Risk factors for type 1 and type 2 myocardial infarction.

AIMS: Whilst the risk factors for type 1 myocardial infarction due to atherosclerotic plaque rupture and thrombosis are established, our understanding of the factors that predispose to type 2 myocardial infarction during acute illness is still emerging. Our aim was to evaluate and compare the risk factors for type 1 and type 2 myocardial infarction. METHODS AND RESULTS: We conducted a secondary analysis of a multi-centre randomized trial population of 48 282 consecutive patients attending hospital with suspected acute coronary syndrome. The diagnosis of myocardial infarction during the index presentation and all subsequent reattendances was adjudicated according to the Universal Definition of Myocardial Infarction. Cox regression was used to identify predictors of future type 1 and type 2 myocardial infarction during a 1-year follow-up period. Within 1 year, 1331 patients had a subsequent myocardial infarction, with 924 and 407 adjudicated as type 1 and type 2 myocardial infarction, respectively. Risk factors for type 1 and type 2 myocardial infarction were similar, with age, hyperlipidaemia, diabetes, abnormal renal function, and known coronary disease predictors for both (P < 0.05 for all). Whilst women accounted for a greater proportion of patients with type 2 as compared to type 1 myocardial infarction, after adjustment for other risk factors, sex was not a predictor of type 2 myocardial events [adjusted hazard ratio (aHR) 0.82, 95% confidence interval (CI) 0.66-1.01]. The strongest predictor of type 2 myocardial infarction was a prior history of type 2 events (aHR 6.18, 95% CI 4.70-8.12). CONCLUSIONS: Risk factors for coronary disease that are associated with type 1 myocardial infarction are also important predictors of type 2 events during acute illness. Treatment of these risk factors may reduce future risk of both type 1 and type 2 myocardial infarction.

Evaluation of video review tools for assessing non-technical skills in emergency department resuscitation teams: a systematic review.

BACKGROUND AND IMPORTANCE: Use of video review in medicine is established in contexts such as surgery. Although not widely used in the emergency department (ED), some centres use it to evaluate non-technical skills (NTS) to support teaching and quality improvement. OBJECTIVE: There is no consensus on assessment of NTS using video review in the ED and the purpose of this review was to identify tools used in this context. DESIGN, SETTING AND PARTICIPANTS: Studies were identified using Embase, Medline, CINAHL and Google Scholar. Inclusion criterion for the review was NTS of resuscitation teams working within the ED were assessed using video review. A systematic search method was used, and results were synthesised after search criteria was checked by two independent reviewers. Authors settled on the same 9 studies eligible for inclusion. OUTCOME MEASURES AND ANALYSIS: Reliability and validity of tools identified for use in this context. Due to the heterogeneity of studies, no meta-analysis occurred. MAIN RESULTS: There are 9 studies included in the review. The review was registered with PROSPERO (Ref No: CRD42022306129). Four unique tools were identified - 6 studies used T-NOTECHS, 1 used TTCA-24, 1 used CALM and 1 used the Communication tool. T-NOTECHS is validated in the literature for use in this context. CONCLUSION: T-NOTECHS is the tool of choice for assessing ED teams in this context.

Cardiac Troponin Thresholds and Kinetics to Differentiate Myocardial Injury and Myocardial Infarction.

BACKGROUND: Although the 99th percentile is the recommended diagnostic threshold for myocardial infarction, some guidelines also advocate the use of higher troponin thresholds to rule in myocardial infarction at presentation. It is unclear whether the magnitude or change in troponin concentration can differentiate causes of myocardial injury and infarction in practice. METHODS: In a secondary analysis of a multicenter randomized controlled trial, we identified 46 092 consecutive patients presenting with suspected acute coronary syndrome without ST-segment-elevation myocardial infarction. High-sensitivity cardiac troponin I concentrations at presentation and on serial testing were compared between patients with myocardial injury and infarction. The positive predictive value and specificity were determined at the sex-specific 99th percentile upper reference limit and rule-in thresholds of 64 ng/L and 5-fold of the upper reference limit for a diagnosis of type 1 myocardial infarction. RESULTS: Troponin was above the 99th percentile in 8188 patients (18%). The diagnosis was type 1 or type 2 myocardial infarction in 50% and 14% and acute or chronic myocardial injury in 20% and 16%, respectively. Troponin concentrations were similar at presentation in type 1 (median [25th-75th percentile] 91 [30-493] ng/L) and type 2 (50 [22-147] ng/L) myocardial infarction and in acute (50 [26-134] ng/L) and chronic (51 [31-130] ng/L) myocardial injury. The 99th percentile and rule-in thresholds of 64 ng/L and 5-fold upper reference limit gave a positive predictive value of 57% (95% CI, 56%-58%), 59% (58%-61%), and 62% (60%-64%) and a specificity of 96% (96%-96%), 96% (96%-96%), and 98% (97%-98%), respectively. The absolute, relative, and rate of change in troponin concentration were highest in patients with type 1 myocardial infarction (P<0.001 for all). Discrimination improved when troponin concentration and change in troponin were combined compared with troponin concentration at presentation alone (area under the curve, 0.661 [0.642-0.680] versus 0.613 [0.594-0.633]). CONCLUSIONS: Although we observed important differences in the kinetics, cardiac troponin concentrations at presentation are insufficient to distinguish type 1 myocardial infarction from other causes of myocardial injury or infarction in practice and should not guide management decisions in isolation. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01852123.

Widening or narrowing inequalities? The equity implications of digital tools to support COVID-19 contact tracing: A qualitative study.

BACKGROUND: As digital tools are increasingly used to support COVID-19 contact tracing, the equity implications must be considered. As part of a study to understand the public's views of digital contact tracing tools developed for the national 'Test and Protect' programme in Scotland, we aimed to explore the views of groups often excluded from such discussions. This paper reports on their views about the potential for contact tracing to exacerbate inequalities. METHODS: A qualitative study was carried out; interviews were conducted with key informants from organizations supporting people in marginalized situations, followed by interviews and focus groups with people recruited from these groups. Participants included, or represented, minority ethnic groups, asylum seekers and refugees and those experiencing multiple disadvantage including severe and enduring poverty. RESULTS: A total of 42 people participated: 13 key informants and 29 members of the public. While public participants were supportive of contact tracing, key informants raised concerns. Both sets of participants spoke about how contact tracing, and its associated digital tools, might increase inequalities. Barriers included finances (inability to afford smartphones or the data to ensure access to the internet); language (digital tools were available only in English and required a degree of literacy, even for English speakers); and trust (many marginalized groups distrusted statutory organizations and there were concerns that data may be passed to other organizations). One strength was that NHS Scotland, the data guardian, is seen as a generally trustworthy organization. Poverty was recognized as a barrier to people's ability to self-isolate. Some participants were concerned about giving contact details of individuals who might struggle to self-isolate for financial reasons. CONCLUSIONS: The impact of contact tracing and associated digital tools on marginalized populations needs careful monitoring. This should include the contact tracing process and the ability of people to self-isolate. Regular clear messaging from trusted groups and community members could help maintain trust and participation in the programme. PATIENT AND PUBLIC CONTRIBUTION: Our patient and public involvement coapplicant, L. L., was involved in all aspects of the study including coauthorship. Interim results were presented to our local Public and Patient Involvement and Engagement Group, who commented on interpretation and made suggestions about further recruitment.

Systematic review of factors influencing decisions to limit treatment in the emergency department.

BACKGROUND: Emergency physicians are frequently faced with making decisions regarding how aggressive to be in caring for critically ill patients. We aimed to identify factors that influence decisions to limit treatment in the Emergency Department (ED) through a systematic search of the available literature. DESIGN: Prospectively registered systematic review of studies employing any methodology to investigate factors influencing decisions to limit treatment in the ED. Medline and EMBASE were searched from their inception until January 2019. Methodological quality was assessed using the Mixed Methods Appraisal Tool, but no studies were excluded based on quality. Findings were summarised by narrative analysis. RESULTS: 10 studies published between 1998 and 2016 were identified for inclusion in this review, including seven cross-sectional studies investigating factors associated with treatment-limiting decisions, two surveys of physicians making treatment-limiting decisions and one qualitative study of physicians making treatment-limiting decisions. There was significant heterogeneity in patient groups, outcome measures, methodology and quality. Only three studies received a methodology-specific rating of 'high quality'. Important limitations of the literature include the use of small single-centre retrospective cohorts often lacking a comparison group, and survey studies with low response rates employing closed-response questionnaires. Factors influencing treatment-limiting decisions were categorised into 'patient and disease factors' (age, chronic disease, functional limitation, patient and family wishes, comorbidity, quality of life, acute presenting disorder type, severity and reversibility), 'hospital factors' (colleague opinion, resource availability) and 'non-patient healthcare factors' (moral, ethical, social and cost factors). CONCLUSIONS: Several factors influence decisions to limit treatment in the ED. Many factors are objective and quantifiable, but some are subjective and open to individual interpretation. This review highlights the complexity of the subject and the need for more robust research in this field.

Usability of novel major TraumaApp for digital data collection.

BACKGROUND: Delivery of major trauma care is complex and often fast paced. Clear and comprehensive documentation is paramount to support effective communication during complex clinical care episodes, and to allow collection of data for audit, research and continuous improvement. Clinical events are typically recorded on paper-based records that are developed for individual centres or systems. As one of the priorities laid out by the Scottish Trauma Network project was to develop an electronic data collection system, the TraumaApp was created as a data collection tool for major trauma that could be adopted worldwide. METHODS: The study was performed as a service evaluation based at the Queen Elizabeth University Hospital Emergency Department. Fifty staff members were recruited in pairs and listened to five paired major trauma standby and handover recordings. Participants were randomised to input data to the TraumaApp and one into the existing paper proforma. The time taken to input data add into was measured, along with time for clarifications and any errors made. Those using the app completed a System Usability Score. RESULTS: No statistically significant difference was demonstrated between times taken for data entry for the digital and paper documentation, apart from the Case 5 Handover (p < 0.05). Case 1 showed a significantly higher time for clarifications and number of errors with digital data collection (p = 0.01 and p = 1.79E-05 respectively). There were no other differences between data for the app and the proforma. The mean System Usability score for this cohort was 75 out of 100, with a standard deviation of 17 (rounded to nearest integer). CONCLUSION: Digital real-time recording of clinical events using a tool such as the TraumaApp is comparable to completion of paper proforma. The System Usability Score for the TraumaApp was above the internationally validated standard of acceptable usability. There was no evidence of improvement in use over time or familiarity, most likely due to the brevity of the assessments and the refined user interface. This would benefit from further research, exploring data completeness and a potential mixed methods approach to explore training requirements for use of the TraumaApp.

Ceilings of treatment: a qualitative study in the emergency department.

BACKGROUND: Decision-making concerning the limitation of potentially life-prolonging treatments is often challenging, particularly in the Emergency Department (ED). Current literature in this area of Emergency Medicine is limited and heterogeneous. We seek to determine the factors that influence ceiling of treatment institution in the ED. METHODS: We conducted a phenomenological qualitative study employing semi-structured interviews. Emergency Medicine Consultants were recruited via a sample of convenience from 5 hospitals in the West of Scotland. Data saturation was achieved after 15 interviews. Interviews were recorded, anonymised, transcribed, coded, and an iterative thematic analysis was carried out. RESULTS: A model was created to illustrate the identified themes. Patient wishes are central to decision-making. Acute clinical factors and patient-specific factors lay the foundations of ceiling of treatment decisions. This is heavily contextualised by family input, collateral information, anticipated outcome, and whether the patient is accepted for higher care. This decision-making process flows through a 'filter' of cultural and environmental factors. The overarching nature of patient benefit was found to be of key importance, framing all aspects of ceiling of treatment institution. Ultimately, all ceiling of treatment decisions result in one of three common patient pathways: full escalation, limited escalation, and maintenance of current care with the option of palliative care initiation. CONCLUSIONS: We present a conceptual model composed of 10 major thematic factors that influence Consultant ceiling of treatment decision-making in the ED. Clinicians should be cognizant of influential factors and associated biases when making these important and challenging decisions.

Comparison of Glasgow Admission Prediction Score and Amb Score in predicting need for inpatient care.

AIM: We compared the abilities of two established clinical scores to predict emergency department (ED) disposition: the Glasgow Admission Prediction Score (GAPS) and the Ambulatory Score (Ambs). METHODS: The scores were compared in a prospective, multicentre cohort study. We recruited consecutive patients attending ED triage at two UK sites: Northern General Hospital in Sheffield and Glasgow Royal Infirmary, between February and May 2016. Each had a GAPS and Ambs calculated at the time of triage, with the triage nurses and treating clinicians blinded to the scores. Patients were followed up to hospital discharge. The ability of the scores to discriminate discharge from ED and from hospital at 12 and 48 hours after arrival was compared using the area under the curve (AUC) of their receiving-operator characteristics (ROC). RESULTS: 1424 triage attendances were suitable for analysis during the study period, of which 567 (39.8%) were admitted. The AUC for predicting admission was significantly higher for GAPS at 0.807 (95% CI 0.785 to 0.830), compared with 0.743 (95% CI 0.717 to 0.769) for Ambs, P<0.00001. Similar results were seen when comparing ability to predict hospital stay of >12 hour and >48 hour. GAPS was also more accurate as a binary test, correctly predicting 1057 outcomes compared with 1004 for Ambs (74.2vs70.5%, P=0.012). CONCLUSION: The GAPS is a significantly better predictor of need for hospital admission than Ambs in an unselected ED population.

Optimising clinical performance during resuscitation using video evaluation.

Video evaluation of resuscitation is becoming increasingly integrated into practice in a number of clinical settings. The purpose of this review article is to examine how video may enhance clinical care during resuscitation. As healthcare and available therapeutic interventions evolve, re-evaluation of accepted paradigms requires data to describe current practice and support change. Analysis of video recordings affords creation of a framework to evaluate individual and team performance and develop unique and tailored strategies to optimise care delivery. While video has been used in a number of non-clinical settings, there has been a recent increase of video systems in the prehospital and other clinical areas. This paper reviews the key opportunities in the emergency department-based resuscitation setting to enhance ergonomics, technical and non-technical skills-at both team and individual level-through video-assisted care performance analysis and feedback.

Predicting admission at triage: are nurses better than a simple objective score?

AIM: We compared two methods of predicting hospital admission from ED triage: probabilities estimated by triage nurses and probabilities calculated by the Glasgow Admission Prediction Score (GAPS). METHODS: In this single-centre prospective study, triage nurses estimated the probability of admission using a 100 mm visual analogue scale (VAS), and GAPS was generated automatically from triage data. We compared calibration using rank sum tests, discrimination using area under receiver operating characteristic curves (AUC) and accuracy with McNemar's test. RESULTS: Of 1829 attendances, 745 (40.7%) were admitted, not significantly different from GAPS' prediction of 750 (41.0%, p=0.678). In contrast, the nurses' mean VAS predicted 865 admissions (47.3%), overestimating by 6.6% (p<0.0001). GAPS discriminated between admission and discharge as well as nurses, its AUC 0.876 compared with 0.875 for VAS (p=0.93). As a binary predictor, its accuracy was 80.6%, again comparable with VAS (79.0%), p=0.18. In the minority of attendances, when nurses felt at least 95% certain of the outcome, VAS' accuracy was excellent, at 92.4%. However, in the remaining majority, GAPS significantly outperformed VAS on calibration (+1.2% vs +9.2%, p<0.0001), discrimination (AUC 0.810 vs 0.759, p=0.001) and accuracy (75.1% vs 68.9%, p=0.0009). When we used GAPS, but 'over-ruled' it when clinical certainty was ≥95%, this significantly outperformed either method, with AUC 0.891 (0.877-0.907) and accuracy 82.5% (80.7%-84.2%). CONCLUSIONS: GAPS, a simple clinical score, is a better predictor of admission than triage nurses, unless the nurse is sure about the outcome, in which case their clinical judgement should be respected.

Exploring situational awareness in emergency medicine: developing a shared mental model to enhance training and assessment.

Non-technical skills (NTS) are gaining increasing prominence within the field of emergency medicine. Situational awareness (SA), one key component of NTS, is a key skill for emergency physicians (EPs) during initial training and throughout their career. Furthermore, the majority of frameworks used to evaluate clinical performance incorporate SA as one key component. This review seeks to define and explore the concept of SA within the context of emergency medicine. We describe SA at an individual, team and departmental level. Development of this ability enables EPs to function effectively within the challenging environment of the emergency department (ED). Enhancing our understanding of SA may develop the cognitive process that underpins our clinical performance. We propose a model for consideration to support evaluation and training of SA within the ED, linking the model to the novice expert continuum.

Exploring the impact of traumatic injury on mortality: An analysis of the certified cause of death within one year of serious injury in the Scottish population.

BACKGROUND: Few studies effectively quantify the long-term incidence of death following injury. The absence of detailed mortality and underlying cause of death data results in limited understanding and a potential underestimation of the consequences at a population level. This study takes a nationwide approach to identify the one-year mortality following injury in Scotland, evaluating survivorship in relation to pre-existing comorbidities and incidental causes of death. STUDY DESIGN: This retrospective cohort study assessed the one-year mortality of adult trauma patients with an Injury Severity Score ≥ 9 during 2020 using the Scottish Trauma Audit Group (STAG) registry linked to inpatient hospital data and death certificate records. Patients were divided into three groups: trauma death, trauma-contributed death, and non-trauma death. Kaplan-Meier curves were used for survival analysis to evaluate mortality, and cox proportional hazards regression analysed risk factors linked to death. RESULTS: 4056 patients were analysed with a median age 63 years (58-88) and male predominance (55.2 %). Falls accounted for 73.1 % of injuries followed by motor vehicle accidents (16.3 %) and blunt force (4.9 %). Extremity was the most commonly injured region overall followed by chest and head. However, head injury prevailed in those who died. The registry demonstrated a one-year mortality of 19.3 % with 55 % deaths occurring post-discharge. Of all deaths reported, 35.3 % were trauma deaths, and 47.7 % were trauma-contributed deaths. These groups accounted for over 70 % of mortality within 30 days of hospital admission and continued to represent the majority of deaths up to 6 months post-injury. Patients who died after 6 months were mainly the result of non-traumatic causes, frequently circulatory, neoplastic, and respiratory diseases (37.7 %, 12.3 %, 9.1 %, respectively). Independent risk factors for one-year mortality included a GCS ≤ 8, modified Charlson Comorbidity score >5, Injury Severity Score >25, serious head injury, age and sex. CONCLUSION: With a one-year mortality of 19.3 %, and post-discharge deaths higher than previously appreciated, patients can face an extended period of survival uncertainty. As mortality due to index trauma lasted up to 6 months post-admission, short-term outcomes fail to represent trauma burden and so cogent survival predictions should be avoided in clinical and patient settings.

Assessing the effectiveness of artificial intelligence (AI) in prioritising CT head interpretation: study protocol for a stepped-wedge cluster randomised trial (ACCEPT-AI).

INTRODUCTION: Diagnostic imaging is vital in emergency departments (EDs). Accessibility and reporting impacts ED workflow and patient care. With radiology workforce shortages, reporting capacity is limited, leading to image interpretation delays. Turnaround times for image reporting are an ED bottleneck. Artificial intelligence (AI) algorithms can improve productivity, efficiency and accuracy in diagnostic radiology, contingent on their clinical efficacy. This includes positively impacting patient care and improving clinical workflow. The ACCEPT-AI study will evaluate Qure.ai's qER software in identifying and prioritising patients with critical findings from AI analysis of non-contrast head CT (NCCT) scans. METHODS AND ANALYSIS: This is a multicentre trial, spanning four diverse sites, over 13 months. It will include all individuals above the age of 18 years who present to the ED, referred for an NCCT. The project will be divided into three consecutive phases (pre-implementation, implementation and post-implementation of the qER solution) in a stepped-wedge design to control for adoption bias and adjust for time-based changes in the background patient characteristics. Pre-implementation involves baseline data for standard care to support the primary and secondary outcomes. The implementation phase includes staff training and qER solution threshold adjustments in detecting target abnormalities adjusted, if necessary. The post-implementation phase will introduce a notification (prioritised flag) in the radiology information system. The radiologist can choose to agree with the qER findings or ignore it according to their clinical judgement before writing and signing off the report. Non-qER processed scans will be handled as per standard care. ETHICS AND DISSEMINATION: The study will be conducted in accordance with the principles of Good Clinical Practice. The protocol was approved by the Research Ethics Committee of East Midlands (Leicester Central), in May 2023 (REC (Research Ethics Committee) 23/EM/0108). Results will be published in peer-reviewed journals and disseminated in scientific findings (ClinicalTrials.gov: NCT06027411) TRIAL REGISTRATION NUMBER: NCT06027411.

AI assisted reader evaluation in acute CT head interpretation (AI-REACT): protocol for a multireader multicase study.

INTRODUCTION: A non-contrast CT head scan (NCCTH) is the most common cross-sectional imaging investigation requested in the emergency department. Advances in computer vision have led to development of several artificial intelligence (AI) tools to detect abnormalities on NCCTH. These tools are intended to provide clinical decision support for clinicians, rather than stand-alone diagnostic devices. However, validation studies mostly compare AI performance against radiologists, and there is relative paucity of evidence on the impact of AI assistance on other healthcare staff who review NCCTH in their daily clinical practice. METHODS AND ANALYSIS: A retrospective data set of 150 NCCTH will be compiled, to include 60 control cases and 90 cases with intracranial haemorrhage, hypodensities suggestive of infarct, midline shift, mass effect or skull fracture. The intracranial haemorrhage cases will be subclassified into extradural, subdural, subarachnoid, intraparenchymal and intraventricular. 30 readers will be recruited across four National Health Service (NHS) trusts including 10 general radiologists, 15 emergency medicine clinicians and 5 CT radiographers of varying experience. Readers will interpret each scan first without, then with, the assistance of the qER EU 2.0 AI tool, with an intervening 2-week washout period. Using a panel of neuroradiologists as ground truth, the stand-alone performance of qER will be assessed, and its impact on the readers' performance will be analysed as change in accuracy (area under the curve), median review time per scan and self-reported diagnostic confidence. Subgroup analyses will be performed by reader professional group, reader seniority, pathological finding, and neuroradiologist-rated difficulty. ETHICS AND DISSEMINATION: The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved 13 December 2022). The use of anonymised retrospective NCCTH has been authorised by Oxford University Hospitals. The results will be presented at relevant conferences and published in a peer-reviewed journal. TRIAL REGISTRATION NUMBER: NCT06018545.

Socioeconomic deprivation and illness trajectory in the Scottish population after COVID-19 hospitalization.

BACKGROUND: The associations between deprivation and illness trajectory after hospitalisation for coronavirus disease-19 (COVID-19) are uncertain. METHODS: A prospective, multicentre cohort study was conducted on post-COVID-19 patients, enrolled either in-hospital or shortly post-discharge. Two evaluations were carried out: an initial assessment and a follow-up at 28-60 days post-discharge. The study encompassed research blood tests, patient-reported outcome measures, and multisystem imaging (including chest computed tomography (CT) with pulmonary and coronary angiography, cardiovascular and renal magnetic resonance imaging). Primary and secondary outcomes were analysed in relation to socioeconomic status, using the Scottish Index of Multiple Deprivation (SIMD). The EQ-5D-5L, Brief Illness Perception Questionnaire (BIPQ), Patient Health Questionnaire-4 (PHQ-4) for Anxiety and Depression, and the Duke Activity Status Index (DASI) were used to assess health status. RESULTS: Of the 252 enrolled patients (mean age 55.0 ± 12.0 years; 40% female; 23% with diabetes), deprivation status was linked with increased BMI and diabetes prevalence. 186 (74%) returned for the follow-up. Within this group, findings indicated associations between deprivation and lung abnormalities (p = 0.0085), coronary artery disease (p = 0.0128), and renal inflammation (p = 0.0421). Furthermore, patients with higher deprivation exhibited worse scores in health-related quality of life (EQ-5D-5L, p = 0.0084), illness perception (BIPQ, p = 0.0004), anxiety and depression levels (PHQ-4, p = 0.0038), and diminished physical activity (DASI, p = 0.002). At the 3-month mark, those with greater deprivation showed a higher frequency of referrals to secondary care due to ongoing COVID-19 symptoms (p = 0.0438). However, clinical outcomes were not influenced by deprivation. CONCLUSIONS: In a post-hospital COVID-19 population, socioeconomic deprivation was associated with impaired health status and secondary care episodes. Deprivation influences illness trajectory after COVID-19.

In our study, we aimed to understand how socioeconomic factors impact recovery from COVID-19 following hospitalisation. We followed 252 patients, collecting health data and utilising advanced imaging techniques. We discovered that individuals from deprived areas experienced more severe health complications, reported worse quality of life, and required more specialist care. However, their clinical outcomes were not significantly different. This underscores that socioeconomic deprivation affects health recovery, underlining the need for tailored care for these individuals. Our findings emphasise the importance of considering socioeconomic factors in recovery plans post-COVID-19, potentially improving healthcare for those in deprived areas.

High-Sensitivity Cardiac Troponin for Risk Assessment in Patients With Chronic Coronary Artery Disease.

BACKGROUND: Cardiac troponin is used for risk stratification of patients with acute coronary syndromes; however, the role of testing in other settings remains unclear. OBJECTIVES: The aim of this study was to evaluate whether cardiac troponin testing could enhance risk stratification in patients with chronic coronary artery disease independent of disease severity and conventional risk measures. METHODS: In a prospective cohort of consecutive patients with symptoms suggestive of stable angina attending for outpatient coronary angiography, high-sensitivity cardiac troponin I was measured before angiography, and clinicians were blinded to the results. The primary outcome was myocardial infarction or cardiovascular death during follow-up. RESULTS: In 4,240 patients (age 66 years [IQR: 59-73 years], 33% female), coronary artery disease was identified in 3,888 (92%) who had 255 (6%) primary outcome events during a median follow-up of 2.4 years (IQR: 1.3-3.6 years). In patients with coronary artery disease, troponin concentrations were 2-fold higher in those with an event compared with those without (6.7 ng/L [IQR: 3.2-14.2 ng/L] vs 3.3 ng/L [IQR: 1.7-6.6 ng/L]; P < 0.001). Troponin concentrations were associated with the primary outcome after adjusting for cardiovascular risk factors and coronary artery disease severity (adjusted HR: 2.3; 95% CI: 1.7-3.0, log10 troponin; P < 0.001). A troponin concentration >10 ng/L identified patients with a 50% increase in the risk of myocardial infarction or cardiovascular death. CONCLUSIONS: In patients with chronic coronary artery disease, cardiac troponin predicts risk of myocardial infarction or cardiovascular death independent of cardiovascular risk factors and disease severity. Further studies are required to evaluate whether routine testing could inform the selection of high-risk patients for treatment intensification. (Myocardial Injury in Patients Referred for Coronary Angiography [MICA]; ISRCTN15620297).

Long-Term Usage and Improved Clinical Outcomes with Adoption of a COPD Digital Support Service: Key Findings from the RECEIVER Trial.

Purpose: Digital tools may improve chronic obstructive pulmonary disease (COPD) management, but further evidence of significant, persisting benefits are required. The RECEIVER trial was devised to evaluate the Lenus COPD support service by determining if people with severe COPD would continue to utilize the co-designed patient web application throughout study follow-up and to explore the impact of this digital service on clinical outcomes with its adoption alongside routine care. Patients and Methods: The prospective observational cohort hybrid implementation-effectiveness study began in September 2019 and included 83 participants. Recruitment stopped in March 2020 due to COVID-19, but follow-up continued as planned. A contemporary matched control cohort was identified to compare participant clinical outcomes with and minimize biases associated with wider COVID-19 impacts. Utilization was determined by daily COPD assessment test (CAT) completion through the application. Survival metrics and post-index date changes in annual hospitalizations were compared between the RECEIVER and control cohorts. Longitudinal quality of life and symptom burden data and community-managed exacerbation events were also captured through the application. Results: High and sustained application utilization was noted across the RECEIVER cohort with a mean follow-up of 78 weeks (64/83 participants completed at least one CAT entry on ≥50% of possible follow-up weeks). Subgroup analysis of participants resident in more socioeconomically deprived postcode areas revealed equivalent utilization. Median time to death or a COPD or respiratory-related admission was higher in the RECEIVER cohort compared to control (335 days vs 155 days). Mean reduction in annual occupied bed days was 8.12 days vs 3.38 days in the control cohort. Quality of life and symptom burden remained stable despite the progressive nature of COPD. Conclusion: The sustained utilization of the co-designed patient application and improvements in participant outcomes observed in the RECEIVER trial support scale-up implementation with continued evaluation of this digital service.

Natural history of long-COVID in a nationwide, population cohort study.

Previous studies on the natural history of long-COVID have been few and selective. Without comparison groups, disease progression cannot be differentiated from symptoms originating from other causes. The Long-COVID in Scotland Study (Long-CISS) is a Scotland-wide, general population cohort of adults who had laboratory-confirmed SARS-CoV-2 infection matched to PCR-negative adults. Serial, self-completed, online questionnaires collected information on pre-existing health conditions and current health six, 12 and 18 months after index test. Of those with previous symptomatic infection, 35% reported persistent incomplete/no recovery, 12% improvement and 12% deterioration. At six and 12 months, one or more symptom was reported by 71.5% and 70.7% respectively of those previously infected, compared with 53.5% and 56.5% of those never infected. Altered taste, smell and confusion improved over time compared to the never infected group and adjusted for confounders. Conversely, late onset dry and productive cough, and hearing problems were more likely following SARS-CoV-2 infection.

An Artificial Neural Network for Nasogastric Tube Position Decision Support.

Purpose: To develop and validate a deep learning model for detection of nasogastric tube (NGT) malposition on chest radiographs and assess model impact as a clinical decision support tool for junior physicians to help determine whether feeding can be safely performed in patients (feed/do not feed). Materials and Methods: A neural network ensemble was pretrained on 1 132 142 retrospectively collected (June 2007-August 2019) frontal chest radiographs and further fine-tuned on 7081 chest radiographs labeled by three radiologists. Clinical relevance was assessed on an independent set of 335 images. Five junior emergency medicine physicians assessed chest radiographs and made feed/do not feed decisions without and with artificial intelligence (AI)-generated NGT malposition probabilities placed above chest radiographs. Decisions from the radiologists served as ground truths. Model performance was evaluated using receiver operating characteristic analysis. Agreement between junior physician and radiologist decision was determined using the Cohen κ coefficient. Results: In the testing set, the ensemble achieved area under the receiver operating characteristic curve values of 0.82 (95% CI: 0.78, 0.86), 0.77 (95% CI: 0.71, 0.83), and 0.98 (95% CI: 0.96, 1.00) for satisfactory, malpositioned, and bronchial positions, respectively. In the clinical evaluation set, mean interreader agreement for feed/do not feed decisions among junior physicians was 0.65 ± 0.03 (SD) and 0.77 ± 0.13 without and with AI support, respectively. Mean agreement between junior physicians and radiologists was 0.53 ± 0.05 (unaided) and 0.65 ± 0.09 (AI-aided). Conclusion: A simple classifier for NGT malposition may help junior physicians determine the safety of feeding in patients with NGTs.Keywords: Neural Networks, Feature Detection, Supervised Learning, Machine Learning Supplemental material is available for this article. Published under a CC BY 4.0 license.

True prevalence of long-COVID in a nationwide, population cohort study.

Long-COVID prevalence estimates vary widely and should take account of symptoms that would have occurred anyway. Here we determine the prevalence of symptoms attributable to SARS-CoV-2 infection, taking account of background rates and confounding, in a nationwide population cohort study of 198,096 Scottish adults. 98,666 (49.8%) had symptomatic laboratory-confirmed SARS-CoV-2 infections and 99,430 (50.2%) were age-, sex-, and socioeconomically-matched and never-infected. While 41,775 (64.5%) reported at least one symptom 6 months following SARS-CoV-2 infection, this was also true of 34,600 (50.8%) of those never-infected. The crude prevalence of one or more symptom attributable to SARS-CoV-2 infection was 13.8% (13.2%,14.3%), 12.8% (11.9%,13.6%), and 16.3% (14.4%,18.2%) at 6, 12, and 18 months respectively. Following adjustment for potential confounders, these figures were 6.6% (6.3%, 6.9%), 6.5% (6.0%, 6.9%) and 10.4% (9.1%, 11.6%) respectively. Long-COVID is characterised by a wide range of symptoms that, apart from altered taste and smell, are non-specific. Care should be taken in attributing symptoms to previous SARS-CoV-2 infection.