Can SAPS II Predict Operative Mortality More Accurately Than POSSUM and P-POSSUM in Patients with Colorectal Carcinoma Undergoing Resection?

Background This study assessed the APACHE II (Acute Physiology and Chronic Health Evaluation II), SAPS II (Simplified Acute Physiology Score-II), POSSUM (Physiologic and Operative Severity Score for Enumeration of Morbidity and Mortality), and P-POSSUM (Portsmouth-POSSUM) in patients with colorectal cancer undergoing curative or palliative resection. Methods Predicted mortality rates and the observed/expected mortality ratio were computed by means of each scoring system. The results were compared between survivors and nonsurvivors and between elective and emergency operations. Each model was assessed for its accuracy to predict the risk of death using receiver operator characteristic (ROC) curve analysis, and risk stratification was generated as well. Results Some 224 patients were enrolled in the study. The overall 30-day mortality rate was 3.6% (n = 8). Predicted mortality rates generated by APACHE II, SAPS II, POSSUM, and P-POSSUM were 9.1%, 3.7%, 13.4%, and 5.2%, respectively. All the scoring systems assigned higher scores to those patients who died than to those who survived. Areas under the curve calculated by ROC curve analysis for APACHE II, SAPS II, POSSUM, and P-POSSUM were 0.786, 0.854, 0.793, and 0.831, respectively. Best stratification was achieved by the SAPS II score. Conclusions SAPS II and P-POSSUM were determined to be better predictors for patients with colorectal cancer undergoing resection. SAPS II also was found to have a higher degree of discriminatory power in colorectal resection for carcinoma. The predictive value of this useful severity score in several surgical subgroups must be examined to evaluate its routine use in risk-adjusted audit.     

A comparative study of four intensive care outcome prediction models in cardiac surgery patients

Background  

Outcome prediction scoring systems are increasingly used in intensive care medicine, but most were not developed for use in cardiac surgery patients. We compared the performance of four intensive care outcome prediction scoring systems (Acute Physiology and Chronic Health Evaluation II [APACHE II], Simplified Acute Physiology Score II [SAPS II], Sequential Organ Failure Assessment [SOFA], and Cardiac Surgery Score [CASUS]) in patients after open heart surgery.     

Assessment of three different mortality prediction models in four well-defined critical care patient groups at two points in time: a prospective cohort study

BACKGROUND AND OBJECTIVE: Mortality prediction systems have been calculated and validated from large mixed ICU populations. However, in daily practice it is often more important to know how a model performs in a patient subgroup at a specific ICU. Thus, we assessed the performance of three mortality prediction models in four well-defined patient groups in one centre. METHODS: A total of 960 consecutive adult patients with either severe head injury (n = 299), multiple injuries (n = 208), abdominal aortic aneurysm (n = 267) or spontaneous subarachnoid haemorrhage (n = 186) were included. Calibration, discrimination and standardized mortality ratios were determined for Simplified Acute Physiology Score II, Mortality Probability Model II (at 0 and 24 h) and Injury Severity Score. Effective mortality was assessed at hospital discharge and after 1 yr. RESULTS: Eight hundred and fifty-five (89%) patients survived until hospital discharge. Over all four patient groups, Mortality Probability Model II (24 h) had the best predictive accuracy (standardized mortality ratio 0.62) and discrimination (area under the receiver operating characteristic curve 0.9), but Simplified Acute Physiology Score II performed well for patients with subarachnoid haemorrhage. Overall calibration was poor for all models (Hosmer-Lemeshow Type C-values between 20 and 26). Injury Severity Score had the worst discrimination in trauma patients. All models over-estimated hospital mortality in all four patient groups, and these estimates were more like the mortality after 1 yr. CONCLUSIONS: In our surgical ICU, Mortality Probability Model II (24 h) performed slightly better than Simplified Acute Physiology Score II in terms of overall mortality prediction and discrimination; Injury Severity Score was the worst model for mortality prediction in trauma patients.     

Sequential physiology scoring facilitates objective assessment of resuscitation in patients with an intra-abdominal emergency

BACKGROUND: Patients who present with an intra-abdominal emergency often require urgent surgery. Before surgery a period of resuscitation is undertaken pre-emptively, or to correct any overt physiological derangement. The assessment of response to resuscitation and the decision when to operate is subjective. This study examined the role of sequential physiology scores in assessing the response to resuscitation objectively. METHODS: Sequential physiology scores were recorded in 92 patients with abdominal pathology that subsequently required urgent or emergency surgery. The physiology component of the Physiological and Operative Severity Score for enUmeration of Mortality and morbidity (POSSUM), Acute Physiology And Chronic Health Evaluation (APACHE) II and III, and Simplified Acute Physiology Score (SAPS) II were determined at presentation, during resuscitation and immediately before surgery. RESULTS: There were 76 survivors;16 patients died. All scoring systems showed an improvement during resuscitation but subsequent deterioration before surgery. The POSSUM, and APACHE II and III physiology scores differentiated more effectively between survivors and patients who died than SAPS II. CONCLUSION: Sequential physiology scores may facilitate the assessment of patients' response to resuscitation. Patients who fail to respond to resuscitation when identified may benefit from more expedient surgery.     

The predictive value of the Oxford Acute Severity of Illness Score for clinical outcomes in patients with acute kidney injury

ObjectiveTo compare the performance of the Oxford Acute Severity of Illness Score (OASIS), the Acute Physiology and Chronic Health Evaluation II (APACHE II) score, the Simplified Acute Physiology Score II (SAPS II), and the Sequential Organ Failure Assessment (SOFA) score in predicting 28-day mortality in acute kidney injury (AKI) patients.MethodsData were extracted from the Beijing Acute Kidney Injury Trial (BAKIT). A total of 2954 patients with complete clinical data were included in this study. Receiver operating characteristic (ROC) curves were used to analyze and evaluate the predictive effects of the four scoring systems on the 28-day mortality risk of AKI patients and each subgroup. The best cutoff value was identified by the highest combined sensitivity and specificity using Youden’s index.ResultsAmong the four scoring systems, the area under the curve (AUC) of OASIS was the highest. The comparison of AUC values of different scoring systems showed that there were no significant differences among OASIS, APACHE II, and SAPS II, which were better than SOFA. Moreover, logistic analysis revealed that OASIS was an independent risk factor for 28-day mortality in AKI patients. OASIS also had good predictive ability for the 28-day mortality of each subgroup of AKI patients.ConclusionOASIS, APACHE II, and SAPS II all presented good discrimination and calibration in predicting the 28-day mortality risk of AKI patients. OASIS, APACHE II, and SAPS II had better predictive accuracy than SOFA, but due to the complexity of APACHE II and SAPS II calculations, OASIS is a good substitute.Trial RegistrationThis study was registered at www.chictr.org.cn (registration number Chi CTR-ONC-11001875). Registered on 14 December 2011.     

Performance assessment of Acute Physiology and Chronic Health Evaluation II and Simplified Acute Physiology Score II in a referral respiratory intensive care unit in Iran

Background/purposeNowadays, Acute Physiology and Chronic Health Evaluation II (APACHE II) and Simplified Acute Physiology Score II (SAPS II) scoring systems have drawn much attention for the evaluation and prediction of disease process in patients admitted to intensive care units (ICUs). To use these scoring tools, their predicting power must be initially validated for the target patients. This study was conducted to evaluate the performance of these two scoring systems in an ICU for respiratory diseases in Iran.Material and methodsAll records of patients admitted during a 1-year period were retrospectively reviewed, and the APACHE II and SAPS II scores were calculated accordingly. Information gathering was performed using a questionnaire.ResultsA total of 415 records were used. The mean age of patients was 49.28 ± 0.94 years. Using receiver operating-characteristic curve, cutoff points for 80% sensitivity and specificity of mortality prediction for APACHE and SAPS scores were 13.5 and 27.5, respectively. Calibration and discrimination studies indicated an acceptable status for both scales, but APACHE II scoring system seemed to show rewarding outcomes.ConclusionResults indicate that APACHE II scoring system can be considered as a reliable method for predicting mortality in our referral respiratory ICU.     

Scoring systems do not accurately predict outcome following abdominal aortic aneurysm repair

BACKGROUND: Abdominal aortic aneurysm repair is associated with significant morbidity and mortality. This study aims to evaluate the efficiency of scoring systems in a group of patients undergoing abdominal aortic aneurysm repair. METHODS: A prospective study of 152 patients undergoing aneurysm repair was conducted. Each patient was scored according to the Acute Physiology and Chronic Health Evaluation II, Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity and Simplified Acute Physiology Score II systems. The predicted mortality for each patient was calculated. Chi(2) analysis was carried out to determine the accuracy of mortality predictions. Receiver-operator curves were drawn to compare scoring systems in terms of sensitivity and specificity. RESULTS: In the elective aneurysm repair group, all scoring systems tended to overestimate mortality. Receiver-operator curves showed inaccuracies in identifying patients who were at high risk from surgery. In contrast, predicted mortalities underestimated the true death rate among the ruptured aneurysm group. Receiver-operator curves showed better efficiency of scoring systems in the ruptured aneurysm group than in the elective repair group. There was no significant correlation between predicted and observed mortalities in either group. CONCLUSION: In this study, all systems showed significant errors when predicting mortality. Therefore, although useful as an audit tool, scoring systems should not be used on an individual basis to guide treatment and assess prognosis after surgery.     

Quantification of mortality risk after abdominal aortic aneurysm repair

BACKGROUND: The study was designed to evaluate the Acute Physiology And Chronic Health Evaluation (APACHE) II risk scoring system in abdominal aortic aneurysm (AAA) surgery. The aim was to create an APACHE-based risk stratification model for postoperative death. METHODS: Prospective postoperative APACHE II data were collected from patients undergoing AAA repair over a 9-year interval from 24 intensive care units (ICUs) in the Thames region. A multilevel logistic regression model (APACHE-AAA) for in-hospital mortality was developed to adjust for both case mix and the variation in outcome between ICUs. RESULTS: A total of 1896 patients were studied. The in-hospital mortality rate among the 1289 patients who had elective AAA repair was 9.6 (95 per cent confidence interval (c.i.) 8.0 to 11.2) per cent and that among the 605 patients who had an emergency repair was 46.9 (95 per cent c.i. 43.0 to 50.9) per cent. Four independent predictors of death were identified: age (odds ratio (OR) 1.05 (95 per cent c.i. 1.03 to 1.07) per year increase), Acute Physiology Score (OR 1.14 (95 per cent c.i. 1.12 to 1.17) per unit increase), emergency operation (OR 4.86 (95 per cent c.i. 3.64 to 6.52)) and chronic health dysfunction (OR 1.43 (95 per cent c.i. 1.04 to 1.97)). The APACHE-AAA model was internally valid, as shown by calibration (Hosmer-Lemeshow C statistic: chi(2) = 6.14, 8 d.f., P = 0.632), discrimination properties (area under receiver-operator characteristic curve 0.845) and subgroup analysis. There was no significant variation in outcome between hospitals. CONCLUSION: APACHE-AAA was shown to be an accurate risk-stratification model that could be used to quantify the risk of death after AAA surgery. It might also be used to determine the relative impact of ICU over high-dependency unit care.     

A comparison of SAPS II and SAPS 3 in a Norwegian intensive care unit population

Background: Simplified Acute Physiology Score (SAPS II) is the most widely used general severity scoring system in European intensive care medicine. Because its performance has been questioned in several external validation studies, SAPS 3 was recently released. To our knowledge, there are no published validation studies of SAPS II or SAPS 3 in the Scandinavian countries. We aimed to evaluate and compare the performance of SAPS II and SAPS 3 in a Norwegian intensive care unit (ICU) population.
Method: Prospectively collected data from adult patients admitted to two general ICUs at two different hospitals in Norway were used. Probability of mortality was calculated using the SAPS 3 global equation (SAPS 3 G), the SAPS 3 Northern European equation (SAPS 3 NE), and the original SAPS II equation. Performance was assessed by the standardized mortality ratio (SMR), area under receiving operating characteristic, and the Hosmer and Lemeshow goodness-of-fit Ĉ test.
Results: One thousand eight hundred and sixty-two patients were included after excluding readmissions, and patients who were admitted after coronary surgery or burns. The SMRs were SAPS 3 G 0.71 (0.65, 0.78), SAPS 3 NE 0.74 (0.68, 0.81), and SAPS II 0.82 (0.75, 0.91). Discrimination was good in all systems. Only the SAPS 3 equations displayed satisfactory calibration, as measured by the Hosmer–Lemeshow test.
Conclusion: The performance of SAPS 3 was satisfactory, but not markedly better than SAPS II. Both systems considerably overestimated mortality and exhibited good discrimination, but only the SAPS 3 equations showed satisfactory calibration. Customization of these equations based on a larger cohort is recommended.     

Risk predictors,scoring systems and prognostic models in anesthesia and intensive care. Part II. Intensive Care

The aim of the second part of this review article was to describe common scoring systems in intensive care, and to point out their possible benefits and limitations. Intensive care medicine multipurpose scoring-systems are currently used to estimate severity of illness, mortality and the amount of treatment required. Costs (only commercial available scores e.g. Acute Physiology and Chronic Health Evaluation [APACHE] III) and time needed for calculation have to be taken into consideration. Prognostic models of the third generation (APACHE III, Simplified Acute Physiology Score [SAPS] II, Mortality Prediction Model [MPM] II) should be preferred having better prognostic performance compared to scoring systems of prior generations. Although no prospective study exists comparing these three common scoring systems, it appears that all three systems are able to provide useful information to the clinician and researcher. These scoring systems were designed to classify severity of illness or the course of diagnostic and therapeutic interventions and to perform a risk stratification for scientific studies in a standardized way. In quality management and cost control, scoring systems and predictors are used for risk adjustment and evaluation of care performance.     

Application of the Simplified Acute Physiology Score II (SAPS II) in a medical intensive care unit

OBJECTIVE: Our study was to assess the validity of SAPS II (New Simplified Acute Physiology Score) to predict the probability of in hospital mortality, in a cohort of patient admitted to a medical intensive care unit. STUDY DESIGN: Prospective study. PATIENTS AND METHODS: Out of 467 the 525 patients admitted were included. SAPS score and in hospital mortality prediction were calculated for each of them. RESULTS: In this group, SAPS II offered a satisfactory discrimination power with an area under the curve of 0.843. However, calibration showed a lack of fit (chi 2 = 28.5, P < 0.001), with an overall under prediction of mortality (observed versus expected ratio of 1.12). CONCLUSION: This SAPS II lower predicting accuracy in a specific population and for individual outcome prediction may reduce its interest in clinical decision-making.     

Comparison of Acute Physiology and Chronic Health Evaluation (APACHE) II score with organ failure scores to predict hospital mortality

This study compared the performance of the Acute Physiology and Chronic Health Evaluation (APACHE) II score with two organ failure scores in predicting hospital mortality of critically ill patients. A total of 1311 consecutive adult patients in a tertiary 22-bed multidisciplinary intensive care unit (ICU) in Western Australia were considered. The APACHE II score had a better calibration and discrimination than the Max Sequential Organ Failure Score (Max SOFA) (area under receiver operating characteristic (ROC) curve 0.858 vs 0.829), Admission SOFA (area under ROC 0.858 vs 0.791), and the first day or cumulative 5-day Royal Perth Hospital Intensive Care Unit (RPHICU) organ failure score (area under ROC 0.858 vs 0.822 and 0.819, respectively) in predicting hospital mortality. The APACHE II score predicted hospital mortality of critically ill patients better than the SOFA and RPHICU organ failure scores in our ICU.     

Evaluation of POSSUM and P-POSSUM scoring systems in patients undergoing colorectal surgery

BACKGROUND: The Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (POSSUM) and Portsmouth POSSUM (P-POSSUM) equations were derived from a heterogeneous general surgical population and have been used successfully as audit tools to provide risk-adjusted operative mortality rates. Their applicability to high-risk emergency colorectal operations has not been established. METHODS: POSSUM variables were recorded for 1017 patients undergoing major elective (n = 804) or emergency (n = 213) colorectal surgery in ten hospitals. Subgroup analysis was performed to investigate the predictive capability of POSSUM and P-POSSUM in emergency and elective surgery and in patients in different age groups. RESULTS: The overall operative mortality rate was 7.5 per cent (POSSUM-estimated mortality rate 8.2 per cent; P-POSSUM-estimated mortality rate 7.1 per cent). In-hospital deaths increased exponentially with age. Both scoring systems overpredicted mortality in young patients and underpredicted mortality in the elderly (P < 0.001). Death was underpredicted by both systems for emergency cases, significantly so at a simulated emergency caseload of 47.9 per cent (P < 0.05). CONCLUSION: There is a lack of calibration of POSSUM and P-POSSUM systems at the extremes of age and high emergency workload. This has important implication in clinical practice, as consultants with a high emergency workload may seem to underperform when these scoring systems are applied. Recalibration or remodelling strategies may facilitate the application of POSSUM-based systems in colorectal surgery.     

Presentation, treatment, and multivariate analysis of risk factors for obstructive and perforative colorectal carcinoma

BACKGROUND: It is known that emergency surgery for colorectal cancer is associated with high morbidity and mortality. The aim of this study was to assess the presentation, treatment, and outcome of patients with complicated colorectal cancer. Risk factors for morbidity and mortality were also evaluated. METHODS: From 1991 to 2002, the medical records of 107 consecutive patients undergoing emergency surgery for obstructing or perforating colorectal carcinoma were retrospectively reviewed. Information regarding patient and tumor characteristics, treatment, and outcome was recorded. Risk factors were assessed by multivariate analysis. RESULTS: Eighty-three patients (78%) had complete obstruction and 24 (22%) had perforation. Overall and major complications occurred in 70% and 34%, respectively. The mortality rate was 15%. Independent risk factors for major morbidity were perioperative blood transfusion and high American Society of Anesthesiologists (ASA) class, whereas those for mortality were older age and high Acute Physiology and Chronic Health Evaluation II (APACHE II) score. CONCLUSIONS: Emergency surgery for complicated colorectal carcinoma carries high rates of morbidity and mortality. To achieve improvements in outcome, intensive treatment after surgery in patients with risk factors is recommended.     

Combining sequential organ failure assessment (SOFA) score with acute physiology and chronic health evaluation (APACHE) II score to predict hospital mortality of critically ill patients

The ability to accurately adjust for the severity of illness in outcome studies of critically ill patients is essential. Previous studies have showed that Sequential Organ Failure Assessment (SOFA) score and Acute Physiology and Chronic Health Evaluation (APACHE) II score can predict hospital mortality of critically ill patients. The effects of combining these two scores to predict hospital mortality of critically ill patients has not been evaluated. This cohort study evaluated the performance of combining the APACHE II score with SOFA score in predicting hospital mortality of critically ill patients. A total of 1311 consecutive adult patients admitted to a tertiary 22-bed multidisciplinary intensive care unit (ICU) in Western Australia were considered. The APACHE II, Admission SOFA, Delta SOFA and maximum SOFA score were all related to hospital survival in the univariate analyses. Combining Max SOFA (area under receiver operating characteristic curve 0.875 vs. 0.858, P = 0.014; Nagelkerke R2: 0.411 vs. 0.371; Brier Score: 0.086 vs. 0.090) or Delta SOFA score (area under receiver operating characteristic curve 0.874 vs. 0.858, P = 0.003; Nagelkerke R2: 0.412 vs. 0.371; Brier Score: 0.086 vs. 0.090) with the APACHE II score improved the discrimination and overall performance of the predictions when compared with using the APACHE II score alone, especially in the emergency ICU admissions. Combining Max SOFA or Delta SOFA score with the APACHE II score may improve the accuracy of risk adjustment in outcome studies of critically ill patients.     

Prognostic factors for cancer patients in the postanesthetic recovery unit

OBJECTIVE: To assess the prognosis of cancer patients in an intensive care unit (ICU), to compare the capabilities of severity scoring systems to predict hospital death, and to improve prediction by adding new variables. PATIENTS AND METHODS: Cohort study in a medical-surgical ICU of a university hospital. Demographic and oncologic characteristics were collected along with death records for all nonsurgical cancer patients admitted between January 1995 and June 2000. Severity scores and risk of death were calculated. RESULTS: In the cohort of 250 patients studied, the hospital mortality rate was 58% and the ICU mortality rate was 38.8%. The best predictions were made with the third version of the Acute Physiology and Chronic Health Evaluation (APACHE III), the total maximum Sequential Organ Failure Assessment (SOFA) score, and the total maximum Multiple Organ Dysfunction Score (MODS). The APACHE II and the Simplified Acute Physiology Score (SAPS), version II, were good predictors, whereas the systems of the International Council on Mining and Metals overestimated hospital mortality and the Modality Prediction Model at 0 and 24 hours (MPM0 and MPM24) and the Logistic Organ Dysfunction System underestimated it. The total maximum SOFA and MODS scores had the greatest discriminating capability and the SOFA0, the MODS0, MPM0, and MPM24 had the poorest. All assessment systems except the APACHE III improved when we added new mortality-associated variables: prior functional status, diabetes, radiographic lung infiltrates, mechanical ventilation, and vasoactive support. CONCLUSIONS: Medical oncology patients should not all be denied intensive care. None of the systems assessed offer clinically relevant advantages for predicting hospital mortality in nonsurgical oncology patients in the ICU, although we recommend the SAPS II because it includes oncologic variables, is easy to score, and has good prognostic capability.     

Comparison of four severity scores in patients with head trauma

This study examines the performance of four severity scores in a group of 70 consecutive patients with head trauma, hospitalized in the same neurosurgical ICU. On day of admission to the ICU, data were collected from each patient to compute Acute Physiology Score (APS), Simplified Acute Physiology Score (SAPS), Glasgow Coma Scale (GCS), and Therapeutic Intervention Scoring System (TISS). These four scores were statistically correlated with the outcome of patients. Results also indicate that GCS was superior to any other score in predicting outcome of patients and allowed better specificity and sensitivity. Regarding the level of care needed by patients, TISS was superior to any other scoring system. The difficulty in estimating individual prognosis is discussed, since scoring systems are aimed at classifying groups of patients, not individuals. It is concluded that GCS is a simpler and less time consuming method in predicting outcome of patients with head trauma.     

Informed prognosis [corrected] after abdominal aortic aneurysm repair using predictive modeling techniques [corrected

OBJECTIVE: To identify the best method for the prediction of postoperative mortality in individual abdominal aortic aneurysm surgery (AAA) patients by comparing statistical modelling with artificial neural networks' (ANN) and clinicians' estimates. METHODS: An observational multicenter study was conducted of prospectively collected postoperative Acute Physiology and Chronic Health Evaluation II data for a 9-year period from 24 intensive care units (ICU) in the Thames region of the United Kingdom. The study cohort consisted of 1205 elective and 546 emergency AAA patients. Four independent physiologic variables-age, acute physiology score, emergency operation, and chronic health evaluation-were used to develop multiple regression and ANN models to predict in-hospital mortality. The models were developed on 75% of the patient population and their validity tested on the remaining 25%. The results from these two models were compared with the observed outcome and clinicians' estimates by using measures of calibration, discrimination, and subgroup analysis. RESULTS: Observed in-hospital mortality for elective surgery was 9.3% (95% confidence interval [CI], 7.7% to 11.1%) and for emergency surgery, 46.7% (95% CI, 42.5 to 51.0%). The ANN and the statistical models were both more accurate than the clinicians' predictions. Only the statistical model was internally valid, however, when applied to the validation set of observations, as evidenced by calibration (Hosmer-Lemeshow C statistic, 14.97; P = .060), discrimination properties (area under receiver operating characteristic curve, 0.869; 95% CI, 0.824 to 0.913), and subgroup analysis. CONCLUSIONS: The prediction of in-hospital mortality in AAA patients by multiple regression is more accurate than clinicians' estimates or ANN modelling. Clinicians can use this statistical model as an objective adjunct to generate informed prognosis.     

Evaluation of P-POSSUM and CR-POSSUM Scores in Patients with Colorectal Cancer Undergoing Resection

Introduction The aim of this study was to evaluate the predictive accuracy of P-POSSUM and CR-POSSUM models on patients undergoing colorectal resection. Methods P-POSSUM and CR-POSSUM predictor equations for mortality were applied retrospectively to 321 patients who had undergone colorectal resection for cancer. P-POSSUM and CR-POSSUM scores were validated by assessing their calibration and discrimination. Calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test and the corresponding calibration curves. Evaluation of the discriminative capability of both models was performed using receiver-operating characteristic (ROC) curve analysis. Results Overall, 22 deaths were observed. CR-POSSUM predicted 25 deaths (χ² = 12.20, P = 0.13), and P-POSSUM predicted 29 deaths (χ² =18.85, P = 0.002). ROC curves analysis revealed that CR-POSSUM has reasonable discriminatory power for mortality. Conclusions These data suggest that CR-POSSUM may provide a better estimate of the risk of mortality for patients who undergoing colorectal resection.     

Decompressive craniectomy in trauma patients with severe brain injury

Decompressive craniectomy in the treatment of severe traumatic brain injury (TBI) is controversial. We conducted a retrospective review of prospectively collected data on all patients requiring surgery for TBI from 1995 through 2001 at Cedars-Sinai Medical Center. Patients were separated into two groups: Group A, craniectomy, and Group B, craniotomy. We had 120 patients; 24 (20%) had craniectomy and 96 (80%) had craniotomy. There were no significant differences in demographics or Injury Severity Scores. The craniectomy group had significantly more TBI as evidenced by more frequently collapsed basilar cisterns on CT scan (P = 0.0001). There was no significant difference in actuarial survival between the groups: 52.8 per cent in the craniectomy group and 79.2 per cent in the craniotomy group (P = 0.08). Calculated mortality for craniectomy was 37.5 per cent versus 18.8 per cent for craniotomy (P = NS). We found four preoperative findings to be significant predictors of mortality: 1) Glasgow Coma Scale score, 2) Injury Severity Score, 3) Simplified Acute Physiology Score, and 4) Acute Physiology and Chronic Health Evaluation II. The type of surgery was not found to be a significant predictor of death even when adjusted for severity of injury. Craniectomy may be helpful for patients with TBI associated with preoperative CT scan evidence of basilar cistern collapse. This is evidenced by similar survival rates between the two groups despite clinical evidence of greater TBI among craniectomy patients.