title: Predicting Postoperative Mortality in Patients Undergoing Colorectal Surgery POSSUM, p-POSSUM, and Cr-POSSUM: Implementation Issues in a United States Health Care System for Prediction of Outcome for Colon Cancer Resection Risk‐adjusted surgical audit with the POSSUM scoring system in a developing country Risk‐adjusted analysis of surgeon performance: A 1‐year study Comparison of POSSUM and P-POSSUM for risk-adjusted audit of patients undergoing emergency laparotomy. A newly devised scoring system for prediction of mortality in patients with colorectal cancer: a prospective study. Predicting post-operative mortality in patients undergoing colorectal surgery using P-POSSUM and CR-POSSUM scores: a prospective study POSSUM and Portsmouth POSSUM for predicting mortality. Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity. A model for national outcome audit in vascular surgery. Validation of the CR-POSSUM Risk-Adjusted Scoring System for Major Colorectal Cancer Surgery in a Single Center A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. POSSUM: A scoring system for surgical audit Comparison of individual surgeon's performance Comparison of POSSUM with APACHE II for prediction of outcome from a surgical high‐dependency unit
abstract: Well-known and suitable instruments for surgical audit are the POSSUM and P-POSSUM scoring systems . But these scores have not been well vali date d across the countries . The objective of the present study was to assess the predictive value of scores for colorectal surgery in France . Patients operated on for colorectal malignant or diverticular diseases , whether electively or on emergency basis , within a 4-month period were included in a prospect i ve multicenter study conducted by the French Association for Surgery ( Association Française de Chirurgie , AFC ) . The main outcome measure was postoperative in-hospital mortality . Independent factors leading to death were assessed by multivariate logistic regression analysis ( AFC-index ) . The ratio of expected versus observed deaths was calculated , and the predictive value of the POSSUM and P-POSSUM scores were analyzed by the receiver operating characteristic ( ROC ) curve . A total of 1426 patients were included . The in-hospital death rate was 3.4 % . Four independent preoperative factors ( AFC-index ) have been found : emergency surgery , loss of more than 10 % of weight , neurological disease history , and age > 70 years . POSSUM had a poor predictive value ; it overestimated postoperative death in all cases . P-POSSUM had a good predictive value , except for elective surgery , where it overestimated postoperative death twofold . The predictive value of the AFC-index was also good . It had the same sensitivity and specificity as the P-POSSUM . POSSUM has not been vali date d in France in the field of colorectal surgery . P-POSSUM was as predictive as the AFC-index which is a simpler instrument based on four clinical parameters ( without any mathematical formulas ) PURPOSE : The Physiologic and Operative Severity Score for the enUmeration of Mortality and morbidity ( POSSUM ) , Portsmouth revision (p)-POSSUM , and colorectal (Cr)-POSSUM scoring systems were developed as audit tools for comparing outcomes in surgical and colorectal patients on the basis of operative risk assessment . The aim of this study was to evaluate the applicability of these systems to a cohort of colon cancer patients undergoing surgery in the United States . METHODS : POSSUM factors from 890 consecutive patients undergoing major surgical procedures for colon cancer in nine United States hospitals over a two-year period from January 2000 through December 2001 were prospect ively collected . The observed over the expected hospital mortality was compared by means of the POSSUM , p-POSSUM , and Cr-POSSUM scoring systems . The effect of missing data on the utility of this process for outcome assessment was assessed with three methods for data imputation . RESULTS : The number of resections per institution ranged from 13 to 437 . The observed mortality rate ranged from 0.8 percent to 15.4 percent among the institutions , with an overall operative mortality of 2.3 percent . The POSSUM , p-POSSUM , and Cr-POSSUM predicted mortality was 10.7 percent , 11.2 percent , and 4.9 percent , respectively . The POSSUM and p-POSSUM models overpredicted mortality in all institutions ( P < 0.01 ) , whereas the Cr-POSSUM demonstrated an observed over expected hospital mortality ratio of > 1 in three institutions . The calculations were unaffected by the various methods of inserting missing data . CONCLUSION : An apparent overprediction of mortality for colon cancer resection was evident with all three POSSUM variants . This implies that a calibration process is required for use of these variants in the United States health care system . Missing data may be treated as normal values without influencing outcome . The Cr-POSSUM appeared to be the most promising audit tool for colorectal cancer surgery ; however , it will require further refinement to provide process control graphs for identification of potential outliers and improvement in the quality of care in the United States The Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity  ( POSSUM ) is an objective and appropriate scoring system for risk‐adjusted comparative general surgical audit . This score was devised in the UK and has been used widely , but application of POSSUM to centres outside the UK has been limited , especially in developing countries . This prospect i ve study vali date d its application in a surgical practice with a different population and level of re sources A 1‐year prospect i ve analysis was undertaken of all non‐day‐case general surgery in a district general hospital . Using the  Physiological and Operative Severity Score for the en Umeration of Mortality and Morbidity  ( POSSUM ) scoring system 3004 patients were assessed . From the predictions of mortality and morbidity so obtained , a quality measure , the ratio of observed to expected numbers of deaths and complications ( O : E ratio ) was determined for each surgeon , both overall and within specialty zones . The present study demonstrates the serious hazard in using ‘ raw ’ uncorrected mortality and morbidity statistics to compare surgeon performance . Mortality rates varied from 1·0 to 4·9 per cent whereas O : E ratios ranged from 0·83 to 1·06 ; morbidity rates varied from 5·3 to 12·6 per cent with O : E ratios 0·86‐1·02 . Great misunderst and ing may result from the publication of surgeon or hospital ‘ league tables ’ . The present study demonstrates a technique that might allow surgeon performance to be monitored adequately and accurately BACKGROUND The Physiological and Operative Severity Score for Enumeration of Mortality and Morbidity  ( POSSUM ) scoring system , derived from a heterogeneous population , has been used successfully as an audit tool , but it has appeared to over-predict mortality in low-risk groups for which an alternative system , the Portsmouth predictor equation for mortality ( P-POSSUM ) was design ed and used successfully . In this prospect i ve study , we compared these two equations in predicting death and tested their validity in predicting morbidity and mortality in patients undergoing emergency laparotomy in a tertiary hospital . METHODS Eighty-two patients who underwent emergency laparotomy were included in this study .  Actual morbidity and mortality rates were compared with the predicted mortality and morbidity rates using both POSSUM and P-POSSUM equations by linear regression and exponential methods of analysis . RESULTS POSSUM equation significantly over-predicted both morbidity and mortality when linear regression analysis was used , but predicted well when exponential analysis was used . Prediction of mortality by P-POSSUM was good using both linear and exponential analyses , and exponential method was better than linear regression analysis . CONCLUSION P-POSSUM is a better equation than POSSUM in predicting mortality , and exponential method is better than linear regression analysis . Both equations are useful tools for risk-adjusted surgical audit of patients undergoing emergency laparotomy BACKGROUND Postoperative morbidity and mortality from colorectal cancer varies widely across hospitals in the UK . We aim ed to assess whether a newly developed score from the Association of Coloproctology of Great Britain and Irel and ( ACPGBI ) could predict mortality from colorectal cancer surgery as accurately as the Physiology and Operative Severity Score for enUmeration of Mortality and morbidity ( POSSUM ) , Portsmouth POSSUM ( P-POSSUM ) , or the ColoRectal POSSUM ( CR-POSSUM ) . METHODS We analysed prospect ively 618 patients with histologically confirmed colorectal cancer who had surgery to remove primary tumours done by colorectal surgeons or non-colorectal surgeons in a 3-year period . We compared observed mortality with those predicted by the ACPGBI , POSSUM , P-POSSUM , and CR-POSSUM scoring systems using the Hosmer-Lemeshow test and Receiver Operating Characteristic ( ROC ) curve analysis . FINDINGS Between April 1 , 2002 , and May 31 , 2005 , 618 consecutive patients with colorectal cancer had surgery to remove primary tumours . Overall observed 30-day mortality over the 3 years was 10.2 % ( 95 % CI 8.0 - 12.9 ) . Overall predicted mortality ( mean score ) by use of POSSUM was 12.7 % ( 11.7 - 13.7 ) , by use of P-POSSUM was 4.4 % ( 3.4 - 5.4 ) , by use of CR-POSSUM was 9.6 % ( 8.6 - 10.6 ) , and by use of ACPGBI score was 8.1 % ( 7.3 - 8.8 ) . INTERPRETATION POSSUM overpredicted mortality , whereas P-POSSUM underpredicted mortality from colorectal-cancer surgery . CR-POSSUM was a more-accurate predictor of mortality in most analyses than was POSSUM and P-POSSUM . Although CR-POSSUM gave the closest prediction of overall mortality , analyses of subgroups of patients showed that ACPGBI score predicted overall mortality most accurately Introduction POSSUM and its variants Portsmouth POSSUM ( P-POSSUM ) and Colorectal POSSUM ( CR-POSSUM ) equations were derived from a heterogeneous general surgical population , which have been used successfully to provide risk-adjusted operative mortality rates . CR-POSSUM utilises fewer parameters , allowing ease of use . The aim of this study was to predict the mortality outcome in colorectal surgery using these scoring systems compared to the observed mortality and to devise a new scoring system with improved accuracy . Methods The study was conducted prospect ively on all consecutive patients requiring elective and emergency colorectal surgery between April 2002 and May 2005 . The outcome parameter was defined as 30-day mortality . The observed mortality was compared with predicted mortality by the scoring systems . Hosmer and Lemeshow test was used to assess statistical accuracy of POSSUM . Results Eight hundred ninety-nine patients underwent colorectal surgery during the study period . There were 619 elective and 281 emergency patients .  Observed 30-day mortality rate was 9 % , compared with predicted mortality rate of 13.5 % with POSSUM , 5 % with P-POSSUM and 9.5 % with CR-POSSUM . Conclusion POSSUM ’s mortality rate was overestimated , while P-POSSUM ’s mortality rate was underestimated . CR-POSSUM , the simplest system of all three , most accurately predicted mortality in our unit BACKGROUND There is a need for an accurate measure of surgical outcomes so that hospitals and surgeons can be compared properly regardless of case mix . POSSUM ( Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity ) uses a physiological score and an operative severity score to calculate risks of mortality and morbidity . In a previous small study it was found that Portsmouth POSSUM ( P-POSSUM ; a modification of the POSSUM system ) provided a more accurate prediction of mortality . METHODS Some 10000 general surgical interventions ( excluding paediatric and day cases ) were studied prospect ively between August 1993 and November 1995 . The POSSUM mortality equation was applied to the full 10000 surgical episodes . The 10000 patients were arranged in chronological order and the first 2500 were used as a training set to produce the modified P-POSSUM predictor equation . This was then applied prospect ively to the remaining 7500 patients arranged chronologically in five groups of 1500 . RESULTS The original POSSUM logistic regression equation for mortality overpredicts the overall risk of death by more than twofold and the risk of death for patients at lowest risk ( 5 per cent or less ) by more than sevenfold . The P-POSSUM equation produced a very close fit with the observed in-hospital mortality . CONCLUSION P-POSSUM provides an accurate method for comparative surgical audit OBJECTIVE The aim was to model vascular surgical outcome in a national study using POSSUM scoring . METHODS One hundred and twenty-one British and Irish surgeons completed data question naires on patients undergoing arterial surgery under their care ( mean 12 patients , range 1 - 49 ) in May/June 1998 . A total of 1480 completed data records were available for logistic regression analysis using P-POSSUM methodology . Information collected included all POSSUM data items plus other factors thought to have a significant bearing on patient outcome : " extra items " . The main outcome measures were death and major postoperative complications . The data were checked and inconsistent records were excluded . The remaining 1313 were divided into two sets for analysis . The first " training " set was used to obtain logistic regression models that were applied prospect ively to the second " test " data set . RESULTS using POSSUM data items alone , it was possible to predict both mortality and morbidity after vascular reconstruction using P-POSSUM analysis . The addition of the " extra items " found significant in regression analysis did not significantly improve the accuracy of prediction . It was possible to predict both mortality and morbidity derived from the preoperative physiology components of the POSSUM data items alone . CONCLUSION this study has shown that P-POSSUM methodology can be used to predict outcome after arterial surgery across a range of surgeons in different hospitals and could form the basis of a national outcome audit . It was also possible to obtain accurate models for both mortality and major morbidity from the POSSUM physiology scores alone Purpose Scoring systems to predict mortality from surgery are important tools used to give information to the operator and patient and in the auditing of clinical practice . This study was design ed to vali date the recently developed the Colorectal Physiologic and Operative Severity Score for the Enumeration of Mortality and Morbidity ( CR-POSSUM ) scoring system in a single center for colorectal cancer surgery . We also analyzed whether albumin may have a role in the CR-POSSUM model . Methods We compared this model with two other scoring systems : POSSUM and Portsmouth-POSSUM ( P-POSSUM ) models .  In-hospital mortality was used as the outcome , and Hosmer-Lemeshow statistic was used to determine goodness of fit . Results Complete data were collected prospect ively from 304 patients from 1990 to the present . The overall operative mortality was 6.5 percent . Observed to expected ratios were used to compare the scoring systems at a given predicted mortality . The overall observed to expected ratio was 1.25 for CR-POSSUM , 1.59 for P-POSSUM , and 3.37 for POSSUM . The CR-POSSUM model showed a good fit with the data ( Hosmer-Lemeshow statistic , 3.86 ; P = 0.795 ) and the area under the receiver operator curve was 0.74 . After correcting for factors used in the CR-POSSUM , logistic regression showed a significant correlation between albumin and mortality ( P = 0.016 ) . Conclusions We have shown that the CR-POSSUM model is an accurate predictor of outcome for major colorectal surgery . The POSSUM and P-POSSUM models over-predicted mortality . Albumin , which is not a factor included in these three systems , may be an important addition in improving the accuracy of the CR-POSSUM model OBJECTIVE To develop and vali date a new Simplified Acute Physiology Score , the SAPS II , from a large sample of surgical and medical patients , and to provide a method to convert the score to a probability of hospital mortality . DESIGN AND SETTING The SAPS II and the probability of hospital mortality were developed and vali date d using data from consecutive admissions to 137 adult medical and /or surgical intensive care units in 12 countries . PATIENTS The 13,152 patients were r and omly divided into developmental ( 65 % ) and validation ( 35 % ) sample s. Patients younger than 18 years , burn patients , coronary care patients , and cardiac surgery patients were excluded . OUTCOME MEASURE Vital status at hospital discharge . RESULTS The SAPS II includes only 17 variables : 12 physiology variables , age , type of admission ( scheduled surgical , unscheduled surgical , or medical ) , and three underlying disease variables ( acquired immunodeficiency syndrome , metastatic cancer , and hematologic malignancy ) . Goodness-of-fit tests indicated that the model performed well in the developmental sample and vali date d well in an independent sample of patients ( P = .883 and P = .104 in the developmental and validation sample s , respectively ) . The area under the receiver operating characteristic curve was 0.88 in the developmental sample and 0.86 in the validation sample . CONCLUSION The SAPS II , based on a large international sample of patients , provides an estimate of the risk of death without having to specify a primary diagnosis . This is a starting point for future evaluation of the efficiency of intensive care units POSSUM , a Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity , is described . This system has been devised from both a retrospective and prospect i ve analysis and the present paper attempts to vali date it prospect ively . Logistic regression analysis yielded statistically significant equations for both mortality and morbidity ( P < 0.001 ) . When displayed graphically zones of increasing morbidity and mortality rates could be defined which could be of value in surgical audit . The scoring system produced assessment s for morbidity and mortality rates which did not significantly differ from observed rates Comparison of outcome after  colorectal resection  among different surgeons is difficult . Crude rates of morbidity and mortality can be misleading because such rates make no allowance for differences in case mix and fitness of patients . AIM : The aim of this study was to compare outcome among five surgeons by means of the simple , well-vali date d scoring system POSSUM for risk-adjusted analysis . METHODS : A total of 438 patients were studied prospect ively . Each patient underwent colorectal resection  by one of the five surgeons .  Demographic details , operative procedure , and postoperative course were recorded , and physiologic and operative severity scores were determined . Risk of morbidity and mortality was calculated for each patient . RESULTS : Incidence of morbidity varied sharply among the five surgeons , from 13.6 to 30.6 percent , and the 30-day mortality varied from 4.5 to 6.9 percent . However , application of POSSUM to allow risk-adjusted analysis of the data demonstrated that the incidence of morbidity and mortality predicted by POSSUM based on patients physiologic and operative risks factors was very similar to the observed outcome for each surgeon . CONCLUSION : Direct comparison of individual surgeon 's performance based on crude rates of morbidity and mortality can be misleading . Risk-adjusted analysis allows more meaningful comparison POSSUM and APACHE II scores from 117 consecutive admissions to a high‐dependency unit after major surgery were correlated with 30‐day morbidity and mortality rates . Thirteen patients ( II per cent ) died and 59 ( 50 per cent ) developed a postoperative complication . Receiveroperating characteristic curve analysis showed POSSUM to have good predictive value for mortality ( area under curve 0.75 ) and morbidity ( area under curve 0.82 ) . APACHE II scores had a significantly inferior predictive value for mortality ( area under curve 0.54 ) ( P < 0.002 ) .  POSSUM  was superior to APACHE II in prediction of mortality in patients admitted to a high‐dependency unit after general surgery . Prediction of postoperative complications by POSSUM is accurate and may be useful for audit
target: Conclusions P-POSSUM was the most accurate model for predicting post-operative mortality after colorectal cancer surgery .
  The original POSSUM model was accurate in predicting post-operative complications
background: Introduction The Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity  ( POSSUM ) model and its Portsmouth ( P-POSSUM ) and colorectal ( CR-POSSUM ) modifications are used extensively to predict and audit post-operative mortality and morbidity .
  This aim of this systematic review was to assess the predictive value of the POSSUM models in colorectal cancer surgery .