Operative and non-operative management for intestinal emergencies: findings from a single-centre retrospective cohort study.

BACKGROUND: Patients with an intestinal emergency who do not have surgery are poorly characterised. This study used electronic healthcare records to provide a rapid insight into the number of patients admitted with an intestinal emergency and compare short-term outcomes for non-operative and operative management. METHODS: A single-centre retrospective cohort study was conducted at a tertiary NHS hospital (from 1 December 2013 to 31 January 2020). Patients were identified using diagnosis codes for intestinal emergencies, based on the inclusion criteria for the National Emergency Laparotomy Audit. Relevant data were extracted from electronic healthcare records (n=3,997). RESULTS: Nearly half of patients admitted with an intestinal emergency received nonoperative management (43.7%). Of those who underwent surgery, 63.7% were started laparoscopically. The non-operative group had a shorter hospital stay (median: 5.4 days vs 8.2 days [started laparoscopically] or 16.8 days [started open]) and fewer unintended intensive care admissions than the surgical group (2.4% vs 8.7% [started laparoscopically] 21.1% [started open]). However, 30-day mortality for non-operative treatment was double that for surgery (22.4% vs 10.1%). The 30-day mortality rate was found to be even higher for non-operative management (50.3%) compared with surgery (19.5%) in a sub-analysis of patients with admission National Early Warning Score ≥4 (n=683). CONCLUSION: The proportion of patients with intestinal emergencies who do not have surgery is greater than expected, and it appears that many respond well to non-operative treatment. However, 30-day mortality for non-operative management was high, and the low number of admissions to intensive care suggests that major invasive treatment was not appropriate for most in this group.

Novel predictors of mortality in emergency bowel surgery: a single-centre cohort study.

Pre-operative risk stratification is a key part of the care pathway for emergency bowel surgery, as it facilitates the identification of high-risk patients. Several novel risk scores have recently been published that are designed to identify patients who are frail or significantly unwell. They can also be calculated pre-operatively from routinely collected clinical data. This study aimed to investigate the ability of these scores to predict 30-day mortality after emergency bowel surgery. A single centre cohort study was performed using our local data from the National Emergency Laparotomy Audit database. Further data were extracted from electronic hospital records (n = 1508). The National Early Warning Score, Laboratory Decision Tree Early Warning Score and Hospital Frailty Risk Score were then calculated. The most abnormal National or Laboratory Decision Tree Early Warning Score in the 24 or 72 h before surgery was used in analysis. Individual scores were reasonable predictors of mortality (c-statistic 0.699-0.740) but all were poorly calibrated. A National Early Warning Score ≥ 4 was associated with a high overall mortality rate (> 10%). A logistic regression model was developed using age, National Early Warning Score, Laboratory Decision Tree Early Warning Score and Hospital Frailty Risk Score as predictor variables, and its performance compared with other established risk models. The model demonstrated good discrimination and calibration (c-statistic 0.827) but was marginally outperformed by the National Emergency Laparotomy Audit score (c-statistic 0.861). All other models compared performed less well (c-statistics 0.734-0.808). Pre-operative patient vital signs, blood tests and markers of frailty can be used to accurately predict the risk of 30-day mortality after emergency bowel surgery.

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.

Weekend admission and mortality from acute exacerbations of chronic obstructive pulmonary disease in winter.

Historically, acute medical staffing numbers have been lower on weekends and in winter numbers of medical admissions rise. An analysis of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) admissions to Portsmouth Hospitals over a seven-year period was undertaken to examine the effects of admission on a weekend, of winter, and with the opening of a medical admissions unit (MAU). In total, 9,915 admissions with AECOPD were identified. Weekend admissions accounted for 2,071 (20.9%) of cases, winter accounted for 3,026 (30.5%) admissions, and 522 (34.4%) deaths. Adjusted odds ratio (OR) for death on day 1 after winter weekend admission was 2.89 (95% confidence interval (CI) 1.035 to 8.076). After opening the MAU, the OR for death day 1 after weekend winter admission fell from 3.63 (95% CI 1.15 to 11.5) to 1.65 (95% CI 0.14 to 19.01). AECOPD patients have an increased risk of death after admission over a weekend in winter and this effect was reduced by opening a MAU. These findings have implications for the planning of acute care provision in different seasons.

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.

Improving accuracy and efficiency of early warning scores in acute care.

BACKGROUND: Early warning scores (EWS) are an integral part of the care of acutely ill patients. Unfortunately, in the few studies where the accuracy of EWS has been tested it has been found to be lacking, with serious implications for quality of care. AIM: To determine if the provision of computer-aided scoring could increase the accuracy and efficiency of EWS calculations, when compared with the traditional pen-and-paper method, and to determine if it was acceptable to users. DESIGN: 26 nurses from two surgical assessment wards in two hospitals were studied. The study was conducted in three phases. Phase 1--a classroom-based exercise where nurses were given ten patient vignettes and asked to derive EWS using traditional pen-and-paper methods; Phase 2--the same as phase 1, but using a hand-held computer to derive EWS; Phase 3--the same as phase 2, but was a follow-up exercise undertaken in the ward environment, 4 weeks after computer-aided scoring was implemented in the two wards. Each phase closed with a user perception/attitudes questionnaire. RESULTS: Accuracy and efficiency--phase 1 was associated with a significantly lower overall accuracy (152/260, 58%) compared with phase 2 (96%; difference in proportions 38%, 95% confidence interval 31-44%, P < 0.0001). There was a small but significant reduction in accuracy from phase 2 (96%) to phase 3 (88%) (8% difference, P=0.006). The mean time to derive an EWS reduced from 37.9 seconds in phase 1 to 35.1 seconds in phase 2 (P=0.016), down to 24.0 seconds in phase 3 (P<0.0001). User acceptability: in phase 1, nurses favoured the pen-and-paper method in all respects except accuracy. In phase 2, nurses' views shifted significantly in favour of the hand-held computer, with little deterioration in the follow-up phase 3. CONCLUSIONS: A hand-held computer helps to improve the accuracy and efficiency of EWS in acute hospital care and is acceptable to nurses.

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.

Comparison of different methods of risk stratification in urgent and emergency surgery.

BACKGROUND: The aim was to compare a number of risk scoring systems prospectively in a cohort of patients who underwent non-elective surgery. METHODS: This was a cohort study of 2349 consecutive patients who had urgent or emergency surgery in a district general hospital in the UK. All patients were scored prospectively using the Revised Goldman Cardiac Risk Index (RGCRI), Portsmouth modification of the Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (P-POSSUM), Surgical Risk Score (SRS) and Biochemistry and Haematology Outcome Models (BHOM). Actual 30-day and 1-year survival rates were compared with the predicted outcomes using receiver-operator characteristic (ROC) curves and Hosmer-Lemeshow analysis. RESULTS: Some 141 patients (6.0 per cent) died within 30 days of operation. This increased to 254 (10.8 per cent) by 1 year. The area under the ROC curve for death within 30 days was 0.90 for P-POSSUM, 0.85 for SRS, 0.84 for BHOM and 0.73 for RGCRI. Only the first three risk scores were able to discriminate accurately within the groups (area under ROC curve over 0.8), with no significant variation between expected and observed mortality rates confirmed by Hosmer-Lemeshow analysis. Similar results were found for the ability of each score to predict outcome at 1 year. CONCLUSION: P-POSSUM, SRS and BHOM scoring systems were all able to predict outcome after emergency and urgent surgery, but the SRS had the advantage of ease of calculation. BHOM requires only the most commonly available blood test data and the computer holding these data can easily perform the calculation.

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 (chi(2) = 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.

Descriptive study comparing routine hospital administrative data with the Vascular Society of Great Britain and Ireland's National Vascular Database.

OBJECTIVE: To compare patient volume and outcomes in vascular surgery between an administrative data set (Hospital Episode Statistics) and a clinical database (National Vascular Database). DESIGN: Descriptive study. METHODS: Volume of cases determined by age, sex, year and procedure and in-hospital mortality by procedure for both datasets for patients undergoing either repair of abdominal aortic aneurysm, carotid endarterectomy or infrainguinal bypass over a three year period between 1st April 2001 and 31st March 2004. RESULTS: There were 32,242 admissions with a mention of the three selected vascular procedures within the administrative data set compared to 8462 within the clinical database. For NHS trusts common to both datasets, there were twice as many procedures (16,923) recorded within the administrative dataset compared to the clinical database. Patient characteristics were similar across both databases. Further analysis limiting the administrative data to records attributed to consultants known to contribute to the clinical database showed much closer agreement with only 11% more repairs of abdominal aortic aneurysm recorded within the administrative dataset compared to the National Vascular Database. CONCLUSIONS: There are significant differences in total numbers between HES and the NVD. If the National Vascular Database is to become a credible source of information on activity and outcomes for vascular surgery, there is a clear need to increase the number of contributing surgeons and to increase the completeness of data submitted. Further analysis at individual record level is needed to identify other reasons for discrepancies which could help to enhance data quality, both within Hospital Episode Statistics and within the National Vascular Database.

Sub-specialization in general surgery: the problem of providing a safe emergency general surgical service.

BACKGROUND: The increasing subspecialization of general surgeons in their elective work may result in deskilling and create problems in providing expert care for emergency cases. To evaluate the size of the problem this study determined how often complex emergency surgical cases are treated by general surgeons working outside their own elective subspecialty. METHOD: In a district general hospital in the south of the UK serving a population of 550 000 where there is almost complete subspecialization within general surgery, 1554 patients having emergency general surgical operations were studied in a one-year review. The time an operation occurred, the seniority of the operating surgeon, the subspecialty interest of the consultant responsible for the case compared with the specialist nature of the operation was determined. RESULTS: Of 1554 patients having emergency general surgical operations, 23% (352/1554) were of a high category of complexity. Ninety were vascular procedures and were dealt with by specialist vascular surgeons on a separate rota. Of the remaining 262 operations, 78 (30%) did not match the subspecialty of the consultant surgeon responsible for their care; 56 (72%) of these occurred out of hours of which 14 (18%) had a consultant surgeon present and scrubbed in the theatre; one per month of the study. Seventy-three percent (57/78) of these were complex colorectal operations. CONCLUSION: The mismatch between the subspecialist elective interests of the consultant general surgeon and out of hours specialist major surgery needing consultant involvement occurred infrequently, and was mainly due to major lower gastrointestinal cases managed by upper gastrointestinal and breast surgeons. This has important implications for the future training of general surgeons and the provision of an emergency nonvascular general surgical service.

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 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 (chi(2) = 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.