Prevalence of Frailty in European Emergency Departments (FEED): an international flash mob study

INTRODUCTION

Current emergency care systems are not optimized to respond to multiple and complex problems associated with frailty. Services may require reconfiguration to effectively deliver comprehensive frailty care, yet its prevalence and variation are poorly understood. This study primarily determined the prevalence of frailty among older people attending emergency care.

METHODS

This cross-sectional study used a flash mob approach to collect observational European emergency care data over a 24-h period (04 July 2023). Sites were identified through the European Task Force for Geriatric Emergency Medicine collaboration and social media. Data were collected for all individuals aged 65 + who attended emergency care, and for all adults aged 18 + at a subset of sites. Variables included demographics, Clinical Frailty Scale (CFS), vital signs, and disposition. European and national frailty prevalence was determined with proportions with each CFS level and with dichotomized CFS 5 + (mild or more severe frailty).

RESULTS

Sixty-two sites in fourteen European countries recruited five thousand seven hundred eighty-five individuals. 40% of 3479 older people had at least mild frailty, with countries ranging from 26 to 51%. They had median age 77 (IQR, 13) years and 53% were female. Across 22 sites observing all adult attenders, older people living with frailty comprised 14%.

CONCLUSION

40% of older people using European emergency care had CFS 5 + . Frailty prevalence varied widely among European care systems. These differences likely reflected entrance selection and provide windows of opportunity for system configuration and workforce planning.

1750 Prediction of 6 month Trauma PROMS using in-hospital data

Aims, Objectives and Background

Trauma audit has used lived/died as an outcome for 30 years, but Patient Reported Outcome Measures (PROMS) have also been collected by the Trauma Audit and Research Network (TARN) for the past 5 years across major trauma centres. These are measured at 6 months after injury and include two measures of health-related quality of life, EQ5D-5 and GOSE, employment status and three patient experience questions. It is not known if 6-month PROMS can be predicted after major trauma.

Method and Design

The TARN PROMS data was extracted and randomly divided into a model development (training) and a model testing (test) dataset. There is no standard way of using this type of complex data, so three different modelling approaches were used: (1) conventional logistic regression, (2) artificial intelligence (AI) selection of ‘nearest neighbours’, and (3) AI decision trees. The performance of each model was evaluated using the test dataset.

Results and Conclusion

There were 5791 patients in the training set and 1447 patients in the test set. All three of the methods achieved an ROC AUC between 0.69 and 0.77 – implying that this might be the limit of prediction based on this type of data. When tested against the binary Glasgow Outcome Score the results are shown in the table 1. The AI method of ‘k Nearest Neighbours’ achieved a balance between sensitivity (72%) and specificity (71%).

Abstract 1750 Table 1GOSE Groupk Nearest NeighboursLogistic RegressionDecision treesSensitivity72%91%79%Specificity71%26%65%PPV90%82%39%NPV42%45%91%ROC0.7640.6850.77ROC 95% CI0.733 – 0.7950.648 – 0.723

Conclusion

patient reported outcomes at 6 months after injury can be predicted from in-hospital data. This potentially gives a new method for clinical audit and comparison of trauma outcomes using a measure that is more relevant to survivors of major trauma than the current ‘lived/died’.

A population-based cohort study of obesity, ethnicity and COVID-19 mortality in 12.6 million adults in England

Obesity and ethnicity are known risk factors for COVID-19 outcomes, but their combination has not been extensively examined. We investigate the association between body mass index (BMI) and COVID-19 mortality across different ethnic groups using linked national Census, electronic health records and mortality data for adults in England from the start of pandemic (January 2020) to December 2020. There were 30,067 (0.27%), 1,208 (0.29%), 1,831 (0.29%), 845 (0.18%) COVID-19 deaths in white, Black, South Asian and other ethnic minority groups, respectively. Here we show that BMI was more strongly associated with COVID-19 mortality in ethnic minority groups, resulting in an ethnic risk of COVID-19 mortality that was dependant on BMI. The estimated risk of COVID-19 mortality at a BMI of 40 kg/m² in white ethnicities was equivalent to the risk observed at a BMI of 30.1 kg/m², 27.0 kg/m², and 32.2 kg/m² in Black, South Asian and other ethnic minority groups, respectively.

Robust and Scalable Learning of Complex Intrinsic Dataset Geometry via ElPiGraph

Multidimensional datapoint clouds representing large datasets are frequently characterized by non-trivial low-dimensional geometry and topology which can be recovered by unsupervised machine learning approaches, in particular, by principal graphs. Principal graphs approximate the multivariate data by a graph injected into the data space with some constraints imposed on the node mapping. Here we present ElPiGraph, a scalable and robust method for constructing principal graphs. ElPiGraph exploits and further develops the concept of elastic energy, the topological graph grammar approach, and a gradient descent-like optimization of the graph topology. The method is able to withstand high levels of noise and is capable of approximating data point clouds via principal graph ensembles. This strategy can be used to estimate the statistical significance of complex data features and to summarize them into a single consensus principal graph. ElPiGraph deals efficiently with large datasets in various fields such as biology, where it can be used for example with single-cell transcriptomic or epigenomic datasets to infer gene expression dynamics and recover differentiation landscapes.

Functional CT imaging for identification of the spatial determinants of small-airways disease in adults with asthma

BACKGROUND

Asthma is a disease characterized by ventilation heterogeneity (VH). A number of studies have demonstrated that VH markers derived by using impulse oscillometry (IOS) or multiple-breath washout (MBW) are associated with key asthmatic patient-related outcome measures and airways hyperresponsiveness. However, the topographical mechanisms of VH in the lung remain poorly understood.

OBJECTIVES

We hypothesized that specific regionalization of topographical small-airway disease would best account for IOS- and MBW-measured indices in patients.

METHODS

We evaluated the results of paired expiratory/inspiratory computed tomography in a cohort of asthmatic (n = 41) and healthy (n = 11) volunteers to understand the determinants of clinical VH indices commonly reported by using IOS and MBW. Parametric response mapping (PRM) was used to calculate the functional small-airways disease marker PRMfSAD and Hounsfield unit (HU)-based density changes from total lung capacity to functional residual capacity (ΔHU); gradients of ΔHU in gravitationally perpendicular (parallel) inferior-superior (anterior-posterior) axes were quantified.

RESULTS

The ΔHU gradient in the inferior-superior axis provided the highest level of discrimination of both acinar VH (measured by using phase 3 slope analysis of multiple-breath washout data) and resistance at 5 Hz minus resistance at 20 Hz measured by using impulse oscillometry (R5-R20) values. Patients with a high inferior-superior ΔHU gradient demonstrated evidence of reduced specific ventilation in the lower lobes of the lungs and high levels of PRMfSAD. A computational small-airway tree model confirmed that constriction of gravitationally dependent, lower-zone, small-airway branches would promote the largest increases in R5-R20 values. Ventilation gradients correlated with asthma control and quality of life but not with exacerbation frequency.

CONCLUSIONS

Lower lobe-predominant small-airways disease is a major driver of clinically measured VH in adults with asthma.

A random six-phase switch regulates pneumococcal virulence via global epigenetic changes

Streptococcus pneumoniae (the pneumococcus) is the world’s foremost bacterial pathogen in both morbidity and mortality. Switching between phenotypic forms (or ‘phases’) that favour asymptomatic carriage or invasive disease was first reported in 1933. Here, we show that the underlying mechanism for such phase variation consists of genetic rearrangements in a Type I restriction-modification system (SpnD39III). The rearrangements generate six alternative specificities with distinct methylation patterns, as defined by single-molecule, real-time (SMRT) methylomics. The SpnD39III variants have distinct gene expression profiles. We demonstrate distinct virulence in experimental infection and in vivo selection for switching between SpnD39III variants. SpnD39III is ubiquitous in pneumococci, indicating an essential role in its biology. Future studies must recognize the potential for switching between these heretofore undetectable, differentiated pneumococcal subpopulations in vitro and in vivo. Similar systems exist in other bacterial genera, indicating the potential for broad exploitation of epigenetic gene regulation.

Pneumococci can alternate between harmless and highly virulent forms. Here the authors show that such variation may be due to random rearrangements in a genetic locus encoding a restriction-modification system, resulting in epigenetic changes that affect expression of many genes.