i

EVALUATION OF PHYSIOLOGIC AND OPERATIVE SEVERITY

SCORE FOR ENUMERATION OF MORTALITY AND MORBIDITY

AND PORTSMOUTH PREDICTOR MODIFICATION IN PATIENTS

UNDERGOING EMERGENCY AND ELECTIVE LAPAROTOMY

DR. NAVEED ABBAS AKBAR

MD

SUPERVISOR

DR. SYED ASGHAR NAQI

FCPS, FRCS Ed.

South Surgical ward, Mayo Hospital

King Edward Medical University, Lahore.

Date,

CERTIFICATE

ii

I, hereby, certify that Dr. Naveed Abbas Akbar having Enrolment

Number: 62102 & RTMC Number: SGR-2006-066-3213 has been

working under my direct supervision with effect from 06/12/2005 to

date in West & South surgical wards, Mayo hospital Lahore.

The enclosed Dissertation titled:

‘EVALUATION OF PHYSIOLOGIC AND OPERATIVE SEVERITY SCORE

FOR ENUMERATION OF MORTALITY AND MORBIDITY AND

PORTSMOUTH PREDICTOR MODIFICATION IN PATIENTS

UNDERGOING EMERGENCY AND ELECTIVE LAPAROTOMY’ IS prepared

according to the “FCPS Dissertation-Guidelines” under my direct

supervision.

I have read the Dissertation and have found it satisfactory for FCPS

Part II examination in the subject.

-----------------------------------

DR SYED ASGHAR NAQI

FCPS.FRCS

MAYO HOSPITALLAHORE

DATED:

iii

DEDICATION

To my mother, who is spirit of my life

To my brother who is really our soul

iv

PART I

v

ACKNOWLEDGEMENTS

It is a great pleasure to acknowledge the help of many individuals

without whose help this dissertation could not have been

completed. First and foremost I am indebted to Dr. Khalid Masood

Gondal Professor of South Surgical Ward Mayo Hospital, who

moulded me into a surgeon. His ultimate effort, attention and

valuable suggestions were vital in preparation of this dissertation.

I am thankful to Dr. Asghar Naqi associate professor for helping me

to select the topic for dissertation, and Dr. Nadeem Aslam associate

professor, Dr. Salman Akhtar consultant surgeons, for critical review

of my research work. I am indebted to Dr. Hassan Muhammad Khan

and Dr. Muhammad Zahid, Dr. Zakryia, Dr. Junaid, Dr. Samina

Consultant Surgeons, for imparting their lifetime experience and for

eagerness to help at every step. I owe plentiful thanks to many

people. To name few of them are my colleagues Dr. Mohsin and Dr.

Abrar who helped me in collecting data. Special thanks are owed to

Dr. Kaleen Abbas Asghar, my younger brother for helpful tips on

statistical analysis. I am very grateful to all my beloved patients who

kindly consented for my study. It would be remiss if I failed to

acknowledge my family. I am thankful for their tolerance of my

absences, for their freely offered encouragement.

Dr. Naveed Abbas Akbar

vi

TABLE OF CONTENTS

Chapter page

1. ABSTRACT ……………………………………………………..2

2. INTRODUCTION…………………………………….……………………… 5

3. LITERATURE REVIEW …………………………………….…………. 11

4. OBJECTIVE………….………………………………………………………. 45

5. HYPOTHESIS …………………………………………………………………46

6. OPERATIONAL DEFINATION………………………………………. 47

7. MATERIAL AND METHODS………………………………………….. 51

8. RESULTS…………………………………………………………........... 56

9. DISCUSSION…………………………………………………….……………84

10. CONCLUSION…………………………………………………..……………91

11.

APPENDIX……………………………………………………………………..93

12. BIBLIOGRAPHY……………………………………………………….…….99

vii

LIST OF TABLES

TITLE

PAGE

1. Indications for elective

laparotomy…………………………………………60

2. Complications in elective surgery

……………………………………………61

3. Surgeons on elective list …..

……………………………………………………..62

4. Sum of observed and predicted outcomes in elective cases....64

5. Comparison of observed and predicted mortality using POSSUM

equation in elective laparotomy

………………………………………………….65

6. Comparison of observed and predicted morbidity using POSSUM

equation in elective laparotomy

………………………………………………….66

7. Comparison of observed and predicted mortality using P-POSSUM

equation in elective laparotomy

………………………………………………….67

viii

8. Pearson’s correlation in elective morbidity……………………………

68

9. Pearson correlation in POSSUM elective mortality……………….69

10. Pearson correlation in P-POSSUM elective mortality……………70

11. Hosmer and Lemeshow

test………………………………………………………71

12. Indications for emergency

laparotomy…………………………………….72

13. Complications in emergency

laparotomy…………………………………73

14. Surgeons in

emergency……………………………………………………………74

15. Sum of observed and predicted outcomes in emergency cases76

16. Comparison of observed and predicted mortality using POSSUM

equation in emergency laparotomy

……………………………………………….77

17. Comparison of observed and predicted morbidity using POSSUM

equation in emergency laparotomy

……………………………………………….78

ix

18. Comparison of observed and predicted mortality using P-

POSSUM equation in emergency laparotomy

……………………………….79

19. Pearson’s correlation in emergency morbidity

……………………….80

20. Pearson’s correlation in POSSUM emergency

mortality………….81

21. Pearson’s correlation in P-POSSUM emergency mortality…….82

22. Hosmer and Lemeshow

test…………………………………………………….83

LIST OF FIGURES/GRAPH

1. Gender distribution in elective cases…………………………63

2. Gender distribution in emergency cases……………………75

x

LIST OF ABBREVIATIONS

POSSUM: physiologic and operative severity score for enumeration

of mortality and morbidity.

P-POSSUM: Portsmouth predictor modification.

O.mort: Observed mortality

xi

O.morb: Observed morbidity

P.mort: Predicted mortality by POSSUM

P.morb: Predicted morbidity by POSSUM

PP.mort: Predicted mortality by p-possum

T.B ABDOMEN: Tuberculosis abdomen

FAI: fire arm injury

PID: Pelvic inflammatory disease

OPD: out door patients

C.A: carcinoma

T.B: tuberculosis

UTI: urinary tract infection

PART 2

1. ABSTRACT

INTRODUCTION

Crude morbidity and mortality rates are inaccurate and

misleading indicators of quality of care. Physiological and

Operative severity score for the enumeration of mortality and

morbidity (POSSUM) is currently the most tested system for

assessing outcomes in risk adjusted analysis. This is used

widely to predict 30 days mortality and morbidity. The

Portsmouth predictor modification (P-POSSUM) was developed

to overcome the overpredicted mortality of POSSUM in low

risk patients.

OBJECTIVE

The aim of my study was to evaluate the predictive accuracy

of POSSUM and P-POSSUM scoring systems in patients

undergoing emergency and elective laparotomy.

MAIN OUTCOME MEASURES

The main outcome studied was observed and predicted

mortality and morbidity, and the comparison of both these

figures.

STUDY DESIGN

Prospective observational follow up study

SETTING

South Surgical unit Mayo Hospital Lahore, duration of study

was 6 months.

METHODS

Total 100 cases, 50 from emergency and 50 from

elective cases undergoing elective or emergency laparotomy

meeting the inclusion criteria were taken for study.

Comparison of observed and predicted mortality and

morbidity was carried out by using Pearson’s correlation.

RESULTS

In elective laparotomy observed mortality was 3(6%) and

morbidity was 15 (30%) while predicted mortality and

morbidity by POSSUM was 4.8 (9.6%) and 14.67(29.34%) and

P-POSSUM predicted mortality was 2.93(5.96%).

In emergency laparotomy observed mortality was 6(12%) and

morbidity was 22(44%) while predicted mortality and

morbidity by POSSUM was 9 (18%) and 28.17(57.34%) and P-

POSSUM predicted mortality was 6(12%).

CONCLUSION

POSSUM scoring system can predict morbidity well in both

elective and emergency laparotomy but it overpredicts

mortality in low risk patients while P-POSSUM is more accurate

in predicting mortality.

KEY WORDS

POSSUM, P-POSSUM, mortality, morbidity

2. INTRODUCTION:

‘If a doctor inflicts a serious wound with his operation knife on

a free man’s slave and kills him, the doctor must replace the

slave with another. If a doctor has treated a free man but

caused a serious injury from which the man dies, or if he has

opened an abscess and the man goes blind, the man is to cut

off his hands’. King Hammurabi of Babylon, 1750 BC

[Engraved on black diorite stone discovered in Susa in Iran].1

The notion that every doctor, especially surgeon, must be

completely accountable for the outcome of the treatment has

been continuing from ancient times. But the outcome of any

surgical intervention whether in form of mortality or morbidity

is not only dependent on the performance of the surgeon but

status of the patient is also quite important for the final

outcome of the patient. . His acute and chronic physiological

status, current illness that requires surgical correction, nature

and extent of surgical intervention and co- morbid conditions

have a major impact on the outcome.1

We live in challenging and changing times. No longer can we,

as surgeon, work and operate with the autonomy to which we

have historically been accustomed. The healthcare system as

a whole especially surgeons are increasingly accountable for

their actions to local multidisciplinary teams, to their own

professional organizations through re-validation, to health

authorities, the government, the media and the population

whom we serve.

In the last few years surgical practice has moved to objective

assessment and accountability in the context of clinical

governance. Since the introduction of the NHS Plan (national

health services), it became more important to show that one

is following accepted evidence based practice and also that

one is striving to perform towards nationally accepted

standards of practice. The focus of clinical effectiveness and

quality improvement now lies with the individual patient which

now defines the patient centered approach.2

Since outcome of the patient does not solely depend upon

damage inflicted by the surgeon, raw morbidity and mortality

rate are not true reflection of a particular surgeon. Raw

mortality and morbidity rate will ignore the effect of case-mix

i.e. different sorts of patients, both fit and unfit, undergoing

treatment. For example if a surgeon operates on relatively fit

patients, his mortality and morbidity will be low but if another

surgeon working in a tertiary referral center operates on

relatively unfit patients, his outcome will be dismal.1

It is well accepted that the use of crude mortality and

morbidity rates are inaccurate and misleading for comparative

surgical audit when results of emergency surgery are

compared between different units and hospitals.3, 4

An accurate system to predict mortality and morbidity is

essential for auditing surgical patients.3 For this purpose a

scoring system that classifies the patients on the basis of

severity of their illness before surgery may be a meaningful

tool for predicting mortality and morbidity.4

POSSUM stands for physiologic and operative Severity Score

for the enumeration of Mortality and Morbidity. It was

developed by Copeland et al in 1991 and has since been

applied to a number of surgical groups including orthopedics

patients, vascular surgery (AAA, carotid endarterectomy etc),

head and neck surgery and GI/Colorectal surgery.3, 4

In a recent surgical review article on the subject of Risk

Scoring in Surgical Patients (Jones 1999a) it was concluded

that "the POSSUM Score is the most appropriate of the

currently available scores for general surgical practice" in the

estimation of risk of dying. Despite first being reported in

1991 (Copeland 1991), POSSUM has not become widely used

in the UK although it is more popular in the North West of

England, close to its original base, and has been adopted by a

few enthusiasts across the world. A data base of 250,000

patients has apparently been gathered. Surgeons seem to be

generally more aware of POSSUM than anesthetists who

regularly use ASA for general patients and APACHE for the

critically ill. This is probably because most of the relevant

publications have been in surgical journals.1

The ideal risk assessment tool would be quick and easy to use,

widely applicable, include elective and emergency work and

accurately predict outcome. The initial researchers examined

62 factors. As in many similar areas of mathematical

prediction, multi-variate analysis was able to identify the most

powerful predictors and reduced these to just 12 physiological

and 6 operative parameters. Other factors no doubt do predict

Outcome but so strongly duplicate these 18 that they offer no

additive predictive power. Each of these 18 factors were

divided into two, three or four levels and computer analysis

calculated that a weighting of 1,2,4 or 8 approximated well to

the relative predictive power, much simplifying the

calculation.

It is valuable for risk adjustment and comparison.4 It was

devised specially from surgical patients and is used for

surgical audit. In this scoring system all 12 physiological and

six operative variables are used to predict 30 days mortality

and morbidity.3,7-8 These variables are basic medical

information that should be present in the medical record of

every patient. The Portsmouth predictor modification

(P.POSSUM) was developed to overcome the over prediction of

mortality by possum in low risk patients.3, 4 In p-possum same

physiologic variables are used but equation and method of

analysis is different.3

Since 1990, this scoring system has been used extensively in

UK and in some other countries to compare surgical patients

treated in different health care systems.5 The Variety of cases

undergoing emergency and elective laparotomy in developing

countries is different from UK. Their nutritional status, low

socioeconomic status and delayed presentation also affect

their prognosis. Comparing the outcome of such patients

using data from developed countries may be misleading.6

The aim of present study is to evaluate the predictive

accuracy of possum and p-possum in predicting mortality and

morbidity in patients undergoing emergency and elective

laparotomy in our circumstances. It will also compare the

results of possum and p-possum scores in emergency and

elective surgery. This system may be useful to audit different

surgeons, units and hospitals in future.

The need for accountability, the desire of professional bodies

to maintain standards and wish of the patients for ever more

information is leading to increasingly sophisticated

information appearing in the public domain. In this culture of

increased scrutiny, surgeons must be able to demonstrate

clearly and accurately how they perform through comparative

surgical audit.

3. REVIEW OF LITERATURE

3.1. SURGICAL AUDIT

Surgeons view on audit

We must formulate some method of hospital report showing

as nearly as possible what the results of treatment obtained at

different institution are. This report must be made out and

published by each hospital in a uniform manner, so that

comparison will be possible. With such report as a starting

point, those interested can begin to ask questions as to

management and efficiency. (Taken from lecture by Ernest

Amory Codman) 7

Surgery with out audit is like playing cricket without keeping

the score. (H.B. Devlin, founding director of the surgical

epidemiology and audit unit, Royal College of surgeons of

England) 7

The roots of modern audit lie in the regular morbidity and

mortality meetings held in many hospitals of USA, when

transferred to British Isles in 1950, the American drive for

change based on findings and the educational importance

were missing. Until 1990 there was no funding or allocation of

time for audit activity. Health authorities are now agreed to

audit every specialty, particularly surgery.8

Clinical audit is defined by the UK department of health as:

the systemic, critical analysis of the quality of medical care,

including the procedures used for diagnosis and treatment,

the use of resources and the resulting outcome and the

quality of life for the patient and states that an effective

program of medical audit will also help to provide reassurance

to doctors, their patients and managers that the best quality

of service is being achieved, having regard to the resources

available(department of health). 8

Clinical audit is a process used by clinician to improve patient

care. This process involves comparing aspect of care, if care

falls short of the criteria chosen, some change in the way of

care is proposed. This change may be at individual,

institutional, national or international level.

3.2. METHODS OF AUDIT8

Donabedian identified three main elements in the delivery of

health care: structure, process, and outcome. Structures

include the quality and type of resources available and are

generally easy to measure. Process defines what is done to

the patient. It includes way of operation and prescribed

medications, the adequacy of notes. Outcome is the result of

clinical intervention and may represent the success or failure

of process. 8

A number of audit techniques have evolved and found a place

in the regular assessment of clinical practice.

Basic clinical audit: It analyzes and assesses complications,

morbidity, and mortality. A review of such data is undertaken

every 3rd month. The essential feature is to find any deviation

from the accepted norm and then to investigate the reason for

this observation.

Incident review: It involves discussing strategies to be

adopted under certain clinical situations. Incident may be

leaking aortic aneurysm. The discussion may lead to clear

policies for future actions and use with the construction of

local guidelines.

Clinical record review: A member of another firm of the

same or similar specialty reviews a random selection of case

notes and compares it with specific standard given. It is

simple and requires little time or resources.

Criterion audit: it is more advanced and structured form of

incident audit. Retrograde analysis of clinical records is made

and judged against a number of carefully chosen criteria.

It can compare, waiting time, investigations ordered,

treatment given, and outcome and follow-up strategies.

Adverse occurrence screening: it identifies the adverse

events like wound infection, unplanned readmission, delayed

or wrong diagnosis. Occurrences are recorded, reviewed and

avoided in future.

Comparative audit: it implies the collection of data and its

comparison across units, health authorities and even through

whole region. The Royal College of surgeons set up a

comparative audit service in which all surgeons supply

information under a confidential number for comparison with

their peers at regular meetings. This regional audit

significantly influences and improves surgical practice.

National studies: this was used first to study perinatal

mortality in obstetric. First report of the perioperative death,

considered the factors involved in death of patients who died

with in 30 days of surgery. Much was learned to upgrade the

training of the doctors to prevent disasters.

Outcome audit: it is a review of the whole process of

healthcare delivery during a patient’s hospital contact. It

measures all the skills of the medical and nursing staff, the

hospital administration and all those who are in contact with

patient. Commonly used measures of outcome are mortality

and morbidity.

MORBIDITY:

It is a valuable end point for assessing surgical performance

and has implication for both patient’s quality of life as well as

consumption of healthcare resources. It is most important for

those operations and specialties which have a low mortality

rate.

MORTALITY:

It is a more finite and indisputable end-point than morbidity

has been used to greater extent and success in competitive

surgical audit.

Most series have measured in hospital mortality but recent

work suggests that this may underestimate true mortality up

to 20% when compared to 30-day mortality rates. 9

3.3. RISK ADJUSTED ANALYSIS

Simple collection of crude numbers of the dead or the injured

alone is insufficient to reflect quality of care as, to compare

morbidity or mortality directly; the original population should

be identical. True comparison can only be made if risk

adjustment is used to allow for the difference in case mix

between units and surgeons. This is termed risk adjusted

analysis. It is based upon the principle of identifying those

variables that affect outcome like mode of presentation,

physiological parameters and the nature, extent and difficulty

of surgery.

In this model multivariate logistic regression analysis is used.

It identifies outcome affecting variables and generates an

equation which predicts the likelihood of the outcome

occurring in any given patient.

An alternative approach to risk stratification is to use a

Bayesian model.13 Rather than producing a rigid equation, this

method allows the probability of an event to be revised as

additional relevant information is obtained. The Bayes, model

is then tested in much the same way by using ROC curves and

calibration curves.

3.4. RISK SCORING SYSTEMS

Risk scoring systems used to predict complicated outcome or

mortality are not a new phenomenon and there are many

examples (ASA, APACHE, Ranson, SAPS, Cardiac index,

Revised trauma score).however they were not designed

specifically for the purpose of comparative surgical audit.

3.4.1. ASA GRADING 10

The best and most widely used example is the classification of

preoperative physical status described by American society of

Anesthesiologists (ASA score) in 1963.Patients are allocated to

one of five categories based on general medical history and

examination with out requiring any specific investigation.

I. Normal healthy patients

II. Mild systemic disease

III. Severe systemic disease, not incapacitating

IV. Incapacitating systemic disease that is threat to life

V. Moribund, not expected to survive 24 h with or without

an Operation

“E” can be added to denote emergency surgery and signifies a

worse prognosis. This score is simple and already in universal

use. There is potential for interobsever subjective error. This is

effective in predicting mortality when used alone.11

3.4.2. APACHE

The acute physiology and chronic health evaluation score

(APACHE) scoring system has been used extensively in the

intensive care setting. Its aim is to allow for classification of

patients’ on the basis of severity of illness to facilitate

comparison of outcome, the evaluation of new therapies and

as an indicator of daily progress. The original APACHE system

had 34 physiological variables, taking the worst value

obtained in first 24 h of admission in intensive care, combined

with a simple evaluation of chronic health. More recently the

APACHE system has been modified and simplified to create

APACHE II12 APACHE III13 although these still require 12 or 18

physiological variables.

APACHE II has been validated both in general and surgical

intensive care patients with some success, although results

have not always been accurate. It is limited for risk adjusted

analysis in comparative surgical audit due to its complexity

and requirement to collate variables over 24 h.

3.5. METHODS OF RISK ADJUSTED ANALYSIS IN

COMPARATIVE SURGICAL AUDIT

A number of methods have been proposed for standardizing

patient’s data to permit direct, meaningful comparisons of

patient outcome irrespective of differences in population for

the purpose of audit.

POSSUM (physiology and operative severity score for

enumeration of morbidity and mortality)

Surgical Risk Scale

E –PASS (estimation of physiologic ability and surgical stress)

National Veterans Affairs Surgical Risk Study

Specialty Registries’

These are few methods which are useful for comparative

surgical audit. All these methods include operative variables

also.

3.5.1. POSSUM

A Physiological and Operative Severity Score for the

enumeration of Mortality and Morbidity was developed by G P

Copland et al. Driven by the need to develop a simple risk

scoring system applicable to diverse general surgical

populations, whose main use would be in surgical audit,

POSSUM scoring system was developed. It was first published

in British Journal of Surgery March 1991; 78:356-360.

Copland et al conducted a prospective study over a period of

six months in 1372 patients undergoing operation in general

surgery units. It is an attempt to quantify the quality of

surgical care and to allow comparison between different

surgeons, units, hospitals and regions. Authors also stressed

the usefulness of POSSUM as an adjunct to surgical audit.14

It is currently the most tested system for assessing outcomes

by risk-adjusted analysis in the UK. It is simple, easy and

applicable to all general surgical procedures. It is more

popular in the North West of England, close to its original

base, and has been adopted by a few enthusiasts across the

world. A data base of 250,000 patients has apparently been

gathered. Surgeons seem to be generally more aware of

POSSUM than anesthetists who regularly use ASA for general

patients and APACHE for the critically ill. This is probably

because most of the relevant publications have been in

surgical journals. If ASA is considered to be too simplistic and

highly subjective whilst APACHE is too complex for general

use, then POSSUM neatly fits into the gap, requiring only 12

physiological and 6 operative facts, all easily available from

routine admission and operation data.

Possum was developed as a tool to compare morbidity and

mortality in a wide ranging basket of general surgical

procedures and should be applied at the time of induction of

anesthesia before operation to patients of all risk categories.

However it is of relevance to mortality audits as an objective

and qualitative assessment of risk in patients who died after

surgery. Some Scottish surgical centers have taken an interest

in POSSUM and took the opportunity of piloting POSSUM more

widely at the same time.

COMPONENTS OF POSSUM

POSSUM is a two part scoring system that includes a

physiological assessment and measure of operative severity.

There are 12 physiological and 6 operative variables which are

divided into 4 grades with an increase in score. The

physiological variables are those that are present at the time

of surgery. These include symptoms and signs, results of

simple biochemical and hematological investigations and

electrocardiographic changes. If a variable is not present, a

score of 1 is allocated. Some variables are assessed by means

of changes on chest radiograph.

Physiological Operative

Age Operative severity

Cardiac history Multiple procedures

Respiratory history Total blood loss

Blood pressure peritoneal soiling

Pulse rate Presence of malignancy

Glasgow coma score Mode of surgery Hemoglobin

White blood count

Serum urea

Serum sodium

Serum potassium

Electrocardiogram

The minimum score is 12, with a maximum of 88.once these

scores are known, it is possible to estimate the predicted risk

of mortality and morbidity using complex equation.

3.5.2. P-POSSUM

In his article Whitely et al in 1996 showed that the original

POSSUM regression equation failed to work in patients in

Portsmouth.15 He found that POSSUM over predicted mortality

of low risk patients in a study of 1485 patients. It was still

possible to use the POSSUM y data set, but a different

regression equation was needed. This regression equation

became the Portsmouth predictor equation, or P-POSSUM. It

used a different constant and weighted value for physiology

and operative scores.

P-Possum used standard method of analysis described by

Hosmer and Lemeshow. In this system, the risk applies to an

individual.

A lively correspondence between the Portsmouth group and

the originators of POSSUM appeared in British Journal of

Surgery. The debate culminated in a direct comparison of the

two possible methods of analysis. Wijisinghe et al explained

how the original POSSUM equation used exponential analysis,

while P-POSSUN used linear analysis. They employed both

methods in a series of 312 patients who had vascular surgical

procedure and showed that each was effective if appropriate

analysis was used. Both scoring systems failed when the

incorrect analysis was used.16

3.5.1.1. POSSUM IN GENERAL SURGERY

Since first published, POSSUM scoring system has been

validated by many authors. Although this system is more

popular around the North England in, many authors use it all

over the worlds. A review article was published in 1999 annual

report of SASM (Scottish Audit of Surgical Mortality) describing

POSSUM scoring system. The aim of that review was to

increase the general awareness of POSSUM across Scottish

surgeons and anesthetics.

It was recommended to include POSSUM in SASM forms during

the following year for some surgical specialties. 17

Possum also used in developing countries like India and

Pakistan. A study conducted recently by R.S. Mohil and

colleagues in India. In their study 120 patients who underwent

emergency laparotomy in a single unit studied. Predicted

mortality and morbidity rates were calculated by POSSUM and

P-POSSUM equation using both linear and exponential method

of analysis. When the linear method of analysis was used

POSSUM over predicted morbidity, and there was a significant

difference between the observed and predicted values

(observed to expected (O: E) ratio 0.68). The prediction was

more accurate when the exponential method was used (O: E

ratio0.91). Possum also significantly over predicted mortality

when analyzed by the linear method (O: E ratio 0.39), but the

prediction improved when exponential analysis was used (O: E

ratio 0.62). Applying linear and exponential analyses for P-

POSSUM, the O: E ratios for mortality were 0.66 and 0.88

respectively. 4

Another recently conducted study in center for the study of

liver disease and department of surgery, university of Hong

Kong, C.M. Lam and colleagues used POSSUM scoring systems

for audit of major hepatectomy. A retrospective analysis was

performed on data collected prospectively over a 6 year

interval from January 1997 to December 2002.the mortality

risk was calculated using POSSUM and the P-POSSUM

equations. In this study 259 patients underwent major

hepatectomy. There were 6.6% postoperative deaths. On

multivariate analysis only the physiological and operative

severity scores were independent variables. The POSSUM

system over predicted mortality (14.2%) and there was

significant lack of fit in these patients. The mortality rate

predicted by P-POSSUM was 4.2% and showed no significant

lack of fit.3

In another study in academic department of surgery, king, s

college hospital London, UK, Tekkis PP and colleagues studied

risk-adjusted prediction of operative mortality in

oesophagogastric surgery with O-POSSUM. This was designed

to develop a dedicated oesophagogastric model for prediction

of risk adjusted postoperative mortality in upper

gastrointestinal surgery. Using 1042 patients undergoing

esophageal surgery between 1994 and 2000, the Portsmouth

predictor equation for mortality scoring system was compared

with a standard logistic regression O-POSSUM model. The

overall mortality was 12% (elective 9.4 and emergency

26.9).P-POSSUM over predicted mortality 14.5%, particularly

in elective group of patients. The multilevel model offered

higher discrimination than single level O-POSSUM and P-

POSSUM models. When observed to expect outcomes were

evaluated, the multilevel O-POSSUM model was found

superior.5

R. Sutton, S. Sarin and Brooks compared the surgical risk

score, POSSUM and p-POSSUM in higher risk patients. The aim

of study was to compare the accuracy of mortality prediction

using that of POSSUM and p-POSSUM in a cohort of higher risk

patients. The surgical risk score (SRS) has been show

equivalent accuracy but was validated using a cohort that

contained a high proportion of patients. Some 949 inpatients

undergoing procedures in a district general hospital under the

care of surgeon were analyzed. The observed mortality rate

was 8.4%.mean mortality predicted by SRS, POSSUN and p-

POSSUM were 5.9, 12.6, and 7.3% respectively.6

Gocmen E, Koc M and Tez M from fifth department of surgery,

Ankara Numune Education and research hospital Turkey

evaluated O-POSSUM and P-POSSUM scores in patients with

gastric cancer undergoing resection. They studied

retrospectively 126 patients undergoing elective resection in

stomach cancer. They compared observed and predicted

mortality using both these models. Overall fourteen deaths

were observed-POSSUM predicted 15 deaths and P-POSSUM

predicted 20.this data suggest that O-POSSUM predicts

mortality more accurately than P-POSSUM.18

Mahesh Gopashetty, Gabriel Rodrigues and colleagues

Evaluated P-Possum Mortality Predictor Equation and Its Use

as a Tool in Surgical Audit. Patients admitted and operated

over a period of four months in six general surgery units of

Kasturba Medical College and Hospital, Manipal, India were

included in the study. Copeland's scoring system was used to

classify patients and the data was analyzed using P-POSSUM

mortality equation. Predicted mortality rate was calculated

and was compared with observed mortality rate. Results were

evaluated by χ2 test. A total of 493(n) patients were operated

during this period of study. Of these, 103 patients underwent

emergency surgeries. Among 493 patients operated, had a

mortality of 26. The raw mortality rate in surgical unit II was

3.96% and 5.45% in unit VI. It was lowest in unit V (1.69%)

and highest in unit IV (6.41%). After adjusting for risk, it was

noted that Observed: Expected mortality ratio was almost

equal in unit II and unit VI (0.83 and 0.8 respectively), while it

ranged from 0.66 in unit V to 1.25 in unit IV. It was also

observed that mortality rates were not significantly different

from predicted mortality rates. Thus, at the end of the study it

was concluded that P-POSSUM mortality predictor equation

predicts death accurately in general surgical patients.

Comparing risk adjusted mortality rate is more meaningful

than comparing "raw" mortality rate.19

3.5.1.2. POSSUM in specialties

Oesophagogastric surgery

In Department of Surgery, Crosshouse Hospital, Kilmarnock,

UK University Department of Surgery, Glasgow Royal

Infirmary, Glasgow, UK Comparison of P-POSSUM and O-

POSSUM in predicting mortality after oesophagogastric

resections made. Elective oesophagogastric cancer resections

in a district general hospital from 1990 to 2002 were scored by

P-POSSUM and O-POSSUM methods. Observed mortality rates

were compared to predicted mortality rates in six risk groups

for each model using the Hosmer–Lemeshow goodness-of-fit

test. 313 patients underwent oesophagogastric resections. 32

died within 30 days (10.2%). P-POSSUM predicted 36 deaths,

O-POSSUM predicted 49. Neither model accurately predicted

the risk of postoperative death. P-POSSUM provided a better fit

to observed results than O-POSSUM, which over predicted

total mortality. P-POSSUM also had superior discriminatory

power.20

Hisao   Wakabayashi and colleagues in their study validated the

usefulness of POSSUM and-POSSUM for a surgical audit in

elective digestive surgery for elderly patients. This study

involved 153 patients aged 75years and older who underwent

elective gastric or colorectal surgery between July 2004 and

June 2006. A retrospective analysis was performed on data

collected prior to each surgery. The predicted mortality and

morbidity rates were calculated using each of the scoring

systems and were used to obtain the observed/predicted (O/E)

mortality and morbidity ratios. New logistic regression

equations for morbidity and mortality were then calculated

using the scores from the POSSUM system and applied

retrospectively. The O/E ratio for morbidity obtained from

POSSUM score was 0.23. The O/E ratios for mortality from the

POSSUM score and the P-POSSUM were 0.15 and 0.38,

respectively. Utilizing the new equations using scores from the

POSSUM, the O/E ratio increased to 0.88. Both the POSSUM

and P-POSSUM over-predicted the morbidity and mortality in

elective gastrointestinal surgery for malignant tumors in

elderly patients. However, if a surgical unit makes appropriate

calculations using its own patient series and updates these

equations, the POSSUM system can be useful in the risk

assessment for surgery in elderly patients.21

3.5.1.3. COLORECTAL SURGERY

Department of Surgery of Queen Mary Hospital, HONG-KONG,

they examined the accuracy of (P-POSSUM) in predicting the

mortality of patients who underwent operations for

obstructing colorectal cancer. They attempted to analyze the

actual mortality and the predicted P-POSSUM mortality of

different surgical options for obstructing left-sided cancer.

They studied 160 patients from 1998 to 2002. 18 patients died

postoperatively. The operative mortality was 11.3 percent. P-

POSSUM predicted overall mortality of 15 percent. The

observed and predicted mortality was found to have no

significant difference. They concluded that P-POSSUM system

is valid for prediction of overall mortality in patients with

operations for obstructing colorectal cancer.22

Watanabe Makoto and colleagues from Isesaki Municipal

Hospital, Japan, Estimated Mortality and Morbidity Risk in

Colorectal Surgery using POSSUM Predictor Equation.

Physiological and operative severity scores in 119 patients

undergoing elective and emergency colorectal surgery were

recorded retrospectively. Observed morbidity and mortality

were compared with the prediction by POSSUM or P-POSSUM

using linear analysis. The Hosmer-Lemeshow goodness of fit

test indicated that the POSSUM morbidity equation had a

significant lack of fit with observed complications. The

POSSUM mortality equation overestimated deaths with this

analysis. But the mortality rate estimated by P-POSSUM did

not differ significantly from the observed death rate. These

results suggest that the POSSUM scoring system for morbidity

risk must be modified, and the P-POSSUM scoring system for

mortality risk is useful for patients undergoing colorectal

surgery.23

3.5.1.4. PANCREATIC SURGERY

W. Pratt and S. Joseph in their article studied the predictive

accuracy of POSSUM in pancreatic resection. 326 consecutive

pancreatic resections (227 pancreaticoduodenectomies, 87

distal, 7 central, and 5 total pancreatectomies) were

performed between October 2001 and January 2007. Logistic

regression analysis was used to identify specific POSSUM

parameters predictive of postoperative morbidity. Observed

and Expected morbidity rates were equivalent (53.1% vs.

55.5%) with an overall O/E ratio of 0.96. they concluded that

POSSUM is a valuable perioperative scoring system for

pancreatic resections and outcomes, and can be employed to

guide management decisions that impact postoperative

recovery.24

Abdaal W Khan, Sudeep R Shah and colleagues from

Department of Surgery, Royal Free and University College

Medical School, London, UK Evaluated the POSSUM scoring

system for comparative audit in pancreatic surgery. They

analyzed Retrospectively 50 patients undergoing partial

pancreaticoduodenectomy (PD) with POSSUM and P-POSSUM

equations. The predicted results compared with observed

values. The POSSUM-predicted mortality was 26%, and P-

POSSUM predicted mortality was 6%, while actual mortality

was 4%.Using POSSUM for morbidity, the predicted value was

76%. The observed morbidity was 46%.While P-POSSUM

appeared satisfactory for predicting mortality risk; POSSUM

overestimated morbidity and mortality for

pancreaticoduodenectomies in a specialist centre.

Modifications are needed prior to its application for

comparative audit in pancreatic surgery.25

3.5.1.5. VASCULAR SURGERY

In Vascular Unit of Derriford Hospital, Derriford Plymouth,

Midwinter M. J Tytherleigh M., ASHLEY S, estimated vascular

mortality and morbidity by using POSSUM and P-POSSUM

equations. Physiological and operative severity scores in 221

patients undergoing elective and emergency arterial surgery

in a pure vascular practice under a single consultant were

recorded prospectively. Observed morbidity and mortality

rates were compared with the rates predicted by POSSUM and

P-POSSUM using a linear method of analysis. The POSSUM

overestimated deaths but the mortality rate estimated by P-

POSSUM was not significantly different from the observed

death rate. The risk of morbidity predicted by POSSUM was

not significantly different from the observed complication rate.

They concluded that POSSUM combined with the P-POSSUM

adjustment for death allows satisfactory prediction of

mortality and morbidity rates in patients undergoing vascular

surgery.26

Prytherch, Sutton, and Boyle in their study evaluated P-

PSSUM model for abdominal aortic aneurysm surgery.

Patients with a ruptured abdominal aortic aneurysm (AAA) are,

however, very different from elective patients and it may be

hypothesized that they require their own specific risk model.

They studied 444 (213 emergency, 231 elective) admissions

for AAA surgery between August 1993 and July 2000 and

analyzed using the P-POSSUM equation for general surgery

and the P-POSSUM equations developed for vascular surgery.

All models failed in emergency aneurysms while predicted

successfully the elective aneurysms. They suggested that

admission method is an important factor for patients with

AAA. They concluded that Ruptured AAAs appears to be

different from elective AAAs and other vascular cases and

require their own risk model.27

3.5.1.6. ORTHOPEDICS SURGERY

A cohort study was conducted in Queen's Medical Centre,

Nottingham over a period of nearly 2 years .in their study,

they assessed the predictive capability of POSSUM for 30-day

mortality after surgery for fractured neck of femur. Complete

data from 1164 patients were analyzed to compare the

mortality predicted by POSSUM and the observed mortality.

POSSUM risk of death was calculated using the original

POSSUM equation, with modifications to the operative score

appropriate for orthopaedic surgery. POSSUM predicted 181

(15.6%) deaths and the observed mortality was 119

(10.2%).POSSUM overpredicts mortality in hip fracture

patients. It should be used with caution whether as an audit

tool or for preoperative triage.28

An article published in Chinese journal of traumatology in

2006 Evaluated P. POSSUM scoring system in predicting

mortality in patients with hip joint arthroplasty. A total of 141

patients (75 males and 66females, aged 63.22 years ± 14.45

years on an average) undergoing hip joint arthroplasty during

January 2002 and March 2005 were studied retrospectively

with P-POSSUM. Their average physiological score and

operative severity score were 17.48 ± 5.16 and 12.43 ± 3.05,

respectively. The predicted postoperative mortality with P-

POSSUM was compared with the observed value. Analysis was

performed to investigate the predictive capability of P-

POSSUM. POSSUM scoring system was used as the control.

The average physiological scores were 32.33 ±9.87 in the

death group and 17.16 ±4.56 in the survival group. The

former was obviously higher than the latter, which showed

statistical difference between the two groups but a strong

relation was found between the observed death number and

the predicted death number calculated by P-POSSUM, though

POSSUM overestimated the overall mortality.

3.5.1.7. GYNECOLOGICAL SURGERY

DAS N, TALAAT A. S in their article published in European

journal of surgical oncology, assessed POSSUM and its validity

for use in gynecological oncology surgery.  All patients

undergoing gynecological oncology surgery at the Northern

Gynecological Oncology Centre (NGOC) Gateshead, UK over a

period of 12 months (2002-2003) were assessed

prospectively. Mortality and morbidity predicted by P-POSSUM

were compared to the actual outcomes. During this period 468

patients were assessed. It predicted a 7% mortality rate while

observed rate was 2% (35 predicted deaths in comparison to

10 observed deaths), a difference that was statistically

significant. They concluded that P-POSSUM overestimates the

risk of mortality for gynecological oncology patients

undergoing surgery and it needs further modifications prior to

adoption for gynecological cancer surgery as a risk adjusted

surgical audit tool.29

3.5.1.8. BARIATRIC SURGERY

Cagigas and Escalante studied the Application of POSSUM

System in Bariatric Surgery. 20 patients were scored by the

POSSUM system. All underwent elective bariatric surgery

during 1997. All patients were scored at the time of surgery

with the physiologic score (FIS) and at discharge with the

operative severity score (IQ). The mean POSSUM score was

23.9. The mean FIS was 13.95 (12-22), and the mean IQ was

9.4 (7-16). The distribution of patients was performed for BMI.

The group with BMI 35-45 (n = 4 patients) had a mean

POSSUM score of 22.75, a mean FIS of 13.75, and a mean IQ

of 9.0. The group with BMI >45 (n = 16 patients) had a mean

POSSUM score of 24.18, a mean FIS of 14.62, and a mean IQ

of 9.5. The morbidities were gastric fistula with peritonitis and

deep venous thrombosis. The two complications had similar

POSSUM scores with different BMIs. No mortality was

observed. According to their experience, the POSSUM scoring

system appears to provide an indicator of minor risk of

morbidity and mortality in bariatric surgery with vertical

banded gastroplasty.30

3.5.1.9. OTOLARYNGOLOGYL SURGERY

Department of ENT and the Head and Neck Unit, University

Hospital of Wales, they studied applicability of POSSUM in

head and neck surgery. They also applied the P-Possum

(Portsmouth Possum) equation for mortality. They compared

observed with the predicted outcomes. They introduced two

new variables, radiotherapy and previous surgery to the

operative site, to test their association with outcome. They

found that Possum is valid for morbidity but predicts more

accurately for high-risk than for low-risk groups. Neither

Possum nor P-Possum accurately predicts mortality.31

3.5.1.10. EMERGENCY SURGERY

Hobson and. Sutton in Department of General and Colorectal

Surgery, Leicester General Hospital, studied the comparison of

POSSUM and P-POSSUM with clinical assessment of mortality

following emergency surgery. Data were collected

prospectively from 163 patients. Details of the physiological

and operative severity scores were recorded for POSSUM and

P-POSSUM. The estimates of both the surgeon and anesthetist

for 30-day and in-hospital mortality were also recorded pre-

operatively. The accuracies of the four predictions were then

compared with actual mortalities using linear and exponential

analysis and receiver operator characteristics (ROC). Results:

P-POSSUM gave the most accurate prediction of 30-day

mortality using linear analysis [observed to expected ratio (O:

E) = 1.0]. POSSUM gave the most accurate prediction using

exponential analysis (O: E = 1.15). Clinical judgment of

mortality from both operating surgeons and anesthetists

compared favorably with the scoring systems for 30-day

mortality (O:E = 0.83 and O: E = 0.93, respectively). They

conclude POSSUM and P-POSSUM appear to be useful

indicators for the prediction of mortality. Clinical judgment

compares strongly with scoring systems in predicting post-

operative mortality, but may underestimate mortality in very

high-risk patients with more than 90% mortality.32

3.5.1.11. POSSUM IN PAKISTAN

Qamar Hafeez Kiani et al conducted a study on the topic of

Surgical Audit Using POSSUM Scoring System. A total of 500

case were studied. The scoring system provided the

assessments for mortality and morbidity, which did not

significantly differ from observed rates (p<0.001).The

POSSUM score provided a reasonably effective means of

achieving comparison among the two-thirds of patients who

underwent surgical procedure. It was concluded that POSSUM

provides a good assessment of the risk of mortality and

morbidity in general surgical patients. This score can be

effectively applied in all surgical setups in Pakistan and can be

used as an adjunct to surgical and can be used as an adjunct

to surgical audit.33, 34

3.6. COMPUTER PROGRAM FOR POSSUM AND P-

POSSUM CALCULATIONS

There is a computer program freely available that provides a

single screen to calculate the POSSUM and P-POSSUM

morbidity and mortality risk .This is available on the site

www.sfar.org/score2/P-POSSUM2.html .

POSSUM formula:

Morbidity: ln R/1-R = –5.91 + (0.16 * physiological score) + (0.19 * operative score)

Mortality: Ln R/1-R = -7.04 + (0.13 x physiological score) + (0.16 x operative severity score)

P-POSSUM formula:

Mortality: Ln R/1-R = -9.065 + (0.1692 x physiological score) + (0.1550 x operative severity score)

Where R = predicted risk of mortality

3.7. VALIDATION OF POSSUM AND P-POSSUM

PREDICTION EQUATIONS

Observed and predicted outcomes, derived from POSSUM and

P-POSSUM equation, correlated by using Pearson correlation

coefficient. The resultant of Pearson correlation is shown in

tables. Pearson correlation coefficient(r) measures the

strength of association between two variables.

The value of r ranges between +1 and –1

+1=A positive or direct correlation

-1=A negative or inverse correlation

0=A zero correlation (no relationship)

The results are tested by using chi-square (χ²) test. The value

of P≤0.05 is taken as significant. The result of chi-square is

illustrated in table no.11 and 22.

THE

STUDY

4 . OBJECTIVES:

THE OBJECTIVE OF THIS STUDY IS TO:

Evaluate the predictive accuracy of POSSUM and P-POSSUM

scoring systems in patients undergoing emergency and

elective laparotomy.

5. HYPOTHESIS:

P-POSSUM scoring system is better than POSSUM in

predicting mortality in patients undergoing

emergency and elective laparotomy.

6. OPERATIONAL DEFINITIONS:

1. PREDICTIVE ACCURACY: This will be measured in terms

of:

2. MORBIDITY:

2.1. Wound haemorrhage: Local haematoma requiring

evacuation.

2.2. Deep haemorrhage: Postoperative bleeding requiring

re-exploration.

2.3. Chest infection: Production of purulent sputum with

positive bacteriological cultures, with or without chest

radiography changes or pyrexia, or consolidation seen on

chest radiograph.

2.4. Wound infection: Wound cellulites or the discharge of

purulent exudates.

2.5. Urinary infection: The presence of > 10 5 bacteria / ml

with the presence of white cells in the urine, in previously

clear urine.

2.6. Deep infection: The presence of an intra-abdominal

collection confirmed clinically or radiologically.

2.7. Septicemia: Positive blood culture.

2.8. Pyrexia of unknown origin: Any temperature above

37°C for more than 24 h occurring after the original pyrexia

following surgery (if present) had settled, for which no obvious

cause could be found.

2.9. Wound dehiscence: Superficial or deep wound

breakdown.

2.10. Deep venous thrombosis and pulmonary embolus:

when suspected, confirmed radiologically by venography or

ventilation/ perfusion scanning or diagnosed at post mortem.

2.11. Cardiac failure: Symptoms or signs of left ventricular

or congestive cardiac failure (alteration from preoperative

measures).

2.12. Impaired renal function: Arbitrarily defined as an

increase in blood urea of > 5mmol / l from preoperative levels.

2.13. Hypotension: A fall in systolic blood pressure below

90mmHg for more than 2 H as determined by

sphygmomanometry or arterial pressure transducer

measurement.

2.14. Respiratory failure: Respiratory difficulty requiring

emergency ventilation.

2.15. Anastomotic leak: Discharge of bowel content via the

drain, wound or abnormal orifice.

2.16. Moderate surgery: Cholecystectomy,

appendicectomy, mastectomy, transurethral resection of

prostate.

2.17. Major surgery: Any laparotomy, bowel resection,

cholecystectomy with choledochotomy, peripheral vascular

procedure or major amputation.

2.18. Major surgery+: Any aortic procedure,

abdominoperineal resection, pancreatic or liver resection,

oesophagogast-rectomy.

3. Mortality: Number of deaths with in 30 days of surgery.

4. Emergency Laparotomy: Laparotomy which is done with

in 24 hours of presentation to emergency department

5. Elective Laparotomy: Laparotomy for etiologies, which

can be postponed till patient, is fully optimized and risk

factors evaluated.

6. POSSUM:

Physiology and operative severity score for enumeration of

morbidity and mortality.

7. P-POSSUM:

Portsmouth- Physiology and operative severity score for

enumeration of morbidity and mortality

7. MATERIAL AND METHODS:

SETTING: South Surgical Unit, Mayo Hospital, Lahore.

DURATION OF STUDY: 6 months after the approval of the

synopsis.

SAMPLE SIZE:

50 emergency laparotomies (done within 24 hours of

presentation) and 50 laparotomies elective (done after patient

is optimized).

SAMPLING TECHNIQUE:

Non Probability convenience sampling.

STUDY DESIGN:

It is a prospective, observational follow-up study

SAMPLE SELECTION:

INCLUSION CRITERIA:

All adult cases being admitted to Mayo Hospital through

emergency or OPD for etiologies requiring laparotomy

Both genders

Cases above 13 years of age.

EXCLUSION CRITERIA:

Patients unfit for GA

Patients requiring CPR

before surgery

Elective

cholecystectomy

Mentally retarded

patients

Appendicectomy

DATA COLLECTION:

Total 100 cases, 50 from emergency and 50 from elective list

undergoing elective or emergency laparotomy meeting the

inclusion criteria were taken for study. An informed consent

was obtained from patients. Their demographic information’s

(age, sex, weight, etc) was recorded. The physiological

variables like pulse rate, systolic blood pressure, respiratory

rate, cardiac signs and Glasgow coma scale, hemoglobin,

white blood count, Urea, Sodium, Potassium, ECG and CXR

were recorded just before surgery. During the surgical

procedure six operative variables including operative severity,

total blood loss, multiple procedures, peritoneal soiling, cancer

and mode of surgery were recorded by the operating

surgeons. Their final physiological and operative score

calculated from possum data sheet (attached). The predicted

mortality and morbidity was calculated by POSSUM and P-

POSSUM equations. After surgery the patient’s observed

mortality and morbidity were noted for one month and

compared with the predicted outcomes. The patients were

followed up for 1 month on 1st, 3rd, 7th, 15th, 30th post

operative days for morbidity (list attached in operational

definitions) and mortality.

Data analysis:

All the information’s gathered will be entered in the SPSS

version 10.0 and analyzed. The source of the data will be

emergency and elective laparotomy. 12 physiological

variables i.e. age, pulse rate, systolic ,blood pressure,

respiratory rate, cardiac signs, and Hb, W.B.C, Urea, Sodium,

Potassium, and ECG & six operative variables i.e. operative

severity, total blood loss, multiple procedures, peritoneal

soiling, cancer and mode of surgery were recorded.

Demographic variables of the patients included in this study

were analyzed using the simple descriptive statistics.

Frequency distribution tables were made for source of data

(emergency/elective). Final prediction of the mortality and

morbidity of each patient was calculated using POSSUM and P-

POSSUM calculator available on the internet and recorded.

The observed mortality and morbidity was recorded within 30

days post-operatively and compared with predicted outcomes

Mortality and morbidity tables were made to calculate the

observed/predicted (O/P) ratios. Pearson correlation was used

to correlate the observed and predicted morbidity and

mortality. Chi-square analysis was made for the test of

significance. A p-value of .05 or less was taken as significant.

RESULTS

8. RESULTS

The main indications of surgery on elective list were C.A Colon

(20%), T. B Abdomen (12%) and Fibroid Uterus (10%)

respectively and the main complication was wound infection

10% (table 1, 2). Most of the surgeries were performed by

associate professor and consultants, 36% and 32%

respectively (table 3).

In elective cases 26(52%) were female and 24(48%) were

male (Figure 1). Mean age of elective patients was 40.58

years (SD +14.34) with age range 14 to 70 years and mean

weight was 54.48 KG (SD+ 6.72) with weight range 50 to 68

KG.

On elective, sum of observed mortality and morbidity was 3(6

%) & 15(30%) while predicted mortality and morbidity by

POSSUM was 4.8(9.6%) & 14.67(29.34%) and P-POSSUM

2.93(5.86%).The O/P ratio (observed / predicted) of mortality

by POSSUM in elective laparotomy was .625 and for morbidity

was 1.02 and by using P-POSSUM, the mortality was 1.02

(table 5-7).

Pearson’s correlation in elective for POSSUM observed and

predicted morbidity was 1.000 &.707 and mortality was 1.00

& .867 and for P-POSSUM was 1.000 & .901 (table 8-10).

On elective, there was no significant difference between the

observed and predicted values for morbidity(x2 =66.4, 27 df.

p=. 000), for POSSUM mortality(x2 =88.240, 15 df. p=. 000),

and for P-POSSUM mortality(x2 =114.160, 11 df. p=. 000),

(table 11).

The main indication of surgery in emergency was T.B

Abdomen 20%, fire arm injury abdomen (FAI) 16% and blunt

trauma abdomen 14% and major complication was wound

infection (table 12,13).Most of the surgeries in emergency

were performed by residents (54%) and consultants (46%).

Mostly patients in emergency were male (88%). Mean age of

the emergency patients was 36 years (SD + 16.50) with age

range 15 to 75 years and weight was 62.00 KG (SD+ 11.78)

with weight range 40 to 100 KG.

In emergency, sum of observed mortality and morbidity was

6(12 %) & 22(44%) while predicted mortality and morbidity by

POSSUM was 9(18%) & 28.17(56.34%) and P-POSSUM

6(12%).The O/P ratio (observed / predicted) of mortality by

POSSUM in emergency laparotomy was .66 and for morbidity

was .78 and by P-POSSUM, the mortality was 1.00 (table 16-

18).

Pearson’s correlation in emergency for POSSUM observed and

predicted morbidity was 1.000 &.736 and mortality was 1.00

& .707 and for P-POSSUM was 1.000 & .858 (table 19-21).

In emergency also, there was no significant difference

between the observed and predicted values for morbidity(x2

=45.00, 24 df. p=. 006), for POSSUM mortality(x2 =34.840, 20

df. p=. 021), and for P-POSSUM mortality(x2 =104.160, 14 df.

p=. 000), (Table 22).

Table 1: INDICATIONS FOR ELECTIVE LAPAROTOMY

N=50

Frequency

(%age)

CA. Colon 10(20) T.B Abdomen 6(12) Fibroid uterus 5 (10) CA. Rectum 5(10) CA. Ovary 4 (8) Rectovaginal Fistula 2 (4) Intestinal Obstruction 2 (4) CA. Head of pancreas 2 (4) Graham patch leak 1 (2) Ovarian cyst 1 (2) CA. Gall bladder 1 (2) Pseudocyst 1 (2) Typhoid perforation 1 (2) Choledochal cyst 1 (2) Perforated Appendicitis 1 (2) Adhesive bowel disease 1 (2) pelvic collection 1 (2) vesicovaginal fistula 1 (2) Liver Abscess 1 (2) Intussusceptions 1 (2) Liposarcoma 1 (2) Gastric outlet obstruction 1 (2) Total 50

TABLE 2: COMPLICATIONS IN ELECTIVE SURGERY

N=50

Frequency(%age)

Wound infection 5 (10)

Multiple* 2 (4)

Wound infection and deep

infection 2 (4)

Wound hemorrhage 1 (2)

Respiratory failure 1 (2)

Anastomotic leak and wound

dehiscence 1 (2)

Wound infection and UTI 1 (2)

Deep infection 1 (2)

Anastomotic leak 1 (2)

Total 15

*Multiple: wound infection, deep infection, dehiscence,

Table 3: SURGEON ON ELECTIVE LIST

N= 50

Frequency

PROFESSOR 7 (14)

ASSOCIATE PROFSSOR 18 (36)

ASSISTANT PROF 9 (18)

SENIOR REGISTRAR 16 (32)

TOTAL 50

FIGURE 1: GENDER DISTRIBUTION IN ELECTIVE CASES

N: 50

Table 4: SUM OF OBSERVED AND PREDICTED

OUTCOMES IN ELECTIVE CASES

O.mort

Sum

O.morb

Sum

P.mort

Sum

P.morb

Sum

PP.mort

Sum

3.00 15.00 4.80 14.67 2.93

KeysO.mort: observed mortality

O.morb: observed morbidity

P.mort: predicted mortality by POSSUM

P.morb: predicted morbidity by POSSUM

PP.mort: predicted mortality by p-possum

TABLE 5: COMPARISON OF OBSERVED AND PREDICTED

MORTALITY USING POSSUM EQUATION IN ELECTIVE

LAPAROTOMY

Range

of age

in years

Frequency O.mort P.mortO/p

Ratio

14-29 11 2 2.58 .775

30-44 16 1 1.12 .89

45-59 17 0 .88 0

60-74 6 0 .22 0

50 3 4.80 .625

Keys

O.mort: observed mortality

P.mort: predicted mortality by POSSUM

O/P : Observed/predicted

TABLE 6: COMPARISON OF OBSERVED AND PREDICTED

MORBIDITY USING POSSUM EQUATION IN ELECTIVE

LAPAROtomy

Range

of age

in years

Frequency O.morb P.morbO/p

Ratio

14-29 11 8 5.44 1.47

30-44 16 2 3.92 .51

45-59 17 4 4.08 .98

60-74 6 1 1.23 .81

50 15 14.67 1.02

Keys

O.morb: observed morbidity

P.morb: predicted morbidity by POSSUM

O/P : Observed/predicted

Table 7: COMPARISON OF OBSERVED AND PREDICTED

MORTALITY USING P-POSSUM EQUATION IN ELECTIVE

LAPAROTOMY

Range

of age

in years

Frequency O.mort PP.mortO/p

Ratio

14-29 11 2 1.98 1.01

30-44 16 1 .95 1.05

45-59 17 0 0 0

60-74 6 0 0 0

TOTAL 50 3 2.93 1.02

Keys

O.mort: observed mortality

PP.mort: predicted mortality by P-POSSUM

O/P : Observed/predicted

TABLE 8: Pearson’s correlation in elective morbidity

Observed

morbidity

Predicted

morbidity

Observed

morbidity

Pearson

Correlation

1.000 .707

Sig. (2-tailed). .000

N50 50

Predicted

morbidity

Pearson

Correlation

.707 1.000

Sig. (2-tailed).000 .

N

50 50

** Correlation is significant at the 0.01 level (2-tailed).

TABLE 9: Pearson’s correlation in POSSUM elective

mortality

Observed

mortality

Predicted

mortality

Observed

mortality

Pearson

Correlation

1.000 .867

Sig. (2-tailed). .000

N50 50

Predicted

mortality

Pearson

Correlation

.867 1.000

Sig. (2-tailed).000 .

N

50 50

** Correlation is significant at the 0.01 level (2-tailed).

TABLE 10: Pearson’s correlation in P-POSSUM elective

mortality

Observed

mortality

Predicted

mortality

Observed

mortality

Pearson

Correlation

1.000 .901

Sig. (2-tailed). .000

N50 50

Predicted

mortality

Pearson

Correlation

.901 1.000

Sig. (2-tailed).000 .

N

50 50

** Correlation is significant at the 0.01 level (2-tailed).

GOODNESS OF FIT TEST

TABLE 11: Hosmer and Lemeshow Test in elective cases

Chi-Square df Sig.

Predicted

morbidity

66.480 27 .000

POSSUM

Predicted

mortality

88.240 15 .000

P-POSSUM

Predicted

mortality

114.160 11 .000

TABLE 12: INDICATIONS FOR EMERGENCY

LAPAROTOMY

Frequency

T.B Abdomen 10(20%)

FAI Abdomen 8(16%)

Blunt trauma abdomen 7(14%)

Duodenal ulcer Perforation 6(12%)

Typhoid perforation 3(6%)

Mesenteric ischemia 2(4%)

CA. Colon 2(4%)

liver abscess 1(2%)

Band obstruction 1(2%)

Intussusceptions 1(2%)

Adhesive bowel disease 1(2%)

Post appendectomy peritonitis 1(2%)

Ectopic pregnancy 1(2%)

Rectal injury 1(2%)

*PID 1(2%)

Fecal fistula 1(2%)

Stab abdomen 1(2%)

CA. Testis 1(2%)

Perforated appendicitis 1(2%)

Total 50

*PID: pelvic inflammatory disease

Table 13: Complications of Emergency Laparotomy

Frequency Wound infection 5(10%)

Anastomotic leak 2(4)%

Wound Dehiscence 3(6%)

Deep infection 2(4%)

Sepsis 1(2%)

Cardiac failure 1(2%)

Chest infection 1(2%)

Jaundice, Fistula 1(2%)

Urinary fistula 1(2%)

Pulmonary Embolus 1(2%) Liver failure 1(2%) Renal failure 1(2%) Stomach leak 1(2%) *UTI 1(2%) Total 22

*UTI: urinary tract infection

Table 14: SURGEONS IN EMERGENCY

Frequency

Resident 27(54%)

SR 23(46%)

Total 50

FIGURE 2: GENDER DISTRIBUTION IN EMERGENCY

CASES

Table 15: SUM OF OBSERVED AND PREDICTED

OUTCOMES IN EMERGENCY CASES

O.morb

Sum

O.mort

Sum

P.morb

Sum

P.mort

Sum

PP.mort

Sum

22.00 6.00 28.17 9.00 6.00

KeysO.mort: observed mortality

O.morb: observed morbidity

P.mort: predicted mortality by POSSUM

P.morb: predicted morbidity by POSSUM

PP.mort: predicted mortality by p-possum

Table 16: COMPARISON OF OBSERVED AND PREDICTED

MORTALITY USING POSSUM EQUATION IN EMERGENCY

LAPAROTOMY

RANGE OF

AGE IN

YEARS

FREQUENCY O.MORT P.MORT O/P

RATIO

15-30 27 1 4.45 .224

31-45 10 1 1.56 .64

46-60 7 0 0.36 0

61-75 6 4 2.63 1.52

50 6 9 .66

Keys

O.mort: observed mortality

P.mort: predicted mortality by POSSUM

O/P : Observed/predicted

Table 17: COMPARISON OF OBSERVED AND PREDICTED

MORBIDITY USING POSSUM EQUATION IN EMERGENCY

LAPAROTOMY

RANGE OF

AGE IN

YEARS

FREQUENCY O.MORB P.MOR

B

O/P

RATIO

15-30 27 9 15.04 .59

31-45 10 5 5.15 .97

46-60 7 1 2.16 .46

61-75 6 7 5.82 1.20

50 22 28.17 .78

Keys

O.morb: observed morbidity

P.morb: predicted morbidity by POSSUM

O/P : Observed/predicted

Table 18: COMPARISON OF OBSERVED AND PREDICTED

MORTALITY USING P-POSSUM EQUATION IN

EMERGENCY LAPAROTOMY

RANGE OF

AGE IN

YEARS

FREQUENC

Y

O.MOR

T

PP.MOR

T

O/P

RATIO

15-30 27 1 2.41 .41

31-45 10 1 .67 1.49

46-60 7 0 0 0

61-75 6 4 2.92 1.36

50 6 6 1

Keys

O.mort: observed mortality

PP.mort: predicted mortality by P-POSSUM

O/P : Observed/predicted

Table 19: Pearson’s correlation in emergency morbidity

Observed

morbidity

Predicted

morbidity

Observed

morbidity

Pearson

Correlation

1.000 .736

Sig. (2-tailed). .000

N50 50

Predicted

morbidity

Pearson

Correlation

.736 1.000

Sig. (2-tailed).000 .

N

50 50

** Correlation is significant at the 0.01 level (2-tailed).

TABLE 20: Pearson’s correlation in POSSUM emergency

mortality

Observed

mortality

Predicted

mortality

Observed

mortality

Pearson

Correlation

1.000 .707

Sig. (2-tailed). .000

N50 50

Predicted

mortality

Pearson

Correlation

.707 1.000

Sig. (2-tailed).000 .

N

50 50

** Correlation is significant at the 0.01 level (2-tailed).

TABLE 21: Pearson’s correlation in P-POSSUM

emergency mortality

Observed

mortality

Predicted

mortality

Observed

mortality

Pearson

Correlation

1.000 .858

Sig. (2-tailed). .000

N50 50

Predicted

mortality

Pearson

Correlation

.858 1.000

Sig. (2-tailed).000 .

N

50 50

** Correlation is significant at the 0.01 level (2-tailed).

GOODNESS OF FIT TEST

TABL 22: Hosmer and Lemeshow Test in elective cases

Chi-Square df Sig.

Predicted

morbidity

45.000 24 .006

POSSUM

Predicted

mortality

34.840 20 .021

P-POSSUM

Predicted

mortality

104.800 14 .000

9. DISSCUSSION

In this culture of increased scrutiny surgeons must be able to

demonstrate clearly and accurately how they perform through

comparative audit of mortality and morbidity rates. Thus audit

of an individual surgeon, a unit or a hospital can be done

simply by monthly meetings of mortality and morbidity or by

many sophisticated scoring systems. POSSUM is such a

scoring system which predicts mortality & morbidity.

The main indication of laparotomy in emergency was trauma

32% (16 % FAI & 14 % blunt trauma abdomen, & 2% stab

abdomen) and abdomen tuberculosis (20%). Main bulk of the

patients were younger 27(54 %) between 15-30 years and

trauma was more common in young patients. Joosse P et al,

Talwar S et al, Qureshi MA, Aharonson-Daniel L et al, Nijboer

JMet al. shown that trauma is more prevalent in young

males.35-39

On elective most patients were older 17(34%) between 45-

59 years and main indication was Colon cancer (20%) and

abdominal tuberculosis was second major cause (12%).Wound

infection was major complication in both elective and

emergency cases (10%). Bhatti AA et al. (2004) reported post

operative complications in trauma patients in frequency of

wound infection (24%), chest infections (13.34%) and renal

failure (1.34%). 46 Ali AA et. al. (1996) reported that wound

infection and respiratory complications were the most

common (18%) in blunt trauma patients.47 Saidi HS et al.

reported complication rate of 15%.48 This indicates low

complication rate in our set up.

On elective surgery observed mortality was 3(6 %), morbidity

was 15(30%) and predicted mortality and morbidity by

POSSUM was 4.8(9.6%) and 14.67(29.34%) while P-POSSUM

predicted mortality was 2.93(5.86%).In my study it was shown

that POSSUM predicted morbidity well but overpredicted

mortality while P-POSSUM predicted mortality more accurately

in elective cases.

Lam CM et al. reported the observed mortality rate in major

hepatectomy 6.6% and POSSUM system over predicted

mortality (14.2%).The mortality rate predicted by P-POSSUM

was 4.2%.3 This shows P-POSSUM is more accurate.

Tekkis pp et al. reported that in oesophagogastric surgery O-

POSSUM was superior.5

Sutton R et al. reported the observed mortality rate 8.4%

while mean mortality predicted by SRS (surgical risk score),

POSSUN and p-POSSUM were 5.9, 12.6, and 7.3%

respectively.6 This shows P-POSSUM is more accurate.

Gocmen E et al. reported that O-POSSUM predicts mortality

more accurately than P-POSSUM.18

Mahesh G et al. also reported that P-POSSUM mortality

predictor equation predicts death accurately in general

surgical patients.19

Nagabhushsn S et al. also reported that in elective

oesophagogastric cancer surgery, observed mortality was

32(10.2%) and P-POSSUM predicted 36 and O-POSSUM

49.They concluded neither model accurately predicted the risk

of postoperative death. P-POSSUM provided a better fit to

observed results than O-POSSUM, which over predicted total

mortality. P-POSSUM also had superior discriminatory power.20

  Wakabayashi H et al. reported that in elective digestive

surgery, the POSSUM system can be useful in the risk

assessment for surgery in elderly patients.21

Jensen TC et al, Watanabe M et al, also found that in colorectal

cancer surgery P-POSSUM predicted mortality well while

POSSUM over-predicted mortality.22, 23

Pratt W et al. reported that POSSUM is a valuable

perioperative scoring system for pancreatic resections and

outcomes, and can be employed to guide management

decisions that impact postoperative recovery.24

Abdul wk et al. also reported that The POSSUM-predicted

mortality was 26%, and P-POSSUM predicted mortality was

6%, while actual mortality was 4%.Using POSSUM for

morbidity, the predicted value was 76%. The observed

morbidity was 46%. While P-POSSUM appeared satisfactory for

predicting mortality risk; POSSUM overestimated morbidity

and mortality for Pancreaticoduodenectomies in a specialist

centre.25

Midwinter M et al, Khan AW et al, found that POSSUM over-

predicted mortality while P-POSSUM predicted well.26, 28

Das N et al. reported that P-POSSUM overestimates the risk of

mortality for gynecological cancer.29

In my study mortality and morbidity was highest between 14

to 29 years of patients because most of these had

complicated form of tuberculosis (fecal fistula). In this age

group observed mortality was 2(4%) and POSSUM predicted

2.58(5.16%) and P-POSSUM 1.98(3.96%).In age groups

between 45 to 59 and 60 to 74, there was no observed

mortality and P-POSSUM also predicted zero mortality while

POSSUM predicted .88 and .22 respectively. This shows the

inaccuracy of POSSUM in predicting the mortality in low risk

patients because the lowest rate of mortality is 2% in major

surgery like laparotomy.

Griffiths H et al. also reported that Possum is valid for

morbidity but predicts more accurately for high-risk than for

low-risk groups.31

Prytherch DR et al, Whitely MS et al, Sagar PM et al, Tekkis PP

et al also shown this in accuracy of POSSUM.41-44

In emergency surgery observed mortality was 6 (12%),

morbidity was 22 (44 %) while POSSUM predicted 9 (18%)

deaths and 28.12 (56.24 %) and P-POSSUM predicted

mortality 6(12%).In emergency most of the patients were

between 15-30 years (54%) and their observed mortality (1)

and morbidity (9) was minimum compared to other groups but

their predicted mortality(4.45) and morbidity (15.04) by

POSSUM and P-POSSUM (2.41) was high. These patients were

younger, relatively fit with out any co morbid condition. The

predicted outcomes were higher because in gut perforation

minimum morbidity was more then 40% and mortality was

10%.In older age group 61 to 75, observed mortality (4) and

morbidity (7) was high and POSSUM and P-POSSUM both

predicted well.

Mohil R.S et al. (2004) in their study on emergency

laparotomy reported O/P ratio of POSSUM mortality 0.62 and

morbidity 0.92 and P-POSSUM 0.88. He found both POSSUM

and P-POSSUM predicted well. 4

Hobson and Sutton concluded that both POSSUM and P-

POSSUM appeared to be useful indicators for the prediction of

mortality in emergency surgery. 32

Qamar HK et al. reported that POSSUM score can be

effectively applied in all surgical setups in Pakistan and can be

used as an adjunct to surgical and can be used as an adjunct

to surgical audit.33, 34

In my study, we observed that P-POSSUM predicted mortality

more accurately in both elective (O/P 1.02) and emergency

(O/P 1) while POSSUM over predicted mortality and morbidity

in both cases.

Whilst POSSUM and P-POSSUM have performed well in

mortality and morbidity prediction and comparative surgical

audit, it does have certain limitations. Most notably it

persistently over predicts mortality in low risk patients.

Secondly the formula used in this system is complex and still

matter of some debate. Thirdly data required for it, is not

available for every patient.

10. CONCLUSION.

Based on this study, POSSUM and P-POSSUM can be used to

predict 30 days mortality and morbidity in general surgical

procedures. How ever P-POSSUM predicts mortality equally

well in both emergency and elective setups and is more

accurate compared to POSSUM. POSSUM over-predicts

mortality in both elective and emergency cases but predicts

morbidity well. However application of the POSSUM and P-

POSSUM scoring systems in larger patient setup is required to

rectify its prediction tendency.

This scoring system also can be used for risk-adjusted audit in

general surgery department. It can be used for predicting

outcome and pre-operative counseling of patients and their

careers, as a part of the process of informed consent and can

be used to evaluate the technique of pre -optimization in high

risk patients. Application of this tool would allow valid

comparison between surgeons and hospitals

This study showed that although POSSUM overpredicts

mortality but both these scores are good methods of risk

evaluation in general surgery ward in our set up as its

predicted morbidity and mortality matched with the observed

mortality rates. This scoring system can be applied in our set

up for the surgical audit.

11. PROFORMA

EVALUATION OF PHYSIOLOGIC AND OPERATIVE SEVERITY

SCORE FOR ENUMERATION OF MORTALITY AND MORBIDITY

AND PORTSMOUTH PREDICTOR MODIFICATION IN PATIENTS

UNDERGOING EMERGENCY AND ELECTIVE LAPAROTOMY.

Case no # Registration no# Date#

Name of patient# Age# Sex#

Socio economic status#

Weight# Profession#

Provisional diagnosis#

Final diagnosis#

Mode of admission # OPD / Emergency

Type of surgery # Elective/emergency

Date of operation#

Procedure#

POSSUM DATA SHEET PHYSIOLOGICAL SCORE

1 2 4 8

AGE <60 61-70 >71

Cardiac sign + CXR no failure

diuretic, digoxin,

anti-anginal or

anti-hypertensive

peripheral oedema

warfarin

borderline cardiomegaly

raised JVP

cardiomegaly

Respiratory History +CXR

no dyspnoea dyspnoea on exertion

limiting dyspnoea (one flight) moderate COAD

dyspnoea at rest RR>30min fibrosis or consolidation

Blood pressure

(Systolic mmHg)110-130

131-170

100-109

>171

90-99<90

Pulse (Beats/min) 50-8081-100

40-49101-120

>121

<40

Glasgow coma scale

15 12-14 9-11 <8

Haemoglobin g/100ml

13-1611.5-12.9

16.1-17.0

10.0-11.4

17.1-18.0

<9.9

>18.1

White cell count (x1012/L)

4-1010.1-20.0

3.1-4.0

>20.1

<3.0

Urea (mmol/L) <7.5 7.6-10.0 10.1-15.0 >15.1

Sodium (mmol/L) >136 131-135 126-130 <125

Potassium (mmol/L)

3.5-5.03.2-3.4

5.1-5.3

2.9-3.1

5.4-5.9

<2.8

>6.0

ECG NormalAtrial fibrillation (rate 60-90/min)

any abnormal rhythm

>5 ectopics / min, Q waves

ST/T wave changes

POSSUM DATA SHEET OPERATIVE SEVERITY SCORE

1 2 4 8

Operative severity score

MINOR MODERATE MAJOR MAJOR +

Multiple procedures

1 2 > 2

Total blood loss (mls)

< 100 101-500 501-999 >999

Contamination NoneMinor (serous fluid)

Local pusFree bowel content, pus or blood

Presence of malignancy

None Primary onlynodal metastases

Distant metastases

Mode of surgery

Elective

Emergency Resus >2hrs

Possible op <24 hrs after admission

Emergency (Immediate

surgery < 2hrs needed

Possum score: Physiological ______________Operative: ______________

OBSERVED MORBIDITY:

Complications1st day

Y/N

3rd day

Y/N

7th day

Y/N

15th

day

Y/N

30th

day

Y/N

Wound hemorrhage

Deep hemorrhage

Chest infection

Urinary infection

Wound infection

Deep infection

Septicemia

Pyrexia of unknown origin

Wound dehiscence

DVT and P. Embolus

Cardiac failure

Impaired renal function

Hypotension

Respiratory failure

Anastomotic leak

POSSUM

(Physiologic and Operative Severity Score for the enumeration of Mortality and Morbidity) 

Top of Form

Age Glasgow Respiratory

Urea Pulse (beats/min) Cardiac signs

Hb (g/dL) W.B.C. ECG

Potassium (mEql/L)

Sodium (mEql/L)

Systolic Blood Pressure

Physiologic Score

Operative Severity

Multiple procedures

Total Blood Loss

Peritoneal soiling

Cancer Mode of surgery

Predicted Morbidity Rate(Definitions are following)

Predicted Mortality Rate

x = (0.16* physiologic score)+(0.19*operative score)- 5.91Predicted Morbidity Rate = 1/(1+ e(-x))

y=(0.13* physiologic score)+(0.16*operative score)-7.04Predicted Mortality Rate = 1/(1+ e(-

y))

 

Portsmouth – POSSUM

(Physiologic and Operative Severity Score for the enumeration of Mortality and Morbidity) 

Top of Form

Age Glasgow Respiratory

Urea Pulse (beats/min) Cardiac signs

Hb (g/dL) W.B.C. ECG

Potassium (mEql/L)

Sodium (mEql/L) Systolic Blood Pressure

Physiologic Score

Operative Severity

Multiple procedures Total Blood Loss

Peritoneal soiling

Cancer Mode of surgery

Operative Score

  Predicted Death Rate

R= (0.1692 * physiologic score)+(0.1550 *operative score)-9.065Predicted Death Rate = 1 / ( 1+ e(-R) )

 

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