An evaluation of POSSUM and P-POSSUM scoring in predicting post-operative mortality in a level 1 critical care setting

Background POSSUM and P-POSSUM are used in the assessment of outcomes in surgical patients. Neither scoring systems’ accuracy has been established where a level 1 critical care facility (level 1 care ward) is available for perioperative care. We compared POSSUM and P-POSSUM predicted with observed mortality on a level 1 care ward. Methods A prospective, observational study was performed between May 2000 and June 2008. POSSUM and P-POSSUM scores were calculated for all postoperative patients who were admitted to the level 1 care ward. Data for post-operative mortality were obtained from hospital records for 2552 episodes of patient care. Observed vs expected mortality was compared using receiver operating characteristic (ROC) curves and the goodness of fit assessed using the Hosmer-Lemeshow equation. Results ROC curves show good discriminative ability between survivors and non-survivors for POSSUM and P-POSSUM. Physiological score had far higher discrimination than operative score. Both models showed poor calibration and poor goodness of fit (Hosmer-Lemeshow). Observed to expected (O:E) mortality ratio for POSSUM and P-POSSUM indicated significantly fewer than expected deaths in all deciles of risk. Conclusions Our data suggest a 30-60% reduction in O:E mortality. We suggest that the use of POSSUM models to predict mortality in patients admitted to level 1 care ward is inappropriate or that a recalibration of POSSUM is required to make it useful in a level 1 care ward setting

SEND: a system for electronic notification and documentation of vital sign observations

Background: Recognising the limitations of a paper-based approach to documenting vital sign observations and responding to national clinical guidelines, we have explored the use of an electronic solution that could improve the quality and safety of patient care. We have developed a system for recording vital sign observations at the bedside, automatically calculating an Early Warning Score, and saving data such that it is accessible to all relevant clinicians within a hospital trust. We have studied current clinical practice of using paper observation charts, and attempted to streamline the process. We describe our user-focussed design process, and present the key design decisions prior to describing the system in greater detail. Results: The system has been deployed in three pilot clinical areas over a period of 9 months. During this time, vital sign observations were recorded electronically using our system. Analysis of the number of observations recorded (21,316 observations) and the number of active users (111 users) confirmed that the system is being used for routine clinical observations. Feedback from clinical end-users was collected to assess user acceptance of the system. This resulted in a System Usability Scale score of 77.8, indicating high user acceptability. Conclusions: Our system has been successfully piloted, and is in the process of full implementation throughout adult inpatient clinical areas in the Oxford University Hospitals. Whilst our results demonstrate qualitative acceptance of the system, its quantitative effect on clinical care is yet to be evaluated

The Procedural Index for Mortality Risk (PIMR): an index calculated using administrative data to quantify the independent influence of procedures on risk of hospital death

<p>Abstract</p> <p>Background</p> <p>Surgeries and other procedures can influence the risk of death in hospital. All published scales that predict post-operative death risk require clinical data and cannot be measured using administrative data alone. This study derived and internally validated an index that can be calculated using administrative data to quantify the independent risk of hospital death after a procedure.</p> <p>Methods</p> <p>For all patients admitted to a single academic centre between 2004 and 2009, we estimated the risk of all-cause death using the Kaiser Permanente Inpatient Risk Adjustment Methodology (KP-IRAM). We determined whether each patient underwent one of 503 commonly performed therapeutic procedures using Canadian Classification of Interventions codes and whether each procedure was emergent or elective. Multivariate logistic regression modeling was used to measure the association of each procedure-urgency combination with death in hospital independent of the KP-IRAM risk of death. The final model was modified into a scoring system to quantify the independent influence each procedure had on the risk of death in hospital.</p> <p>Results</p> <p>275 460 hospitalizations were included (137,730 derivation, 137,730 validation). In the derivation group, the median expected risk of death was 0.1% (IQR 0.01%-1.4%) with 4013 (2.9%) dying during the hospitalization. 56 distinct procedure-urgency combinations entered our final model resulting in a Procedural Index for Mortality Rating (PIMR) score values ranging from -7 to +11. In the validation group, the PIMR score significantly predicted the risk of death by itself (c-statistic 67.3%, 95% CI 66.6-68.0%) and when added to the KP-IRAM model (c-index improved significantly from 0.929 to 0.938).</p> <p>Conclusions</p> <p>We derived and internally validated an index that uses administrative data to quantify the independent association of a broad range of therapeutic procedures with risk of death in hospital. This scale will improve risk adjustment when administrative data are used for analyses.</p

Predictive Value of POSSUM and ACPGBI Scoring in Mortality and Morbidity of Colorectal Resection: A Case–Control Study

Contains fulltext : 97239.pdf (publisher's version ) (Open Access)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

Evaluation of the effects of implementing an electronic early warning score system: protocol for a stepped wedge study

Background: An Early Warning Score is a clinical risk score based upon vital signs intended to aid recognition of patients in need of urgent medical attention. The use of an escalation of care policy based upon an Early Warning Score is mandated as the standard of practice in British hospitals. Electronic systems for recording vital sign observations and Early Warning Score calculation offer theoretical benefits over paper-based systems. However, the evidence for their clinical benefit is limited. Previous studies have shown inconsistent results. The majority have employed a “before and after” study design, which may be strongly confounded by simultaneously occurring events. This study aims to examine how the implementation of an electronic early warning score system, System for Notification and Documentation (SEND), affects the recognition of clinical deterioration occurring in hospitalised adult patients. Methods: This study is a non-randomised stepped wedge evaluation carried out across the four hospitals of the Oxford University Hospitals NHS Trust, comparing charting on paper and charting using SEND. We assume that more frequent monitoring of acutely ill patients is associated with better recognition of patient deterioration. The primary outcome measure is the time between a patient’s first observations set with an Early Warning Score above the alerting threshold and their subsequent set of observations. Secondary outcome measures are in-hospital mortality, cardiac arrest and Intensive Care admission rates, hospital length of stay and system usability measured using the System Usability Scale. We will also measure Intensive Care length of stay, Intensive Care mortality, Acute Physiology and Chronic Health Evaluation (APACHE) II acute physiology score on admission, to examine whether the introduction of SEND has any effect on Intensive Care-related outcomes. Discussion: The development of this protocol has been informed by guidance from the Agency for Healthcare Research and Quality (AHRQ) Health Information Technology Evaluation Toolkit and Delone and McLeans’s Model of Information System Success. Our chosen trial design, a stepped wedge study, is well suited to the study of a phased roll out. The choice of primary endpoint is challenging. We have selected the time from the first triggering observation set to the subsequent observation set. This has the benefit of being easy to measure on both paper and electronic charting and having a straightforward interpretation. We have collected qualitative measures of system quality via a user questionnaire and organisational descriptors to help readers understand the context in which SEND has been implemented