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18Feb2010

A 5 Year Audit of the High Risk General Surgical Patient

Claire Martin MB, BS, FRCS
Joanna Seward MB, BS, FRCS
Graham O’Dair MB, BS, FRCS
Graham Copeland MB, BS, FRCS, ChM Consultant Surgeon North Cheshire Hospitals NHS Trust

Keywords: Audit, POSSUM scoring system, high risk surgical patient

Abstract

The current study represents a five year audit of the surgical high risk patient (predicted risk over 10%) and the effects of service provision and clinical practice on ultimate mortality and morbidity.

14890 consecutive non-day procedures were scored using the POSSUM scoring system during this period of which 18.1% had a predicted risk greater than 10% and 12.1% greater than 20%. This latter group had increased in number 31% over the five year period. This was however accompanied by a 35% decrease in those patients whose predicted risk exceeded 80%.

There appeared to be a close relationship between variations in high dependency bed numbers and outcome as expressed by the overall O/E ratio (observed versus predicted numbers of deaths). Similarly O/E ratios deteriorated as total ITU/HDU bed availability decreased. Transferring the high risk patient immediately following surgery appeared to adversely affect subsequent outcome and this effect was only partially mitigated by intensive optimisation prior to transfer.

Introduction

In a general surgical environment the volume and management of the high risk surgical patient (the patient in whom the risk of subsequent mortality exceeds 10%) can have a major impact on indices of surgical performance. Although this is widely recognised amongst surgeons it is not so well appreciated by the press and the public at large. The speciality interest of a general surgeon can have a marked influence on observed mortality rates following surgical intervention being in general highest in vascular surgeons and lowest in urologists and breast surgeons.

Since the development of the POSSUM1 system in the early 1990s it has been noted by one of the authors, from interrogation of the international POSSUM database, that variation in individual surgeon performance can be attributed to the management of patients in the risk band between 10-80%23.

In previous studies we have addressed the effect of case mix on surgical outcome45 but we have not examined the effects of variation in availability of facilities which could assist in the management of high risk patients. It has previously been suggested by Jones and de Cossart6 that surgical outcome can be affected by the absolute number of high dependency beds available within an acute trust but the effect of variation in such facilities has not be assessed.

The current paper describes a method for estimating the volume and chronological changes in volume over time in the number of general surgical high risk patients and attempts to assess the effects of variation in availability of intensive care and high dependency beds on surgical outcome by an audit of our own experiences in a district general hospital setting using the POSSUM system for surgical audit1.

Methods

All consecutive general surgical patients admitted to Warrington Hospital during a 5 year period (Jan 2000 – Dec 2004) in whom an operative procedure was performed on a non day case basis were assessed using the POSSUM system.

The POSSUM system1 includes a physiological assessment and an operative severity assessment. The physiological assessment includes twelve variables each divided into four grades with an exponentially increasing score value (1,2,4 and 8) (see table1). The majority of score variables were available for all patients but where a variable was not assessed a score of 1 was allocated. The operative severity score included six score variables each divided into four grades with an exponentially increasing score value (see table 1). Definitions of operative magnitude are illustrated in table 2. Where an operation was not listed the most close approximate to an operation grouping was chosen. The ‘number of operations variable’ indicates the chronology of procedures occurring within thirty days of the preceding operation, each operation being scored separately. All operative severity score variables were available in all patients, although later histological confirmation was necessary in some individuals to complete all score elements. Outcome was assessed as 30 day mortality and morbidity. Complications were assessed where applicable using our previous published definitions1.

Mortality and morbidity predictions (R1 for mortality and R2 for morbidity) for individual patients were estimated using previously determined equations1,7.

Loge R1/(1-R1) = -7.04 + (0.13 x physiological score) + (0.16 x operative severity score)

Loge R2/(1-R2) = -5.91 + (0.16 x physiological score) + (0.19 x operative severity score)

Our previously described methodology of assessing the quality of overall care, the O/E ratio (ratio of observed to expected mortality) was applied were applicable to measure the outcome of care4.

During the study period the number of intensive care and high dependency beds varied, the current total being eleven of which seven are designated as intensive care and four as high dependency. The intensive care beds may be used as high dependency beds at certain times depending on need and nursing staff availability. In the main the numbers of high dependency beds varied more than intensive care bed numbers. For some of the five year period the affect of bed availability was assessed using the arbitrary cut of a predicted mortality risk of 10-20% as requiring at least high dependency care and a risk of greater than 20% are requiring intensive care. Although these limits are arbitrary they do correlate well with the observed admission policy for the units concerned. In all bed availability was assessed for three of the five years but ceased with the opening of the general surgical close monitoring unit. This is a surgical ward based medium care facility based on an acute general surgical ward which houses four beds. The ratio of nursing staff to patient is 1 to 3 and is aimed at patients in the risk band 10-20% who require less intensive and in the main non-invasive monitoring.

During the early stages of the audit period where intensive care bed numbers were few and the unit often full it was common practice to transfer patients immediately post-surgery to other intensive care units. These cases were universally emergency cases and comprised patients undergoing surgery for ruptured abdominal aortic aneurysm, perforated abdominal viscus and upper gastrointestinal haemorrhage. In some cases this transfer could require journey times of up to one hour. The effects of such transfer were assessed in two study periods of one year each.

Results

During the study period 14890 patients underwent non day case surgery. Of these 12.1% of patients had a predicted risk in excess of 20% and 18.1% had a predicted risk in excess of 10%. The risk spectrum distribution is shown in table 3. As the main group under investigation are that group of patients whose predicted risk exceeds 10% the risk spectrum distribution for this group is shown in table 4.The distribution is in the main exponential although there is a small peak in the extreme high risk group which represents mainly patients undergoing surgery for ruptured abdominal aortic aneurysm.

Over the five year study period there has been a steady increase in the number of patients undergoing operative intervention whose predictive risk for mortality exceeds 20% (see table 5). During the whole period there was a 31% increase in the patients whose risk exceeds 20% and an 11% increase in those whose risk exceeds 40%. However despite these increases there has been a 35% decrease in those patients undergoing surgery whose risk exceeds 80%.

The availability of high dependency beds has varied (from 2 to 5) over the study period while the intensive care compliment has remained fairly static. The effect of this variation can be seen in table 6.Using O/E ratios where the norm is 1.00 and a value <1.00 indicates better performance and a value >1.00 worse performance it can be seen that as bed numbers decreased outcome deteriorated and only recovered when bed numbers were restored.

A similar relationship to total intensive care and high dependency bed availability and outcome can be seen in table 7. As availability decreased patient outcomes also deteriorated both with regard to mortality and morbidity.

During the early part of the study period it was common practice to transfer patients immediately following surgery. Two separate periods were investigated to determine whether this had any impact on outcome. During the earlier period (study period 1) of 163 patients requiring intensive care 15 were transferred to other units. The O/E ratio for resident patients during this period was 0.97 but was 1.6 for transferred patients. In an attempt to improve outcomes a stabilisation bay was developed in the recovery suite to optimise patients prior to transfer and during the later study period (study period 2) of 149 patients requiring intensive care 12 were transferred. The O/E ratio for the resident group was 0.97 and for the transferred group 1.18 (see table 8).

Since the introduction of the close monitoring surgical unit the numbers of intensive care and high dependency beds have remained stable but a number of patients who would have been managed on the high dependency unit are now treated in the close monitoring unit and high dependency length of stay has been reduced. Over the past twelve months, in combination with the above, inter hospital referrals have dramatically reduced and the overall O/E ratio has remained static for the past twelve months at 0.97.

Discussion

The present study represents an audit of the effect of intensive and high dependency care on patient outcomes particularly with regard to the high risk surgical patient in a general hospital setting. In an attempt to quantify this effect the POSSUM scoring system for surgical audit has been utilised. The POSSUM system has been widely validated both in the United Kingdom and internationally813 and is the audit system recommended by the Royal College of Surgeons of Edinburgh and England as well as NCEPOD, the Vascular Society of Great Britain and Ireland14, the Association of Upper Gastrointestinal Surgeons15 and the Association of Colorectal Surgeons16.

Previous authors have utilised the system to assess and quantify the effects of pre-operative resuscitation and pre and peri-operative optimisation1719. It would appear therefore that the POSSUM system is an ideal system to examine the effects of service provision on surgical outcome.

The present study demonstrates that the POSSUM system allows an accurate assessment of the volume of the high risk surgical patient and could at the very least be used as a means of assessing service provision for high dependency and intensive care facilities. That the requirement for such facilities is increasing is well shown in table 5. It could be argued that this increase merely represents an increase in ‘futile procedures’ but the numbers of patients whose risk exceeds 80% have steadily declined over the past five years indicating that NCEPOD advice is being followed. In our own unit we have suggested that the ‘3 eights rule’ be applied for referral to a more senior colleague. If 3 POSSUM physiological variables score 8 the patient’s care plan should be discussed with a senior staff member. This has radically reduced such ‘futile cases’.

It would appear that the increase in high risk patients represents improvements in surgical technique and anaesthestic methodologies such that patients previously considered unsuitable for surgery are now surgical candidates. That the necessary facilities to achieve these aims are not readily available are well shown in tables 6 and 7.

Intensive care and high dependency facilities are costly to develop and maintain but if surgical care is to improve and develop the number of such beds must keep pace with advancing surgical and anaesthestic practice. The United Kingdom is underprovided with such facilities and it has been suggested that this may represent one of the reasons why surgical outcomes appear to be better in the United States of America when compared to the United Kingdom20. In our own unit we have developed a stop-gap facility, the Close Monitoring Unit, which at present appears to be maintaining our performance but the audit of this facility is still in its early stages and it will be essential to identify whether this type of facility can truly reinforce and compliment high dependency care.

From table 8 there can be little doubt that the transfer of patients immediately following surgical intervention results in a poorer outcome. The cause from the present analysis remains obscure. It may be related to the difficulties in maintaining stability during a prolonged transfer but are we not always more committed to patients we have operated on and anaesthestised ourselves? The establishment of a stabilisation bay and optimisation prior to transfer does appear to improve the situation but outcomes are still not as good as for non transferred patients. The practice of transferring these high risk surgical patients in the acute phase, immediately following surgery, needs to be questioned unless it is necessary for particular specialist treatment.

There is no doubt that surgical and anaesthestic skills will continue to improve. Indeed Wilson et al18 have shown that aggressive optimisation in an intensive care environment can produce spectacular improvements in outcome. If we are to replicate these results we must have suitable and more flexible high dependency facilities and they must keep pace with medical advances. The POSSUM does offer at least a methodology for their assessment and may be a useful adjunct in the selection criteria for admission to such facilities. Further studies are however necessary to see whether a step down facility between general ward and high dependency care can maintain performance while their more expensive cousins are planned and commissioned.

References

  1. Copeland GP, Jones D, Walters M. POSSUM: a scoring system for surgical audit. Br J Surg 1991; 78: 355-360.
  2. Copeland GP. The POSSUM system of surgical audit. Arch Surg 2002; 137: 15-19
  3. Copeland GP. Assessing the surgeon: 10 years experience with the POSSUM system. J Clin Excell 2000; 2: 187-190.
  4. Copeland GP, Jones D, Wilcox A et al. Comparative vascular audit using the POSSUM scoring system. Ann R Coll Surg Engl 1993; 75:175-177.
  5. Copeland GP, Sagar P, Brennan J et al. Risk adjusted analysis of surgeon performance. Br J Surg 1995; 82: 408-411.
  6. Sagar PM, Hartley MN, MacFie J et al. Comparison of individual surgeon’s performance. Dis Colon Rectum 1996; 38: 654-658.
  7. Jones HJS, de Cossart L. Risk scoring in surgical patients. Br J Surg 1999; 86:149-157.
  8. Mohamed K, Copeland GP, Boot DA et al. An assessment of the POSSUM scoring system in orthopaedic surgery. J Bone Joint Surg 2002; 84: 735-739
  9. Wijesinghe LD, Mahmood T, Scott DLA et al. Comparison of POSSUM and the Portsmouth predictor equation for predicting death following vascular surgery. Br J Surg 1998; 85: 209-212.
  10. Tekkis PP, Kocher HM, Bentley AJ et al. Operative mortality rates among surgeons: comparison of POSSUM and p-POSSUM scoring systems in gastrointestinal surgery. Dis Colon Rectum 2000; 43: 1528-1532.
  11. Brunelli A, Fianchini A, Xiume F et al. Evaluation of the POSSUM scoring system in lung surgery. Thorac Cardiovasc Surg 1998; 46: 141-146.
  12. Cajigas JC, Escalante CF, Ingelmo A. Application of the POSSUM system in bariatric surgery. Obesity Surg 1999; 9(3): 279-281
  13. Gotonda N, Iwagaki H, Itano S. Can POSSUM, a scoring system for perioperative surgical risk, predict postoperative clinical course? Acta Med Okayama 1998; 52: 325-329.
  14. Prytherch D, Whiteley MS, Higgins B et al. POSSUM and Portsmouth POSSUM for predicting mortality. Br J Surg 1998; 85: 1217-1220.
  15. Prytherch DR, Ridler BM, Beard JD et al. A model for national audit in vascular surgery. Eur J Vasc Endovasc Surg 2001; 21: 477-483.
  16. Tekkis PP, McCulloch P, Poloniecki JD et al. Risk adjusted operative mortality in oesophagogastric surgery: O-POSSUM scoring system. Br J Surg 2004; 91: 288-295.
  17. Tekkis PP, Prytherch DR, Kocher HM et al. Development of a dedicated risk-adjustment scoring system for colorectal surgery (colorectal POSSUM). Br J Surg 2004; 91: 1174-1182.
  18. Boyd O, Grounds RM, Bennett ED. A randomised clinical trial of the effect of deliberate perioperative increase of oxygen delivery on mortality in high risk surgical patients. J Am Med Ass 1993; 270: 2699-2707.
  19. Wilson J, Woods I, Fawcett J et al. Reducing the risk of major elective surgery: randomised controlled trial of preoperative optimisation of oxygen delivery. BMJ 1999; 318: 1099-1103.
  20. McIlroy B, Miller A, Copeland GP et al. Audit of emergency preoperative resuscitation. Br J Surg 1994; 81: 1492-1494.
  21. Bennett-Guerrero E, Hyam JA, Shaefi S et al . Comparison of p-POSSUM risk adjusted mortality rates after surgery between patients in the United States of America and the United Kingdom. Br J Surg 2003; 90: 1593-1598

Tables

Table 1: POSSUM sheet for physiological score

SCORE 1 2 4 8
Age <60 61 - 70 >71
Cardiac signs Normal On Cardiac drugs or steroid Oedema WarfarinJVP
CXR Border CardioCardio megaly
Resp. signs Normal SOB ExertionSOB stairs SOB rest
CXR Mild COAD Mod COAD Any other change
SYSTOLIC 110 - 130 131 - 170 >171 <89
BP 100 - 109 90 - 99
Pulse 50 - 80 81 - 100 101 - 120 >121
40 - 49 <39
Coma Score 15 12 - 14 9 - 11 <8
Urea <7.5 7.6 - 10 10.1 - 15 >15.1
Na >136 131 - 135 126 - 130 <125
K 3.5 - 5 3.2 - 3.4 2.9 - 3.1 <2.8
5.1 - 5.3 5.4 - 5.9 >6
Hb 13 - 16 11.5 - 12.9 10 - 11.4 <9.9
16.1 - 17 17.1 - 18 >18.1
WCC 4 - 10 10.1 - 20 >20.1
3.1 - 3.9 <3
ECG Normal AF (60 - 90) Any other change

 

Table 2: POSSUM sheet for operative severity score

SCORE 1 2 3 4
OpMinorInterMajorMajor +
No. of ops 1 2>2
Blood loss <100 101 - 500 501 - 999 >1000
Perin soiling No Serous blood ( <250 ) Local pus Any other
alignant No 1o Node mets Distant mets
Time of op Elec. Emerg. resus Emerg. no resus

 

Table 3: Risk spectrum for all patients over the study period. The bands shown represent the percentage of total patients who risk fails within the indicated risk band ie <10%, 10-20%, 20-30% etc

Table 3

Table 3

 

Table 4: The risk spectrum for those patients whose risk exceeds 10% expressed as a percentage of the total number of patients whose exceeds this level.

Table 4 (click to enlarge)

Table 4

 

Table 5: The relationship of the number of patients whose risk exceeds 20% with year of study.

Table 5 (click to enlarge)

Table 5

 

Table 6: The relationship of overall O/E ratios for mortality with high dependency bed availability. The numbers of available beds during each period are shown on the x axis.

Table 6 (click to enlarge)

Table 6

 

Table 7: The effect of reduced ITU/HDU be availability on overall outcome with regard to mortality and morbidity O/E ratios

% Bed availability (combined ITU HDU) Mortality O/E ratio Morbidity O/E ratio
100 0.97 0.98 90
0.99 0.99 70 1.08
1.06 50 1.20 1.08

 

Table 8: The effect of transferring patients immediately post-surgery for intensive care on outcome. O/E ratios refer only to those patients under investigation.

O/E ratio mortalityResident group O/E ratio mortalityTransferred group
Study period 1148 patients vs 15 patients 0.97 1.60
Study period 2137 patients vs 12 patients 0.97 1.18
  • 18 Feb, 2010
  • Claire Bale
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