Core Topics in General and Emergency Surgery

Patient assessment and surgical risk

Chris Deans

Introduction

Surgical risk is an estimation of the likelihood of an adverse event occurring as a consequence of a patient undergoing a particular surgical procedure or intervention. Patient assessmentis a process that attempts to quantify this risk for an individual patient. The ability to undertake patient assessment to determine surgical risk for a patient is fundamental to modern surgical practice. Appropriate patient assessment and estimation of risk will inform surgical decision-making, assist patient decision-making and facilitate informed consent. It is important to remember that risk is not only associated with undertaking surgical procedures, but may also include other treatments or investigations that may pose a particular risk to the patient, for example performing a colonoscopy or interventional radiological procedure. The risks of not performing a particular procedure or intervention should also be considered and the possible implications for the patient of not undertaking a procedure should be part of any informed consent process (Box 15.1).1

 

Box 15.1   GMC definition of risk of investigation/treatment

  1. Side-effects
  2. Complications
  3. Failure of an intervention to achieve the desired aim
  4. The potential outcome of taking no action

Why Assess Surgical Risk?

Estimation of surgical risk is important for several reasons. Firstly, as already stated, determining a patient's risk will influence surgical decision-making and, in turn, facilitate informed consent. This process will ultimately influence choice of treatment options for individual patients. Secondly, identifying higher risk patients will also allow appropriate pre-emptive measures to be undertaken and target particular areas of concern to optimise the patient in the perioperative period (see also Chapter 16). This process may also help anticipate potential adverse events. A further positive effect of this process is to aid case mix adjustment. There is increasing public release of activity/outcome figures (or ‘league tables’) in surgery, which may be crude mortality or complication rates. Case mix adjustment allows units with a greater proportion of high-risk patients to compensate for any differences in their figures with respect to national outcomes and allow for meaningful comparison of data against national audits. This will ensure quality assurance for the future (Box 15.2).

 

Box 15.2   Why assess surgical risk?

  1. Allow informed consent
  2. Facilitate surgical decision-making
  3. To anticipate adverse events
  4. To minimise risk to patients, staff and healthcare system
  5. To allow for meaningful comparison of outcomes

How Can We Assess Surgical Risk?

Determination of surgical risk is complex and is influenced by many variables. However, the process may be simplified by thinking of the assessment according to two main factors – the patient, and those related to the surgical procedure itself. Procedural-related risks are generally easier to quantify where national, local and even individual complication rates may be known. The introduction of national and regional audit programmes in some specialities, as well as the improved quality of data collection in local departments, have enabled a better understanding of the more common risks and complications that are associated with many surgical procedures. However, procedural-related risk will also depend on additional factors, such as the urgency and duration of the procedure, volume of blood loss and type of surgery undertaken. The National Institute for Clinical Excellence (NICE) has attempted to stratify surgical procedures into different grades of severity in an effort to provide guidance on the use of preoperative investigations and estimate the likelihood of perioperative risk (Table 15.1).2

Table 15.1

Examples of surgical procedures by severity grading (NICE)

Patient-related risk factors are less easy to quantify. They may be broadly divided into either subjective or objective factors. Subjective risk assessment includes patient history, clinical examination, pattern recognition, accumulated clinical experience and ‘the end of the bed test’. Objective risk assessment includes formal laboratory results and assessment of comorbidity and physiological function through further investigation. Patient factors will be influenced by the ‘fitness’ of the patient (functional/performance status), age, comorbid illness, the underlying disease process and nutritional status, as well as many other inter-related variables.

Several risk prediction models and scoring systems have been developed in an attempt to help measure surgical risk. In addition, techniques to formally quantify levels of patient fitness (functional assessment), such as exercise testing, and measurements of serum biomarkers have also been introduced specifically to predict perioperative risk, with variable success. This chapter will discuss some of these scoring systems and functional assessment tools that may be used for patient assessment and estimation of surgical risk. These may be broadly classified into risk prediction models (general and specific), functional assessment tools and novel biomarkers.

Estimation of surgical risk

Clinical Assessment

It is the role of the surgeon as a clinician to undertake a thorough clinical assessment of every patient in order to carefully identify individual characteristics of the patient's comorbidity and underlying disease process that may influence surgical risk. Only then may a fully informed decision be made regarding treatment choices for individual patients. Some patient factors may be clearly identifiable, such as the presence of ischaemic heart disease or obesity, whereas others may not yet have been diagnosed. A thorough history and examination should be undertaken and targeted investigations requested, based on the clinical findings. This process may be difficult and more challenging situations should involve consultation with colleagues and other clinicians as part of the wider multidisciplinary team – for example, obtaining a cardiology review or asking for an anaesthetic opinion. In difficult cases a second opinion may be sought from an independent source.

There are in fact data to support the concept that ‘gut instinct’ or ‘surgical intuition’ can be more effective than formal risk prediction models in identifying the patient with a poor prognosis in the context of a particular procedure,3 especially in the elective setting.4 However, there is also epidemiological evidence suggesting that clinicians often fail to identify patients at high risk of complications and as a result do not allocate them to the appropriate level of perioperative care.5

Risk Prediction Models And Scoring Systems

Attempts to improve the accuracy of estimation of risk and to provide a more robust quantitative assessment have led to the development of several scoring systems and risk prediction models. Many of these are freely available online.6 These tools have been developed to objectively estimate morbidity and mortality rates for individual patients prior to the proposed intervention. It should be noted that there is no perfect tool, that the models available must not be used to guide decision-making in isolation and that there is no substitute for the combination of objective markers with surgical experience and intuition. Furthermore, the models available pertain to populations rather than individual patients, and therefore have limitations that need to be recognised before using them to inform surgical risk assessment. For example, the mortality rate for a surgical intervention in a particular population may be 5%, but for the individual patient it can only be 0% or 100%.

The scoring systems available usually incorporate physiological and comorbidity data that have been selected using logistical regression techniques in a large database of patients, which may not be similar to the local population. A coefficient may be assigned in order to weight the variables and the resulting equation provides a numerical indication of risk for the patient, although it is frequently more meaningful to the operating surgeon.

Ideally the patients in the database on which the scoring system is developed and validated should be similar to the individual patient in question. However, publication bias may mean that only the best data are published. While this has inherent flaws, it may also be viewed as an opportunity to benchmark one's own results against those from a centre of excellence.

Finally, the accuracy of predictive models is dynamic and they should be periodically retested against an evolving surgical patient population. When accuracy deteriorates they should be revised and updated.

POSSUM

The Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (POSSUM) was first described in 1991.7 It was designed as a scoring system to estimate morbidity and mortality following a surgical procedure and, by including data on the patient's physiological condition, it provides a risk-adjusted prediction of outcome. This facilitates more accurate comparison of hospital or surgeon performance and it can be used as an audit and clinical governance tool. However, the model involves inclusion of operative variables, such as volume of blood loss and the presence of peritoneal soiling, which precludes its use in the preoperative setting to inform the consent process. Despite this, POSSUM is the most widely applied, validated, surgical risk scoring system in the UK and has been modified by several authors to provide speciality-specific information.

The original POSSUM score was developed after initially subjecting 62 parameters to multivariate analysis in order to determine the most powerful outcome predictors. Twelve physiological and six operative parameters were identified, and each of these factors were weighted to a value of 1, 2, 4 or 8 to simplify the calculation. It was re-evaluated in 1998 in Portsmouth, UK by Whiteley et al., who reported concerns that it overestimated mortality in their patients, particularly in the lowest risk group.8 They modified the POSSUM formula and they constructed the Portsmouth predictor equation for mortality (P-POSSUM; Box 15.3). The modified formula fitted well with the observed mortality rate; however, it still overestimates mortality in low-risk groups, the elderly and in certain surgical subspecialities.9 The latter finding has prompted the development of speciality-specific POSSUM for major elective surgery.

 

Box 15.3   Variables used in the calculation of the P-POSSUM score

Physiological variables

Age

Cardiac disease

Respiratory disease

Electrocardiogram (ECG)

Systolic blood pressure

Pulse rate

Haemoglobin concentration

White cell count

Serum urea concentration

Serum sodium concentration

Serum potassium concentration

Glasgow Coma Scale (GCS)

Operative variables

Operation severity class

Number of procedures

Blood loss

Peritoneal contamination

Malignancy status

Urgency

P-POSSUM formula

Ln R/1 − R = − 9.065 + (0.1692 × physiological score) + (0.1550 × operative severity score)

CR-POSSUM (colorectal)

The value of POSSUM and P-POSSUM in predicting in-hospital mortality was examined in patients undergoing colorectal surgery in France. Both POSSUM and P-POSSUM performed well but overestimated postoperative death in elective surgery and the authors concluded that it had not been validated in France in the field of colorectal surgery.10 The original POSSUM score and P-POSSUM were derived from a heterogeneous population of general surgical patients. Subgroup analysis of high-risk colorectal surgery patients found that the models under-predicted death in the emergency patients.11 There was also a lack of calibration at the extremes of age in both emergency and elective work. This resulted in remodelling of the POSSUM score for colorectal surgery patients and led to the development of the Colorectal-POSSUM model (CR-POSSUM). The CR-POSSUM model was superior to the P-POSSUM model in predicting operative mortality in a study involving almost 7000 patients undergoing emergency and elective colorectal surgery in the UK.12

External validation of CR-POSSUM was derived from three multicentre, UK studies involving a total of 16 006 patients: the original CR-POSSUM study population (n = 6883), the Association of Coloproctology of Great Britain and Ireland (ACPGBI) Colorectal Cancer (CRC) Database (n = 8077) and the ACPGBI Malignant Bowel Obstruction (MBO) Study (n = 1046). Of the different risk models that were tested, CR-POSSUM was superior in predicting postoperative death.13

This conflicted to a certain extent with a later study in New Zealand involving 308 patients undergoing major colorectal surgery. In this cohort POSSUM, P-POSSUM and CR-POSSUM were all satisfactory predictive tools for postoperative mortality but the latter tended to be relatively less accurate. However, the authors noted that CR-POSSUM requires fewer individual patient parameters to be calculated and is therefore simpler to use.14 A more recent systematic review pooled data from 18 studies to compare the accuracy of POSSUM, P-POSSUM and CR-POSSUM in predicting postoperative mortality for patients undergoing colorectal cancer surgery.15 This study also reported greater predictive accuracy for the P-POSSUM model compared with CR-POSSUM.

CR-POSSUM is not an accurate predictor of disease-specific colorectal mortality. A study from the Netherlands on patients undergoing elective sigmoid resection for either carcinoma or diverticular disease demonstrated that CR-POSSUM over-predicted mortality in the patients with malignant disease and under-predicted it in patients with benign disease. However, in the whole group CR-POSSUM predicted postoperative mortality accurately.16

A possible modification to the CR-POSSUM system was suggested following a single-centre UK study in 304 patients. CR-POSSUM proved to be a more accurate predictive model than POSSUM and P-POSSUM and, interestingly, logistic regression demonstrated a significant correlation between albumin and mortality. It may therefore be possible to improve the accuracy of CR-POSSUM further by modifying the equation to include serum albumin.17

O-POSSUM (oesophagogastric)

A UK study of 204 patients demonstrated that POSSUM did not accurately predict morbidity and mortality in patients undergoing oesophagectomy.18 A dedicated oesophagogastric model (O-POSSUM) developed in a study population of 1042 patients was described in 2004. The O-POSSUM model used the following independent factors: age, physiological status, mode of surgery, type of surgery and histological stage. It provided a more accurate risk-adjusted prediction of death from oesophageal and gastric surgery for individual patients than P-POSSUM.19 However, to date there have been several conflicting studies examining the predictive value and accuracy of O-POSSUM. A Dutch study of 663 patients undergoing potentially curative oesophagectomy in a tertiary referral centre (in-hospital mortality 3.6%) demonstrated that O-POSSUM over-predicted in-hospital mortality threefold and could not identify those patients with an increased risk of death.20 This was supported in similar studies from both the UK and Hong Kong, which found that P-POSSUM provided the most accurate prediction of in-hospital mortality and O-POSSUM again over-predicted mortality in patients, particularly with low physiological scores and in older patients.21,22 A recent systematic review comparing P-POSSUM with O-POSSUM, which included data from 10 studies, concluded that P-POSSUM was the most accurate predictor of postoperative mortality and O-POSSUM consistently overestimated postoperative mortality in gastro-oesophageal cancer patients.23

In summary, the data reported in the original study to construct O-POSSUM have not been validated in other centres. It may be that individual units have to modify the O-POSSUM model to take account of local factors.

V-POSSUM (vascular)

V-POSSUM was devised for use specifically in patients undergoing arterial surgery. One study examined the records of 1313 patients and added ‘extra items’ to the original POSSUM dataset, although this did not appear to significantly improve the accuracy of prediction.24 The model has, however, been used in further studies and has been modified further to only take account of the physiology component of the score, with improved prediction accuracy (V-POSSUM physiology only). However, a current UK study involving almost 11 000 patients undergoing elective abdominal aortic aneurysm repair evaluated the accuracy of five risk prediction models, including V-POSSUM.25 V-POSSUM performed poorly, with the Medicare and Vascular Governance North West (VGNW) models demonstrating the best discrimination, leaving the authors to conclude that V-POSSUM should not be used for risk prediction for these patients. Neither the V-POSSUM nor P-POSSUM models appear to be accurate in predicting mortality in the context of ruptured aortic aneurysms.24 The finding that V-POSSUM may be of limited value in the context of emergency arterial surgery was confirmed by a larger and more recent evaluation of the appropriate POSSUM models in the context of ruptured abdominal aortic aneurysms (RAAAs).26 When the P-POSSUM, RAAA-POSSUM, RAAA-POSSUM (physiology only), V-POSSUM and V-POSSUM (physiology only) models were all compared in 223 patients with RAAA (in-hospital mortality was 32.4%), all except V-POSSUM and P-POSSUM (physiology only) demonstrated no significant lack of fit.

As one may expect, the various POSSUM models are not accurate predictive tools in the context of elective carotid surgery. Both POSSUM and V-POSSUM over-predicted mortality in a large single-centre study (n = 499) of patients undergoing carotid endarterectomy.27 This is not surprising given the nature of the surgery and the reduced surgical insult compared to body cavity procedures.

A recent evaluation of V-POSSUM in New Zealand has indicated that it is a useful tool not only in the assessment of outcome, but of longitudinal surgical performance in major vascular surgery. Major vascular procedures (n = 454) were prospectively scored for V-POSSUM over a 10-year period. There was a trend towards improved surgical performance over time, with a drop in the observed to predicted ratios of deaths. This novel role has not yet been tested in the other POSSUM models, but given these data there may be the potential to use them to evaluate surgical training and performance in other surgical subspecialities.28

 

POSSUM is the most widely applied, validated, surgical risk scoring system currently used in the UK. The original POSSUM equation has been modified in an effort to increase its accuracy as a risk prediction tool for in-hospital surgical mortality. Of these, P-POSSUM is the most widely used and validated general modification. Speciality-specific POSSUM has also been developed for use in colorectal, oesophagogastric and vascular patients, with some variable improvement in risk stratification. The available data, however, suggest that the various POSSUM models have a tendency to overestimate mortality rates. Within these limitations, the POSSUM models provide a useful tool for risk assessment, audit, and comparing outcomes between different units and within the same unit over a period of time. However, due to the variables included in the calculation, POSSUM cannot be used in the preoperative setting to inform risk.

ASA

In 1963 the American Society of Anesthesiologists (ASA) adopted a five-point classification system for assessing the physical status of a patient prior to elective surgery. A sixth category was added later (Box 15.4).

 

Box 15.4   ASA classification

  1. A normal healthy patient
  2. A patient with mild systemic disease
  3. A patient with severe systemic disease
  4. A patient with severe systemic disease that is a constant threat to life
  5. A moribund patient who is not expected to survive without the operation
  6. A declared brain-dead patient whose organs are being removed for donor purposes

Note: If the surgery is an emergency, the ASA grade is followed by ‘E’ (for emergency), for example ‘3E’. Category 5 is always an emergency so should not be written without ‘E’.

The ASA grade is essentially a combination of the subjective opinion of the anaesthetist taken in conjunction with a more objective assessment of the patient's general fitness for surgery, and is used routinely in most centres in the UK. There are a number of studies assessing the utility and accuracy of the ASA grade in determining surgical risk and, as anticipated given the nature of this scoring system, the literature is conflicting.

One study of 113 anaesthetists in the UK demonstrated such marked variation in the inter-individual assessment of 10 hypothetical patients that the authors concluded that the ASA grade should not be used on its own to predict surgical risk.29 A further study of 97 anaesthetists demonstrated that the agreement for the assessment of each hypothetical patient varied from 31% to 85%. The overall correlation was only fair, and the inter-observer inconsistency was similar to that in a study from 20 years previously.30

However, the largest study to date is more encouraging.31 Of 16 227 patients undergoing elective surgery over a 5-year period, 215 died within 4 weeks of operation. There was a significant correlation between perioperative mortality and the ASA grade. The mortality was lowest (0.4%) when the ASA grade was less than or equal to 2 and increased up to 7.3% in ASA grade 4 patients. The authors concluded that perioperative mortality can be predicted using the ASA grade.

 

The ASA classification remains a quick, simple, widely used and reasonably accurate assessment of surgical risk in both the elective and emergency settings.

Surgical mortality probability model

The surgical mortality probability model (SMPM) was derived from a retrospective data analysis of almost 300 000 patients using the American College of Surgeons National Surgical Quality Improvement Program Database.32 The primary outcome was 30-day mortality for patients undergoing non-cardiac surgery. The model identified three risk factors – ASA status; emergency or elective surgery; and surgery risk class – as the main determinants of outcome. Points are allocated in accordance with these three factors and predicted 30-day mortality is calculated from the total score (Table 15.2). Patients with a total risk score less than 5 had a predicted mortality of less than 0.5%, whereas a risk score greater than 6 predicted a mortality of more than 10%. This new model has yet to be fully externally and prospectively validated but may prove to be a useful risk tool for the future.

Table 15.2

The surgical mortality probability model (SMPM)

Risk factor

Points

ASA

 

I

0

II

2

III

4

IV

5

V

6

Procedure severity

 

Low

0

Intermediate

1

High

2

Urgency

 

Elective

0

Emergency

1

Total points

Mortality risk

0–4

< 0.5%

 

5–6

1.5–4%

 

7–9

> 10%

 

The SMPM was developed to predict 30-day mortality for patients undergoing non-cardiac surgery. The model utilises three risk factors (ASA status, severity of procedure and urgency of the procedure). A score is awarded for each variable and the mortality risk is calculated form the total score.

 

The surgical mortality probability model (SMPM) is a newly devised risk assessment tool to predict 30-day surgical mortality. It has the advantage of using simple data readily available at the bedside. Whether the SMPM becomes widely established as a risk model will depend on the results of future validation studies.

Revised Cardiac Risk Index

The risk models described thus far have been designed to predict general mortality and morbidity for a patient population. Some risk models have been developed with the aim of predicting specific complications, such as risk of cardiac or pulmonary complications in the postoperative period. Of these, the Revised Cardiac Risk Index (RCRI) is the most commonly used. This model was published in 1999 with the aim to develop an index of risk for cardiac complications in major elective non-cardiac surgery.33 Six independent predictors of complications were identified and included high-risk type of surgery, history of ischaemic heart disease, history of heart failure, history of stroke, diabetes requiring insulin, and elevated baseline serum creatinine. The risk of myocardial infarction and cardiac death could then be predicted according to the number of risk factors that were present (Table 15.3Fig. 15.1). A recent systematic review was performed to evaluate the current accuracy of the RCRI to predict cardiac complications and death after non-cardiac surgery.34 The authors concluded that the RCRI discriminated moderately well between patients at low versus high risk for cardiac events, but it did not perform well at predicting cardiac events after vascular surgery or at predicting death within 30 days.

Table 15.3

The Revised Cardiac Risk Index: six risk factors to predict mortality and cardiovascular complications following surgery

Risk factor

Number of risk factors

Risk of death/ myocardial infarction

Major (high risk) surgery

0

0.4%

History of ischaemic heart disease

1

1%

History of heart failure

2

2.4%

History of cerebrovascular disease

3

5.4%

Diabetes requiring insulin treatment

   

Serum creatinine concentration > 177 μmol/L

   

FIGURE 15.1 Risk of major cardiac complications predicted by the Revised Cardiac Risk Index according to type of surgical procedure performed. The greater the number of risk factors present, the greater the risk of complications, irrespective of the type of surgery undertaken. Reproduced from Lee TH, Marcantonio ER, Mangione CM et al. Derivation and prospective validation of a simple index for prediction of cardiac risk of major noncardiac surgery. Circulation 1999; 100(10):1043–9. With permission from Wolters Kluwer Health.

Other risk prediction models

There are many other published risk prediction models that have not been presented here for reasons of brevity. Despite showing initial promise, many of these models have been disappointing when validated against external patient populations. The inability of these models to reproduce initial predictive accuracy has resulted in many failing to gain widespread acceptance. It is fair to say that at present there is no single model that can accurately predict surgical risk for all patient populations.

Functional Assessment

Assessment of exercise capacity provides useful information about the functional status of a patient and their response to physiological stress. This information can then be used to inform about how the patient might respond to surgical stress and may therefore be used to predict perioperative risk. Patients with higher exercise tolerance usually have lower risk. Evaluation of exercise capacity may be subjective or objective, where formal exercise testing is performed.

Subjective assessment of exercise tolerance can usually be easily undertaken by asking some simple questions to assess the functional capacity of the patient. Determination of how many stairs a patient can climb before stopping due to limitation by symptoms, or how far they can walk on the flat without stopping, are commonly employed questions. There is evidence to suggest that these simple assessments of exercise capacity correlate with surgical risk. In a study of 600 patients undergoing major non-cardiac surgery serious postoperative complications, especially cardiac complications, were twice as common for those patients who were unable to climb two flights of stairs preoperatively.35 Inability to climb two flights of stairs was associated with a positive predictive value of 82% for the development of cardiopulmonary complications in patients undergoing major thoracic and abdominal surgery and stair-climbing ability was inversely related to duration of hospital stay.36

Exercise capacity may be more objectively measured. Metabolic equivalent of tasks (METs) is a measure of energy expenditure related to physical activity. One MET may be considered as the resting metabolic rate (RMR) and is defined as energy consumption at a rate of 3.5 mL O2 per kg per minute. Physical activities may be measured as a ratio compared to the RMR. For example, ironing clothes is equivalent to 1.8 METs and climbing two flights of stairs is equivalent to 4 METs. Some further examples are given in Table 15.4. This process may then be used to identify patients with reduced exercise capacity and who may benefit from a more objective assessment of their functional status. A full list of physical activities and the MET equivalents can be found at the web address listed in Ref. 37.

Table 15.4

Examples of common activities and their metabolic equivalents (METs)

Activity

MET value

Watching television

1

Showering

2

Playing the piano

2.3

Washing the dishes

2.5

Playing snooker

2.5

Walking the dog

3

Slow ballroom dancing

3

Lawn bowls

3

Moderate housework

3.5

Climbing two flights of stairs

4

Golf (using an electric cart)

3.5

Golf (carrying clubs)

4.3

Mowing the lawn

5.5

Moderate swimming

5.8

Jogging

7

Running (10 minute/mile pace)

9.8

Cardiopulmonary exercise testing (CPEX)

Cardiopulmonary exercise testing (CPEX) has been introduced in an attempt to provide a measurable, objective assessment of cardiorespiratory function for the assessment of surgical risk. In 1993 Older et al. performed CPEX testing among a group of elderly patients undergoing major surgery. An anaerobic threshold (AT) of less than 11 O2 mL/min/kg was associated with a mortality rate of 18% compared with a mortality rate of less than 1% for those patients with an AT greater than 11 O2 mL/min/kg.38 Subsequent studies have similarly shown that an AT of less than 11 O2 mL/min/kg was associated with increased hospital mortality following major elective abdominal and vascular surgery.39,40 CPEX testing was also predictive of longer-term outcome. In a study of 102 patients undergoing elective abdominal aortic aneurysm repair, CPEX testing was not only predictive of 30-day mortality, but was also predictive of longer-term survival at 30 months.40 Other studies have identified different values for the optimal discriminatory level for the anaerobic threshold. CPEX testing was undertaken in patients with a low functional capacity (less than 7 METs) who subsequently underwent major surgery. A lower AT value was associated with increased likelihood of postoperative complications and the optimal AT threshold for the study group was identified at 10.1 O2 mL/min/kg.41 An AT cut-off of 11 mL/kg/min was also a poor predictor of postoperative cardiopulmonary morbidity for patients undergoing oesophagectomy for cancer.42 However, this study did demonstrate an association between lower exercise capacity and risk of complications, suggesting that this study was underpowered and/or an alternative AT threshold may be more suitable for this group of patients. The optimal cut-off value for the anaerobic threshold is generally accepted at 11 O2 mL/min/kg.43 It is interesting that this value closely relates to 4 METs (14 O2 mL/min/kg) and, in turn, ability to climb two flights of stairs. This would suggest that patients who are able to climb two flights of stairs would have an anaerobic threshold greater than11 O2 mL/min/kg. Stair climbing therefore has the potential to be used as a screening tool for the identification of patients who would benefit from further assessment by CPEX testing. However, it remains unclear whether this is the optimal AT value or indeed if alternative thresholds should be adopted for different patient groups or for different surgical procedures.

Other limitations of CPEX testing relate to the process of conducting the test itself. Patients are required to exercise, usually on a cycle ergometer, and full assessment may be limited by physical ability rather than limitations due to cardiorespiratory function – for example, patients with arthritis or amputees. Another potential limitation of CPEX testing relates to availability and cost. The equipment and expertise to perform the test are not widely available in the UK at present. A survey conducted in England during 2008 found that only 30 (17%) hospitals had a CPEX service, with an additional 12 (7%) in the process of setting one up.43 Despite these limitations, CPEX testing is becoming an increasingly adopted tool for preoperative assessment of higher risk patients undergoing major surgery.

 

Cardiopulmonary exercise testing (CPEX) is the ‘gold standard’ measure of cardiorespiratory function. An anaerobic threshold (AT) less than 11 O2 mL/min/kg has been associated with increased risk of postoperative complications and mortality, although the exact threshold AT value may need to be modified for different patient groups or different surgical procedures. CPEX testing requires specialist equipment and expertise to perform, and it is not widely available in the UK at present, but it is likely to be increasingly used for assessment of perioperative risk in selected high-risk patient populations.

Other objective measures of exercise capacity

The incremental shuttle walk test (ISWT) requires the patient to walk between two markers placed 10 metres apart within a set time period. This time period becomes progressively shorter, requiring more effort from the patient to make the distance within the shorter time. The test stops when the patient cannot reach the end of the 10-metre course within the given time. The ISWT has been shown to correlate with measured oxygen consumption in patients with cardiac and chronic lung disease.44 A small study investigated the role of ISWT to predict 30-day mortality following oesophagogastrectomy.45 No patients with a walk distance greater than 350 metres died in the postoperative period. Patients who managed to walk a distance less than 350 metres had a 50% 30-day mortality. Distance achieved on the shuttle walk test was compared with CPEX measurements in a study of 50 patients undergoing abdominal surgery. All patients who walked in excess of 360 metres had an anaerobic threshold (AT) greater than 11 O2 mL/min/kg.46 It was also noted that some patients who walked less than 360 metres may also have had satisfactory CPEX results, suggesting that the ISWT was good at identifying patients with a good AT, but could not accurately identify those who had a poor anaerobic threshold (i.e. a good positive predictive value, but poor negative predictive value). The study was not, however, sufficiently powered to investigate surgical outcomes. These data suggest that the ISWT may be used as a screening tool to identify patients who may then benefit from more formal exercise testing with CPEX (i.e. those who walked less than 350–360 metres).

The 6-minute walk test is another standardised assessment tool for estimation of exercise capacity. The test involves measuring the distance that a patient can cover during a 6-minute period. The patient is instructed to walk as fast as they can to cover the maximum possible distance. The AT determined by CPEX testing was compared with maximum distance achieved during the 6-minute walk test in a study of 110 patients awaiting major general surgery. Patients who completed in excess of 563 metres during the 6-minute test had an AT greater than 11 O2 mL/min/kg and those who managed less than 427 metres had an AT less than 11 O2 mL/min/kg.47 The authors recommended that those patients who completed 563 metres did not require formal exercise testing, whereas those who could not manage more than 427 metres should undergo CPEX assessment. Those patients who walked between 427 and 563 metres belong to a group of ‘clinical uncertainty’, and other clinical risk factors and magnitude of surgery should be incorporated into the decision-making process.

 

The incremental shuttle walk test (ISWT) and the 6-minute walk test are simple tools to objectively assess exercise capacity. They are indirect tests of oxygen consumption and have been shown to correlate with formal exercise testing values (CPEX). The main value of these tests is to identify higher-risk patient populations who may benefit from formal exercise testing.

Biomarkers To Assess Risk

There is emerging evidence that estimation of serum concentration of biomarkers in the preoperative period may assist risk stratification for patients undergoing surgery. Brain natriuretic peptide (BNP) and C-reactive protein (CRP) are the most promising biomarkers for risk assessment. BNP is released from cardiac ventricles in response to excessive stretching and elevated serum concentrations are correlated with prognosis in heart failure.48 Elevated preoperative serum concentration of BNP (> 40 pg/mL) was associated with an increased risk of death and perioperative cardiac events in a study of 204 patients undergoing non-cardiac surgery.49 A further study of 190 patients undergoing elective non-cardiac surgery also identified elevated serum NT-proBNP (a co-secretory product of BNP) as a predictor of postoperative cardiac complications, which was independently prognostic on multivariate analysis.50 A recent meta-analysis examined the predictive value of preoperative serum BNP concentrations for predicting postoperative mortality and cardiac complications following vascular surgery.51 The authors concluded that elevated BNP concentrations were predictive of adverse outcome, but there was wide variation in the serum concentration of BNP that was chosen as the threshold for discrimination (range 35–100 pg/mL). The optimal discriminatory concentration remains unknown and it is likely that threshold values may vary depending on the patient group under investigation.

CRP is a marker of systemic inflammation and serum concentrations are associated with atherosclerotic disease and adverse outcomes in cancer. A preoperative serum CRP concentration greater than 6.5 mg/L was associated with increased 30-day mortality and postoperative cardiac complication rates in a study involving 592 patients undergoing vascular surgery (odds ratio 2.5; 95% confidence interval 1.5–4.3).52 Moreover, this association was independent of serum BNP concentration and also established cardiac risk factors. The association between elevated CRP concentration and adverse perioperative outcome may be due, in part, to a correlation between markers of systemic inflammation and exercise capacity. Elevated serum CRP concentrations have been demonstrated to be inversely correlated with VO2 max in male subjects without evidence of coronary heart disease.53 Further study is required to determine the true value of serum biomarkers in risk assessment for surgical patients.

 

Brain natriuretic peptide (BNP) and C-reactive protein (CRP) are the most promising biomarkers for risk assessment. Elevated preoperative serum concentrations have been associated with increased risk of mortality and cardiac complications in surgical patients; however, the optimal threshold cut-off value remains unknown. The real value of serum biomarkers may lie in the selection of patients into high- or low-risk groups and therefore help identify which patients merit further assessment.

Communicating risk

The use of risk prediction models, scoring systems, exercise tests and serum biomarkers as adjuncts to decision-making is an increasingly important part of surgical practice. This information must then be communicated effectively to the patient to allow fully informed choice. GMC guidance on this issue states that:

Clear, accurate information about the risks of any proposed investigation or treatment, presented in a way patients can understand, can help them make informed decisions. The amount of information about risk that the clinician should share with patients will depend on the individual patient and what they want, or need, to know. Discussions with patients should therefore focus on their individual situation and the risk to them.1

In communicating risk there are several techniques to impart the concept of how likely it is that the patient will have a complication of the procedure, or die as a result of it. These broadly fall into using numerical data or descriptive details of risk. As always, this communication must be tailored to the needs and expectations of the individual patient and it is likely that a combination of these techniques will be most appropriate.

Percentages alone are often not well understood, and as they apply to a population rather than an individual patient, they may be misleading. Odds, relative risk and absolute risk may be too complex, but quoting for example ‘a 1 in 10 or 1 in 100 chance’ may be helpful. Using relativity (comparison with a concept the patient understands) or examples (‘of the last 50 patients this has happened to …’) may also clarify the concept of surgical risk to the patient.

Finally, it is worth remembering that the perceived surgical risk that concerns the surgeon is not necessarily what the patient is worried about. Assessing, discussing and communicating risk has the primary aim of allowing patients to understand what may happen to them, and to help them make an informed choice about investigation or therapeutic options. However, as a consequence of this, coupled with careful documentation, it affords the surgeon some protection against litigation.

 

Key points

  • Estimation of surgical risk is vital to enhance treatment decision-making and facilitate informed consent, anticipate potential complications and target aspects of care to optimise the patient, and allow meaningful comparison of clinical outcomes, audit and quality assurance.
  • Determination of surgical risk is complex, but may be more simply considered in terms of patient-relatedrisks and procedural-related risks.
  • Patient-related risk factors will be influenced by patient age, comorbidity, the underlying disease process, nutritional status and the performance status of the patient.
  • Procedural-related risk factors include the grade of severity of the procedure planned, urgency of the procedure, volume of blood loss and other technical aspects.
  • Risk prediction models and scoring systems (such as POSSUM, ASA and the Revised Cardiac Risk Index) have been developed in an attempt to improve risk prediction. These tools work best for patient populations (groups) rather than individual patients, and therefore their main value is for audit purposes and comparing outcomes between different units and within the same units over time. There is no perfect risk prediction model.
  • Assessment of functional capacity may be easily undertaken through the use of simple screening questions. More objective measurements may be performed by using standardised walking tests or CPEX testing.
  • Serum biomarkers, such as BNP and CRP, may have a future role in identifying high-risk surgical patient groups, who may then benefit from more detailed assessment.
  • Estimation of surgical risk should include a thorough clinical assessment, an assessment of the functional capacity of the patient (through simple questions relating to METs) and should take into account the severity of the surgical procedure proposed. If this process identifies the patient to be at high risk, then further testing should be considered – for example, objective exercise testing (CPEX).

References

  1. GMC. Consent: patients and doctors making decisions together. General Medical Council; 2008.
  2. NICE. The use of routine preoperative tests for elective surgery. National Institute for Clinical Excellence; 2003.
  3. Hartley, M.N., Sagar, P.M. The surgeon's ‘gut feeling’ as a predictor of post-operative outcome. Ann R Coll Surg Engl. 1994;76(6, Suppl.):277–278.
  4. Markus, P.M., Martell, J., Leister, I., et al, Predicting postoperative morbidity by clinical assessment. Br J Surg. 2005;92(1):101–106. 15635697
  5. Pearse, R.M., Holt, P.J.E., Growcott, M.P.W. Managing perioperative risk in patients undergoing elective non-cardiac surgery. Br Med J. 2011;343:5759.
  6. http://www.riskprediction.org.uk/; [accessed 25.09.12].
  7. Copeland, G.P., Jones, D., Walters, M., POSSUM: a scoring system for surgical audit. Br J Surg. 1991;78(3):355–360. 2021856
  8. Whiteley, M.S., Prytherch, D.R., Higgins, B., et al, An evaluation of the POSSUM surgical scoring system. Br J Surg. 1996;83(6):812–815. 8696749
  9. Wakabayashi, H., Sano, T., Yachida, S., et al, Validation of risk assessment scoring systems for an audit of elective surgery for gastrointestinal cancer in elderly patients: an audit.Int J Surg. 2007;5(5):323–327. 17462968
  10. Slim, K., Panis, Y., Alves, A., et al. Predicting postoperative mortality in patients undergoing colorectal surgery. World J Surg. 2006;30(1):100–106.
  11. Tekkis, P.P., Kessaris, N., Kocher, H.M., et al, Evaluation of POSSUM and P-POSSUM scoring systems in patients undergoing colorectal surgery. Br J Surg. 2003;90(3):340–345.12594670
  12. Tekkis, P.P., Prytherch, D.R., Kocher, H.M., et al, Development of a dedicated risk-adjustment scoring system for colorectal surgery (colorectal POSSUM). Br J Surg. 2004;91(9):1174–1182. 15449270
  13. Al-Homoud, S., Purkayastha, S., Aziz, O., et al. Evaluating operative risk in colorectal cancer surgery: ASA and POSSUM-based predictive models. Surg Oncol. 2004;13(2–3):83–92.
  14. Vather, R., Zargar-Shoshtari, K., Adegbola, S., et al. Comparison of the POSSUM, P-POSSUM and Cr-POSSUM scoring systems as predictors of postoperative mortality in patients undergoing major colorectal surgery. Aust N Z J Surg. 2006;76(9):812–816.
  15. Richards, C., Leith, F., Horgan, P.G., et al. Predicting post-operative mortality in colorectal cancer surgery: a systematic review of the accuracy of POSSUM, P-POSSUM and CR-POSSUM. Gastroenterology. 2010;38(5, Suppl. 1):S-853.
  16. Oomen, J.L., Cuesta, M.A., Engel, A.F., Comparison of outcome of POSSUM, P-POSSUM, and CR-POSSUM scoring after elective resection of the sigmoid colon for carcinoma or complicated diverticular disease. Scand J Gastroenterol. 2007;42(7):841–847. 17558908
  17. Bromage, S.J., Cunliffe, W.J., Validation of the CR-POSSUM risk-adjusted scoring system for major colorectal cancer surgery in a single center. Dis Colon Rectum. 2007;50(2):192–196. 17164963
  18. Zafirellis, K.D., Fountoulakis, A., Dolan, K., et al, Evaluation of POSSUM in patients with oesophageal cancer undergoing resection. Br J Surg. 2002;89(9):1150–1155. 12190681
  19. Tekkis, P.P., McCulloch, P., Poloniecki, J.D., et al, Risk-adjusted prediction of operative mortality in oesophagogastric surgery with O-POSSUM. Br J Surg. 2004;91(3):288–295.14991628
  20. Lagarde, S.M., Maris, A.K., de Castro, S.M., et al, Evaluation of O-POSSUM in predicting in-hospital mortality after resection for oesophageal cancer. Br J Surg. 2007;94(12):1521–1526. 17929231
  21. Nagabhushan, J.S., Srinath, S., Weir, F., et al, Comparison of P-POSSUM and O-POSSUM in predicting mortality after oesophagogastric resections. Postgrad Med J. 2007;83(979):355–358. 17488869
  22. Lai, F., Kwan, T.L., Yuen, W.C., et al, Evaluation of various POSSUM models for predicting mortality in patients undergoing elective oesophagectomy for carcinoma. Br J Surg. 2007;94(9):1172–1178. 17520711
  23. Dutta, S., Horgan, P.G., McMillan, D.C., POSSUM and its related models as predictors of postoperative mortality and morbidity in patients undergoing surgery for gastro-oesophageal cancer: a systematic review. World J Surg. 2010;34(9):2076–2082. 20556607
  24. Prytherch, D.R., Sutton, G.L., Boyle, J.R., Portsmouth POSSUM models for abdominal aortic aneurysm surgery. Br J Surg. 2001;88(7):958–963. 11442527
  25. Grant, S.W., Grayson, A.D., Mitchell, D.C., et al, Evaluation of five risk prediction models for elective abdominal aortic aneurysm repair using the UK National Vascular Database.Br J Surg. 2012;99(5):673–679. 22415901
  26. Bown, M.J., Cooper, N.J., Sutton, A.J., et al. The postoperative mortality of ruptured abdominal aortic aneurysm repair. Eur J Vasc Endovasc Surg. 2004;27(1):65–74.
  27. Kuhan, G., Abidia, A.F., Wijesinghe, L.D., et al, POSSUM and P-POSSUM overpredict mortality for carotid endarterectomy. Eur J Vasc Endovasc Surg. 2002;23(3):209–211. 11914006
  28. Mosquera, D., Chiang, N., Gibberd, R. Evaluation of surgical performance using V-POSSUM risk-adjusted mortality rates. Aust N Z J Surg. 2008;78(7):535–539.
  29. Haynes, S.R., Lawler, P.G., An assessment of the consistency of ASA physical status classification allocation. Anaesthesia. 1995;50(3):195–199. 7717481
  30. Mak, P.H., Campbell, R.C., Irwin, M.G., The ASA Physical Status Classification: inter-observer consistency. American Society of Anesthesiologists. Anaesth Intensive Care. 2002;30(5):633–640. 12413266
  31. Prause, G., Ratzenhofer-Comenda, B., Pierer, G., et al, Can ASA grade or Goldman's cardiac risk index predict perioperative mortality? A study of 16,227 patients. Anaesthesia. 1997;52(3):203–206. 9124658
  32. Glance, L.G., Lustik, S.J., Hannan, E.L., et al, The surgical mortality probability model: derivation and validation of a simple risk prediction rule for noncardiac surgery. Ann Surg2012;255:696–702. 22418007
  33. Lee, T.H., Marcantonio, E.R., Mangione, C.M., et al, Derivation and prospective validation of a simple index for prediction of cardiac risk for noncardiac surgery. Circulation1999;100:1043–1049. 10477528
  34. Ford, M.K., Beattie, S., Wijeysundera, D.N., Systematic review: prediction of perioperative cardiac complications and mortality by the Revised Cardiac Risk Index. Ann Intern Med. 2010;152(1):26–35. 20048269
  35. Reilly, D.F., McNeely, M.J., Doerner, D., et al, Self-reported exercise tolerance and the risk of serious perioperative complications. Arch Intern Med. 1999;159(18):2185–2192.10527296
  36. Girish, M., Trayney, E., Dammann, O., et al, Symptom-limited stair climbing as a predictor of postoperative cardiopulmonary complications after high-risk surgery. Chest2001;120:1147–1151. 11591552
  37. Ainsworth, B.E., Haskell, W.L., Herrmann, S.D., et al, Compendium of physical activities: a second update of codes and MET values. Med Sci Sports Exercise. 2011;43(8):1575–1581. [accessed 25.09.12]. https://sites.google.com/site/compendiumofphysicalactivities/home
  38. Older, P., Smith, R., Courtney, P., et al, Preoperative evaluation of cardiac failure and ischemia in elderly patients by cardiopulmonary exercise testing. Chest. 1993;104(3):701–704.8365279
  39. Wilson, R.J., Davies, S., Yates, D., Impaired functional capacity is associated with all-cause mortality after major elective abdominal surgery. Br J Anaesth2010;105:297–303.20573634
  40. Thompson, A.R., Peters, N., Lovegrove, R.E., et al, Cardiopulmonary exercise testing provides a predictive tool for early and late outcomes in abdominal aortic aneurysm patients.Ann R Coll Surg Engl. 2011;93(6):474–481. 21929919
  41. Snowden, C., Prentis, J., Anderson, H.L., et al, Submaximal cardiopulmonary exercise testing predicts complications and hospital length of stay in patients undergoing major elective surgery. Ann Surg. 2010;251(3):535–541. 20134313
  42. Forshaw, M.J., Strauss, D.C., Davies, A.R., et al, Is cardiopulmonary exercise testing a useful test before oesophagectomy? Ann Thorac Surg2008;85:294–299. 18154826
  43. Simpson, J.C., Sutton, H., Grocott, M.P. Cardiopulmonary exercise testing – a survey of current use in England. J Intensive Care Soc. 2009;10:275–278.
  44. Singh, S.J., Morgan, M.D., Hardman, A.E., et al, Comparison of oxygen uptake during a conventional treadmill test and the shuttle walking test in chronic airflow limitation. Eur Respir J1994;7:2016–2020. 7875275
  45. Whiting, P., Murray, P., Hutchinson, S., et al. The role of the shuttle walking test in predicting mortality and morbidity post oesophagogastric surgery. Critical Care. 2005;9(Suppl. 1):P43.
  46. Struthers, R., Erasmus, P., Holmes, K., et al, Assessing fitness for surgery: a comparison of questionnaire, incremental shuttle walk and cardiopulmonary exercise testing in general surgical patients. Br J Anaesth. 2008;101(6):774–780. 18953057
  47. Sinclair, R.C.F., Batterham, A.M., Davies, S., et al. Validity of the 6 min walk test in prediction of the anaerobic threshold before major non-cardiac surgery. Br J Anaesth. 2012;108(1):30–35.
  48. Maisel, A., Krishnaswamy, P., Nowak, R., et al, Rapid measurement of B-type natriuretic peptide in the emergency diagnosis of heart failure. N Engl J Med. 2002;347(3):161–167.12124404
  49. Cuthbertson, B.H., Amiri, A.R., Croal, B.L., et al, The utility of B-type natriuretic peptide in predicting peri-operative cardiac events after major non-cardiac surgery. Br J Anaesth. 2007;99(2):170–176. 17573389
  50. Yeh, H.M., Lau, H.P., Lin, J.M., et al, Preoperative plasma N-terminal pro-brain natriuretic peptide as a marker of cardiac risk in patients undergoing elective non-cardiac surgery.Br J Surg2005;92:1041–1045. 15997451
  51. Rodseth, R.N., Padayachee, L., Biccard, B.M., A meta-analysis of the utility of pre-operative brain natriuretic peptide in predicting early and intermediate-term mortality and major adverse cardiac events in vascular surgical patients. Anaesthesia2008;63:1226–1233. 18673363
  52. Goei, D., Hoeks, S.E., Boersma, E., et al, Incremental value of high-sensitivity C-reactive protein and N-terminal pro-B-type natriuretic peptide for the prediction of postoperative cardiac events in noncardiac vascular surgery patients. Coron Artery Dis2009;20:219–224. 19322079
  53. Kullo, I.J., Khaleghi, M., Hensrud, D.D., Markers of inflammation are inversely associated with VO2max in asymptomatic men. J Appl Physiol 2007;102:1374–1379. 17170204