Major trauma presentations and patient outcomes in English hospitals during the COVID-19 pandemic: An observational cohort study.

BACKGROUND: Single-centre studies suggest that successive Coronavirus Disease 2019 (COVID-19)-related "lockdown" restrictions in England may have led to significant changes in the characteristics of major trauma patients. There is also evidence from other countries that diversion of intensive care capacity and other healthcare resources to treating patients with COVID-19 may have impacted on outcomes for major trauma patients. We aimed to assess the impact of the COVID-19 pandemic on the number, characteristics, care pathways, and outcomes of major trauma patients presenting to hospitals in England. METHODS AND FINDINGS: We completed an observational cohort study and interrupted time series analysis including all patients eligible for inclusion in England in the national clinical audit for major trauma presenting between 1 January 2017 and 31 of August 2021 (354,202 patients). Demographic characteristics (age, sex, physiology, and injury severity) and clinical pathways of major trauma patients in the first lockdown (17,510 patients) and second lockdown (38,262 patients) were compared to pre-COVID-19 periods in 2018 to 2019 (comparator period 1: 22,243 patients; comparator period 2: 18,099 patients). Discontinuities in trends for weekly estimated excess survival rate were estimated when lockdown measures were introduced using segmented linear regression. The first lockdown had a larger associated reduction in numbers of major trauma patients (-4,733 (21%)) compared to the pre-COVID period than the second lockdown (-2,754 (6.7%)). The largest reductions observed were in numbers of people injured in road traffic collisions excepting cyclists where numbers increased. During the second lockdown, there were increases in the numbers of people injured aged 65 and over (665 (3%)) and 85 and over (828 (9.3%)). In the second week of March 2020, there was a reduction in level of major trauma excess survival rate (-1.71%; 95% CI: -2.76% to -0.66%) associated with the first lockdown. This was followed by a weekly trend of improving survival until the lifting of restrictions in July 2020 (0.25; 95% CI: 0.14 to 0.35). Limitations include eligibility criteria for inclusion to the audit and COVID status of patients not being recorded. CONCLUSIONS: This national evaluation of the impact of COVID on major trauma presentations to English hospitals has observed important public health findings: The large reduction in overall numbers injured has been primarily driven by reductions in road traffic collisions, while numbers of older people injured at home increased over the second lockdown. Future research is needed to better understand the initial reduction in likelihood of survival after major trauma observed with the implementation of the first lockdown.

External validation of triage tools for adults with suspected COVID-19 in a middle-income setting: an observational cohort study.

BACKGROUND: Tools proposed to triage ED acuity in suspected COVID-19 were derived and validated in higher income settings during early waves of the pandemic. We estimated the accuracy of seven risk-stratification tools recommended to predict severe illness in the Western Cape, South Africa. METHODS: An observational cohort study using routinely collected data from EDs across the Western Cape, from 27 August 2020 to 11 March 2022, was conducted to assess the performance of the PRIEST (Pandemic Respiratory Infection Emergency System Triage) tool, NEWS2 (National Early Warning Score, version 2), TEWS (Triage Early Warning Score), the WHO algorithm, CRB-65, Quick COVID-19 Severity Index and PMEWS (Pandemic Medical Early Warning Score) in suspected COVID-19. The primary outcome was intubation or non-invasive ventilation, death or intensive care unit admission at 30 days. RESULTS: Of the 446 084 patients, 15 397 (3.45%, 95% CI 34% to 35.1%) experienced the primary outcome. Clinical decision-making for inpatient admission achieved a sensitivity of 0.77 (95% CI 0.76 to 0.78), specificity of 0.88 (95% CI 0.87 to 0.88) and the negative predictive value (NPV) of 0.99 (95% CI 0.99 to 0.99). NEWS2, PMEWS and PRIEST scores achieved good estimated discrimination (C-statistic 0.79 to 0.82) and identified patients at risk of adverse outcomes at recommended cut-offs with moderate sensitivity (>0.8) and specificity ranging from 0.41 to 0.64. Use of the tools at recommended thresholds would have more than doubled admissions, with only a 0.01% reduction in false negative triage. CONCLUSION: No risk score outperformed existing clinical decision-making in determining the need for inpatient admission based on prediction of the primary outcome in this setting. Use of the PRIEST score at a threshold of one point higher than the previously recommended best approximated existing clinical accuracy.

What influences decisions to transfer older care-home residents to the emergency department? A synthesis of qualitative reviews.

BACKGROUND: care home residents aged over 65 have disproportionate rates of emergency department (ED) attendance and hospitalisation. Around 40% attendances may be avoidable, and hospitalisation is associated with harms. We synthesised the evidence available in qualitative systematic reviews of different stakeholders' experiences of decisions to transfer residents to the ED. METHODS: six electronic databases, references and citations of included reviews and relevant policy documents were searched. Reviews of qualitative studies exploring factors that influenced care home staff, medical practitioners, residents' family or residents' experiences and factors influencing decisions to transfer residents to the ED were included. Thematic analysis was used to synthesise findings. RESULTS: six previous reviews were included, which synthesised the findings of 34 primary studies encompassing 152 care home residents, 283 resident family members or carers and 447 care home staff. Of the primary studies, 19 were conducted in the North America, seven in Australia, five were conducted in Scandinavia, two in the United Kingdom and one in Holland. Three themes were identified: (i) power dynamics between residents, family members, care home staff and health care professionals (external to the care home) influence decisions; (ii) admission can be necessary; however, (iii) some decisions may be driven by factors other than clinical need. CONCLUSION: transfer decisions are complex and are determined not just by changes in health status interventions aimed at reducing avoidable transfers need to address the key role family members have in transfer decisions, the medical legal fears of care home staff and barriers to accessing community services.

Predicting need for hospital admission in patients with traumatic brain injury or skull fractures identified on CT imaging: a machine learning approach.

BACKGROUND: Patients with mild traumatic brain injury on CT scan are routinely admitted for inpatient observation. Only a small proportion of patients require clinical intervention. We recently developed a decision rule using traditional statistical techniques that found neurologically intact patients with isolated simple skull fractures or single bleeds <5 mm with no preinjury antiplatelet or anticoagulant use may be safely discharged from the emergency department. The decision rule achieved a sensitivity of 99.5% (95% CI 98.1% to 99.9%) and specificity of 7.4% (95% CI 6.0% to 9.1%) to clinical deterioration. We aimed to transparently report a machine learning approach to assess if predictive accuracy could be improved. METHODS: We used data from the same retrospective cohort of 1699 initial Glasgow Coma Scale (GCS) 13-15 patients with injuries identified by CT who presented to three English Major Trauma Centres between 2010 and 2017 as in our original study. We assessed the ability of machine learning to predict the same composite outcome measure of deterioration (indicating need for hospital admission). Predictive models were built using gradient boosted decision trees which consisted of an ensemble of decision trees to optimise model performance. RESULTS: The final algorithm reported a mean positive predictive value of 29%, mean negative predictive value of 94%, mean area under the curve (C-statistic) of 0.75, mean sensitivity of 99% and mean specificity of 7%. As with logistic regression, GCS, severity and number of brain injuries were found to be important predictors of deterioration. CONCLUSION: We found no clear advantages over the traditional prediction methods, although the models were, effectively, developed using a smaller data set, due to the need to divide it into training, calibration and validation sets. Future research should focus on developing models that provide clear advantages over existing classical techniques in predicting outcomes in this population.

Performance of the Hull Salford Cambridge Decision Rule (HSC DR) for early discharge of patients with findings on CT scan of the brain: a CENTER-TBI validation study.

BACKGROUND: There is international variation in hospital admission practices for patients with mild traumatic brain injury (TBI) and injuries on CT scan. Only a small proportion of patients require neurosurgical intervention, while many guidelines recommend routine admission of all patients. We aim to validate the Hull Salford Cambridge Decision Rule (HSC DR) and the Brain Injury Guidelines (BIG) criteria to select low-risk patients for discharge from the emergency department. METHOD: A cohort from 18 countries of Glasgow Coma Scale 13-15 patients with injuries on CT imaging was identified from the multicentre Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) Study (conducted from 2014 to 2017) for secondary analysis. A composite outcome measure encompassing need for ongoing hospital admission was used, including seizure activity, death, intubation, neurosurgical intervention and neurological deterioration. We assessed the performance of our previously derived prognostic model, the HSC DR and the BIG criteria at predicting deterioration in this validation cohort. RESULTS: Among 1047 patients meeting the inclusion criteria, 267 (26%) deteriorated. Our prognostic model achieved a C-statistic of 0.81 (95% CI: 0.78 to 0.84). The HSC DR achieved a sensitivity of 100% (95% CI: 97% to 100%) and specificity of only 4.7% (95% CI: 3.3% to 6.5%) for deterioration. Using the BIG criteria for discharge from the ED achieved a higher specificity (13.3%, 95% CI: 10.9% to 16.1%) and lower sensitivity (94.6%, 95% CI: 90.5% to 97%), with 12/105 patients recommended for discharge subsequently deteriorating, compared with 0/34 with the HSC DR. CONCLUSION: Our decision rule would have allowed 3.5% of patients to be discharged, none of whom would have deteriorated. Use of the BIG criteria may select patients for discharge who have too high a risk of subsequent deterioration to be used clinically. Further validation and implementation studies are required to support use in clinical practice.

Prognostic accuracy of triage tools for adults with suspected COVID-19 in a prehospital setting: an observational cohort study.

BACKGROUND: Tools proposed to triage patient acuity in COVID-19 infection have only been validated in hospital populations. We estimated the accuracy of five risk-stratification tools recommended to predict severe illness and compared accuracy to existing clinical decision making in a prehospital setting. METHODS: An observational cohort study using linked ambulance service data for patients attended by Emergency Medical Service (EMS) crews in the Yorkshire and Humber region of England between 26 March 2020 and 25 June 2020 was conducted to assess performance of the Pandemic Respiratory Infection Emergency System Triage (PRIEST) tool, National Early Warning Score (NEWS2), WHO algorithm, CRB-65 and Pandemic Medical Early Warning Score (PMEWS) in patients with suspected COVID-19 infection. The primary outcome was death or need for organ support. RESULTS: Of the 7549 patients in our cohort, 17.6% (95% CI 16.8% to 18.5%) experienced the primary outcome. The NEWS2 (National Early Warning Score, version 2), PMEWS, PRIEST tool and WHO algorithm identified patients at risk of adverse outcomes with a high sensitivity (>0.95) and specificity ranging from 0.3 (NEWS2) to 0.41 (PRIEST tool). The high sensitivity of NEWS2 and PMEWS was achieved by using lower thresholds than previously recommended. On index assessment, 65% of patients were transported to hospital and EMS decision to transfer patients achieved a sensitivity of 0.84 (95% CI 0.83 to 0.85) and specificity of 0.39 (95% CI 0.39 to 0.40). CONCLUSION: Use of NEWS2, PMEWS, PRIEST tool and WHO algorithm could improve sensitivity of EMS triage of patients with suspected COVID-19 infection. Use of the PRIEST tool would improve sensitivity of triage without increasing the number of patients conveyed to hospital.

The burden of traumatic brain injury from low-energy falls among patients from 18 countries in the CENTER-TBI Registry: A comparative cohort study.

BACKGROUND: Traumatic brain injury (TBI) is an important global public health burden, where those injured by high-energy transfer (e.g., road traffic collisions) are assumed to have more severe injury and are prioritised by emergency medical service trauma triage tools. However recent studies suggest an increasing TBI disease burden in older people injured through low-energy falls. We aimed to assess the prevalence of low-energy falls among patients presenting to hospital with TBI, and to compare their characteristics, care pathways, and outcomes to TBI caused by high-energy trauma. METHODS AND FINDINGS: We conducted a comparative cohort study utilising the CENTER-TBI (Collaborative European NeuroTrauma Effectiveness Research in TBI) Registry, which recorded patient demographics, injury, care pathway, and acute care outcome data in 56 acute trauma receiving hospitals across 18 countries (17 countries in Europe and Israel). Patients presenting with TBI and indications for computed tomography (CT) brain scan between 2014 to 2018 were purposively sampled. The main study outcomes were (i) the prevalence of low-energy falls causing TBI within the overall cohort and (ii) comparisons of TBI patients injured by low-energy falls to TBI patients injured by high-energy transfer-in terms of demographic and injury characteristics, care pathways, and hospital mortality. In total, 22,782 eligible patients were enrolled, and study outcomes were analysed for 21,681 TBI patients with known injury mechanism; 40% (95% CI 39% to 41%) (8,622/21,681) of patients with TBI were injured by low-energy falls. Compared to 13,059 patients injured by high-energy transfer (HE cohort), the those injured through low-energy falls (LE cohort) were older (LE cohort, median 74 [IQR 56 to 84] years, versus HE cohort, median 42 [IQR 25 to 60] years; p < 0.001), more often female (LE cohort, 50% [95% CI 48% to 51%], versus HE cohort, 32% [95% CI 31% to 34%]; p < 0.001), more frequently taking pre-injury anticoagulants or/and platelet aggregation inhibitors (LE cohort, 44% [95% CI 42% to 45%], versus HE cohort, 13% [95% CI 11% to 14%]; p < 0.001), and less often presenting with moderately or severely impaired conscious level (LE cohort, 7.8% [95% CI 5.6% to 9.8%], versus HE cohort, 10% [95% CI 8.7% to 12%]; p < 0.001), but had similar in-hospital mortality (LE cohort, 6.3% [95% CI 4.2% to 8.3%], versus HE cohort, 7.0% [95% CI 5.3% to 8.6%]; p = 0.83). The CT brain scan traumatic abnormality rate was 3% lower in the LE cohort (LE cohort, 29% [95% CI 27% to 31%], versus HE cohort, 32% [95% CI 31% to 34%]; p < 0.001); individuals in the LE cohort were 50% less likely to receive critical care (LE cohort, 12% [95% CI 9.5% to 13%], versus HE cohort, 24% [95% CI 23% to 26%]; p < 0.001) or emergency interventions (LE cohort, 7.5% [95% CI 5.4% to 9.5%], versus HE cohort, 13% [95% CI 12% to 15%]; p < 0.001) than patients injured by high-energy transfer. The purposive sampling strategy and censorship of patient outcomes beyond hospital discharge are the main study limitations. CONCLUSIONS: We observed that patients sustaining TBI from low-energy falls are an important component of the TBI disease burden and a distinct demographic cohort; further, our findings suggest that energy transfer may not predict intracranial injury or acute care mortality in patients with TBI presenting to hospital. This suggests that factors beyond energy transfer level may be more relevant to prehospital and emergency department TBI triage in older people. A specific focus to improve prevention and care for patients sustaining TBI from low-energy falls is required.

Prognostic accuracy of emergency department triage tools for adults with suspected COVID-19: the PRIEST observational cohort study.

BACKGROUND: The WHO and National Institute for Health and Care Excellence recommend various triage tools to assist decision-making for patients with suspected COVID-19. We aimed to compare the accuracy of triage tools for predicting severe illness in adults presenting to the ED with suspected COVID-19. METHODS: We undertook a mixed prospective and retrospective observational cohort study in 70 EDs across the UK. We collected data from people attending with suspected COVID-19 and used presenting data to determine the results of assessment with the WHO algorithm, National Early Warning Score version 2 (NEWS2), CURB-65, CRB-65, Pandemic Modified Early Warning Score (PMEWS) and the swine flu adult hospital pathway (SFAHP). We used 30-day outcome data (death or receipt of respiratory, cardiovascular or renal support) to determine prognostic accuracy for adverse outcome. RESULTS: We analysed data from 20 891 adults, of whom 4611 (22.1%) died or received organ support (primary outcome), with 2058 (9.9%) receiving organ support and 2553 (12.2%) dying without organ support (secondary outcomes). C-statistics for the primary outcome were: CURB-65 0.75; CRB-65 0.70; PMEWS 0.77; NEWS2 (score) 0.77; NEWS2 (rule) 0.69; SFAHP (6-point rule) 0.70; SFAHP (7-point rule) 0.68; WHO algorithm 0.61. All triage tools showed worse prediction for receipt of organ support and better prediction for death without organ support. At the recommended threshold, PMEWS and the WHO criteria showed good sensitivity (0.97 and 0.95, respectively) at the expense of specificity (0.30 and 0.27, respectively). The NEWS2 score showed similar sensitivity (0.96) and specificity (0.28) when a lower threshold than recommended was used. CONCLUSION: CURB-65, PMEWS and the NEWS2 score provide good but not excellent prediction for adverse outcome in suspected COVID-19, and predicted death without organ support better than receipt of organ support. PMEWS, the WHO criteria and NEWS2 (using a lower threshold than usually recommended) provide good sensitivity at the expense of specificity. TRIAL REGISTRATION NUMBER: ISRCTN56149622.

Post-exertion oxygen saturation as a prognostic factor for adverse outcome in patients attending the emergency department with suspected COVID-19: a substudy of the PRIEST observational cohort study.

BACKGROUND: Measurement of post-exertion oxygen saturation has been proposed to assess illness severity in suspected COVID-19 infection. We aimed to determine the accuracy of post-exertional oxygen saturation for predicting adverse outcome in suspected COVID-19. METHODS: We undertook a substudy of an observational cohort study across 70 emergency departments during the first wave of the COVID-19 pandemic in the UK. We collected data prospectively, using a standardised assessment form, and retrospectively, using hospital records, from patients with suspected COVID-19, and reviewed hospital records at 30 days for adverse outcome (death or receiving organ support). Patients with post-exertion oxygen saturation recorded were selected for this analysis. We constructed receiver-operating characteristic curves, calculated diagnostic parameters, and developed a multivariable model for predicting adverse outcome. RESULTS: We analysed data from 817 patients with post-exertion oxygen saturation recorded after excluding 54 in whom measurement appeared unfeasible. The c-statistic for post-exertion change in oxygen saturation was 0.589 (95% CI 0.465 to 0.713), and the positive and negative likelihood ratios of a 3% or more desaturation were, respectively, 1.78 (1.25 to 2.53) and 0.67 (0.46 to 0.98). Multivariable analysis showed that post-exertion oxygen saturation was not a significant predictor of adverse outcome when baseline clinical assessment was taken into account (p=0.368). Secondary analysis excluding patients in whom post-exertion measurement appeared inappropriate resulted in a c-statistic of 0.699 (0.581 to 0.817), likelihood ratios of 1.98 (1.26 to 3.10) and 0.61 (0.35 to 1.07), and some evidence of additional prognostic value on multivariable analysis (p=0.019). CONCLUSIONS: Post-exertion oxygen saturation provides modest prognostic information in the assessment of selected patients attending the emergency department with suspected COVID-19. TRIAL REGISTRATION NUMBER: ISRCTN Registry (ISRCTN56149622) http://www.isrctn.com/ISRCTN28342533.

Electrocardiographic characteristics in patients with heart failure and normal ejection fraction: A systematic review and meta-analysis.

BACKGROUND: Little is known about ECG abnormalities in patients with heart failure and normal ejection fraction (HeFNEF) and how they relate to different etiologies or outcomes. METHODS AND RESULTS: We searched the literature for peer-reviewed studies describing ECG abnormalities in HeFNEF other than heart rhythm alone. Thirty five studies were identified and 32,006 participants. ECG abnormalities reported in patients with HeFNEF include atrial fibrillation (prevalence 12%-46%), long PR interval (11%-20%), left ventricular hypertrophy (LVH, 10%-30%), pathological Q waves (11%-18%), RBBB (6%-16%), LBBB (0%-8%), and long JTc (3%-4%). Atrial fibrillation is more common in patients with HeFNEF compared to those with heart failure and reduced ejection fraction (HeFREF). In contrast, long PR interval, LVH, Q waves, LBBB, and long JTc are more common in patients with HeFREF. A pooled effect estimate analysis showed that QRS duration ≥120 ms, although uncommon (13%-19%), is associated with worse outcomes in patients with HeFNEF. CONCLUSIONS: There is high variability in the prevalence of ECG abnormalities in patients with HeFNEF. Atrial fibrillation is more common in patients with HeFNEF compared to those with HeFREF. QRS duration ≥120 ms is associated with worse outcomes in patients with HeFNEF. Further studies are needed to address whether ECG abnormalities correlate with different phenotypes in HeFNEF.

Postmortem ICD interrogation in mode of death classification.

BACKGROUND: The definition of sudden death due to arrhythmia relies on the time interval between onset of symptoms and death. However, not all sudden deaths are due to arrhythmia. In patients with an implantable cardioverter defibrillator (ICD), postmortem device interrogation may help better distinguish the mode of death compared to a time-based definition alone. OBJECTIVE: This study aims to assess the proportion of "sudden" cardiac deaths in patients with an ICD that have confirmed arrhythmia. METHODS: We conducted a literature search for studies using postmortem ICD interrogation and a time-based classification of the mode of death. A modified QUADAS-2 checklist was used to assess risk of bias in individual studies. Outcome data were pooled where sufficient data were available. RESULTS: Our search identified 22 studies undertaken between 1982 and 2015 with 23,600 participants. The pooled results (excluding studies with high risk of bias) suggest that ventricular arrhythmias are present at the time of death in 76% of "sudden" deaths (95% confidence interval [CI] 67-85; range 42-88). CONCLUSION: Postmortem ICD interrogation identifies 24% of "sudden" deaths to be nonarrhythmic. Postmortem device interrogation should be considered in all cases of unexplained sudden cardiac death.

CT head imaging in patients with head injury who present after 24†h of injury: a retrospective cohort study.

BACKGROUND: National Institute for Health and Care Excellence guidelines used to triage patients with head injury to CT imaging are based on research conducted in populations presenting within 24 h of injury.We aim to compare guideline use, and outcomes, in patients with head injury that undergo CT imaging presenting within, and after 24 h of injury. METHODS: ED trauma CT head scan requests over a period of 6 months were matched to ED records. Case note review of adult patients with head injury that had undergone CT imaging was completed. Logistic regression was used to assess whether presentation after 24 h affected the guideline's ability to predict significant injuries. RESULTS: 650 case records were available for analysis. 8.6% (56/650) showed a traumatic abnormality, 1.5% (10/650) required neurosurgery or died. 15.5% (101/650) of CT scans were for patients presenting after 24 h. 8.4% (46/549) of those presenting within, and 9.9% (10/101) of those presenting after 24 h had traumatic CT abnormalities.The sensitivity of the guidelines for intracranial injuries was 98% (95% CI 87.0% to 99.9%) in those presenting within 24 h and 70% (95% CI 35.4% to 91.9%) in those presenting after 24 h of injury. The presence of a guideline indication statistically predicted significant injury, and this was unaffected by time of presentation. CONCLUSIONS: Patients with head injury presenting after 24 h of injury are a clinically significant population. Existing guidelines appear to predict traumatic CT abnormalities irrespective of timing of presentation. However, their sole use in patients presenting after 24 h may result in significant injuries not being identified.

Prognostic accuracy of eight triage scores in suspected COVID-19 in an Emergency Department low-income setting: An observational cohort study.

Introduction: Previous studies deriving and validating triage scores for patients with suspected COVID-19 in Emergency Department settings have been conducted in high- or middle-income settings. We assessed eight triage scores' accuracy for death or organ support in patients with suspected COVID-19 in Sudan. Methods: We conducted an observational cohort study using Covid-19 registry data from eight emergency unit isolation centres in Khartoum State, Sudan. We assessed performance of eight triage scores including: PRIEST, LMIC-PRIEST, NEWS2, TEWS, the WHO algorithm, CRB-65, Quick COVID-19 Severity Index and PMEWS in suspected COVID-19. A composite primary outcome included death, ventilation or ICU admission. Results: In total 874 (33.84 %, 95 % CI:32.04 % to 35.69 %) of 2,583 patients died, required intubation/non-invasive ventilation or HDU/ICU admission . All risk-stratification scores assessed had worse estimated discrimination in this setting, compared to studies conducted in higher-income settings: C-statistic range for primary outcome: 0.56-0.64. At previously recommended thresholds NEWS2, PRIEST and LMIC-PRIEST had high estimated sensitivities (≥0.95) for the primary outcome. However, the high baseline risk meant that low-risk patients identified at these thresholds still had a between 8 % and 17 % risk of death, ventilation or ICU admission. Conclusion: None of the triage scores assessed demonstrated sufficient accuracy to be used clinically. This is likely due to differences in the health care system and population (23 % of patients died) compared to higher-income settings in which the scores were developed. Risk-stratification scores developed in this setting are needed to provide the necessary accuracy to aid triage of patients with suspected COVID-19.

Reducing unplanned hospital admissions from care homes: a systematic review.

Background: Care homes predominantly care for older people with complex health and care needs, who are at high risk of unplanned hospital admissions. While often necessary, such admissions can be distressing and provide an opportunity cost as well as a financial cost. Objectives: Our objective was to update a 2014 evidence review of interventions to reduce unplanned admissions of care home residents. We carried out a systematic review of interventions used in the UK and other high-income countries by synthesising evidence of effects of these interventions on hospital admissions; feasibility and acceptability; costs and value for money; and factors affecting applicability of international evidence to UK settings. Data sources: We searched the following databases in December 2021 for studies published since 2014: Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews; Cumulative Index to Nursing and Allied Health Literature; Health Management Information Consortium; Medline; PsycINFO; Science and Social Sciences Citation Indexes; Social Care Online; and Social Service Abstracts. 'Grey' literature (January 2022) and citations were searched and reference lists were checked. Methods: We included studies of any design reporting interventions delivered in care homes (with or without nursing) or hospitals to reduce unplanned hospital admissions. A taxonomy of interventions was developed from an initial scoping search. Outcomes of interest included measures of effect on unplanned admissions among care home residents; barriers/facilitators to implementation in a UK setting and acceptability to care home residents, their families and staff. Study selection, data extraction and risk of bias assessment were performed by two independent reviewers. We used published frameworks to extract data on intervention characteristics, implementation barriers/facilitators and applicability of international evidence. We performed a narrative synthesis grouped by intervention type and setting. Overall strength of evidence for admission reduction was assessed using a framework based on study design, study numbers and direction of effect. Results: We included 124 publications/reports (30 from the UK). Integrated care and quality improvement programmes providing additional support to care homes (e.g. the English Care Homes Vanguard initiatives and hospital-based services in Australia) appeared to reduce unplanned admissions relative to usual care. Simpler training and staff development initiatives showed mixed results, as did interventions aimed at tackling specific problems (e.g. medication review). Advance care planning was key to the success of most quality improvement programmes but do-not-hospitalise orders were problematic. Qualitative research identified tensions affecting decision-making involving paramedics, care home staff and residents/family carers. The best way to reduce end-of-life admissions through access to palliative care was unclear in the face of inconsistent and generally low-quality evidence. Conclusions: Effective implementation of interventions at various stages of residents' care pathways may reduce unplanned admissions. Most interventions are complex and require adaptation to local contexts. Work at the interface between health and social care is key to successful implementation. Limitations: Much of the evidence identified was of low quality because of factors such as uncontrolled study designs and small sample size. Meta-analysis was not possible. Future work: We identified a need for improved economic evidence and the evaluation of integrated care models of the type delivered by hospital-based teams. Researchers should carefully consider what is realistic in terms of study design and data collection given the current context of extreme pressure on care homes. Study registration: This study is registered as PROSPERO database CRD42021289418. Funding: This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (award number NIHR133884) and will be published in full in Health and Social Care Delivery Research; Vol. 11, No. 18. See the NIHR Journals Library website for further project information.

Older people living in care homes often have complex health problems such as dementia and frailty, and they may need to be taken to hospital at short notice. This can lead to them being admitted for further tests and treatment. We know that some of these unplanned hospital admissions might be avoided if health and social care services worked together to meet residents' needs. We looked for published research on methods (interventions) used in health and social care to reduce these admissions. Interventions could be carried out by care home staff, general practitioners, nurses, paramedics or other specialists individually or in teams. We asked which interventions have been evaluated, how strong is the evidence that they work and how acceptable they are to care home residents, family carers and staff. We also looked for information on how easy or difficult they are to implement and whether they represent good value for money. We included 124 research studies (30 from the UK). We found that integrated care programmes linking care homes with general practitioners and community services can be effective but need time and support (such as extra money and specialist staff) to implement them. Quality improvement programmes and training to improve staff skills may also reduce admissions. For care home residents nearing the end of life, advance care planning and palliative care can ensure that wishes are followed and avoid potentially burdensome admissions to hospital. Hospital-based teams providing 'outreach' services to care homes have been evaluated in Australia and could be suitable for UK research. We found limited evidence on interventions involving paramedics and on 'value for money'. We found many barriers to implementing new services in the UK, particularly staff shortages and high staff turnover, together with care homes closing down or changing ownership. Successful interventions have often been based on existing services and relationships.

LMIC-PRIEST: Derivation and validation of a clinical severity score for acutely ill adults with suspected COVID-19 in a middle-income setting.

BACKGROUND: Uneven vaccination and less resilient health care systems mean hospitals in LMICs are at risk of being overwhelmed during periods of increased COVID-19 infection. Risk-scores proposed for rapid triage of need for admission from the emergency department (ED) have been developed in higher-income settings during initial waves of the pandemic. METHODS: Routinely collected data for public hospitals in the Western Cape, South Africa from the 27th August 2020 to 11th March 2022 were used to derive a cohort of 446,084 ED patients with suspected COVID-19. The primary outcome was death or ICU admission at 30 days. The cohort was divided into derivation and Omicron variant validation sets. We developed the LMIC-PRIEST score based on the coefficients from multivariable analysis in the derivation cohort and existing triage practices. We externally validated accuracy in the Omicron period and a UK cohort. RESULTS: We analysed 305,564 derivation, 140,520 Omicron and 12,610 UK validation cases. Over 100 events per predictor parameter were modelled. Multivariable analyses identified eight predictor variables retained across models. We used these findings and clinical judgement to develop a score based on South African Triage Early Warning Scores and also included age, sex, oxygen saturation, inspired oxygen, diabetes and heart disease. The LMIC-PRIEST score achieved C-statistics: 0.82 (95% CI: 0.82 to 0.83) development cohort; 0.79 (95% CI: 0.78 to 0.80) Omicron cohort; and 0.79 (95% CI: 0.79 to 0.80) UK cohort. Differences in prevalence of outcomes led to imperfect calibration in external validation. However, use of the score at thresholds of three or less would allow identification of very low-risk patients (NPV ≥0.99) who could be rapidly discharged using information collected at initial assessment. CONCLUSION: The LMIC-PRIEST score shows good discrimination and high sensitivity at lower thresholds and can be used to rapidly identify low-risk patients in LMIC ED settings.

Training and testing of a gradient boosted machine learning model to predict adverse outcome in patients presenting to emergency departments with suspected covid-19 infection in a middle-income setting.

COVID-19 infection rates remain high in South Africa. Clinical prediction models may be helpful for rapid triage, and supporting clinical decision making, for patients with suspected COVID-19 infection. The Western Cape, South Africa, has integrated electronic health care data facilitating large-scale linked routine datasets. The aim of this study was to develop a machine learning model to predict adverse outcome in patients presenting with suspected COVID-19 suitable for use in a middle-income setting. A retrospective cohort study was conducted using linked, routine data, from patients presenting with suspected COVID-19 infection to public-sector emergency departments (EDs) in the Western Cape, South Africa between 27th August 2020 and 31st October 2021. The primary outcome was death or critical care admission at 30 days. An XGBoost machine learning model was trained and internally tested using split-sample validation. External validation was performed in 3 test cohorts: Western Cape patients presenting during the Omicron COVID-19 wave, a UK cohort during the ancestral COVID-19 wave, and a Sudanese cohort during ancestral and Eta waves. A total of 282,051 cases were included in a complete case training dataset. The prevalence of 30-day adverse outcome was 4.0%. The most important features for predicting adverse outcome were the requirement for supplemental oxygen, peripheral oxygen saturations, level of consciousness and age. Internal validation using split-sample test data revealed excellent discrimination (C-statistic 0.91, 95% CI 0.90 to 0.91) and calibration (CITL of 1.05). The model achieved C-statistics of 0.84 (95% CI 0.84 to 0.85), 0.72 (95% CI 0.71 to 0.73), and 0.62, (95% CI 0.59 to 0.65) in the Omicron, UK, and Sudanese test cohorts. Results were materially unchanged in sensitivity analyses examining missing data. An XGBoost machine learning model achieved good discrimination and calibration in prediction of adverse outcome in patients presenting with suspected COVID19 to Western Cape EDs. Performance was reduced in temporal and geographical external validation.

An Unusual cause of Endocarditis.

A man in his 20s attended the emergency department with three days of fever, headache, reduced appetite and a sore throat. COVID-19 point-of-care test was negative. Blood cultures grew a gram-negative coccobacillus, Neisseria elongata Following an episode of confusion, MRI head revealed septic emboli. Prolapse of the mitral valve with regurgitation was noted on echocardiography. Infection was found to have originated from multiple dental caries and treatment required a combination of dental extraction, prolonged antibiotic therapy and surgery for mitral valve repair.N. elongata is part of the normal oropharyngeal flora but is also a rare cause of endocarditis. There are no established treatment guidelines for endocarditis of this aetiology. N. elongata endocarditis may present atypically, with a murmur only developing several days later. 'Classical' stigmata should not be relied on to make a diagnosis. N. elongata predominantly affects the left side of the heart and predisposes to embolic events.

Factors associated with increased Emergency Department transfer in older long-term care residents: a systematic review.

The proportion of adults older than 65 years is rapidly increasing. Care home residents in this age group have disproportionate rates of transfer to the Emergency Department (ED) and around 40% of attendances might be avoidable. We did a systematic review to identify factors that predict ED transfer from care homes. Six electronic databases were searched. Observational studies that provided estimates of association between ED attendance and variables at a resident or care home level were included. 26 primary studies met the inclusion criteria. Seven common domains of factors assessed for association with ED transfer were identified and within these domains, male sex, age, presence of specific comorbidities, polypharmacy, rural location, and care home quality rating were associated with likelihood of ED transfer. The identification of these factors provides useful information for policy makers and researchers intending to either develop interventions to reduce hospitalisations or use adjusted rates of hospitalisation as a care home quality indicator.

LMIC-PRIEST: Derivation and validation of a clinical severity score for acutely ill adults with suspected COVID-19 in a middle-income setting.

Background: Uneven vaccination and less resilient health care systems mean hospitals in LMICs are at risk of being overwhelmed during periods of increased COVID-19 infection. Risk-scores proposed for rapid triage of need for admission from the emergency department (ED) have been developed in higher-income settings during initial waves of the pandemic. Methods: Routinely collected data for public hospitals in the Western Cape, South Africa from the 27 th August 2020 to 11 th March 2022 were used to derive a cohort of 446,084 ED patients with suspected COVID-19. The primary outcome was death or ICU admission at 30 days. The cohort was divided into derivation and Omicron variant validation sets. We developed the LMIC-PRIEST score based on the coefficients from multivariable analysis in the derivation cohort and existing triage practices. We externally validated accuracy in the Omicron period and a UK cohort. Results: We analysed 305,564, derivation 140,520 Omicron and 12,610 UK validation cases. Over 100 events per predictor parameter were modelled. Multivariable analyses identified eight predictor variables retained across models. We used these findings and clinical judgement to develop a score based on South African Triage Early Warning Scores and also included age, sex, oxygen saturation, inspired oxygen, diabetes and heart disease. The LMIC-PRIEST score achieved C-statistics: 0.82 (95% CI: 0.82 to 0.83) development cohort; 0.79 (95% CI: 0.78 to 0.80) Omicron cohort; and 0.79 (95% CI: 0.79 to 0.80) UK cohort. Differences in prevalence of outcomes led to imperfect calibration in external validation. However, use of the score at thresholds of three or less would allow identification of very low-risk patients (NPV ≥0.99) who could be rapidly discharged using information collected at initial assessment. Conclusion: The LMIC-PRIEST score shows good discrimination and high sensitivity at lower thresholds and can be used to rapidly identify low-risk patients in LMIC ED settings. What is already known on this subject: Uneven vaccination in low- and middle-income countries (LMICs) coupled with less resilient health care provision mean that emergency health care systems in LMICs may still be at risk of being overwhelmed during periods of increased COVID-19 infection.Risk-stratification scores may help rapidly triage need for hospitalisation. However, those proposed for use in the ED for patients with suspected COVID-19 have been developed and validated in high-income settings. What this study adds: The LMIC-PRIEST score has been robustly developed using a large routine dataset from the Western Cape, South Africa and is directly applicable to existing triage practices in LMICs.External validation across both income settings and COVID-19 variants showed good discrimination and high sensitivity (at lower thresholds) to a composite outcome indicating need for inpatient admission from the ED. How this study might affect research practice or policy: Use of the LMIC-PRIEST score at thresholds of three or less would allow identification of very low-risk patients (negative predictive value ≥0.99) across all settings assessedDuring periods of increased demand, this could allow the rapid identification and discharge of patients from the ED using information collected at initial assessment.