Adult Deterioration Detection System (ADDS): An evaluation of the impact on MET and Code blue activations in a regional healthcare service

How do nurses use early warning system vital signs observation charts in rural, remote and regional health care facilities: A scoping review

INTRODUCTION

Physiological signs of clinical deterioration are known to occur in the hours preceding a serious adverse event. As a result, track and trigger systems known as early warning systems (EWS) were introduced and routinely implemented as patient observation tools to trigger an alert in the presence of abnormal vital signs.

OBJECTIVE

The objective aimed to explore the literature pertaining to EWS and their utilisation in rural, remote and regional health care facilities.

DESIGN

The Arksey and O'Malley's methodological framework was used to guide the scoping review. Only studies reporting on rural, remote and regional health care settings were included. All four authors participated in the screening, data extraction and analysis process.

FINDINGS

Our search strategy yielded 3869 peer-reviewed articles published between 2012 and 2022, with six studies ultimately included. Collectively, the studies included in this scoping review examined the complex interaction between patient vital signs observation charts and recognition of patient deterioration.

DISCUSSION

Whilst rural, remote and regional clinicians use EWS to recognise and respond to clinical deterioration, noncompliance dilutes the tool's effectiveness. This overarching finding is informed by three contributing factors: documentation, communication and challenges specific to the rural context.

CONCLUSION

The success of EWS relies on accurate documentation and effective communication within the interdisciplinary team to support appropriate responses to clinical patient decline. More research is required to understand the nuances and complexities of rural and remote nursing and to address challenges associated with the use of EWS in rural health care settings.

Experiences of nurses practising mindfulness during self-leadership in delivering a rapid response system for general wards in a private hospital in Gauteng

Background

The increased complexity of the nursing care needs of patients and acuity in general wards present nursing care challenges for nurses. Self-led nurses are attentive, taking responsibility for activating the rapid response service when a patient is starting to deteriorate.

Objectives

The purpose of this article is to describe nurses’ experiences practising mindfulness during self-leadership in delivering a rapid response system (RRS) in a private hospital in Gauteng.

Method

A qualitative, exploratory, descriptive and contextual design was followed. Homogenous purposive sampling was used and a total of eight focus groups were conducted. Focus groups durations were between 45 min and 60 min. The data analysis was carried out through open coding on Atlas.ti using the noticing things, collecting things and thinking about things (NCT) approach. An independent coder met with the researcher during a consensus meeting and finalised the analysis.

Results

The findings indicated an underlying theme of nurses being mindful in their self-leadership through the development of self-motivation and self-direction in the RRS. Three categories with six subcategories emerged from the data analysis, namely self-motivation in an RRS by the team, self-direction through role-modelling to peers and training.

Conclusion

Nurses practised mindfulness during self-leadership, utilising behavioural and natural reward approaches and constructive thought patterns. These findings could assist management with developing training programmes for nurses.

Patient Deterioration on General Care Units: A Concept Analysis

Patient deterioration is a phenomenon that occurs from the inability to recognize it or respond to a change in condition. Despite the published reports on recognizing a deteriorating patient on general care floors, a gap remains in the ability of nurses to describe the concept, affecting patient outcomes. Walker and Avant's approach was applied to analyze patient deterioration. The aim of this article was to explore and clarify the meaning of patient deterioration and identify attributes, antecedents, and consequences. The defining attributes were compared to early warning scores. An operational definition was developed and its value to nurses established.

Predicting clinical deterioration with Q-ADDS compared to NEWS, Between the Flags, and eCART track and trigger tools

BACKGROUND

Early warning tools have been widely implemented without evidence to guide (a) recognition and (b) response team expertise optimisation. With growing databases from MET-calls and digital hospitals, we now have access to guiding information. The Queensland Adult-Deterioration-Detection-System (Q-ADDS) is widely used and requires validation.

AIM

Compare the accuracy of Q-ADDS to National Early Warning Score (NEWS), Between-the-Flags (BTF) and the electronic Cardiac Arrest Risk Triage Score (eCART)).

METHODS

Data from the Chicago University hospital database were used. Clinical deterioration was defined as unplanned admission to ICU or death. Currently used NEWS, BTF and eCART trigger thresholds were compared with a clinically endorsed Q-ADDS variant.

RESULTS

Of 224,912 admissions, 11,706 (5%) experienced clinical deterioration. Q-ADDS (AUC 0.71) and NEWS (AUC 0.72) had similar predictive accuracy, BTF (AUC 0.64) had the lowest, and eCART (AUC 0.76) the highest. Early warning alert (advising ward MO review) had similar NPV (99.2-99.3%), for all the four tools however sensitivity varied (%: Q-ADDS = 47/NEWS = 49/BTF = 66/eCART = 40), as did alerting rate (% vitals sets: Q-ADDS = 1.4/NEWS = 3.5/BTF = 4.1/eCART = 3.4). MET alert (advising MET/critical-care review) had similar NPV for all the four tools (99.1-99.2%), however sensitivity varied (%: Q-ADDS = 14/NEWS = 24/BTF = 19/eCART = 29), as did MET alerting rate (%: Q-ADDS = 1.4/NEWS = 3.5/BTF = 4.1/eCART = 3.4). High-severity alert (advising advanced ward review, Q-ADDS only): NPV = 99.1%, sensitivity = 26%, alerting rate = 3.5%.

CONCLUSION

The accuracy of Q-ADDS is comparable to NEWS, and higher than BTF, with eCART being the most accurate. Q-ADDS provides an additional high-severity ward alert, and generated significantly fewer MET alerts. Impacts of increased ward awareness and fewer MET alerts on actual MET call numbers and patient outcomes requires further evaluation.

A review of Code Blue activations in a single Regional Australian Healthcare Service: A retrospective descriptive study of RISKMAN data

BACKGROUND

In the case of life-threatening conditions such as respiratory or cardiac arrest, or the clinical deterioration of the patient, a Code Blue activation may be instigated. A Code Blue activation involves a team of advanced trained clinicians attending the emergency needs of the patient.

AIMS AND OBJECTIVES

The aim of the study was to explore the number of cases of Code Blue activations, looking at the timing, clinical ward, diagnosis and activation criteria while noting cases where escalation from a Medical Emergency Team (MET) call occurs in one Regional Healthcare Service in Victoria, Australia, over a six-year period.

METHODS

A quantitative retrospective descriptive study of Code Blue emergencies over a six-year period from June 2010 to June 2016 was conducted. Data collected from the RISKMAN program operating at a single site was imported into SPSS (V 22) for descriptive statistical analysis. A STROBE EQUATOR checklist was used for this study (see File S1).

FINDINGS

The majority of Code Blue activations were male (59%, n = 127) and aged between 70 and 89 years of age (43%, n = 93). A Code Blue activation was more likely to occur at 08:00 hr, 14:00 hr or 22:00 hr, corresponding to the nurses' change in shift, with the majority of Code Blues (27.8%, n = 60) occurring in the emergency department. Cardiac arrest was the main activation criterion with 54.6% (n = 118) cases followed by respiratory arrest (14%, n = 32). Interestingly, 20% (n = 45) of the Code Blue activations were upgraded from a Medical Emergency Team (MET) call.

CONCLUSION

This project has produced several interesting findings surrounding Code Blue activations at one regional healthcare service which are not present in existing literature and is worthwhile for further investigation.

RELEVANCE TO CLINICAL PRACTICE

Understanding Code Blue activation criteria, common timings (month, time of day) and patient demographics ensures clinicians can remain vigilant in watching for the signs of patient deterioration and improve staff preparedness Code Blue events.

The efficacy of twelve early warning systems for potential use in regional medical facilities in Queensland, Australia

AIM

Early warning system (EWS) validation studies are conducted predominantly in tertiary metropolitan facilities and are not necessarily applicable to regional hospitals. This study evaluates 12 EWSs for use in regional subcritical hospitals.

METHOD

This is a retrospective case-control study of patients who experienced severe adverse events (SAEs) in two regional private hospitals. Vital signs collected over 72 h preceding the SAE were applied to 12 EWSs representing three classes of EWSs. The EWS area under the receiver operator characteristic curve (AUROC), sensitivity, specificity, and number of alerts were calculated.

RESULTS

Data from 159 index and 172 control patients showed no significant differences in demographics, length of stay, and level of comorbidities. Only half of index patients achieved a medical emergency alert threshold score. On average, index patients triggered alerts 20.06 (22.67) hours preceding the SAE and alerted 2.25 (3.87) times over 72 h. The AUROC ranged from 0.628 to 0.747, with a single-parameter EWS having the lowest AUROC and an aggregated weighted EWS, the highest. The sensitivity of the EWS ranges from 0.359 to 0.692. The specificity was greater than 0.9 for all the EWSs tested.

CONCLUSIONS

Based on the EWS sensitivity and AUROC, there is a lack of conclusive evidence of the efficacy of the 12 EWSs tested. However, because the adoption of the EWS in Australian hospitals is mandatory, the implementation of an aggregated weighted EWS, such as Compass, should be considered in subcritical regional private hospitals. Given that only half of SAE achieved an EWS medical alert threshold score, it is important that good clinical judgement be used with EWS.