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Eur J Cardiothorac Surg 2002;21:41-46
© 2002 Elsevier Science NL

Preoperative risk prediction and intraoperative events in cardiac surgery

^a Department of Cardiothoracic Surgery, Papworth Hospital, Papworth Everard, Cambridge CB3 8RE, UK
^b MRC Biostatistics Unit, Cambridge CB2 2SR, UK
^c CHU Fort de France, Martinique, France

Received 2 August 2001; received in revised form 14 October 2001; accepted 19 October 2001.

* Corresponding author. Tel.: +44-1480-830-541; fax: +44-1480-364-744
e-mail: sam.nashef{at}papworth-tr.anglox.nhs.uk

	Abstract

Top Abstract 1. Introduction 2. Patients and methods 3. Results 4. Discussion Appendix A. Conference... References

 
Objective: To examine the relationship between preoperative risk prediction and intraoperative events. Methods: A total of 3118 patients operated in 1999 and 2000 at our institution were analysed, all of whom had their EuroSCORE collected prospectively. The intraoperative variables studied were consultant or trainee operating, long bypass time, long ischaemic time, return on bypass in theatre and use of intra-aortic balloon pump at the end of the procedure. The outcomes are reported as hospital mortality, prolonged length of stay in the intensive therapy unit (pLOS-ITU, >48 h) and death or pLOS-ITU. Risk models were constructed by logistic regression for predicting these three outcomes. Results: With the exception of prolonged cross-clamp time, all variables analysed were independently predictive of a negative outcome. Trainee operating had an apparent protective effect. All risk models performed well. The area under the receiver operating characteristic (ROC) curve (95% CI) increased from 0.857 (0.81, 0.90) for EuroSCORE to 0.874 (0.83, 0.92) for the risk of death model. Similarly, the area under the ROC curve for the pLOS-ITU model increased from 0.687 (0.642, 0.732) to 0.734 (0.691, 0.777) and for the death or pLOS-ITU model from 0.717 (0.677, 0.756) to 0.757 (0.719, 0.795). Conclusions: Knowledge of adverse intraoperative events enhances preoperative risk prediction. This type of analysis could be used for identifying ‘near miss’ outcomes in adult cardiac surgery.

Key Words: EuroSCORE • Mortality • Length of intensive therapy • Near miss

	1. Introduction

Top Abstract 1. Introduction 2. Patients and methods 3. Results 4. Discussion Appendix A. Conference... References

 
Risk stratification in cardiac surgery influences clinical decision-making, patient consent for procedures, audit, training and resource planning. Mortality and morbidity have been the focus of many models based on preoperative, intraoperative and postoperative patient factors or a combination of them. de Leval and colleagues introduced surgical human factors and the concept of near miss into this equation. This is a negative outcome or an equivalent of failure defined as a severe temporary or permanent complication [1]. Using the neonatal arterial switch operations as an index procedure, these authors prospectively investigated a number of perioperative human factors leading to major or minor errors. The multiplicative nature of such negative events is counteracted by the ability of the team to compensate for them and this interaction directly influences the outcome.

Extrapolation of these concepts to adult cardiac surgery is reasonable and necessary. Although it is intuitive that preoperative risk is modified by subsequent events in the operating room and in the intensive therapy unit (ITU), the magnitude of this interaction has not been directly quantified. In an extreme example, a cohort of patients with zero risk of death for coronary revascularisation, according to recognised scores, may experience a high mortality if each procedure is associated with major negative events, be those human errors (e.g. inexperienced surgical team), system errors (e.g. power failure) or a combination of them. Identification of negative events and correction of their causes may be one of the major ways further to improve cardiac surgical outcomes whilst continuing to offer good training. Furthermore, if preoperative outcome prediction is enhanced by adverse events in theatre and in ITU, than this information would be of interest to many, including individual patients, their families and hospital staff (for example by helping to plan ITU bed occupancy). In this paper we set out to describe the association between preoperative risk assessment and adverse intraoperative events in our patient population as a first step for identifying near miss outcomes.

	2. Patients and methods

Top Abstract 1. Introduction 2. Patients and methods 3. Results 4. Discussion Appendix A. Conference... References

 
2.1. Patient population
We evaluated all patients who underwent cardiac operations at our institution between January 1999 and December 2000. EuroSCORE was collected prospectively for all cases and used as a preoperative indicator of the patients’ risk profile. The operative variables analysed were: consultant or trainee operating, cardiopulmonary bypass (CPB) time, cross-clamp (XC) time, return on CPB in theatre and requirement for mechanical support (intra-aortic balloon pump) at the end of the procedure. There were three outcome measures: hospital death, prolonged stay in the ITU (pLOS-ITU, >48 h) as a surrogate marker of morbidity, and the combined outcome of death or pLOS-ITU. A pilot analysis on 2691 consecutive cases showed that the length of anaesthetic time does not influence either of these outcomes and so this variable was not included in the current study [2]. Procedures for which EuroSCORE was thought to be an inappropriate descriptor of risk in this context were excluded: operations not involving cardiopulmonary bypass, all transplants and bridges to transplant, and elective pulmonary thromboendarterectomy. There were no cases of myocardial protection by ventricular fibrillation. Patients who needed a second operation during the study period were retained if there was a new indication (e.g. blocked graft, valvular leak) and such cases were entered in the study with their new EuroSCORE. Cases of return to theatre for immediate complications (e.g. bleeding, tracheostomy) did not represent a new indication. After the selection process, 3118 cases were retained. Twenty-nine patients who died in theatre during the initial procedure or in the first 48 h in the ITU were included in the death analyses but not in the pLOS-ITU analyses.

2.2. Statistical analysis
Prolonged cardiopulmonary bypass time (pCPB) and prolonged cross-clamp time (pXC) were defined as exceeding the 75th centile of their distribution. LOS-ITU was considered prolonged when in excess of 48 h. A risk model incorporating both EuroSCORE and the intraoperative variables was created. Patients operated on in 1999 (n=1575) were used as a derivation data set and the patients treated in 2000 (n=1543) represented the validation set. Multivariate analysis by stepwise forward logistic regression was performed for the three outcomes studied. The Hosmer-Lemeshow goodness of fit ² test was used to evaluate the calibration of the models. In addition, their ability to discriminate between patients with or without the outcome is described by the area under the receiver-operating characteristic (ROC) curve.

	3. Results

Top Abstract 1. Introduction 2. Patients and methods 3. Results 4. Discussion Appendix A. Conference... References

 
Fig. 1 shows the distribution of procedures and Fig. 2 illustrates the case mix by surgical team for all the patients. The overall hospital mortality was 132 (4.2%) and 319 (10.2%) patients had pLOS-ITU. Table 1 presents the frequency of the variables studied in the derivation set and in the test set. Overall 82 patients returned on CPB in theatre, 153 required mechanical support at the end of the procedure and 1235 (39.6%) were operated by a trainee (28.8% assisted by a consultant and 11.8% independently).

View larger version (30K): [in this window] [in a new window]   Fig. 1. Distribution of cases by procedure and by preoperative risk (n=3118).

View larger version (11K): [in this window] [in a new window]   Fig. 2. Case mix by surgical team. Expressed as median EuroSCORE with interquartile ranges. C=consultant, T=trainee. Cases operated by consultant with trainee as first assistant (C+T) had a higher EuroSCORE than the other two groups of patients (P<0.001, Kruskall–Wallis test).

View larger version (14K): [in this window] [in a new window]   Fig. 3. Receiver operating characteristic curves for: (a) death; (b) pLOS-ITU; and (c) death or pLOS-ITU. Morbidity is an elusive outcome and better prediction is rewarding. Of the three models, the highest increase in area under the ROC curve was for pLOS-ITU, from 0.687 to 0.734.

	4. Discussion

Top Abstract 1. Introduction 2. Patients and methods 3. Results 4. Discussion Appendix A. Conference... References

Our results show that knowledge of adverse intraoperative events enhances preoperative risk prediction. The derivation set and the test set were different in some respects (Table 1), but it is reassuring to see that the three models retain their predictive ability in spite of these differences. The odds ratio increase per EuroSCORE percentage increase is >1 for mortality (Table 2) (when it should be very close to 1), showing that there is a true interaction between pre- and intraoperative factors. This is confirmed by the standardised rates calculated in Table 3, all of which are >1. A higher number of outcomes than expected may have several explanations: (1) patient factors not covered by EuroSCORE; (2) intraoperative factors; and (3) postoperative variables. We shall discuss the last two groups of causes.

The fact that training does not increase the mortality risk is in keeping with previous findings [7]. The apparent protective effect of trainee operating on the pLOS-ITU could be explained by the fact that consultants know what cases can be given to trainees to ensure good outcomes. For example, of two patients with the same EuroSCORE, the likelihood is that a consultant will choose to operate on the patient with less favourable anatomy or with other comorbidity not included in EuroSCORE. In other words, risk stratification cannot replace clinical experience. Prolonged ischaemic time did not affect outcome in our population, whereas long bypass times increased both the risk of death and pLOS-ITU. Return on bypass in theatre and use of mechanical support at the end of the procedure are significant independent risk factors for a negative outcome. These measures have been and should be dictated by clinical need. It would be wrong to conclude that aggressive measures solely lead to increased mortality and morbidity. Even in the postoperative period, ‘crash back on bypass’ is worthwhile in selected cases [8]. An unresolved issue in this type of analysis is the use of mechanical support at the end of the case. Like with inotropic choice and dosage, a number of surgeons will accept the complications of this therapy and have a low threshold for using it, whereas others will be more conservative. Our study cannot provide an answer to the ‘prophylactic’ versus ‘therapeutic’ mechanical support, but we feel that by arbitrarily dividing the patients into two groups this source of bias has been adequately dealt with.

The difficulty in establishing that a risk scoring system is superior to others comes from two main causes. First of all, risk scores are based on either preoperative or postoperative risk factors, but rarely include intraoperative events or the complete time sequence [9]. Parsonnet score outperformed three general severity systems in predicting death [10]. A possible explanation is that the majority of cardiac surgical patients have a systemic inflammatory response syndrome (SIRS) after extracorporeal circulation [11]. This may reduce the predictive value of acute physiology scores which, although devised for general ITU patients, have been applied with some success to cardiac surgery [9–13]. One disadvantage of severity scores is that physiological measurements have to be performed repeatedly in the first 24 h. Furthermore, demonstration of escalating SIRS and improved prediction of death requires serial measurements for up to 5 days [11]. The risk profile for coronary revascularisation may be better described by the state of the patient on arrival to ITU [14]. Mortality models can be improved by adding observations that are closer to death, such as extreme bradycardia. Open sternum, mean arterial pressure of less than 60 mmHg, use of inotropes or pressor agents are a number of independent risk factors and this information would be readily available to most units. In summary therefore, although preoperative algorithms seem to offer an advantage over severity scores, mortality prediction is still lacking the desired power [15]. A logical approach would be to combine pre-, intra- and postoperative variables [9]. Such a model, both simple to apply and precise, is not available yet.

Secondly, in terms of outcomes measured, the factors affecting mortality may differ from those determining morbidity. When hospital outcomes are used as an indicator of the quality of care, it is important to note the conclusions of Silber et al. [16]. They elegantly showed that rates of death and failure to rescue tend to be divergent from complication rates for individual hospitals. These outcomes possibly measure different aspects of healthcare delivery, or simply some units are better at rescuing. This brings us back to the role of human factors in the delivery of surgical treatments. A possible way in which patient and medical team risk factors overlap is shown in Fig. 4. Medical system factors are clearly more elusive, but the new practice environment is forcing us to submit them to closer scrutiny. We believe that an analysis such as this could be a first step in identifying major errors leading to negative outcomes. Instead of a blanket approach to audit, those cases could be targeted and analysed so that appropriate lessons are learned.

View larger version (10K): [in this window] [in a new window]   Fig. 4. The chronological interaction between patient risk factors and treatment delivery factors. In the preoperative phase patient factors are more prominent and this is useful in counselling. In the intra- and postoperative phases surgical and medical management factors are increasingly important, with direct implications for departmental quality control and cost containment.

	Acknowledgments

 
Mr S.C. Stoica is supported by a research grant from the Garfield Weston Foundation. We are grateful to Sarah Powell and Frances Carey at Papworth Hospital for their support in collecting the clinical data. Cambridge Clinical Perfusion kindly provided information on mechanical support usage.

	Footnotes

 
Presented at the joint 15th Annual Meeting of the European Association for Cardio-thoracic Surgery and the 9th Annual Meeting of the European Society of Thoracic Surgeons, Lisbon, Portugal, September 16–19, 2001.

	Appendix A. Conference discussion

Top Abstract 1. Introduction 2. Patients and methods 3. Results 4. Discussion Appendix A. Conference... References

 
Dr B. Osswald (Heidelberg, Germany): The EuroSCORE which you evaluated is a preoperative score, which means you have to evaluate, out of the preoperative variables, the risk for mortality. What you included are now the operation-related factors, which most likely predict death after operation much better. What is the aim of your score?

Mr Stoica: To try and make this point I will give you an extreme example. Let's consider a patient with a EuroSCORE of zero, a zero risk of mortality, who has a long bypass run, a long ischemic time, and also they have to return on bypass in theater and come out of the operating room with a balloon pump. It means that something has happened which changed the preoperative risk prediction, and this knowledge helps us to communicate with the patient, if they are awake, or with their family, and also to plan resources in the ITU; it affects the ITU turnover. Separately from that, we can target audit those sort of events because we need to know what is happening with low risk cases who have a bad run in theater, and perhaps we can learn more about these cases. These are the issues. So these are the two main objectives of our exercise.

As I showed in the presentation, risk modelling can be taken a step forward by introducing acute physiology scores or other variables at the time of patient arrival in the ITU. In this way prediction and discriminatory power can be further improved.

Dr S. Hagl (Heidelberg, Germany): I would like to come back to your statement before. You said that you are concentrating, if I understood well, on the patients entering the operation with a very low risk and where you have problems intraoperatively. That is possible, everybody knows. But I really doubt that you have enough numbers that you really can do a risk stratification in those patients. The normal patient, the low risk patient, having trouble intraoperatively, I think we are all happy about that, is the rarity.

Mr Stoica: This is a rare occurrence, of course, and I used this as an extreme example to make the point to the previous question. Our study encompassed the whole EuroSCORE range, and in fact we analyzed the calibration of the model across three EuroSCORE bands, low risk, medium risk and high risk, and it performed well for all of them.

	References

Top Abstract 1. Introduction 2. Patients and methods 3. Results 4. Discussion Appendix A. Conference... References

 

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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 Author home page(s): Serban C. Stoica François Roques Samer A.M. Nashef Permission Requests Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Stoica, S. C. Articles by Nashef, S. A.M. Search for Related Content PubMed PubMed Citation Articles by Stoica, S. C. Articles by Nashef, S. A.M. Related Collections Cardiac - other