Can SAPS II predict operative mortality more accurately than POSSUM and P-POSSUM in patients with colorectal carcinoma undergoing resection?

The impact of frailty syndrome and risk scores on emergency cholecystectomy patients

PURPOSE

Cholecystectomy, which is one of the most common surgical procedures, is also performed in the emergency setting. A number of risk scores have been introduced in recent studies; moreover, over the last few years literature has focused on surgical patients with frailty syndrome. The aim of the present study is to evaluate whether frailty syndrome and the risk scores are correlated with morbidity, post-operative hospital stay and the ICU admission rate following emergency cholecystectomy.

METHODS

Eighty-five consecutive patients of >65 years of age who underwent cholecystectomy were selected from 2306 emergency procedures for inclusion in the present study. The patients were assessed for frailty syndrome and their scores were calculated on the basis of chart review. Univariate analyses were performed to compare severe frailty patients to intermediate frailty and robust patients. ROC and logistic regression analyses were performed with the end-points of morbidity, hospital stay and ICU admission.

RESULTS

In addition to having worse ASA, inflammatory and risk values than robust patients, frailty syndrome patients also had higher rates of morbidity and ICU admission and longer hospitalization periods. A logistic regression analysis showed that the P-Possum was independently correlated with morbidity. Frailty and open surgery were independently correlated with longer hospitalization, whereas ICU admission was correlated with worse ASA and P-Possum values.

CONCLUSIONS

Frailty syndrome significantly impacts the length of hospitalization in patients undergoing emergency cholecystectomy. Although the ORs were limited, the P-Possum value was independently associated with the outcome.

Comparison of Risk Scoring Systems to Predict the Outcome in ASA-PS V Patients Undergoing Surgery: A Retrospective Cohort Study

Operative decision in American Society of Anesthesiology Physical Status (ASA-PS) V patient is difficult as this group of patients expected to have high mortality rate. Another risk scoring system in this ASA-PS V subset of patients can aid to ease this decision. Data of ASA-PS V classified patients between 2011 and 2013 years in a single hospital were analyzed in this study. Predicted mortality of these patients was determined with acute physiology and chronic health evaluations (APACHE) II, simplified acute physiology score (SAPS II), Charlson comorbidity index (CCI), Porthsmouth physiological and operative severity score for enumeration of mortality and morbidity (P-POSSUM), Surgical apgar score (SAS), and Goldman cardiac risk index (GCRI) scores. Observed and predicted mortality rates according to the risk indexes in these patients were compared at survivor and nonsurvivor group of patients. Risk stratification was made with receiver operator characteristic (ROC) curve analysis. Data of 89 patients were included in the analyses. Predicted mortality rates generated by APACHE II and SAPS II scoring systems were significantly different between survivor and nonsurvivor group of patients. Risk stratification with ROC analysis revealed that area under curve was 0.784 and 0.681 for SAPS II and APACHE II scoring systems, respectively. Highest sensitivity (77.3) is reached with SAPS II score. APACHE II and SAPS II are better predictive tools of mortality in ASA-PS V classified subset of patients. Discrimination power of SAPS II score is the best among the compared risk stratification scores. SAPS II can be suggested as an additional risk scoring system for ASA-PS V patients.

Does adding ICU data to the POSSUM score improve the prediction of outcomes following surgery for upper gastrointestinal malignancies?

Surgery for upper gastrointestinal malignancy carries a high postoperative mortality and morbidity risk. The importance of preoperative physiological reserve and intraoperative events in determining clinical outcomes is recognised in the Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM) score that comprises variables relevant to both phases. Whether adding variables linked to ICU admission characteristics improves the predictive capacity of POSSUM is unclear, especially in an Australian/New Zealand healthcare context. This study aimed to evaluate the predictive capacity of the POSSUM score for 30-day mortality and in-hospital morbidity in 80 patients undergoing resection of oesophageal (28%), gastric (26%) or pancreatic (46%) malignancies and admitted to ICU. The 30-day mortality was 8.8% and 65% of patients developed some postoperative complication. Receiver operating characteristics generated an area under the curve (95% CI) to predict mortality by Portsmouth POSSUM of 0.87 (0.77 to 0.93) and morbidity by POSSUM of 0.67 (0.55 to 0.77). Multiple regression analysis including biochemical variables and vital signs on admission to ICU identified renal function parameters, fluid balance and need for cardiorespiratory support beyond the first postoperative day as independent factors associated with mortality and morbidity (in addition to the POSSUM score) but the inclusion of these variables in a logistic regression model did not significantly improve the predictive capacity for mortality (to area under the curve 0.93 [0.85 to 0.97]) or morbidity (to area under the curve 0.67 [0.55 to 0.78]). In conclusion, the POSSUM score provides clinically useful predictive capacity in patients undergoing surgery for upper gastrointestinal malignancies. The incorporation of ICU admission variables to the pre- and intraoperative POSSUM variables did not significantly enhance the precision.

Predicting postoperative mortality after colorectal surgery: a novel clinical model

AIM

The aim of this study was to develop and externally validate a clinically, practical and discriminative prediction model designed to estimate in-hospital mortality of patients undergoing colorectal surgery.

METHOD

All consecutive patients who underwent elective or emergency colorectal surgery from 1990 to 2005, at the Zaandam Medical Centre, The Netherlands, were included in this study. Multivariate logistic regression analysis was performed to estimate odds ratios (ORs) and 95% confidence intervals (CIs) linking the explanatory variables to the outcome variable in-hospital mortality, and a simplified Identification of Risk in Colorectal Surgery (IRCS) score was constructed. The model was validated in a population of patients who underwent colorectal surgery from 2005 to 2011 in Barcelona, Spain. Predictive performance was estimated by calculating the area under the receiver operating characteristic curve.

RESULTS

The strongest predictors of in-hospital mortality were emergency surgery (OR = 6.7, 95% CI 4.7-9.5), tumour stage (OR = 3.2, 95% CI 2.8-4.6), age (OR = 13.1, 95% CI 6.6-26.0), pulmonary failure (OR = 4.9, 95% CI 3.3-7.1) and cardiac failure (OR = 3.7, 95% CI 2.6-5.3). These parameters were included in the prediction model and simplified scoring system. The IRCS model predicted in-hospital mortality and demonstrated a predictive performance of 0.83 (95% CI 0.79-0.87) in the validation population. In this population the predictive performance of the CR-POSSUM score was 0.76 (95% CI 0.71-0.81).

CONCLUSIONS

The results of this study have shown that the IRCS score is a good predictor of in-hospital mortality after colorectal surgery despite the relatively low number of model parameters.

Utility of the Surgical Apgar Score in a district general hospital

BACKGROUND

The Surgical Apgar Score (SAS) is a simple tool for intraoperative risk stratification. The aim of this prospective observational study was to assess its performance in predicting outcome after general/vascular and orthopedic surgery and its utility in a U.K. district general hospital.

METHOD

A prospective cohort of 223 consecutive general, vascular, and orthopedic surgical cases was studied. The SAS was calculated for all patients, and its relationship to 30 day mortality and major complication assessed with reference to the mode of surgery (elective or emergent). Statistical analysis of categorical data was performed with Fisher's exact test and the AUC (area under the curve) on receiver operating characteristic (ROC) analysis. Selected cases were reviewed to assess the potential of the SAS to modify postoperative management.

RESULTS

The proportion of patients who died or experienced major complications increased monotonically with Surgical Apgar Score category in general and vascular but not orthopedic cases. The relative risks of mortality or major complication between SAS categories were less marked than in previous publications. The SAS performed variably on ROC curve analysis, with an AUC of 0.62-0.73. Discrimination achieved significance in general and vascular cases (p = 0.0002) but not in orthopedic cases (p = 0.15). Subgroup analysis of high (SAS < 7) and low risk (SAS ≥ 7) groups demonstrated utility of the score in general surgery and vascular cases overall (p < 0.0001), and in the emergency (p = 0.004) but not elective (p = 0.12) subgroups. Case note review of those patients who died indicated that despite their identification by the SAS, there would have been limited scope to modify outcome.

CONCLUSION

This study provides further evidence that the SAS is a simple and effective predictive tool in the emergency general and vascular surgical setting. It appears to have a limited role in the management of individual patients after orthopedic surgery and elective general/vascular surgery. The SAS has been proven to reliably stratify risk in larger populations and might be applied most usefully as a marker of quality. Further studies are required to determine whether its application can influence outcome.

Is the SAPS II score valid in surgical intensive care unit patients?

AIMS AND OBJECTIVES

We investigated the performance of the simplified acute physiology score II (SAPS II) in a large cohort of surgical intensive care unit (ICU) patients and tested the hypothesis that customization of the score would improve the uniformity of fit in subgroups of surgical ICU patients.

METHODS

Retrospective analysis of prospectively collected data from all 12,938 patients admitted to a postoperative ICU between January 2004 and January 2009. Probabilities of hospital death were calculated for original and customized (C1-SAPS II and C2-SAPS II) scores. A priori subgroups were defined according to age, probability of death according to the SAPS II score, ICU length of stay (LOS), surgical procedures and type of admission.

RESULTS

The median ICU LOS was 1 (1-3) day. ICU and hospital mortality rates were 5.8% and 10.3%, respectively. Discrimination of the SAPS II was moderate [area under receiver operating characteristic curve (aROC) = 0.76 (0.75-0.78)], but calibration was poor. This model markedly overestimated hospital mortality rates [standardized mortality rate: 0.35 (0.33-0.37)]. First-level customization (C1-SAPS II) did not improve discrimination in the whole cohort or the subgroups, but calibration improved in some subgroups. Second-level customization (C2-SAPS II) improved discrimination in the whole cohort [aROC = 0.82 (0.79-0.85)] and most of the subgroups (aROC range 0.65-86). Calibration in this model (C2-SAPS II) improved in the whole cohort and in subgroups except in patients with ICU LOS 4-14 days and those undergoing neuro- or gastrointestinal surgery.

CONCLUSIONS

In this large cohort of surgical ICU patients, performance of the original SAPS II model was generally poor. Although second-level customization improved discrimination and calibration in the whole cohort and most of the subgroups, it failed to simultaneously improve calibration in the subgroups stratified according to the type of surgery, age or ICU LOS.

Predictive value of POSSUM and ACPGBI scoring in mortality and morbidity of colorectal resection: a case-control study

BACKGROUND

Preoperative risk prediction to assess mortality and morbidity may be helpful to surgical decision making. The aim of this study was to compare mortality and morbidity of colorectal resections performed in a tertiary referral center with mortality and morbidity as predicted with physiological and operative score for enumeration of mortality and morbidity (POSSUM), Portsmouth POSSUM (P-POSSUM), and colorectal POSSUM (CR-POSSUM). The second aim of this study was to analyze the accuracy of different POSSUM scores in surgery performed for malignancy, inflammatory bowel diseases, and diverticulitis. POSSUM scoring was also evaluated in colorectal resection in acute vs. elective setting. In procedures performed for malignancy, the Association of Coloproctology of Great Britain and Ireland (ACPGBI) score was assessed in the same way for comparison.

METHODS

POSSUM, P-POSSUM, and CR-POSSUM predictor equations for mortality were applied in a retrospective case-control study to 734 patients who had undergone colorectal resection. The total group was assessed first. Second, the predictive value of outcome after surgery was assessed for malignancy (n = 386), inflammatory bowel diseases (n = 113), diverticulitis (n = 91), and other indications, e.g., trauma, endometriosis, volvulus, or ischemia (n = 144). Third, all subgroups were assessed in relation to the setting in which surgery was performed: acute or elective. In patients with malignancy, the ACPGBI score was calculated as well. In all groups, receiver operating characteristic (ROC) curves were constructed.

RESULTS

POSSUM, P-POSSUM, and CR-POSSUM have a significant predictive value for outcome after colorectal surgery. Within the total population as well as in all four subgroups, there is no difference in the area under the curve between the POSSUM, P-POSSUM, and CR-POSSUM scores. In the subgroup analysis, smallest areas under the ROC curve are seen in operations performed for malignancy, which is significantly worse than for diverticulitis and in operations performed for other indications. For elective procedures, P-POSSUM and CR-POSSUM predict outcome significantly worse in patients operated for carcinoma than in patients with diverticulitis. In acute surgical interventions, CR-POSSUM predicts mortality better in diverticulitis than in patients operated for other indications. The ACPGBI score has a larger area under the curve than any of the POSSUM scores. Morbidity as predicted by POSSUM is most accurate in procedures for diverticulitis and worst when the indication is malignancy.

CONCLUSION

The POSSUM scores predict outcome significantly better than can be expected by chance alone. Regarding the indication for surgery, each POSSUM score predicts outcome in patients operated for diverticulitis or other indications more accurately than for malignancy. The ACPGBI score is found to be superior to the various POSSUM scores in patients who have (elective) resection of colorectal malignancy.

A systematic review of POSSUM and its related models as predictors of post-operative mortality and morbidity in patients undergoing surgery for colorectal cancer

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.

METHODS

Major electronic databases, including Medline, Embase, Cochrane Library and Pubmed were searched for original studies published between 1991 and 2010. Two independent reviewers assessed each study against inclusion and exclusion criteria. All data was specific to colorectal cancer surgery. Predictive value was assessed by calculating observed to expected (O/E) ratios.

RESULTS

Nineteen studies were included in final review. The mortality analysis included ten studies (4,799 patients) on POSSUM, 17 studies (6,576 patients) on P-POSSUM and 14 studies (5,230 patients) on CR-POSSUM. Weighted O/E ratios for mortality were 0.31 (CI 0.31-0.32) for POSSUM, 0.90 (CI 0.88-0.92) for P-POSSUM and 0.64 (CI 0.63-0.65) for CR-POSSUM. The morbidity analysis included four studies (768 patients) on POSSUM with a weighted O/E ratio of 0.96 (CI 0.94-0.98).

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.