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Year : 2015  |  Volume : 5  |  Issue : 1  |  Page : 16-19

Validation of the use of POSSUM and P-POSSUM score in perforation peritonitis in Indian population

Department of General Surgery, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India

Date of Web Publication13-Mar-2015

Correspondence Address:
S K Yadav
Room No. 50, H No. 03, Rajendra Institute of Medical Sciences, Ranchi - 834 009, Jharkhand
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2278-9596.153143

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Background: The Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (POSSUM) model, its Portsmouth (P-POSSUM) modification are two surgical risk scoring systems used extensively to predict post-operative morbidity and mortality in general surgery. The aim was to undertake the study of validity of these models in Indian patients undergoing exploratory laparotomy for perforation peritonitis.
Patients and Methods: A prospective study was performed, in which a total of 103 patients undergoing exploratory laparotomy for perforation peritonitis were included during the period of November 2011 to October 2013. The morbidity and mortality risks were calculated using the POSSUM and P-POSSUM.
Results: Around 44/103 patients developed complications (total morbidity rate of 42.72%) in post-operative period; this was compared with POSSUM predicted morbidity. There was no statistical difference between observed and predicted morbidity (χ2 = 45.607; df = 1; observed/expected ratio (O:E) = 0.82; P value = 0.000). Ten patients died (total mortality rate of 9.7%). The P-POSSUM expected mortality rate was compared. There was no statistical difference between the observed and P-POSSUM predicted mortality rates (χ2 = 17.444, df = 1; P value = 0.000). However, P-POSSUM overpredicts mortality in our study (O:E = 0.25; P value = 0.000).
Conclusion: POSSUM and P-POSSUM appear to be good and valid indices for use in risk prediction of morbidity and mortality, respectively (surgical outcome in perforation peritonitis) in the Indian population. We found that POSSUM accurately predicts morbidity but P-POSSUM is not able to predict mortality accurately.

Keywords: Morbidity, mortality, perforation peritonitis, POSSUM, P-POSSUM

How to cite this article:
Chaubey D, Yadav S K, Yadav J, Kumar P, Sahu S S, Kumar S, Prakash O M. Validation of the use of POSSUM and P-POSSUM score in perforation peritonitis in Indian population. Arch Int Surg 2015;5:16-9

How to cite this URL:
Chaubey D, Yadav S K, Yadav J, Kumar P, Sahu S S, Kumar S, Prakash O M. Validation of the use of POSSUM and P-POSSUM score in perforation peritonitis in Indian population. Arch Int Surg [serial online] 2015 [cited 2024 Mar 1];5:16-9. Available from:

  Introduction Top

The outcome of surgical intervention is not solely dependent on the abilities and techniques of the surgeon. The patients' physiological status and the peri-operative services affect the ultimate outcome. Crude morbidity and mortality rates can be misleading when results of emergency surgery are compared between different units and hospitals. The determination of outcome of surgery helps to plan and implement more effective treatment regimen.

Perforation peritonitis is the most common surgical emergency encountered by surgeons all over the world as well as in India. [1] Common causes of peritonitis are peptic ulcer perforation (Duodenal and Antral), ileal perforation, appendicular perforation, gastric perforation, jejunal perforation, and large gut perforation. [1] If properly treated mortality is <10% in typical cases of surgically correctable peritonitis in otherwise healthy patients. Mortality rises to above 40% in elderly and in those with significant co-morbidities (cardiac, respiratory, renal, etc.) as well as cases that present late (>48 hours). [1]

Many surgical risk scoring systems are available but the Physiologic and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM) model by Copeland et al., [2] was recommended as the most appropriate for general surgery. [3],[4] This model, utilizing scores relating to 12 physiological and 6 operative variables, was developed to predict hospital mortality and morbidity postoperatively. However, POSSUM was then reported as over predicting postoperative mortality, particularly in patients at low risk. This led to a revision: The Portsmouth modification (P-POSSUM) by Whiteley et al. [5]

Few studies have been taken up in developing countries regarding risk adjusted audits of emergency exploratory laporotomy patients. Keeping in mind the different category of patients at our hospital (delayed presentation, malnutrition and limited resources), it was felt that POSSUM and P-POSSUM scoring could be used to assess the healthcare provided, outcome, and compare with others. Hence, this prospective study was taken.

  Patients and Methods Top

This study was carried out in Department of surgery, RIMS., Ranchi. Data was collected prospectively for all peritonitis cases admitted in a single surgical unit from November 2011 to October 2013. All preoperative, intraoperative, and post-operative patient data were collected and entered into a computer database prospectively. The morbidity risk was calculated using the POSSUM equation. The mortality risk was calculated using the P-POSSUM equation. The in-hospital mortality was recorded for each patient.

Statistical analysis

Continuous data was presented as mean ± standard deviation (SD). For calculation of significance between continuous variables between two separate groups, unpaired t-test was used. For calculation of significance between two proportions and percentages, Chi-square and Fischer's test were used.

POSSUM equation for morbidity

Logn R/(1-R) = -5.91 + (0.16 × physiological score) + (0.19 × operative severity score)

Where R = predicted morbidity 8 .

P-POSSUM equation for mortality

Logn R/(1-R) = -7.04 + (0.13 × physiological score) + (0.16 × operative severity score)

Where R = predicted mortality. 8

Using outcome (dead/alive or complicated/uncomplicated) as a dichotomous-dependent variable, we have derived multiple logistic regression equations for both morbidity and mortality. Significance was assessed using model χ2 . Differences between observed and expected outcomes were assessed using χ2 tests. Statistical calculations were carried out with statistical package of social sciences (SPSS) computer software 16.0 (SPSS, Chicago, Illinois, United States). A value of P < 0.05 was considered statistically significant.

  Results Top

Observations and outcome

A total of 103 emergency laparotomies (satisfying study criteria) were performed for perforation peritonitis between September 2011 and September 2013. Duodenal perforation (58.25%) was the most common cause of perforation peritonitis followed by ileal perforation (27.18%) and gastric perforation (8.73%) [Table 1].
Table 1: Site of perforation

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[Table 2] shows the pre-operative parameters of the patients. Where Hypotension is defined by Blood Pressure <110/70 mm Hg, hypokalemia is serum potassium <3.5 meq/dl, dyspnea on exertion is mild COAD = chronic obstructive airway disease, limiting dyspnea is moderate COAD and dyspnea at rest, fibrosis, or consolidation is severe COAD.
Table 2: The pre-operative parameters of patients

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Out of 103 patients of perforation peritonitis, 102 cases were having free bowel content with or without pus and blood as peritoneal contaminant only one was having local pus collection [Table 3].
Table 3: Type of peritoneal contamination

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Primary repair of intestinal perforation was most common surgery performed, followed by ileostomy and resection and anastomosis. Almost all duodenal perforations were of size 0.5 cm × 0.5 cm and was repaired by modified Graham omental patch technique, i.e., primary repair was done. Commonly performed operative procedure for ileal perforation was ileostomy in our study [Table 4].
Table 4: Type of repair

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Total number of patients with complications was 44 which also include 10 patients who died after surgery. More than one complication was observed in many postoperative patients. Most common complication was wound infection (26.21%) [Table 5].
Table 5: Post operative complications

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Statistically significant differences were detected in the postoperative morbidity or in-hospital mortality rate using the χ2 goodness-of-fit test. When comparing predicted morbidity with observed morbidity by POSSUM score, an overall O:E (observed/expected) ratio of 0.82 was found. When linear analysis was used to predict the morbidity, the O:E ratio was 0.61 and it significantly over-predicted morbidity (χ2 = 11.48, df = 9, P = 0.025), but when the same data was used in exponential analysis, the O:E ratio was 0.82. There was no significant difference between the observed and predicted values (χ2 = 9.684, df = 1, P = 0.000). P-POSSUM over-predicted mortality when the linear method was used with an O:E ratio of 0.25 (χ2 = 7.806, df = 1, P = 0.000).

  Discussion Top

The basic tenet in the healthcare is to provide quality healthcare with reduction in adverse outcome. Comparison of adverse outcome rates is necessary to assess the adequacy of care provided and to evolve new strategies for better outcome. The accurate prediction of outcomes after a high-risk procedure such as surgical treatment of perforation peritonitis can early detect postoperative complications, and early referral to a higher medical facility. Better planning and precision can improve individual prognosis and it can help in implementing a tier based medical services in 3 rd world countries with each tier having a predefined permission for surgical intervention. POSSUM and P-POSSUM, a modification of POSSUM, were proposed to overcome shortcomings of crude mortality rates-based comparison.

Post-operative complications and death may result depending on three major factors: The quality of the surgical team, the patient's physiological status, and the degree of surgical stress. [6] But POSSUM and P-POSSUM has to be correlated to the general condition of the local population for it to be effective. [7],[8],[9] This is important for patients in developing countries like India where the general health of the population is variable and presentation frequently delayed. [7],[8]

With a post-operative morbidity rate of 42.72% and an in-hospital mortality rate of 9.7%, our institution lies within the accepted range of complications after emergency laparotomy for perforation peritonitis. In this study, POSSUM has generally over-predicted morbidity significantly when linear method of analysis was used, and though over-prediction of morbidity was insignificant with exponential method of analysis, it was comparable with other studies. [10],[11],[12]

Tekkis and others obtained mortality rate in elective surgery at 3.9% and in emergency 25%, overall mortality rate of 11.1%. [11] P-POSSUM the predicted mortality was 40, an O:E ratio of 0.25 was obtained in this study. This is in contrast to the findings obtained by Yii MK and Ng KJ [13] (O:E = 1.28), Tekkis [11] (O:E = 0.98).

To conclude, POSSUM predicted morbidity well and can be used as a tool to assess level of healthcare provided. But P-POSSUM, in our study, over-predicted mortality; hence, it cannot be recommended for mortality assessment in Indian population.

  References Top

Norman S Williams, Christopher JK, P Ronan o'connell. Bailey and love's short practice of surgery, 26 th edition. CRC press New York.  Back to cited text no. 1
Copeland GP, Jones D, Walters M. POSSUM: A scoring system for surgical audit. Br J Surg 1991;78:355-60.  Back to cited text no. 2
Prytherch DR, Ridler BM, Beard JD, Earnshaw JJ. Audit and Research Committee, The Vascular Surgical Society of Great Britian and Ireland. A model for national outcome audit in vascular surgery. Eur J Vasc Endovasc Surg 2001;21:477-83.  Back to cited text no. 3
Dutta S, Horgan PG, McMillan DC. POSSUM and its related models as predictors of postoperative mortality and morbidity in patients undergoing surgery for gastro-oesophageal cancer: A systematic review. World J Surg 2010;34:2076-82.  Back to cited text no. 4
Whiteley MS, Prytherch DR, Higgins B, Weaver PC, Prout WG. An evaluation of the POSSUM surgical scoring system. Br J Surg 1996;83:812-5.  Back to cited text no. 5
Haga Y, Ikei S, Ogawa M. Estimation of Physiologic Ability and Surgical Stress (E-PASS) as a new prediction scoring system for postoperative morbidity and mortality following elective gastrointestinal surgery. Surg Today 1999;29:219-25.  Back to cited text no. 6
Yii MK, Ng KJ. Risk-adjusted surgical audit with the POSSUM scoring system in a developing country. Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity. Br J Surg 2002;89:110-3.  Back to cited text no. 7
Parihar V, Sharma D, Kohli R, Sharma DB. Risk adjustment for audit of low risk general surgical patients by Jabalpur-POSSUM score. Indian J Surg 2005;67:38-42.  Back to cited text no. 8
Prytherch DR, Whiteley MS, Higgins B, Weaver PC, Prout WG, Powell SJ. POSSUM and Portsmouth POSSUM for predicting mortality. Physiological and Operative Severity Score for the enumeration of Mortality and morbidity. Br J Surg 1998;85:1217-20.  Back to cited text no. 9
Neary WD, Heather BP, Earnshaw JJ. The Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (POSSUM). Br J Surg 2003;90:157-65.  Back to cited text no. 10
Tekkis P, Trotter G, South LM. Comparison of POSSUM and the Portsmouth predictor equation for predicting death following vascular surgery. Br J Surg 1999;86:713-4.  Back to cited text no. 11
Tekkis PP, Kocher HM, Bentley AJ, Cullen PT, South LM, Trotter GA, et al. Operative mortality rates among surgeons: Comparison of POSSUM and p-POSSUM scoring systems in gastrointestinal surgery. Dis Colon Rectum 2000;43:1528-32.  Back to cited text no. 12
Midwinte M, Ashley S. Risk stratification in vascular patients using modified POSSUM scoring system. Br J Surg 1997;84:568-72.  Back to cited text no. 13


  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]


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