Paul E. Schmidt

Impact of introducing an electronic physiological surveillance system on hospital mortality

Background Avoidable hospital mortality is often attributable to inadequate patient vital signs monitoring, and failure to recognise or respond to clinical deterioration. The processes involved with vital sign collection and charting; their integration, interpretation and analysis; and the delivery of decision support regarding subsequent clinical care are subject to potential error and/or failure. Objective To determine whether introducing an electronic physiological surveillance system (EPSS), specifically designed to improve the collection and clinical use of vital signs data, reduced hospital mortality. Methods A pragmatic, retrospective, observational study of seasonally adjusted in-hospital mortality rates in three main hospital specialties was undertaken before, during and after the sequential deployment and ongoing use of a hospital-wide EPSS in two large unconnected acute general hospitals in England. The EPSS, which uses wireless handheld computing devices, replaced a paper-based vital sign charting and clinical escalation system. Results During EPSS implementation, crude mortality fell from a baseline of 7.75% (2168/27 959) to 6.42% (1904/29 676) in one hospital (estimated 397 fewer deaths), and from 7.57% (1648/21 771) to 6.15% (1614/26 241) at the second (estimated 372 fewer deaths). At both hospitals, multiyear statistical process control analyses revealed abrupt and sustained mortality reductions, coincident with the deployment and increasing use of the system. The cumulative total of excess deaths reduced in all specialties with increasing use of the system across the hospital. Conclusions The use of technology specifically designed to improve the accuracy, reliability and availability of patients’ vital signs and early warning scores, and thereby the recognition of and response to patient deterioration, is associated with reduced mortality in this study.

Patterns in the recording of vital signs and early warning scores: compliance with a clinical escalation protocol

Background The recognition of patient deterioration depends largely on identifying abnormal vital signs, yet little is known about the daily pattern of vital signs measurement and charting. Methods We compared the pattern of vital signs and VitalPAC Early Warning Score (ViEWS) data collected from admissions to all adult inpatient areas (except high care areas, such as critical care units) of a NHS district general hospital from 1 May 2010 to 30 April 2011, to the hospitals clinical escalation protocol. Main outcome measures were hourly and daily patterns of vital signs and ViEWS value documentation; numbers of vital signs in the periods 08:00–11:59 and 20:00–23:59 with subsequent vital signs recorded in the following 6 h; and time to next observation (TTNO) for vital signs recorded in the periods 08:00–11:59 and 20:00–23:59. Results 950 043 vital sign datasets were recorded. The daily pattern of observation documentation was not uniform; there were large morning and evening peaks, and lower night-time documentation. The pattern was identical on all days. 23.84% of vital sign datasets with ViEWS ≥ 9 were measured at night compared with 10.12–19.97% for other ViEWS values. 47.42% of patients with ViEWS=7–8 and 31.22% of those with ViEWS ≥ 9 in the period 20:00–23:59 did not have vital signs recorded in the following 6 h. TTNO decreased with increasing ViEWS value, but less than expected by the monitoring protocol. Conclusions There was only partial adherence to the vital signs monitoring protocol. Sicker patients appear more likely to have vital signs measured overnight, but even their observations were often not followed by timely repeat assessments. The observed pattern of monitoring may reflect the impact of competing clinical priorities.

Towards a national clinical minimum data set for general surgery

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.

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.

Development and validation of a decision tree early warning score based on routine laboratory test results for the discrimination of hospital mortality in emergency medical admissions

AIM OF STUDY To build an early warning score (EWS) based exclusively on routinely undertaken laboratory tests that might provide early discrimination of in-hospital death and could be easily implemented on paper. MATERIALS AND METHODS Using a database of combined haematology and biochemistry results for 86,472 discharged adult patients for whom the admission specialty was Medicine, we used decision tree (DT) analysis to generate a laboratory decision tree early warning score (LDT-EWS) for each gender. LDT-EWS was developed for a single set (n=3496) (Q1) and validated in 22 other discrete sets each of three months long (Q2, Q3…Q23) (total n=82,976; range of n=3428 to 4093) by testing its ability to discriminate in-hospital death using the area under the receiver-operating characteristic (AUROC) curve. RESULTS The data generated slightly different models for male and female patients. The ranges of AUROC values (95% CI) for LDT-EWS with in-hospital death as the outcome for the validation sets Q2-Q23 were: 0.755 (0.727-0.783) (Q16) to 0.801 (0.776-0.826) [all patients combined, n=82,976]; 0.744 (0.704-0.784, Q16) to 0.824 (0.792-0.856, Q2) [39,591 males]; and 0.742 (0.707-0.777, Q10) to 0.826 (0.796-0.856, Q12) [43,385 females]. CONCLUSIONS This study provides evidence that the results of commonly measured laboratory tests collected soon after hospital admission can be represented in a simple, paper-based EWS (LDT-EWS) to discriminate in-hospital mortality. We hypothesise that, with appropriate modification, it might be possible to extend the use of LDT-EWS throughout the patients hospital stay.

A Comparison of the Ability of the Physiologic Components of Medical Emergency Team Criteria and the U.k. National Early Warning Score to Discriminate Patients at Risk of a Range of Adverse Clinical Outcomes.

Objective:To compare the ability of medical emergency team criteria and the National Early Warning Score to discriminate cardiac arrest, unanticipated ICU admission and death within 24 hours of a vital signs measurement, and to quantify the associated workload. Design:Retrospective cohort study. Setting:A large U.K. National Health Service District General Hospital. Patients:Adults hospitalized from May 25, 2011, to December 31, 2013. Interventions:None. Measurements and Main Results:We applied the National Early Warning Score and 44 sets of medical emergency team criteria to a database of 2,245,778 vital signs sets (103,998 admissions). The National Early Warning Score’s performance was assessed using the area under the receiver-operating characteristic curve and compared with sensitivity/specificity for different medical emergency team criteria. Area under the receiver-operating characteristic curve (95% CI) for the National Early Warning Score for the combined outcome (i.e., death, cardiac arrest, or unanticipated ICU admission) was 0.88 (0.88–0.88). A National Early Warning Score value of 7 had sensitivity/specificity values of 44.5% and 97.4%, respectively. For the 44 sets of medical emergency team criteria studied, sensitivity ranged from 19.6% to 71.2% and specificity from 71.5% to 98.5%. For all outcomes, the position of the National Early Warning Score receiver-operating characteristic curve was above and to the left of all medical emergency team criteria points, indicating better discrimination. Similarly, the positions of all medical emergency team criteria points were above and to the left of the National Early Warning Score efficiency curve, indicating higher workloads (trigger rates). Conclusions:When medical emergency team systems are compared to a National Early Warning Score value of greater than or equal to 7, some medical emergency team systems have a higher sensitivity than National Early Warning Score values of greater than or equal to 7. However, all of these medical emergency team systems have a lower specificity and would generate greater workloads.

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.

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.

Automated calculation of 'early warning scores'

with any confidence. Barakat and Scott are to be congratulated on their extensive experience with low-dose clonidine ⁄ bupivacaine infusions; if they are able to publish this data as a randomised controlled study, it will contribute significantly to our knowledge on this topic. PROSPECT are due to review the literature on hip arthroplasty in the near future to update our hip arthroplasty review. This update will include all the relevant papers published since May 2004. Should the role of low-dose clonidine and local anaesthetic combination infusions show a clear benefit when used in primary hip arthroplasty, PROSPECT will be pleased to change its recommendations about epidural clonidine for this procedure. We agree that central neuraxial opioids have the potential to cause respiratory depression and other significant risks. On the other hand, there is an extensive literature database confirming the benefits of these agents. Such a database does not yet exist for clonidine. Within the published review there was insufficient space for a detailed discussion of all these points but the web-based, procedure-specific review does highlight some of the risks of central neuraxial opioids and explains why, in reaching the consensus recommendations, the working group took account of these risks when ranking epidural infusions lower than peripheral nerve blocks and intrathecal techniques. In the end consensus recommendations are exactly that – just recommendations – but based on the best available published data. They have been produced to inform and advise the reader, who is free to follow them or reject them or take a more in-depth look at the data on the website as they see fit.

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

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.