost constant within each RACHS-1 group (see Fig. 2). However the risk rises markedly and exponentially with each decreasing day of life in the neonatal period. Previous studies have also demonstrated higher risk in younger patients, but have typically measured risk by age groups (<30 days, 30 days to 1 year, >1 year) [1,3] Our results suggest that comparing results amongst age strata is in fact a fairly crude form of risk-stratification, and that risk models can be significantly refined when age is measured as a continuous variable, remembering the non-linear association with mortality.

It is important to emphasise that this relationship does not imply that neonatal surgery becomes safer if delayed. In many instances, such as simple transposition or hypoplastic left heart syndrome, there may well be a detrimental effect in postponing surgery. Our data have an inherent selection bias because the age at operation has been preselected in every individual case, based on our clinical judgement as to the optimal timing of surgery. All we can safely conclude is that babies who required bypass surgery at a very young age had an importantly higher risk of death than older babies. Trying to prove scientifically the optimal age for surgery in any specific lesion (e.g. tetralogy of Fallot) is probably impossible, as this could only be determined in a prospectively controlled trial.

A fourth factor, weight, was shown to be important but not as significant as age. Although weight and age are closely correlated, it can be concluded that age is the more important determinant of outcome. However this has implications for prematurity, a factor that was not directly measured in our study. As a sensitivity analysis, a proxy measure for prematurity was constructed based on age and weight z-score, on the basis that premature infants tend to have low z-scores when young. Adding this variable to the models made no difference to the results, a finding different to that in the study by Jenkins et al. [1]. The explanation may be that few infants in our own series were premature, and/or that we tend to postpone bypass surgery where possible or offer palliation (closed surgery) in very premature babies. This represents a possible selection bias in our data and we therefore cannot completely discount prematurity as a potentially important risk factor.

Reassuringly, there were no significant differences in mortality amongst surgeons in this series. Interestingly, intraoperative variables such as cross-clamp time, circulatory arrest and transfusion were significant predictors of mortality on univariate testing, but became non-significant once adjusted for RACHS-1 risk category. In an attempt to include the importance of acute physiological status and comorbidity present at the time of surgery, we chose to use the ASA grade (see Appendix C). This widely used grading system is a global index of a patient's immediate preoperative condition and can therefore be representative of a multitude of factors (e.g. ventilator-dependence, congestive heart failure, critical hypoxia). The ASA system, although highly subjective in nature [17], has been shown to be a valid measure of perioperative risk in other types of surgery [18,19]. Somewhat surprisingly, ASA grade was found to have no significant influence over postoperative mortality in multivariate analysis. It may be that age and RACHS-1 risk category already have implications for comorbidity. For example, a patient aged 2 months undergoing VSD closure is likely to have more important congestive heart failure than a patient having VSD closure aged 12 months. Likewise patients in higher RACHS-1 risk categories are usually (but not always) more acutely unwell and operated on more urgently than those in lower grades.

Chromosomal disorders, non-cardiac anomalies and syndromes were not analysed in this study. Since not all patients at our institution are screened for genetic disorders, and not all non-cardiac comorbidity is recorded in our database, these variables were excluded from the study. However as the understanding of genetic determinants of disease continues to advance and genetic screening becomes routine practice, genotype could well be found to play an important role in pre-determining outcome.

4.1. Limitations of this study
As with other statistical risk-stratification models, there are a number of limitations and therefore precautions that should be taken into account when applying this model to clinical audit. Firstly, the data analysed are by definition retrospective and therefore automatically historical. This problem is well recognised in older cardiac surgery risk models such as the Parsonnet score [20], where outcomes by today's standards would be expected to be superior to those at the time the model was published. Secondly, all models fall short of being 100% accurate in predictive ability. Despite a high level of discrimination in our model (which compares favourably with those commonly used in adult cardiac surgery [12,13]), there are finite limitations of statistical methods to measure biological outcomes. We have attempted to address some of the deficiencies of the RACHS-1 risk stratification system in the preceding discussion. Finally, future research may uncover new factors (e.g. genetic) that are subsequently shown to have important effects on outcome. This underscores the need for risk-stratification models to be able to adapt to surgical science and practice that continues to evolve.

4.2. Conclusion
This study identifies independent risk factors for mortality after paediatric open-heart surgery. It validates the recently proposed RACHS-1 method as applied to open-heart surgery, whilst accepting the shortcomings of such a system. The data provide a risk model that can be used to predict outcome and allow comparison of results amongst appropriately risk-stratified groups of patients, provided the necessary safeguards are applied and that the limitations and caveats associated with the model are understood.

	Acknowledgments

 
The authors wish to acknowledge the kind assistance of Mr Ian Laskey (data manager), Mr Brian Reynolds (information technology), Miss Faith Hanstater (research assistant) and Mr Jaroslav Stark (manuscript review). Research at the Institute of Child Health and Great Ormond Street Hospital for Children NHS Trust benefits from Research and Development funding received from the NHS Executive.

	Footnotes

 
Presented at the joint 17th Annual Meeting of the European Association for Cardio-thoracic Surgery and the 11th Annual Meeting of the European Society of Thoracic Surgeons, Vienna, Austria, October 12–15 2003.

	Appendix A. Conference discussion

Top Abstract 1. Introduction 2. Materials and methods 3. Results 4. Discussion Appendix A. Conference... Appendix B. RACHS-1... appendix c. American Society... References

 
Dr C. Tchervenkov (Montreal, Canada): Can you expand a little bit on the issue of age? Did you analyze this for the overall cohort or per lesion? Some lesions have a higher mortality with younger age, perhaps tetralogy of Fallot, while others have a greater risk of death with increasing age, such as the arterial switch operation for transposition or the Norwood operation for hypoplastic left heart syndrome. Can you elaborate this point, since depending on the lesion the impact of age may have an opposite effect.

My second point is regarding category 5 of Jenkins. It doesn't seem to be very practical or relevant, since in her latest paper and your paper, category 5 has almost no patients. So perhaps it ought to be eliminated and either moved up or down to the next category.

Dr Kang: I think you've raised a couple of good points. One of the limitations of our study is that there is a selection bias involved. In other words, we selected what age to operate on what patients. For example, in our institution, most but not all tetralogies would have palliation first with a shunt and then have a repair operation several months down the track. So already we have a selection bias in our risk model. What we can say is that age, based on our data, is independently predictive of death; that means independent of the lesion or independent of the risk category.

I agree completely with you about risk category 5 in the Jenkins system. We had none of about 1000 patients in that category, and I think in the original paper, very few of nearly 10,000 patients were in that risk category. So it sounds like that risk category should be discarded altogether.

Dr Tchervenkov: I really think that the issue of age has to be further studied in the future because the wrong inference may be made based on this paper as to when to when to operate on some patients.

Dr Kang: Exactly. We're not saying that we should delay surgery in neonates for as long as possible for the obvious reasons you've made, and that's why there is a bias in our study, a selection bias. All we're saying is that when you needed to operate at a very young age, in the first week of life or perhaps the first 10 days of life, that the risk was high, and that's the only conclusion we wish to make to the audience.

Dr M. Jacobs (Philadelphia, PA, USA): My question goes to your conclusion that length of cardiopulmonary bypass time is a surrogate for bad outcome. I think it has been observed in now countless studies in congenital surgery, in adult surgery and whatnot that there is a statistical correlation between bypass time and mortality, and I really wondered what part of your analysis led to your conclusions that it was a surrogate. If one looked at your slide, certainly on the right upper quadrant, patients with 5 and 6 h of bypass you made every last effort to salvage and that duration for those patients may very well be a surrogate, but I think if you limited your slide to the left lower quadrant with bypass times out to 120 or 150 min, there probably still is a statistical correlation, and I wondered what part of the analysis led you to conclude that this was a surrogate and not a contributing factor in mortality.

Dr Kang: Well, you might be absolutely right. We drew that conclusion because we didn't want to send a message saying that we should try to reduce our bypass time for every operation by 10 min by going a little bit faster. What we thought was that those cases that had high mortality were ones that, for example, needed a second run on bypass to revise a repair, or where things were just going badly; perhaps we needed to bridge onto mechanical support, and therefore we had a prolonged bypass time. Our statistician told us that around about 220 min is when there was a more critical increase in the risk. So most operations that I can think of where things are going well should take under about 220 min to complete on bypass.

	Appendix B. RACHS-1 classification system [1]

Top Abstract 1. Introduction 2. Materials and methods 3. Results 4. Discussion Appendix A. Conference... Appendix B. RACHS-1... appendix c. American Society... References

 

Risk category 1 Atrial septal defect surgery (including atrial septal defect secundum, sinus venosus atrial septal defect, patent foramen ovale closure) Aortopexy Patent ductus arteriosus surgery at age >30 days Coarctation repair at age >30 days Partially anomalous pulmonary venous connection surgery Risk category 2 Aortic valvotomy or valvuloplasty at age >30 days Subaortic stenosis resection Pulmonary valvotomy or valvuloplasty Pulmonary valve replacement Right ventricular infundibulectomy Pulmonary outflow tract augmentation Repair of coronary artery fistula Atrial septal defect and ventricular septal defect repair Atrial septal defect primum repair Ventricular septal defect repair Ventricular septal defect closure and pulmonary valvotomy or infundibular resection Ventricular septal defect closure and pulmonary artery band removal Repair of unspecified septal defect Total repair of tetralogy of Fallot Repair of total anomalous pulmonary veins at age >30 d Glenn shunt Vascular ring surgery Repair of aorta-pulmonary window Coarctation repair at age 30 d Repair of pulmonary artery stenosis Transection of pulmonary artery Common atrium closure Left ventricular to right atrial shunt repair Risk category 3 Aortic valve replacement Ross procedure Left ventricular outflow tract patch Ventriculomyotomy Aortoplasty Mitral valvotomy or valvuloplasty Mitral valve replacement Valvectomy of tricuspid valve Tricuspid valvotomy or valvuloplasty Tricuspid valve replacement Tricuspid valve repositioning for Ebstein anomaly at age >30 days Repair of anomalous coronary artery without intrapulmonary tunnel Repair of anomalous coronary artery with intrapulmonary tunnel (Takeuchi) Closure of semilunar valve, aortic or pulmonary Right ventricular to pulmonary artery conduit Left ventricular to pulmonary artery conduit Repair of double-outlet right ventricle with or without repair of right ventricular obstruction Fontan procedure Repair of transitional or complete atrioventricular canal with or without valve replacement Pulmonary artery banding Repair of tetralogy of Fallot with pulmonary atresia Repair of cor triatriatum Systemic to pulmonary artery shunt Atrial switch operation Arterial switch operation Reimplantation of anomalous pulmonary artery Annuloplasty Repair of coarctation and ventricular septal defect closure Excision of intracardiac tumor Risk category 4 Aortic valvotomy or valvuloplasty at age 30 d Konno procedure Repair of complex anomaly (single ventricle) by ventricular septal defect enlargement Repair of total anomalous pulmonary veins at age 30 d Atrial septectomy Repair of transposition, ventricular septal defect, and subpulmonary stenosis (Rastelli) Atrial switch operation with ventricular septal defect closure Atrial switch operation with repair of subpulmonary stenosis Arterial switch operation with pulmonary artery band removal Arterial switch operation with ventricular septal defect closure Arterial switch operation with repair of subpulmonary stenosis Repair of truncus arteriosus Repair of hypoplastic or interrupted arch without ventricular septal defect closure Repair of hypoplastic or interrupted aortic arch with ventricular septal defect closure Transverse arch graft Unifocalisation for tetralogy of Fallot and pulmonary atresia Double switch Risk category 5 Tricuspid valve repositioning for neonatal Ebstein anomaly at age 30 d Repair of truncus arteriosus and interrupted arch Risk category 6 Stage 1 repair of hypoplastic left heart syndrome (Norwood operation) Stage 1 repair of nonhypoplastic left heart syndrome conditions Damus–Kaye–Stansel procedure

	appendix c. American Society of Anesthesiology (ASA) Physical Status Classification scheme

Top Abstract 1. Introduction 2. Materials and methods 3. Results 4. Discussion Appendix A. Conference... Appendix B. RACHS-1... appendix c. American Society... References

 

P1

A normal healthy patient

P2

A patient with mild systemic disease

P3

A patient with severe systemic disease

P4

A patient with severe systemic disease that is a constant threat to life

P5

A moribund patient who is not expected to survive without the operation

P6

A declared brain-dead patient whose organs are being removed for donor purposes

Source: (http://www.asahq.org/clinical/physicalstatus.htm)

	References

Top Abstract 1. Introduction 2. Materials and methods 3. Results 4. Discussion Appendix A. Conference... Appendix B. RACHS-1... appendix c. American Society... References

 

1. Jenkins K.J., Gauvreau K., Newburger J.W., Spray T.L., Moller J.H., Iezzoni L.I. Consensus-based method for risk adjustment for surgery for congenital heart disease. J Thorac Cardiovasc Surg 2002;123:110-118.[Abstract/Free Full Text]
2. 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;355:1004-1077.[CrossRef][Medline]
3. 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;72:169-174.[Abstract/Free Full Text]
4. Hannan E.L., Racz M., Kavey R.E., Quaegebeur J.M., Williams R. Pediatric cardiac surgery: the effect of hospital and surgeon volume on in-hospital mortality. Pediatrics 1998;101:963-969.[Abstract/Free Full Text]
5. Jenkins K.J., Newburger J.W., Lock J.E., Davis R.B., Coffman G.A., Iezzoni L.I. In-hospital mortality for surgical repair of congenital heart defects: preliminary observations of variation by hospital caseload. Pediatrics 1995;95:323-330.[Abstract/Free Full Text]
6. 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. Preoperative risk-of-death prediction model in heart surgery with deep hypothermic circulatory arrest in the neonate. J Thorac Cardiovasc Surg 2000;119:347-357.[Abstract/Free Full Text]
7. Gonzalez P.C., Gauvreau K., Demone J.A., Piercey G.E., Jenkins K.J. Regional racial and ethnic differences in mortality for congenital heart surgery in children may reflect unequal access to care. Pediatr Cardiol 2003;24(2):103-108.[CrossRef][Medline]
8. DeMone J.A., Gonzalez P.C., Gauvreau K., Piercey G.E., Jenkins K.J. Risk of death for Medicaid recipients undergoing congenital heart surgery. Pediatr Cardiol 2003;24(2):97-102.[CrossRef][Medline]
9. Gallivan S., Davis K.B., Stark J.F. Early identification of divergent performance in congenital cardiac surgery. Eur J Cardiothorac Surg 2001;20(6):1214-1219.[Abstract/Free Full Text]
10. Lacour-Gayet F. Risk stratification theme for congenital heart surgery. Semin Thorac Cardiovasc Surg Pediatr Card Surg Annu 2002;5(1):148-152.[CrossRef][Medline]
11. Jenkins K.J., Gauvreau K. Center-specific differences in mortality: preliminary analyses using the Risk Adjustment in Congenital Heart Surgery (RACHS-1) method. J Thorac Cardiovasc Surg 2002;124(1):97-104.[Abstract/Free Full Text]
12. Shroyer A.L., Coombs L.P., Peterson E.D., Eiken M.C., DeLong E.R., Chen A., Ferguson T.B., Grover F.L., Edwards F.H. Society of Thoracic Surgeons. The Society of Thoracic Surgeons: 30-day operative mortality and morbidity risk models. Ann Thorac Surg 2003;75(6):1856-1865.[Abstract/Free Full Text]
13. Nashef S.A., Roques F., Hammill B.G., Peterson E.D., Michel P., Grover F.L., Wyse R.K., Ferguson T.B. EuroSCORE Project Group. Validation of European System for Cardiac Operative Risk Evaluation (EuroSCORE) in North American cardiac surgery. Eur J Cardiothorac Surg 2002;22(1):101-105.[Abstract/Free Full Text]
14. Aylin P., Alves B., Best N., Cook A., Elliott P., Evans S.J., Lawrence A.E., Murray G.D., Pollock J., Spiegelhalter D. Comparison of UK paediatric cardiac surgical performance by analysis of routinely collected data 1984–96: was Bristol an outlier?. Lancet 2001;358(9277):181-187.[CrossRef][Medline]
15. Spiegelhalter D., Grigg O., Kinsman R., Treasure T. Risk-adjusted sequential probability ratio tests: applications to Bristol, Shipman and adult cardiac surgery. Int J Qual Health Care 2003;15(1):7-13.[Abstract/Free Full Text]
16. Mavroudis C., Jacobs J.P. Congenital heart disease outcome analysis: Methodology and rationale. J Thorac Cardiovasc Surg 2002;123:6-7.[Free Full Text]
17. Mak P.H., Campbell R.C., Irwin M.G. American Society of Anaesthesiologists. The ASA Physical Status Classification: inter-observer consistency. American Society of Anaesthesiologists. Anaesth Intensive Care 2002;30(5):633-640.[Medline]
18. Stephan F., Boucheseiche S., Hollande J., Flahault A., Cheffi A., Bazelly B., Bonnet F. Pulmonary complications following lung resection: a comprehensive analysis of incidence and possible risk factors. Chest 2000;118(5):1263-1270.[Abstract/Free Full Text]
19. Collins T.C., Daley J., Henderson W.H., Khuri S.F. Risk factors for prolonged length of stay after major elective surgery. Ann Surg 1999;230(2):251-259.[CrossRef][Medline]
20. Parsonnet V., Dean D., Bernstein A.D. A method of uniform stratification of risk for evaluating the results of surgery in acquired adult heart disease. Circulation 1989;79:12-13.

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