Prediction of diabetic foot ulceration: The value of using microclimate sensor arrays

Background: Accurately predicting the risk of diabetic foot ulceration (DFU) could dramatically reduce the enormous burden of chronic wound management and amputation. Yet, current prognostic models are unable to precisely predict DFU events. Typically, efforts have focused on individual factors like temperature, pressure or shear rather than the overall foot microclimate. Method: A systematic review was conducted by searching PubMed reports with no restrictions on start date covering literature published until 20 February 2019 using relevant keywords, including temperature, pressure, shear and relative humidity. We review the use of these variables as predictors of DFU, highlighting gaps in our current understanding and suggesting which specific features should be combined to develop a real-time microclimate prognostic model. Results: Current prognostic models rely either solely on contralateral temperature, pressure or shear measurement; these parameters, however, rarely reach 50% specificity in relation to DFU. There is also considerable variation in methodological investigation, anatomical sensor configuration and resting time prior to temperature measurements (5-20 minutes). Few studies have considered relative humidity and mean skin resistance. Conclusions: Very limited evidence supports the use of single clinical parameters in predicting the risk of DFU. We suggest the microclimate as a whole should be considered to predict DFU more effectively and suggest nine specific features which appear to be implicated for further investigation. Technology supports real-time inshoe data collection and wireless transmission, providing a potentially rich source of data to better predict risk of DFU

Association between lifestyle factors and the incidence of multimorbidity in an older English population.

Background: Evidence on the role of lifestyle factors in relation to multimorbidity, especially in elderly populations, is scarce. We assessed the association between five lifestyle factors and incident multimorbidity (presence of ≥2 chronic conditions) in an English cohort aged ≥50 years. Methods: We used data from wave 4, 5 and 6 of the English Longitudinal Study of Ageing. Data on smoking, alcohol consumption, physical activity, fruit and vegetable consumption and BMI were extracted and combined to generate a sum of unhealthy lifestyle factors for each individual. We examined whether these lifestyle factors individually or in combination predicted during the subsequent wave. We used marginal structural Cox proportional hazard models, adjusted for both time-constant and time-varying factors. Results: A total of 5,476 participants contributed 232,749 person-months of follow-up during which 1,156 cases of incident multimorbidity were recorded. Physical inactivity increased the risk of multimorbidity by 33% (adjusted Hazard Ratio (aHR) 1.33, 95% CI 1.03-1.73). The risk was about two-three times higher when inactivity was combined with obesity (aHR 2.87, 95% CI 1.55-5.31) or smoking (aHR 2.35, 95% CI 1.36-4.08) and about four times when combined with both (aHR 3.98, 95% CI 1.02-17.00). Any combination of 2, 3 and 4 or more unhealthy lifestyle factors significantly increased the multimorbidity hazard, compared to none, from 42% to 114%. Conclusion: This study provides evidence of a temporal association between combinations of different unhealthy lifestyle factors with multimorbidity. Population level interventions should include reinforcing positive lifestyle changes in the population to reduce the risk of developing multimorbidity

Psoriasis and Psyche - What`s New? What is Clinically Relevant?

Physical activity and age-sex standardised prevalence of multimorbidity over time in the restricted sample (DOCX 16 kb

Empagliflozin cardiovascular and renal effectiveness and safety compared to dipeptidyl peptidase-4 inhibitors across 11 countries in Europe and Asia : Results from the EMPagliflozin compaRative effectIveness and SafEty (EMPRISE) study

Background: Continued expansion of indications for sodium-glucose cotransporter-2 inhibitors increases importance of evaluating cardiovascular and kidney efficacy and safety of empagliflozin in patients with type 2 diabetes compared to similar therapies. Methods: The EMPRISE Europe and Asia study is a non-interventional cohort study using data from 2014 -2019 in seven European (Denmark, Finland, Germany, Norway, Spain, Sweden, United Kingdom) and four Asian (Israel, Japan, South Korea, Taiwan) countries. Patients with type 2 diabetes initiating empagliflozin were 1:1 propensity score matched to patients initiating dipeptidyl peptidase-4 inhibitors. Primary end-points included hospitalization for heart failure, all-cause mortality, myocardial infarction and stroke. Other cardiovascular, renal, and safety outcomes were examined.Findings: Among 83,946 matched patient pairs, (0.7 years overall mean follow-up time), initiation of empagli-flozin was associated with lower risk of hospitalization for heart failure compared to dipeptidyl peptidase-4 inhibitors (Hazard Ratio 0.70; 95% CI 0.60 to 0.83). Risks of all-cause mortality (0.55; 0.48 to 0.63), stroke (0. 82; 0.71 to 0.96), and end-stage renal disease (0.43; 0.30 to 0.63) were lower and risk for myocardial infarc-tion, bone fracture, severe hypoglycemia, and lower-limb amputation were similar between initiators of empagliflozin and dipeptidyl peptidase-4 inhibitors. Initiation of empagliflozin was associated with higher risk for diabetic ketoacidosis (1.97; 1.28 to 3.03) compared to dipeptidyl peptidase-4 inhibitors. Results were consistent across continents and regions.Interpretation: Results from this EMPRISE Europe and Asia study complements previous clinical trials and real-world studies by providing further evidence of the beneficial cardiorenal effects and overall safety of empagliflozin compared to dipeptidyl peptidase-4 inhibitors.(c) 2023 The Authors. Published by Elsevier Masson SAS. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)Peer reviewe

Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19–a data-driven retrospective cohort study

Objectives: To use national, pre- and post-pandemic electronic health records (EHR) to develop and validate a scenario-based model incorporating baseline mortality risk, infection rate (IR) and relative risk (RR) of death for prediction of excess deaths. Design: An EHR-based, retrospective cohort study. Setting: Linked EHR in Clinical Practice Research Datalink (CPRD); and linked EHR and COVID-19 data in England provided in NHS Digital Trusted Research Environment (TRE). Participants: In the development (CPRD) and validation (TRE) cohorts, we included 3.8 million and 35.1 million individuals aged ≥30 years, respectively. Main outcome measures: One-year all-cause excess deaths related to COVID-19 from March 2020 to March 2021. Results: From 1 March 2020 to 1 March 2021, there were 127,020 observed excess deaths. Observed RR was 4.34% (95% CI, 4.31–4.38) and IR was 6.27% (95% CI, 6.26–6.28). In the validation cohort, predicted one-year excess deaths were 100,338 compared with the observed 127,020 deaths with a ratio of predicted to observed excess deaths of 0.79. Conclusions: We show that a simple, parsimonious model incorporating baseline mortality risk, one-year IR and RR of the pandemic can be used for scenario-based prediction of excess deaths in the early stages of a pandemic. Our analyses show that EHR could inform pandemic planning and surveillance, despite limited use in emergency preparedness to date. Although infection dynamics are important in the prediction of mortality, future models should take greater account of underlying conditions

Epidemiology of hypoglycaemia: Trends, risk factors and outcomes

Background: Few data are available on the burden, risk factors, and outcomes of hospitalisation for hypoglycaemia. Newer glucose-lowering medications, such as glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium–glucose cotransporter 2 (SGLT2) inhibitors, have been associated with a lower risk of hypoglycaemia in individual randomised controlled trials (RCTs); yet, they have not been systematically compared to older therapies. Lastly, recent observations have also suggested an association between hypoglycaemia and cardiovascular mortality. Methods: This research is structured in three parts. First, I used the NHS Hospital Episode Statistics data to: examine trends of admissions for hypoglycaemia in England between 2005 and 2014; define risk factors for admissions and differences in outcomes; develop and validate prognostic models to calculate risk of inpatient death and length of hospital stay. Second, I compared the risk of hypoglycaemia for once-weekly GLP-1RAs and SGLT2 inhibitors vs other medications with network meta-analyses of RCTs. Third, I investigated the relationship between fasting plasma glucose and risk of arrhythmias in a cohort study, aiming to clarify the pathophysiological mechanisms linking hypoglycaemia to cardiovascular disease. Results: Admissions for hypoglycaemia increased between 2005 and 2010, with more stable trends thereafter. Differences exist across regions in England for both trends and risk factors for admissions: these findings have been instrumental for the development of a tool to calculate individual risk of inpatient mortality and length of hospital stay. Meta-analyses indicated a lower risk of hypoglycaemia for GLP-1RAs and SGLT2 inhibitors compared to older glucose-lowering therapies. Lastly, in the cohort analysis, there was an inverse relationship between fasting plasma glucose and risk of arrhythmias. Conclusion: This thesis can broaden understanding of the burden of hospitalisation for hypoglycaemia and elucidate the link between hypoglycaemia and cardiovascular disease. These results could also assist decision makers in the adoption of individual- and population-level strategies

Risk of Diabetes Following COVID-19: Translating Evidence Into Clinical and Public Health Actions

Over the course of the COVID-19 pandemic, epidemiological investigations have explored the risk of various medical conditions following the acute phase of the COVID-19 infection. Although variable definitions and terms have been proposed, this cluster of conditions—encompassing mental health, neurological, metabolic, cardiorespiratory, gastrointestinal, musculoskeletal, coagulative, renal, and dermatological disorders—is commonly referred to as “post-acute sequelae of SARS-CoV-2 infection” or “long-COVID” syndrome (1). </p

Response to letter to the editor by Abhipsha Sur Roy and Amol Joshi regarding the article: ‘Multimorbidity and SARS-CoV-2 infection in UK Biobank’ (Chudasama et al.)

Letter to the Editor: Response to letter to the editor by Abhipsha Sur Roy and Amol Joshi regarding the article: ‘Multimorbidity and SARS-CoV-2 infection in UK Biobank’ (Chudasama et al.

Incidence of Depression and First-Line Antidepressant Therapy in People with Obesity and Depression in Primary Care.

OBJECTIVE:The aim of this study was to describe the age- and gender-specific incidence of depression, the dose-response relationship between BMI and risk of depression (Cox proportional hazards), and antidepressant drug prescribing in adults with overweight or obesity. METHODS:A retrospective electronic health record study using the Clinical Practice Research Datalink was conducted to identify adults with overweight and obesity (≥ 18 years) with incident depression (no prior depression diagnosis in their records), followed up from 2000 to 2019. RESULTS:Among 519,513 adults, incidence of depression was 9.2 per 1,000 person-years and was higher in women and in 40- to 59-year-old men who had severe obesity. Compared with having overweight, the hazard of depression increased with each BMI category as follows: 1.13 (30-34 kg/m2 ; 95% CI: 1.10-1.16), 1.34 (35-39 kg/m2 ; 1.29-1.40), 1.51 (40-44 kg/m2 ; 1.41-1.61), and 1.67 (45-49 kg/m2 ; 1.48-1.87), attenuating at BMI 50+ kg/m2 (1.54; 2.91-1.84). Antidepressants were prescribed as first-line therapy in two-thirds (66.3%) of cases. Prescriptions for fluoxetine reduced over time (20.4% [2000]; 8.8% [2018]), and prescriptions for sertraline increased (4.3% [2000]; 38.9% [2018]). CONCLUSIONS:We recommend guidance on antidepressant drug prescribing and specific services for people with obesity and depression that address both symptoms and behaviors

Uses and abuses of real-world data in generating evidence during a pandemic.

On 11 March 2020, the World Health Organization declared COVID-19 a pandemic and called for immediate collaborative initiatives for faster access to available data, with a view to generating robust research evidence informing global and local public health policy.1 This urgency has helped a number of national bodies to secure data and their linkages and to provide safe analytical environment for researches to ask important questions, including pseudonymised data linkages, high-throughput computing environment, and access and authentication processes with clear information governance.2,3 Linkages of multiple sources of clinical data within a trusted environment are being granted at a rapid pace and there is a greater provision of access to COVID-19 studies, improved collaboration, expedited governance and ethical approval of studies.4 Some organisations have also been proactive in getting groups together to work collaboratively on relevant research questions which will rapidly benefit clinical care and public health alike