Guest Editorial - Bad NEWS: standing still is risky for patients admitted to hospital (even with normal vital signs).

Acute Physicians care for acutely unwell patients. Recognising and prioritising those at greatest risk of death is therefore at the heart of our specialty. The risk of catastrophic deterioration in the Acute Medical Unit is usually quantified through the measurement of vital signs. These are being summarised into the National Early Warning Score or similar instruments. Those with higher Early Warning Scores are usually prioritised by clinicians in and out of hospital and being seen before those with lower grades of abnormalities and preferably assessed by a more senior clinician.

Acute Medicine: How will we grow? - An analysis of organisational capabilities for quality improvement, research & education from SAMBA 2021.

BACKGROUND: Education, research, and Quality Improvement (QI) are key enablers for high quality care. We aimed to map the capability of Acute Medical Units (AMUs) to facilitate excellence in these areas. METHODS: AMUs were surveyed in an organisational questionnaire within the Society for Acute Medicine Benchmarking Audit 2021. RESULTS: 143 units participated. 80 units had a QI lead, 24 had a research lead and 99 had a medical education lead. 15 units had all three leadership roles. Most QI work considered service structure rather than changes in processes or care outcomes. CONCLUSION: The organisational capability of AMUs in the strategic areas considered is variable. Improving leadership and disseminating learning could help build a strategic foundation for acute medicine to grow.

Society for Acute Medicine's Patients: Learning from Experience Report (SAM-PLER) A service evaluation of patient reported experience in Acute Medicine - establishing the feasibility of a quality improvement collaborative.

Patient reported experience measures (PREMS) are a key part of measured quality. There is no tool currently used in the UK in Acute Medicine. On the 8th of September 2022 10 units based in England, Scotland and Wales collected data for the validated PREM, alongside the EQ-5D and variables from the Society for Acute Medicine's Benchmarking Audit (SAMBA) dataset. 365 patients were screened, 200 were included (55%): 159 patients from AMUs and 41 from SDEC units. Overall experience of patients was rated 8.5/10, patients rated their experience of safety, trust and listening highly. Collection of PREMS was feasible. Further research is required to link experience to clinical outcome and explore tools that capture experience of patients with altered mental status.

Co-design of interventions to improve acute care in hospital: A rapid review of the literature and application of the BASE methodology, a novel system for the design of patient centered service prototypes.

Co-design in acute care is challenged by the inability of unwell patients to participate in the process and the often transient nature of acute care. We undertook a rapid review of the literature on co-design, co-production and co-creation of solutions for acute care that were developed with patients. We found limited little evidence for co-design methods in acute care. We adapted a novel design driven method (BASE methodology) that creates stakeholder groups through epistemological criteria for the rapid development of interventions for acute care. We demonstrated feasibility of the methodology in two case studies: A mHealth application with checklists for patients undergoing treatment for cancer and a patient held record for self-clerking on admission to hospital.

Quality: What does it mean in Acute Medicine and how do we measure it?

Acute Medicine is a specialty that is not defined by a single organ system and sits at the interface between primary and secondary care. In order to document improvements in the quality of care delivered a system of metrics is required. A number of frameworks for measurements exist to quantify quality of care at the level of patients, teams and organisations, such as measures of population health, patient satisfaction and cost per patient. Measures can capture whether care is safe, effective, patient-centred, timely, efficient and equitable. Measurement in Acute Medicine is challenged by the often-transient nature of the contact between Acute Medicine clinicians and patients, the lack of diagnostic labels, a low degree of standardisation and difficulties in capturing the patient experience in the context. In a time of increasing ecological and financial constraints, reflecting about the most appropriate metrics to document the impact of Acute Medicine is required.

Long-term trends in critical care admissions in Wales*.

As national populations age, demands on critical care services are expected to increase. In many healthcare settings, longitudinal trends indicate rising numbers and proportions of patients admitted to ICU who are older; elsewhere, including some parts of the UK, a decrease has raised concerns with regard to rationing according to age. Our aim was to investigate admission trends in Wales, where critical care capacity has not risen in the last decade. We used the Secure Anonymised Information Linkage Databank to identify and characterise critical care admissions in patients aged ≥ 18 years from 1 January 2008 to 31 December 2017. We categorised 85,629 ICU admissions as youngest (18-64 years), older (65-79 years) and oldest (≥ 80 years). The oldest group accounted for 15% of admissions, the older age group 39% and the youngest group 46%. Relative to the national population, the incidence of admission rates per 10,000 population in the oldest group decreased significantly over the study period from 91.5/10,000 in 2008 to 77.5/10,000 (a relative decrease of 15%), and among the older group from 89.2/10,000 in 2008 to 75.3/10,000 in 2017 (a relative decrease of 16%). We observed significant decreases in admissions with high comorbidity (modified Charlson comorbidity index); increases in the proportion of older patients admitted who were considered 'fit' rather than frail (electronic frailty index); and decreases in admissions with a medical diagnosis. In contrast to other healthcare settings, capacity constraints and surgical imperatives appear to have contributed to a relative exclusion of older patients presenting with acute medical illness.

Who is allowed to read and write?

What makes us human? In 2015 Jeremy Vine asked this question to a selection of leading British thinkers and writers. The answers were as diverse as the people he interviewed. While you might have your own views about the complexity of being human I would suggest that being able to articulate thoughts and communicate them to others might be one of the characteristics that distinguishes us from other life forms. And if we think more about the achievements of human culture then being able to communicate thoughts in writing and reading other.

Testing the effects of checklists on team behaviour during emergencies on general wards: An observational study using high-fidelity simulation.

INTRODUCTION: Clinical teams struggle on general wards with acute management of deteriorating patients. We hypothesized that the Crisis Checklist App, a mobile application containing checklists tailored to crisis-management, can improve teamwork and acute care management. METHODS: A before-and-after study was undertaken in high-fidelity simulation centres in the Netherlands, Denmark and United Kingdom. Clinical teams completed three scenarios with a deteriorating patient without checklists followed by three scenarios using the Crisis Checklist App. Teamwork performance as the primary outcome was assessed by the Mayo High Performance Teamwork scale. The secondary outcomes were the time required to complete all predefined safety-critical steps, percentage of omitted safety-critical steps, effects on other non-technical skills, and users' self-assessments. Linear mixed models and a non-parametric survival test were conducted to assess these outcomes. RESULTS: 32 teams completed 188 scenarios. The Mayo High Performance Teamwork scale mean scores improved to 23.4 out of 32 (95% CI: 22.4-24.3) with the Crisis Checklist App compared to 21.4 (20.4-22.3) with local standard of care. The mean difference was 1.97 (1.34-2.6; p < 0.001). Teams that used the checklists were able to complete all safety-critical steps of a scenario in more simulations (40/95 vs 21/93 scenarios) and these steps were completed faster (stratified log-rank test χ2 = 8.0; p = 0.005). The self-assessments of the observers and users showed favourable effects after checklist usage for other non-technical skills including situational awareness, decision making, task management and communication. CONCLUSIONS: Implementation of a novel mobile crisis checklist application among clinical teams was associated in a simulated general ward setting with improved teamwork performance, and a higher and faster completion rate of predetermined safety-critical steps.

Death after Discharge - Every heart beat counts (probably)!

The assumption would be that patients who are discharged from an emergency or acute medicine department have been thoroughly assessed and are good to return to the safety of their own home. An unplanned death after discharge from hospital is the worst-case scenario for patients, families and indeed clinicians. In order to prevent adverse events after patients leave hospital most units have a multi-layered system to capture risk that includes triage, recording of vital signs, basic blood tests, understanding of existing past medical history and assessment by a senior clinician to add experience and intuition. Discharge decisions depend on a balanced review of all these parameters and a discussion with patients about residual risks. Only after this will patients go home. Despite this a small percentage of patients pass away unexpectedly within days after leaving hospital.

Feasibility of measuring psychological resilience in hospitalized patients with acute illness: The Resilience After the Trauma of Acute Illness (RAFT) study.

Resilience is the 'ability to bounce back'. We want to investigate whether measurement of resilience during an acute hospital admission is feasible. We conducted a feasibility study. Resilience was measured using the Brief Resilience Scale. Results were contextualized by measuring chronic disease burden, anxiety, depression, coping strategies and personality traits. 56 or 103 patients approached took part in the study. A group of 12 patients undergoing pulmonary rehabilitation served as a control group. We found evidence of low resilience in 4/44 (9%) patients admitted as medical emergencies. Low resilience was statistically related to the Hospital Anxiety and Depression Scale and a number of coping strategies and personality traits. We found no relation between measures of resilience and previous admissions to hospital. The concept of resilience might be applicable to unscheduled admissions to hospital. Larger studies are required to establish whether low resilience is common and amenable to intervention. REC number 17/WA/0024.

Differences in identification of patients' deterioration may hamper the success of clinical escalation protocols.

BACKGROUND: Timely and consistent recognition of a 'clinical crisis', a life threatening condition that demands immediate intervention, is essential to reduce 'failure to rescue' rates in general wards. AIM: To determine how different clinical caregivers define a 'clinical crisis' and how they respond to it. DESIGN: An international survey. METHODS: Clinicians working on general wards, intensive care units or emergency departments in the Netherlands, the United Kingdom and Denmark were asked to review ten scenarios based on common real-life cases. Then they were asked to grade the urgency and severity of the scenario, their degree of concern, their estimate for the risk for death and indicate their preferred action for escalation. The primary outcome was the scenarios with a National Early Warning Score (NEWS) ≥7 considered to be a 'clinical crisis'. Secondary outcomes included how often a rapid response system (RRS) was activated, and if this was influenced by the participant's professional role or experience. The data from all participants in all three countries was pooled for analysis. RESULTS: A total of 150 clinicians participated in the survey. The highest percentage of clinicians that considered one of the three scenarios with a NEWS ≥7 as a 'clinical crisis' was 52%, while a RRS was activated by <50% of participants. Professional roles and job experience only had a minor influence on the recognition of a 'clinical crisis' and how it should be responded to. CONCLUSION: This international survey indicates that clinicians differ on what they consider to be a 'clinical crisis' and on how it should be managed. Even in cases with a markedly abnormal physiology (i.e. NEWS ≥7) many clinicians do not consider immediate activation of a RRS is required.

Reliability of frailty assessment in the critically ill: a multicentre prospective observational study.

Demand for critical care among older patients is increasing in many countries. Assessment of frailty may inform discussions and decision making, but acute illness and reliance on proxies for history-taking pose particular challenges in patients who are critically ill. Our aim was to investigate the inter-rater reliability of the Clinical Frailty Scale for assessing frailty in patients admitted to critical care. We conducted a prospective, multi-centre study comparing assessments of frailty by staff from medical, nursing and physiotherapy backgrounds. Each assessment was made independently by two assessors after review of clinical notes and interview with an individual who maintained close contact with the patient. Frailty was defined as a Clinical Frailty Scale rating > 4. We made 202 assessments in 101 patients (median (IQR [range]) age 69 (65-75 [60-80]) years, median (IQR [range]) Acute Physiology and Chronic Health Evaluation II score 19 (15-23 [7-33])). Fifty-two (51%) of the included patients were able to participate in the interview; 35 patients (35%) were considered frail. Linear weighted kappa was 0.74 (95%CI 0.67-0.80) indicating a good level of agreement between assessors. However, frailty rating differed by at least one category in 47 (47%) cases. Factors independently associated with higher frailty ratings were: female sex; higher Acute Physiology and Chronic Health Evaluation II score; higher category of pre-hospital dependence; and the assessor having a medical background. We identified a good level of agreement in frailty assessment using the Clinical Frailty Scale, supporting its use in clinical care, but identified factors independently associated with higher ratings which could indicate personal bias.

Using trends in electronic recordings of vital signs to identify patients stable for transfer from acute hospitals.

Patients who are stable might not be required to remain in hospital. We aimed to create objective criteria to indicate stability based on vital signs. An index based on NEWS (NBI) was compared to a Patient Stability Index (PSI) algorithm created by random forest analysis. Data from the VITAL II study was used to train the algorithm and data from the VITAL III study to validate it. Failure rate of the algorithms was set close to the rate of readmission to UK hospitals at 15%. After a training period of two days the NBI identified stability with acceptable failure rates only after a further 96 hours with a subsequent release of 2143 bed days compared to the PSI which identified stability after only 12 hours leading to potential earlier release of 2652 bed days. Vital sign-based algorithms might be able to predict safe transfer from hospital and inform management of flow.

Changes in vital signs post discharge as a potential target for intervention to avoid readmission.

Readmissions are treated as adverse events in many healthcare systems. Causes can be physiological deterioration or breakdown of social support systems. We investigated data from a European multi-centre study of readmissions for changes in vital signs between index admission and readmission. Data sets were graded according to the National Early Warning Score (NEWS). Of 487 patients in whom NEWS could be calculated on discharge and again on re-admission, 39.6% had worse vital signs with a NEWS score difference ≥ 2 points while only 7.6% had improved by ≤ 2 points. Changes in individual vital signs of 20% or more were most common in respiratory rate and heart rate. Monitoring of respiratory rate and pulse rate post-discharge might predict some deteriorations.

Continuous Monitoring of Respiratory Rate on General Wards What might the implications be for Clinical Practice?

A high respiratory rate is a significant predictor of deterioration. The accuracy of measurements has been questioned. We performed a prospective observational study of automated electronic respiratory rate measurements and compared measurements with electronic counts obtained in the 10 minutes prior to the manual measurement. For 182 patients 1331 matching measurements could be compared. The mean age of these patients was 68 (SD 14) years. 96 (53%) of patients were female. While mean and median measurements were similar frequency distributions were significantly different. Manual measurements were markedly lower than electronic measurements in patients with higher respiratory rates. While electronic measurements are likely to be more reliable clinical implications require further investigation to clarify whether existing algorithms including Early Warning Scores will need adjustment.

Anticipating care needs of patients after discharge from hospital: Frail and elderly patients without physiological abnormality on day of admission are more likely to require social services input.

INTRODUCTION: Acute admissions to hospital are rising. As a part of a service evaluation we examined pathways of patients following hospital discharge depending on data available on admission to hospital. METHODS: We merged data available on admission to the Wrexham Maelor hospital from an existing data-base in the Acute Medical Unit with follow up data from local social services as part of a data sharing agreement. Patients requiring support by social services post-discharge were matched with patients not requiring social services from the same post-code. RESULTS: Stepwise logistic regression analysis identified candidate variables predicting likely support need. Decision tree analysis identified sub-groups of patients with higher likelihood to require support by social services after discharge from hospital. We found patients with normal physiology on admission as evidenced by a value of zero for the National Early Warning Score who were frail or older than 85years were most likely to require support after discharge. CONCLUSIONS: Information available on admission to hospital might inform long term care needs. Prospective testing is needed. The algorithms are prone to be dependent on availability of local services but our methodology is expected to be transferable to other organizations.

Impact of consultant specialty on discharge decisions in patients admitted as medical emergencies to hospitals in the United Kingdom.

BACKGROUND: The Society for Acute Medicine's Benchmarking Audit (SAMBA) annually examines Clinical Quality Indicators (CQIs) of the care of patients admitted to UK hospitals as medical emergencies. AIM: The aim of this study is to review the impact of consultant specialty on discharge decisions in the SAMBA data-set. DESIGN AND METHODS: Prospective audit of patients admitted to acute medical units (AMUs) on 25 June 2015 to participating hospitals throughout the UK with subgroup analysis. RESULTS: Eighty-three units submitted patient data from 3138 patients.Nearly 1845 (58%, IQR for units 50-69%) of patients were referrals from Emergency Medicine, 1072 (32%, IQR for units 24-44%) were referrals from Primary Care. The mean age was 65 (SD 20). One hundred and forty-one (4.5%) patients were admitted from care homes and 951 (30%) of patients were at least 'mildly frail' and 407 (13%) had signs of physiological instability. The median and the mean time to being seen by a doctor were 1 h 20 min and 2 h 3 min, respectively. The median and the mean time to being seen by senior specialist were 3 h 55 min and 5 h 56 min, respectively. By 72 h, 29 (1%) patients had died in the AMU, 73 were admitted to critical care units, 1297 (41%) had been discharged to their own home and 60 to nursing or residential homes. For every 100 patients seen specialists in acute medicine discharged 12 more patients than specialists from other disciplines of medicine ( P < 0.001). The difference remained significant after adjustment for case mix. CONCLUSION: Specialist in acute care might facilitate discharge in a higher proportion of patients.