A One-Shot Learning Framework for Assessment of Fibrillar Collagen from Second Harmonic Generation Images of an Infarcted Myocardium

© 2020 IEEE. Myocardial infarction (MI) is a scientific term that refers to heart attack. In this study, we infer highly relevant second harmonic generation (SHG) cues from collagen fibers exhibiting highly non-centrosymmetric assembly together with two-photon excited cellular autofluorescence in infarcted mouse heart to quantitatively probe fibrosis, especially targeted at an early stage after MI. We present a robust one-shot machine learning algorithm that enables determination of 2D assembly of collagen with high spatial resolution along with its structural arrangement in heart tissues post - MI with spectral specificity and sensitivity. Detection, evaluation, and precise quantification of fibrosis extent at early stage would guide one to develop treatment therapies that may prevent further progression and determine heart transplant needs for patient survival

Dexamethasone and oxygen therapy in care home residents with diabetes: a management guide and algorithm for treatment: a rapid response action statement from the European Diabetes Working Party for Older People (EDWPOP) and European Geriatric Medicine Society (EuGMS)

This statement addresses the need to provide clinically relevant and practical guidance for long-term care staff working in care homes and other stakeholders engaged in the care of residents who require consideration for dexamethasone and oxygen therapy. It had been provided following a series of consensus discussions between the EDWPOP and the EuGMS in January and February 2021. Its main aim is to minimise morbidity and mortality from serious acute illnesses including COVID-19 requiring these treatments within the long-term care sector. © 2021, The Author(s)

J Clin Med

Frailty and sarcopenia are characterized by a loss of muscle mass and functionality and are diagnosed mainly by functional tests and imaging parameters. However, more muscle specific biomarkers are needed to improve frailty diagnosis. Plasma 3-methylhistidine (3-MH), as well as the 3-MH-to-creatinine (3-MH/Crea) and 3-MH-to-estimated glomerular filtration rate (3-MH/eGFR) ratios might support the diagnosis of frailty. Therefore, we investigated the cross-sectional associations between plasma 3-MH, 3-MH/Crea and 3-MH/eGFR with the frailty status of community-dwelling individuals (>65 years). 360 participants from two French cohorts of the FRAILOMIC initiative were classified into robust, pre-frail and frail according to Fried's frailty criteria. General linear models as well as bivariate and multiple linear and logistic regression models, which were adjusted for several confounders, were applied to determine associations between biomarkers and frailty status. The present study consisted of 37.8% robust, 43.1% pre-frail and 19.2% frail participants. Frail participants had significantly higher plasma 3-MH, 3-MH/Crea and 3-MH/eGFR ratios than robust individuals, and these biomarkers were positively associated with frailty status. Additionally, the likelihood to be frail was significantly higher for every increase in 3-MH (1.31-fold) and 3-MH/GFR (1.35-fold) quintile after adjusting for confounders. We conclude that 3-MH, 3-MH/Crea and 3-MH/eGFR in plasma might be potential biomarkers to identify frail individuals or those at higher risk to be frail, and we assume that there might be biomarker thresholds to identify these individuals. However, further, especially longitudinal studies are needed

Essential steps in primary care management of older people with Type 2 diabetes: an executive summary on behalf of the European geriatric medicine society (EuGMS) and the European diabetes working party for older people (EDWPOP) collaboration

We present an executive summary of a guideline for management of type 2 diabetes mellitus in primary care written by the European Geriatric Medicine Society, the European Diabetes Working Party for Older People with contributions from primary care practitioners and participation of a patient’s advocate. This consensus document relies where possible on evidence-based recommendations and expert opinions in the fields where evidences are lacking. The full text includes 4 parts: a general strategy based on comprehensive assessment to enhance quality and individualised care plan, treatments decision guidance, management of complications, and care in case of special conditions. Screening for frailty and cognitive impairment is recommended as well as a comprehensive assessment all health conditions are concerned, including end of life situations. The full text is available online at the following address: essential_steps_inprimary_care_in_older_people_with_diabetes_-_EuGMS-EDWPOP___3_.pdf

Metabolic syndrome across Europe: Different clusters of risk factors

BACKGROUND: Metabolic syndrome (MetS) remains a controversial entity. Specific clusters of MetS components - rather than MetS per se - are associated with accelerated arterial ageing and with cardiovascular (CV) events. To investigate whether the distribution of clusters of MetS components differed cross-culturally, we studied 34,821 subjects from 12 cohorts from 10 European countries and one cohort from the USA in the MARE (Metabolic syndrome and Arteries REsearch) Consortium. METHODS: In accordance with the ATP III criteria, MetS was defined as an alteration three or more of the following five components: elevated glucose (G), fasting glucose ≥110 mg/dl; low HDL cholesterol, < 40mg/dl for men or <50 mg/dl for women; high triglycerides (T), ≥150 mg/dl; elevated blood pressure (B), ≥130/≥85 mmHg; abdominal obesity (W), waist circumference >102 cm for men or >88 cm for women. RESULTS: MetS had a 24.3% prevalence (8468 subjects: 23.9% in men vs. 24.6% in women, p < 0.001) with an age-associated increase in its prevalence in all the cohorts. The age-adjusted prevalence of the clusters of MetS components previously associated with greater arterial and CV burden differed across countries (p < 0.0001) and in men and women (p < 0.0001). In details, the cluster TBW was observed in 12% of the subjects with MetS, but was far more common in the cohorts from the UK (32.3%), Sardinia in Italy (19.6%), and Germany (18.5%) and less prevalent in the cohorts from Sweden (1.2%), Spain (2.6%), and the USA (2.5%). The cluster GBW accounted for 12.7% of subjects with MetS with higher occurrence in Southern Europe (Italy, Spain, and Portugal: 31.4, 18.4, and 17.1% respectively) and in Belgium (20.4%), than in Northern Europe (Germany, Sweden, and Lithuania: 7.6, 9.4, and 9.6% respectively). CONCLUSIONS: The analysis of the distribution of MetS suggested that what follows under the common definition of MetS is not a unique entity rather a constellation of cluster of MetS components, likely selectively risky for CV disease, whose occurrence differs across countries

An international Delphi consensus process to determine a common data element and core outcome set for frailty: FOCUS (The Frailty Outcomes Consensus Project)

BACKGROUND: Despite increased recognition of frailty and its importance, high quality evidence to guide decision-making is lacking. There has been variation in reported data elements and outcomes which makes it challenging to interpret results across studies as well as to generalize research findings. The creation of a frailty core set, consisting of a minimum set of data elements and outcomes to be measured in all frailty studies, would allow for findings from research and translational studies to be collectively analyzed to better inform care and decision-making. To achieve this, the Frailty Outcomes Consensus Project was developed to reach consensus from the international frailty community on a set of common data elements and core outcomes for frailty. METHODS: An international steering committee developed the methodology and the consensus process to be followed. The committee formulated the initial list of data elements and outcomes. Participants from across the world were invited to take part in the Delphi consensus process. The Delphi consisted of three rounds. Following review of data after three rounds, a final ranking round of data elements and outcomes was conducted. A required retention rate of 80% between rounds was set a priori. RESULTS: One hundred and eighty-four panelists from 25 different countries participated in the first round of the Delphi consensus process. This included researchers, clinicians, administrators, older adults, and caregivers. The retention rate between rounds was achieved. Data elements and outcomes forming primary and secondary core sets were identified, within the domains of participant characteristics, physical performance, physical function, physical health, cognition and mental health, socioenvironmental circumstances, frailty measures, and other. CONCLUSION: It is anticipated that implementation and uptake of the frailty core set will enable studies to be collectively analyzed to better inform care for persons living with frailty and ultimately improve their outcomes. Future work will focus on identification of measurement tools to be used in the application of the frailty core set. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-022-02993-w

Development and validation of an electronic frailty index using routine primary care electronic health record data

Background: frailty is an especially problematic expression of population ageing. International guidelines recommend routine identification of frailty to provide evidence-based treatment, but currently available tools require additional resource. Objectives: to develop and validate an electronic frailty index (eFI) using routinely available primary care electronic health record data. Study design and setting: retrospective cohort study. Development and internal validation cohorts were established using a randomly split sample of the ResearchOne primary care database. External validation cohort established using THIN database. Participants: patients aged 65–95, registered with a ResearchOne or THIN practice on 14 October 2008. Predictors: we constructed the eFI using the cumulative deficit frailty model as our theoretical framework. The eFI score is calculated by the presence or absence of individual deficits as a proportion of the total possible. Categories of fit, mild, moderate and severe frailty were defined using population quartiles. Outcomes: outcomes were 1-, 3- and 5-year mortality, hospitalisation and nursing home admission. Statistical analysis: hazard ratios (HRs) were estimated using bivariate and multivariate Cox regression analyses. Discrimination was assessed using receiver operating characteristic (ROC) curves. Calibration was assessed using pseudo-R2 estimates. Results: we include data from a total of 931,541 patients. The eFI incorporates 36 deficits constructed using 2,171 CTV3 codes. One-year adjusted HR for mortality was 1.92 (95% CI 1.81–2.04) for mild frailty, 3.10 (95% CI 2.91–3.31) for moderate frailty and 4.52 (95% CI 4.16–4.91) for severe frailty. Corresponding estimates for hospitalisation were 1.93 (95% CI 1.86–2.01), 3.04 (95% CI 2.90–3.19) and 4.73 (95% CI 4.43–5.06) and for nursing home admission were 1.89 (95% CI 1.63–2.15), 3.19 (95% CI 2.73–3.73) and 4.76 (95% CI 3.92–5.77), with good to moderate discrimination but low calibration estimates. Conclusions: the eFI uses routine data to identify older people with mild, moderate and severe frailty, with robust predictive validity for outcomes of mortality, hospitalisation and nursing home admission. Routine implementation of the eFI could enable delivery of evidence-based interventions to improve outcomes for this vulnerable group