National Early Warning Score Accurately Discriminates the Risk of Serious Adverse Events in Patients With Liver Disease

BACKGROUND & AIMS

The National Early Warning Score (NEWS) is used to identify deteriorating adult hospital inpatients. However, it includes physiological parameters frequently altered in patients with cirrhosis. We aimed to assess the performance of the NEWS in acute and chronic liver diseases.

METHODS

We collected vital signs, recorded in real time, from completed consecutive admissions of patients 16 years or older to a large acute-care hospital in Southern England, from January 1, 2010, through October 31, 2014. Using International Classification of Diseases, 10th revision, codes, we categorized patients as having primary liver disease, secondary liver disease, or none. For patients with liver disease, 2 analysis groups were developed: the first was based on clinical group (such as acute or chronic, alcohol-induced, or associated with portal hypertension), and the second was based on a summary of liver-related, hospital-level mortality indicator diagnoses. For each, we compared the abilities of the NEWS and 34 other early warning scores to discriminate 24-hour mortality, cardiac arrest, or unanticipated admission to the intensive care unit using the area under the receiver operating characteristic (AUROC) curve and early warning score efficiency curve analyses.

RESULTS

The NEWS identified patients with primary, nonprimary, and no diagnoses of liver disease with AUROC values of 0.873 (95% CI, 0.860-0.886), 0.898 (95% CI, 0.891-0.905), and 0.879 (95% CI, 0.877-0.881), respectively. High AUROC values were also obtained for all clinical subgroups; the NEWS identified patients with alcohol-related liver disease with an AUROC value of 0.927 (95% CI, 0.912-0.941). The NEWS identified patients with liver diseases with higher AUROC values than other early warning scoring systems.

CONCLUSIONS

The NEWS accurately discriminates patients at risk of death, admission to the intensive care unit, or cardiac arrest within a 24-hour period for a range of liver-related diagnoses. Its widespread use provides a ready-made, easy-to-use option for identifying patients with liver disease who require early assessment and intervention, without the need to modify parameters, weightings, or escalation criteria.

Early warning scores: unravelling detection and escalation

PURPOSE

The purpose of this paper is to increase understanding of how patient deterioration is detected and how clinical care escalates when early warning score (EWS) systems are used.

DESIGN/METHODOLOGY/APPROACH

The authors critically review a recent National Early Warning Score paper published in IJHCQA using personal experience and EWS-related publications, and debate the difference between detection and escalation.

FINDINGS

Incorrect EWS choice or poorly understood EWS escalation may result in unnecessary workloads forward and responding staff.

PRACTICAL IMPLICATIONS

EWS system implementers may need to revisit their guidance materials; medical and nurse educators may need to expand the curriculum to improve EWS system understanding and use.

ORIGINALITY/VALUE

The paper raises the EWS debate and alerts EWS users that scrutiny is required.

Comparison of the National Early Warning Score in non-elective medical and surgical patients

BACKGROUND

The National Early Warning Score (NEWS) is used to identify deteriorating patients in hospital. NEWS is a better discriminator of outcomes than other early warning scores in acute medical admissions, but it has not been evaluated in a surgical population. The study aims were to evaluate the ability of NEWS to discriminate cardiac arrest, death and unanticipated ICU admission in patients admitted to surgical specialties, and to compare the performance of NEWS in admissions to medical and surgical specialties.

METHODS

Hospitalwide data over 31 months, from adult inpatients who stayed at least one night or died on the day of admission, were analysed. The data were categorized as elective or non-elective surgical or medical admissions. The ability of NEWS to discriminate the outcomes above in these different groups was assessed using the area under the receiver operating characteristic curve (AUROC).

RESULTS

There were too few outcomes to permit meaningful comparison of elective admissions, so the analysis was constrained to comparison of non-elective admissions. NEWS performed equally well, or better, for surgical as for medical patients. For death within 24 h the AUROC for surgical admissions was 0·914 (95 per cent c.i. 0·907 to 0·922), compared with 0·902 (0·898 to 0·905) for medical admissions. For the combined outcome of any of death, cardiac arrest or unanticipated ICU admission, the AUROC was 0·874 (0·868 to 0·880) for surgical admissions and 0·874 (0·871 to 0·877) for medical admissions.

CONCLUSION

NEWS discriminated deterioration in non-elective surgical patients at least as well as in non-elective medical patients.

Are observation selection methods important when comparing early warning score performance?

INTRODUCTION

Sicker patients generally have more vital sign assessments, particularly immediately before an adverse outcome, and especially if the vital sign monitoring schedule is driven by an early warning score (EWS) value. This lack of independence could influence the measured discriminatory performance of an EWS.

METHODS

We used a population of 1564,143 consecutive vital signs observation sets collected as a routine part of patients' care. We compared 35 published EWSs for their discrimination of the risk of death within 24h of an observation set using (1) all observations in our dataset, (2) one observation per patient care episode, chosen at random and (3) one observation per patient care episode, chosen as the closest to a randomly selected point in time in each episode. We compared the area under the ROC curve (AUROC) as a measure of discrimination for each of the 35 EWSs under each observation selection method and looked for changes in their rank order.

RESULTS

There were no significant changes in rank order of the EWSs based on AUROC between the different observation selection methods, except for one EWS that included age among its components. Whichever method of observation selection was used, the National Early Warning Score (NEWS) showed the highest discrimination of risk of death within 24h. AUROCs were higher when only one observation set was used per episode of care (significantly higher for many EWSs, including NEWS).

CONCLUSIONS

Vital sign measurements can be treated as if they are independent - multiple observations can be used from each episode of care--when comparing the performance and ranking of EWSs, provided no EWS includes age.

Decision-tree early warning score (DTEWS) validates the design of the National Early Warning Score (NEWS)

AIM OF STUDY

To compare the performance of a human-generated, trial and error-optimised early warning score (EWS), i.e., National Early Warning Score (NEWS), with one generated entirely algorithmically using Decision Tree (DT) analysis.

MATERIALS AND METHODS

We used DT analysis to construct a decision-tree EWS (DTEWS) from a database of 198,755 vital signs observation sets collected from 35,585 consecutive, completed acute medical admissions. We evaluated the ability of DTEWS to discriminate patients at risk of cardiac arrest, unanticipated intensive care unit admission or death, each within 24h of a given vital signs observation. We compared the performance of DTEWS and NEWS using the area under the receiver-operating characteristic (AUROC) curve.

RESULTS

The structures of DTEWS and NEWS were very similar. The AUROC (95% CI) for DTEWS for cardiac arrest, unanticipated ICU admission, death, and any of the outcomes, all within 24h, were 0.708 (0.669-0.747), 0.862 (0.852-0.872), 0.899 (0.892-0.907), and 0.877 (0.870-0.883), respectively. Values for NEWS were 0.722 (0.685-0.759) [cardiac arrest], 0.857 (0.847-0.868) [unanticipated ICU admission}, 0.894 (0.887-0.902) [death], and 0.873 (0.866-0.879) [any outcome].

CONCLUSIONS

The decision-tree technique independently validates the composition and weightings of NEWS. The DT approach quickly provided an almost identical EWS to NEWS, although one that admittedly would benefit from fine-tuning using clinical knowledge. We believe that DT analysis could be used to quickly develop candidate models for disease-specific EWSs, which may be required in future.

Pain: a quality of care issue during patients' admission to hospital

AIM

To determine the extent of clinically significant pain suffered by hospitalized patients during their stay and at discharge.

BACKGROUND

The management of pain in hospitals continues to be problematic, despite long-standing awareness of the problem and improvements, e.g. acute pain teams and patient-controlled analgesia, epidural analgesia. Poorly managed pain, especially acute pain, often leads to adverse physical and psychological outcomes including persistent pain and disability. A systems approach may improve the management of pain in hospitals.

DESIGN

A descriptive cross-sectional exploratory design.

METHOD

A large electronic pain score database of vital signs and pain scores was interrogated between 1st January 2010 and 31st December 2010 to establish the proportion of hospital inpatient stays with clinically significant pain during the hospital stay and at discharge.

FINDINGS

A total of 810,774 pain scores were analysed, representing 38,451 patient stays. Clinically significant pain was present in 38·4% of patient stays. Across surgical categories, 54·0% of emergency admissions experienced clinically significant pain, compared with 48·0% of elective admissions. Medical areas had a summary figure of 26·5%. For 30% patients, clinically significant pain was followed by a consecutive clinically significant pain score. Only 0·2% of pain assessments were made independently of vital signs.

CONCLUSION

Reducing the risk of long-term persistent pain should be seen as integral to improving patient safety and can be achieved by harnessing organizational pain management processes with quality improvement initiatives. The assessment of pain alongside vital signs should be reviewed. Setting quality targets for pain are essential for improving the patient's experience.

Index blood tests and national early warning scores within 24 hours of emergency admission can predict the risk of in-hospital mortality: a model development and validation study

BACKGROUND

We explored the use of routine blood tests and national early warning scores (NEWS) reported within ±24 hours of admission to predict in-hospital mortality in emergency admissions, using empirical decision Tree models because they are intuitive and may ultimately be used to support clinical decision making.

METHODOLOGY

A retrospective analysis of adult emergency admissions to a large acute hospital during April 2009 to March 2010 in the West Midlands, England, with a full set of index blood tests results (albumin, creatinine, haemoglobin, potassium, sodium, urea, white cell count and an index NEWS undertaken within ±24 hours of admission). We developed a Tree model by randomly splitting the admissions into a training (50%) and validation dataset (50%) and assessed its accuracy using the concordance (c-) statistic. Emergency admissions (about 30%) did not have a full set of index blood tests and/or NEWS and so were not included in our analysis.

RESULTS

There were 23248 emergency admissions with a full set of blood tests and NEWS with an in-hospital mortality of 5.69%. The Tree model identified age, NEWS, albumin, sodium, white cell count and urea as significant (p<0.001) predictors of death, which described 17 homogeneous subgroups of admissions with mortality ranging from 0.2% to 60%. The c-statistic for the training model was 0.864 (95%CI 0.852 to 0.87) and when applied to the testing data set this was 0.853 (95%CI 0.840 to 0.866).

CONCLUSIONS

An easy to interpret validated risk adjustment Tree model using blood test and NEWS taken within ±24 hours of admission provides good discrimination and offers a novel approach to risk adjustment which may potentially support clinical decision making. Given the nature of the clinical data, the results are likely to be generalisable but further research is required to investigate this promising approach.

Predicting outcome following colorectal cancer surgery using a colorectal biochemical and haematological outcome model (Colorectal BHOM)

AIM

To present a new biochemistry and haematology outcome model which uses a minimum dataset to model outcome following colorectal cancer surgery, a concept previously shown to be feasible with arterial operations.

METHOD

Predictive binary logistic regression models (a mortality and morbidity model) were developed for 704 patients who underwent colorectal cancer surgery over a 6-year period in one hospital. The variables measured included 30-day mortality and morbidity. Hosmer-Lemeshow goodness of fit statistics and frequency tables compared the predicted vs the reported number of deaths. Discrimination was quantified using the c-index.

RESULTS

There were 573 elective and 131 nonelective interventional cases. The overall mean predicted risk of death was 7.79% (50 patients). The actual number of reported deaths was also 50 patients (χ(2) = 1.331, df = 4, P-value = 0.856; no evidence of lack of fit). For the mortality model, the predictive c-index was = 0.810. The morbidity model had less discriminative power but there was no evidence of lack of fit (χ(2) = 4.198, df = 4, P-value = 0.380, c-index = 0.697).

CONCLUSIONS

The Colorectal Biochemistry and Haematology Outcome mortality model suggests good discrimination (c-index > 0.8) and uses only a minimal number of variables. However, it needs to be tested on independent datasets in different geographical locations.

ViEWS--Towards a national early warning score for detecting adult inpatient deterioration

AIM OF STUDY

To develop a validated, paper-based, aggregate weighted track and trigger system (AWTTS) that could serve as a template for a national early warning score (EWS) for the detection of patient deterioration.

MATERIALS AND METHODS

Using existing knowledge of the relationship between physiological data and adverse clinical outcomes, a thorough review of the literature surrounding EWS and physiology, and a previous detailed analysis of published EWSs, we developed a new paper-based EWS - VitalPAC EWS (ViEWS). We applied ViEWS to a large vital signs database (n=198,755 observation sets) collected from 35,585 consecutive, completed acute medical admissions, and also evaluated the comparative performance of 33 other AWTTSs, for a range of outcomes using the area under the receiver-operating characteristics (AUROC) curve.

RESULTS

The AUROC (95% CI) for ViEWS using in-hospital mortality with 24h of the observation set was 0.888 (0.880-0.895). The AUROCs (95% CI) for the 33 other AWTTSs tested using the same outcome ranged from 0.803 (0.792-0.815) to 0.850 (0.841-0.859). ViEWS performed better than the 33 other AWTTSs for all outcomes tested.

CONCLUSIONS

We have developed a simple AWTTS - ViEWS - designed for paper-based application and demonstrated that its performance for predicting mortality (within a range of timescales) is superior to all other published AWTTSs that we tested. We have also developed a tool to provide a relative measure of the number of "triggers" that would be generated at different values of EWS and permits the comparison of the workload generated by different AWTTSs.

Health technology assessment review: remote monitoring of vital signs--current status and future challenges

Recent developments in communications technologies and associated computing and digital electronics now permit patient data, including routine vital signs, to be surveyed at a distance. Remote monitoring, or telemonitoring, can be regarded as a subdivision of telemedicine - the use of electronic and telecommunications technologies to provide and support health care when distance separates the participants. Depending on environment and purpose, the patient and the carer/system surveying, analysing or interpreting the data could be separated by as little as a few feet or be on different continents. Most telemonitoring systems will incorporate five components: data acquisition using an appropriate sensor; transmission of data from patient to clinician; integration of data with other data describing the state of the patient; synthesis of an appropriate action, or response or escalation in the care of the patient, and associated decision support; and storage of data. Telemonitoring is currently being used in community-based healthcare, at the scene of medical emergencies, by ambulance services and in hospitals. Current challenges in telemonitoring include: the lack of a full range of appropriate sensors, the bulk weight and size of the whole system or its components, battery life, available bandwidth, network coverage, and the costs of data transmission via public networks. Telemonitoring also has the ability to produce a mass of data - but this requires interpretation to be of clinical use and much necessary research work remains to be done.

Measuring clinical performance using routinely collected clinical data

Following the well-publicized problems with paediatric cardiac surgery at the Bristol Royal Infirmary, there is wide public interest in measures of hospital performance. The Kennedy report on the BRI events suggested that such measures should be meaningful to the public, case-mix-adjusted, and based on data collected as part of routine clinical care. We have found that it is possible to predict in-hospital mortality (a measure readily understood by the public) using simple routine data-age, mode of admission, sex, and routine blood test results. The clinical data items can be obtained at a single venesection, are commonly collected in the routine care of patients, are already stored on hospital core IT systems, and so place no extra burden on the clinical staff providing care. Such risk models could provide a metric for use in evidence-based clinical performance management. National application is logistically feasible.

The development of a VBHOM-based outcome model for lower limb amputation performed for critical ischaemia

BACKGROUND

VBHOM (Vascular Biochemistry and Haematology Outcome Models) adopts the approach of using a minimum data set to model outcome and has been previously shown to be feasible after index arterial operations. This study attempts to model mortality following lower limb amputation for critical limb ischaemia using the VBHOM concept.

METHODS

A binary logistic regression model of risk of mortality was built using National Vascular Database items that contained the complete data required by the model from 269 admissions for lower limb amputation. The subset of NVD data items used were urea, creatinine, sodium, potassium, haemoglobin, white cell count, age on and mode of admission. This model was applied prospectively to a test set of data (n=269), which were not part of the original training set to develop the predictor equation.

RESULTS

Outcome following lower limb amputation could be described accurately using the same model. The overall mean predicted risk of mortality was 32%, predicting 86 deaths. Actual number of deaths was 86 (chi(2)=8.05, 8 d.f., p=0.429; no evidence of lack of fit). The model demonstrated adequate discrimination (c-index=0.704).

CONCLUSIONS

VBHOM provides a single unified model that allows good prediction of surgical mortality in this high risk group of individuals. It uses a small, simple and objective clinical data set that may also simplify comparative audit within vascular surgery.

POSSUM models in open abdominal aortic aneurysm surgery

OBJECTIVES

This study evaluated the Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM), Portsmouth (P) POSSUM and Vascular (V) POSSUM. The primary aim was to assess the validity of these scoring systems in a population of patients undergoing elective and emergency open AAA repair. The secondary intention was in the event that these equations did not fit all patients with an aneurysm; a new model would be developed and tested using logistic regression from the local data (Cambridge POSSUM).

METHODS

POSSUM data items were collected prospectively in a group of 452 patients undergoing elective and emergency open AAA repair over an eight-year period. The operative mortality rates were compared with those predicted by POSSUM, P-POSSUM, V-POSSUM and Cambridge POSSUM.

RESULTS

All models except V-POSSUM (physiology only) showed significant lack of fit when predicting mortality after open AAA surgery. It was found that the locally generated single unified model (Cambridge POSSUM) could successfully describe both elective and ruptured AAA mortality with good discrimination (chi(2)=9.24, 7 d.f., p=0.236, c-index=0.880).

CONCLUSIONS

POSSUM, V-POSSUM and P-POSSUM may not be robust tools for comparing mortality between populations undergoing elective and emergency open AAA repair as once thought. The development and successful validation of Cambridge POSSUM provides a unified model to describe both elective and emergency AAAs together and should be validated in other geographical settings.

VBHOM, a data economic model for predicting the outcome after open abdominal aortic aneurysm surgery

Background

Vascular Biochemistry and Haematology Outcome Models (VBHOM) adopted the approach of using a minimum data set to model outcome. This study aimed to test such a model on a cohort of patients undergoing open elective and non-elective abdominal aortic aneurysm (AAA) repair.

Methods

A binary logistic regression model of risk of in-hospital mortality was built from the 2002–2004 submission to the UK National Vascular Database (NVD) (2718 patients). The subset of NVD data items used comprised serum levels of urea, sodium and potassium, haemoglobin, white cell count, sex, age and mode of admission. The model was applied prospectively using Hosmer–Lemeshow methodology to a test data set from the Cambridge Vascular Unit.

Results

The validation set contained 327 patients, of whom 208 had elective AAA repair and 119 had emergency repair of a ruptured AAA. Outcome following elective and non-elective AAA repair could be described accurately using the same model. The overall mean predicted risk of death was 14·13 per cent, and 48 deaths were predicted. The actual number of deaths was 53 (χ2 = 8·40, 10 d.f., P = 0·590; no evidence of lack of fit). The model also demonstrated good discrimination (c-index = 0·852).

Conclusion

The VBHOM approach has the advantage of using simple, objective clinical data that are easy to collect routinely. The VBHOM data items potentially allow prediction of risk in an individual patient before aneurysm surgery.

Hospital-wide physiological surveillance–A new approach to the early identification and management of the sick patient

Hospitalised patients, who suffer cardiac arrest and require unanticipated intensive care unit (ICU) admission or die, often exhibit premonitory abnormalities in vital signs. Sometimes, the deterioration is well documented, though there is little discernable evidence of intervention. In other cases, monitoring and recording of vital signs is infrequent or incomplete. Healthcare providers have introduced "track and trigger" systems to allow early identification of patients with physiological abnormalities, and rapid response teams to facilitate rapid and appropriate management. However, even when "track and trigger" systems are used, the recording of vital signs, patient chart completion and team activation remain sub-optimal. We have developed a system for collecting routine vital signs data at the bedside using standard personal digital assistants (PDA). The PDAs act as "thin clients" linked by a wireless local area network (W-LAN) to the hospital's intranet system, where raw and derived data are integrated with other patient information, e.g., name, hospital number, laboratory results. It is possible for raw physiology data, early warning scores (EWS), vital signs charts and oxygen therapy records to be made instantaneously available to any member of the hospital healthcare team via the W-LAN or hospital intranet. Early and direct contact with members of the patient's primary clinical team or rapid response team can be made through an automated alerting system, triggered by the EWS data. The ability to capture physiological data at the bedside, and to make these available to anyone with appropriate access rights at any time and in any place, should provide previously unattainable, clinical and administrative benefits. Analysis of the raw physiological data and patient outcomes will also make it possible to validate existing and future "track and trigger" systems.

Calculating early warning scores—A classroom comparison of pen and paper and hand-held computer methods

To assist in the early detection of critical illness, many hospitals now use a "track and trigger" system that allocates points to routine vital signs measurements on the basis of their derangement from an arbitrarily agreed "normal" range. These points are summed to provide an early warning score (EWS). Little is known about the accuracy with which EWS are calculated and charted. We compared the speed and accuracy of charting the weighted value attributed to each vital sign, and of calculating the EWS, using the traditional pen and paper method with that using a specially programmed, personal digital assistant (VitalPAC). Incorrect entries or omissions occurred in 24 (29%) of 84 EWS computed using pen/paper compared to 8 (10%) computed using the VitalPAC method. Fewer incorrect clinical actions were indicated using EWS derived via the VitalPAC method (4/84, 5%) than from those calculated using pen/paper (12/84, 14%). The mean time (+/-S.D.) taken for participants to calculate and chart a set of weighted values and EWS using the pen/paper method was 67.6+/-35.3 s (n=84). The corresponding time taken to enter a set of physiological data using the VitalPAC was 43.0+/-23.5 s (n=84). By comparison with the conventional pen/paper method, the use of VitalPAC was on average 1.6-times faster. The use of a device such as VitalPAC offers significant advantages both in speed and accuracy of recording of EWS.

The use of routine laboratory data to predict in-hospital death in medical admissions

The ability to predict clinical outcomes in the early phase of a patient's hospital admission could facilitate the optimal use of resources, might allow focused surveillance of high-risk patients and might permit early therapy. We investigated the hypothesis that the risk of in-hospital death of general medical patients can be modelled using a small number of commonly used laboratory and administrative items available within the first few hours of hospital admission. Matched administrative and laboratory data from 9497 adult hospital discharges, with a hospital discharge specialty of general medicine, were divided into two subsets. The dataset was split into a single development set, Q(1) (n=2257), and three validation sets, Q(2), Q(3) and Q(4) (n(1)=2335, n(2)=2361, n(3)=2544). Hospital outcome (survival/non-survival) was obtained for all discharges. An outcome model was constructed from binary logistic regression of the development set data. The goodness-of-fit of the model for the validation sets was tested using receiver-operating characteristics curves (c-index) and Hosmer-Lemeshow statistics. Application of the model to the validation sets produced c-indices of 0.779 (Q(2)), 0.764 (Q(3)) and 0.757 (Q(4)), respectively, indicating good discrimination. Hosmer-Lemeshow analysis gave chi(2)=9.43 (Q(2)), chi(2)=7.39 (Q(3)) and chi(2)=8.00 (Q(4)) (p-values of 0.307, 0.495 and 0.433) for 8 degrees of freedom, indicating good calibration. The finding that the risk of hospital death can be predicted with routinely available data very early on after hospital admission has several potential uses. It raises the possibility that the surveillance and treatment of patients might be categorised by risk assessment means. Such a system might also be used to assess clinical performance, to evaluate the benefits of introducing acute care interventions or to investigate differences between acute care systems.

Risk-adjusted predictive models of mortality after index arterial operations using a minimal data set

Background

Reducing the data required for a national vascular database (NVD) without compromising the statistical basis of comparative audit is an important goal. This work attempted to model outcomes (mortality and morbidity) from a small and simple subset of the NVD data items, specifically urea, sodium, potassium, haemoglobin, white cell count, age and mode of admission.

Methods

Logistic regression models of risk of adverse outcome were built from the 2001 submission to the NVD using all records that contained the complete data required by the models. These models were applied prospectively against the equivalent data from the 2002 submission to the NVD.

Results

As had previously been found using the P-POSSUM (Portsmouth POSSUM) approach, although elective abdominal aortic aneurysm (AAA) repair and infrainguinal bypass (IIB) operations could be described by the same model, separate models were required for carotid endarterectomy (CEA) and emergency AAA repair. For CEA there were insufficient adverse events recorded to allow prospective testing of the models. The overall mean predicted risk of death in 530 patients undergoing elective AAA repair or IIB operations was 5·6 per cent, predicting 30 deaths. There were 28 reported deaths (χ2 = 2·75, 4 d.f., P = 0·600; no evidence of lack of fit). Similarly, accurate predictions were obtained across a range of predicted risks as well as for patients undergoing repair of ruptured AAA and for morbidity.

Conclusion

A ‘data economic’ model for risk stratification of national data is feasible. The ability to use a minimal data set may facilitate the process of comparative audit within the NVD.

Development of a dedicated risk-adjustment scoring system for colorectal surgery (colorectal POSSUM)

BACKGROUND

The aim of the study was to develop a dedicated colorectal Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (CR-POSSUM) equation for predicting operative mortality, and to compare its performance with the Portsmouth (P)-POSSUM model.

METHODS

Data were collected prospectively from 6883 patients undergoing colorectal surgery in 15 UK hospitals between 1993 and 2001. After excluding missing data and 93 patients who did not satisfy the inclusion criteria, 4632 patients (68.2 per cent) underwent elective surgery and 2107 had an emergency operation (31.0 per cent); 2437 operations (35.9 per cent) for malignant and 4267 (62.8 per cent) for non-malignant diseases were scored. Stepwise logistic regression analysis was used to develop an age-adjusted POSSUM model and a dedicated CR-POSSUM model. A 60:40 per cent split-sample validation technique was adopted for model development and testing. Observed and expected mortality rates were compared.

RESULTS

The operative mortality rate for the series was 5.7 per cent (387 of 6790 patients) (elective operations 2.8 per cent; emergency surgery 12.0 per cent). The CR-POSSUM, age-adjusted POSSUM and P-POSSUM models had similar areas under the receiver-operator characteristic curves. Model calibration was similar for CR-POSSUM and age-adjusted POSSUM models, and superior to that for the P-POSSUM model. The CR-POSSUM model offered the best overall accuracy, with an observed : expected ratio of 1.000, 0.998 and 0.911 respectively (test population).

CONCLUSION

The CR-POSSUM model provided an accurate predictor of operative mortality. External validation is required in hospitals different from those in which the model was developed.

Risk-adjusted prediction of operative mortality in oesophagogastric surgery with O-POSSUM

Introduction

The present study was designed to develop a dedicated oesophagogastric model for the prediction of risk-adjusted postoperative mortality in upper gastrointestinal surgery (O-POSSUM).

Methods

Using 1042 patients undergoing oesophageal (n = 538) or gastric (n = 504) surgery between 1994 and 2000 the Portsmouth predictor equation for mortality (P-POSSUM) scoring system was compared with a standard logistic regression O-POSSUM model and a multilevel O-POSSUM model using the following independent factors: age, physiological status, mode of surgery, type of surgery and histological stage.

Results

The overall mortality rate was 12·0 per cent (elective mortality rate 9·4 per cent and emergency mortality rate 26·9 per cent). P-POSSUM overpredicted mortality (14·5 per cent), particularly in the elective group of patients. The multilevel model offered higher discrimination than the single-level O-POSSUM and P-POSSUM models (area under receiver–operator characteristic curve 79·7 versus 74·6 and 74·3 per cent). When observed to expected outcomes were evaluated, the multilevel O-POSSUM model was found to offer better calibration (Hosmer–Lemeshow χ2 statistic 10·15 versus 10·52 and 28·80).

Conclusion

The multilevel O-POSSUM model provided an accurate risk-adjusted prediction of death from oesophageal and gastric surgery for individual patients. In conjunction with a multidisciplinary approach to patient management, the model may be used in everyday practice for perioperative counselling of patients and their carers.

Comparison of P-POSSUM risk-adjusted mortality rates after surgery between patients in the USA and the UK

BACKGROUND

The Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (POSSUM) criteria have been used to assess surgical risk in patients in the UK. The aim was to determine how applicable these criteria are to patients undergoing surgery in the USA.

METHODS

Two cohorts of patients undergoing major non-cardiac surgery were followed prospectively in the USA (n = 1056) and the UK (n = 1539). Each patient was assigned a risk score for preoperative physiological status and operative severity using the established POSSUM criteria. Death in hospital was the primary outcome measure. For each patient a predicted risk of death was calculated from Portsmouth POSSUM (P-POSSUM) methodology using an established equation. The relationships between predicted and observed mortality rates in each cohort were investigated by means of multivariate logistic regression.

RESULTS

Within each cohort, an increase in risk estimated by P-POSSUM predicted an increase in observed mortality rate (P < 0.001). For any given risk level, however, mortality rates were significantly higher in the UK cohort than in the US cohort (odds ratio 4.50 (95 per cent confidence interval 2.81 to 7.19); Z = 6.25, P < 0.001).

CONCLUSION

An increase in predicted risk, based on the P-POSSUM methodology, was associated with a higher mortality rate in patients from both countries. However, risk-adjusted mortality rates following major surgery were four times higher in the UK cohort. These marked differences warrant validation in a larger number of centres.

Towards a national clinical minimum data set for general surgery

Background

Measurement and comparison of surgical performance is accepted as necessary and inevitable. Risk-stratified (case-mix adjusted) models of clinical outcomes form a metric with which to assess performance, but require accurate data. Collecting such data in the clinical environment is time consuming and difficult. This study aimed to construct effective models, for operative and non-operative admissions, from routine clinical data residing in hospital computers, so minimizing data collection and quality problems, and facilitating national implementation.

Methods

Data for 3181 non-operative emergency, 5039 elective and 3043 emergency operative admissions for the 2 years beginning 1 August 1997 were used to generate logistic regression equations for risk of death, which were applied prospectively to the following 3 years' data.

Results

The models use urea, haemoglobin, white blood cell count, sodium, potassium, age on admission, sex, British United Provident Association (BUPA) Operative Severity Score (for operative admissions) and, implicitly, mode of admission and mortality at discharge. All three models successfully stratified risk into five or more bands.

Conclusion

Effective models of mortality, applicable to all general surgical admissions, can be constructed from existing routine clinical data, largely obtained from a single venesection. The data set is a candidate national clinical minimum data set.

P-POSSUM models for abdominal aortic aneurysm surgery

Background and methods

POSSUM (Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity) data on 444 (213 emergency, 231 elective) admissions for abdominal aortic aneurysm (AAA) surgery between August 1993 and July 2000 were analysed using the P-POSSUM equation for general surgery and the P-POSSUM equations developed for the Vascular Surgical Society of Great Britain and Ireland (physiological and operative and physiology only).

Results

All three equations successfully modelled the elective admissions but failed to fit the emergency admissions and the elective and emergency admissions combined. This suggests that admission method (not a POSSUM data item) is an important factor for patients with AAA. However, with these data it was not possible to generate a model, including admission method, that successfully modelled the combined elective and emergency data. Emergency AAAs had to be treated separately. The 213 emergency admissions were divided into two groups by operation date. The first 106 (training set) were used to form logistic regression models following the P-POSSUM methodology. These models were found successfully to fit the remaining 107 records (test set) on prospective application, as shown in the Table.

Conclusion

Emergency AAAs appear to be fundamentally different from elective AAAs and other vascular cases, and require their own distinct risk model. The ability to model risk for elective AAAs using a physiology-only risk model could perhaps be used to optimize the timing of operation as well as informing all concerned of the risks involved.

Portsmouth POSSUM models for abdominal aortic aneurysm surgery

BACKGROUND

The Portsmouth Physiological and Operative Severity Score for enUmeration of Mortality and morbidity (P-POSSUM) equation permits comparative audit that allows for differences in case mix. The methodology has previously been shown accurately to model general surgical and vascular surgical patients. Patients with a ruptured abdominal aortic aneurysm (AAA) are, however, very different from elective patients and it may be hypothesized that they require their own specific risk model.

METHODS

POSSUM data on 444 (213 emergency, 231 elective) admissions for AAA surgery between August 1993 and July 2000 were analysed using the P-POSSUM equation for general surgery and the P-POSSUM equations developed for vascular surgery.

RESULTS

All equations successfully modelled the elective aneurysms but failed to fit the emergency aneurysms, and the elective and emergency aneurysms combined. This suggested that admission method (not a POSSUM data item) is an important factor for patients with AAA. However, with these data it was not possible to generate a model, including admission method, that successfully modelled the combined elective and emergency data. The 213 emergency aneurysm repairs were divided into two groups by operation date. The first 106 (training set) were used to form logistic regression models following the P-POSSUM methodology. These models were found successfully to fit the remaining 107 records (test set) on prospective application (chi2 = 4.50, 4 d.f., P = 0.345).

CONCLUSION

Ruptured AAAs appear to be different from elective AAAs and other vascular cases and require their own risk model.

A model for national outcome audit in vascular surgery

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 questionnaires 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 prospectively to the second "test" dataset.

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.

POSSUM and Portsmouth POSSUM for predicting mortality. Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity

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 prospectively 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 prospectively 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.

An evaluation of the POSSUM surgical scoring system

POSSUM (Physiological and Operative Severity Score for the enUmeration of Morbidity and mortality) has been studied as a possible surgical audit system for a 9-month interval using a sample of 28 per cent of the general surgical workload. Mortality or survival was analysed as an endpoint. In this sample the published POSSUM predictor equation for mortality overpredicted deaths by a factor of more than two. The bulk of the overprediction occurred in the group at lowest risk (predicted mortality 10 per cent or less), in which death was overpredicted by a factor of six. This is the most important group for audit purposes since it contains the majority of surgical patients and is composed of fit patients undergoing minor surgery. The published predictor equation for mortality returns a minimum predicted mortality of 1.08 per cent, clearly far higher than that expected for a fit patient having minor surgery. Logistic regression was done on a set of 1485 surgical episodes to generate a local predictor equation for mortality. This process gave a predictor equation that fitted well with the observed mortality rate and gave a minimum predicted risk of mortality of 0.20 per cent. The previously published POSSUM predictor equation for mortality performed badly when tested using a standard test of goodness of fit for logistic regression and must be modified.

Studies on the pathogenesis of the early dumping syndrome induced by intraduodenal instillation of hypertonic glucose

A reaction indistinguishable from the early dumping syndrome was induced in four of nine normal volunteers by intraduodenal instillation of a hypertonic glucose meal. Tachycardia and marked peripheral vasodilatation were demonstrated in 'dumpers' by Doppler ultrasound measurements of the arterial blood flow signal. The dumping reaction was not detectably altered by the addition of guar to the meal. Plasma VIP concentration rose and plasma volume fell to a similar degree in 'dumpers' and 'non-dumpers', suggesting that neither event is an integral component of the dumping mechanism. In contrast, the rates of rise of blood glucose and enteroglucagon concentration were markedly greater in 'dumpers'. The results are inconsistent with the conventional explanation that the early dumping syndrome is caused by a large osmotic fluid shift, but are compatible with a mechanism involving an initial period of intestinal hypermotility.

Haemodynamic assessment of the femoropopliteal segment: comparison of pressure and Doppler methods using ROC curve analysis

Combined aorto-iliac and femoropopliteal vascular disease remains a problem in vascular surgery. Arteriography does not provide information on the relative contributions of the two lesions to the presenting symptoms. Aorto-iliac haemodynamics may reveal occult aorto-iliac disease but does not show whether combined proximal and distal reconstruction will be required to provide symptomatic relief. Haemodynamic assessment of both segments may help in this respect. A haemodynamic assessment of the femoropopliteal segment of 72 limbs in 38 patients is reported. The segmental pressure drop between a common femoral arterial cannula and a below-knee occlusion cuff is compared with a non-invasive Doppler method combining both transit time and damping factor. Comparison is made using receiver operating characteristic (ROC) curve analysis. The measurement of segmental pressure drop is more accurate than the Doppler method in detection of femoropopliteal stenoses of greater than 50 per cent of the luminal diameter (P less than 0.05).

Characterisation of aorto-iliac arterial stenoses in terms of pressure and flow

Aorto-iliac stenoses were characterised in terms of pressure drop and flow velocity in a canine model and in patients with occlusive arterial disease. Pressure above and below the stenosis was measured intra-arterially and flow related measurements were made at rest and during reactive hyperaemia in the dog, and following papaverine administration in patients. The addition of flow velocity information to the pressure drop across a stenosis gave an increased separation of stenoses in the experimental animal and also in man.

Haemodynamics of the adjunctive arteriovenous fistula in femorodistal bypass grafting: an experimental study

The results of femorotibial bypass for limb salvage vary a great deal. One of the reasons for this might be the discrepancy between potential inflow and run-off into the foot. An arteriovenous fistula at the distal graft anastomosis may improve results but the best anatomical arrangement for the fistula is unknown. Ileofemoral grafts were performed in dogs after the hind limb was rendered ischaemic. The distal end of the graft was anastomosed proximal to, superimposed upon, or distal to an arteriovenous fistula between the femoral artery and its accompanying femoral vein. The effect of the fistula on graft/run-off haemodynamics was then measured. The adjunctive arteriovenous fistula increased inflow by a mean of 900 per cent and reduced systemic pressure by 10 per cent. Peripheral resistance was reduced by 85 per cent. Distal arterial run-off was maximized with respect to total graft flow when the graft was placed distal to the fistula (P less than 0.05). The venous steal of flow and perfusion pressure produced by the fistula was minimized with the same configuration compared to the two other arrangements (P less than 0.01 and P less than 0.05). Placement of the graft distal to the adjunctive arteriovenous fistula maximized distal arterial flow and pressure, and significantly increased graft flow.