Measures of cognitive reserve: An umbrella review

Objective: Recently, there has been a growing interest in operationalizing and measuring cognitive reserve (CR) for clinical and research purposes. This umbrella review aims to summarize the existing systematic and meta-analytic reviews about measures of CR. Method: A literature search was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and the guidelines of Aromataris et al. (2015) to identify the systematic reviews and meta-analysis involving the assessment of CR. The methodological quality of the papers included in this umbrella review was assessed with A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR-2) and Specialist Unit for Review Evidence (SURE). Results: Thirty-one reviews were identified, sixteen of which were systematic reviews, and fifteen were meta-analyses. Most of the reviews had a critically low quality, according to AMSTAR-2. The reviews included between two and 135 studies. Most of the papers focused on older adults, mainly those with dementia. CR was measured using one to six proxies, but most considered each proxy separately. The most assessed proxies of CR were education on its own, combined with occupation and/or engagement in activities or combined with parental education, bilingualism, and engagement in activities when four CR proxies were studied. Most of the studies included in higher quality reviews focused on three proxies, with education and engagement in activities being the most evaluated using CR questionnaires. Conclusion: Despite the growing interest in measuring CR, its operationalization did not improve since the last umbrella review in this field.

Out- Of- Pocket health expenditure and household consumption patterns in Benin: Is there a crowding out effect?

Health shocks are common and have serious consequences for households in developing countries where health insurance is lacking. In this study, we examine whether out-of-pocket health expenditures crowd out household consumption of non-healthcare necessities, such as education items in Benin using a sample of 14,952 households from the global vulnerability and food security analysis survey. We estimated a system of conditional Engel curves with three stage least squared (3SLS) and seemingly unrelated regression (SURE) for seven categories of goods using the Quadratic Almost Ideal Demand System (QUAIDS) in the form of budget shares corresponding to proportions of total non-health expenditure. Findings show that out of pocket health expenditure leads households to spend more on health care that in fine crowd out expenditure in other necessity goods such as education item. These findings highlight the need for social protection programs to mitigate the impact of health shocks on vulnerable households in Benin.

Once-Weekly Semaglutide Use in Type 2 Diabetes: Real-World Data from the SURE Netherlands Observational Study

INTRODUCTION

SURE Netherlands (NCT03929679) evaluated the use of once-weekly (OW) semaglutide, a glucagon-like peptide 1 receptor agonist (GLP-1RA), in routine clinical care for individuals with type 2 diabetes (T2D).

METHODS

Adults (age ≥ 18 years) with T2D were enrolled into the single-arm study. The primary endpoint was change from baseline to end of study (EOS;  approx. 30 weeks) in glycated haemoglobin (HbA_1c). Secondary endpoints were change from baseline to EOS in body weight (BW) and waist circumference (WC). Proportions of participants achieving predefined HbA_1c targets and weight-loss responses at EOS, safety, health-related quality of life (HRQoL) and treatment satisfaction were assessed.

RESULTS

In total, 211 participants (mean age 60.5 years; diabetes duration 13.3 years) initiated semaglutide; most were receiving metformin (82.9%) and/or basal insulin (59.2%) at baseline, and 6.2% switched from another GLP-1RA. Mean baseline HbA_1c, BW and WC were 8.6%, 105.2 kg and 118.8 cm. In the 186 (88.2%) participants receiving semaglutide at EOS, mean reduction in HbA_1c with semaglutide was - 1.2%-points (95% [confidence interval] CI - 1.3; - 1.0; p < 0.0001), with 124 (70.5%), 95 (54.0%) and 65 (36.9%) participants achieving HbA_1c targets of < 8.0%, < 7.5% and < 7.0%, respectively. Mean reduction in BW was - 7.8 kg [95% CI - 8.7; - 6.8; p < 0.0001], corresponding to relative reduction of - 7.5% [95% CI - 8.4; - 6.6; p < 0.0001]. Improvements in WC (- 8.8 cm [95% CI - 10.4; - 7.2; p < 0.0001]), HRQoL and treatment satisfaction were observed, including across most Short-Form 36 Health Survey domains. One serious adverse drug reaction (cholecystitis) was reported. Eight participants (all receiving concomitant insulin) experienced severe or documented hypoglycaemia.

CONCLUSION

Individuals with T2D treated with OW semaglutide experienced significant and clinically relevant improvements in glycaemic control and BW from baseline. These results from a diverse real-world population in the Netherlands support the use of OW semaglutide in treating adults with T2D in routine clinical practice.

ENSURE: ENSEMBLE STEIN'S UNBIASED RISK ESTIMATOR FOR UNSUPERVISED LEARNING

Deep learning algorithms are emerging as powerful alternatives to compressed sensing methods, offering improved image quality and computational efficiency. Unfortunately, fully sampled training images may not be available or are difficult to acquire in several applications, including high-resolution and dynamic imaging. Previous studies in image reconstruction have utilized Stein's Unbiased Risk Estimator (SURE) as a mean square error (MSE) estimate for the image denoising step in an unrolled network. Unfortunately, the end-to-end training of a network using SURE remains challenging since the projected SURE loss is a poor approximation to the MSE, especially in the heavily undersampled setting. We propose an ENsemble SURE (ENSURE) approach to train a deep network only from undersampled measurements. In particular, we show that training a network using an ensemble of images, each acquired with a different sampling pattern, can closely approximate the MSE. Our preliminary experimental results show that the proposed ENSURE approach gives comparable reconstruction quality to supervised learning and a recent unsupervised learning method.

Validation of SURE, a four-item clinical checklist for detecting decisional conflict in patients

BACKGROUND

We sought to determine the psychometric properties of SURE, a 4-item checklist designed to screen for clinically significant decisional conflict in clinical practice.

METHODS

This study was a secondary analysis of a clustered randomized trial assessing the effect of DECISION+2, a 2-hour online tutorial followed by a 2-hour interactive workshop on shared decision making, on decisions to use antibiotics for acute respiratory infections. Patients completed SURE and also the Decisional Conflict Scale (DCS), as the gold standard, after consultation. We evaluated internal consistency of SURE using the Kuder-Richardson 20 coefficient (KR-20). We compared DCS and SURE scores using the Spearman correlation coefficient. We assessed sensitivity and specificity of SURE scores (cut-off score ≤3 out of 4) by identifying patients with and without clinically significant decisional conflict (DCS score >37.5 on a scale of 0-100).

RESULTS

Of the 712 patients recruited during the trial, 654 completed both tools. SURE scores showed adequate internal consistency (KR-20 coefficient of 0.7). There was a significant correlation between DCS and SURE scores (Spearman's ρ = -0.45, P < 0.0001). The prevalence of clinically significant decisional conflict as estimated by the DCS was 5.2% (95% CI 3.7-7.3). Sensitivity and specificity of SURE ≤3 were 94.1% (95% CI 78.9-99.0) and 89.8% (95% CI 87.1-92.0), respectively.

CONCLUSIONS

SURE shows adequate psychometric properties in a primary care population with a low prevalence of clinically significant decisional conflict. SURE has the potential to be a useful screening tool for practitioners, responding to the growing need for detecting clinically significant decisional conflict in patients.