Determinants of falls after stroke based on data on 5065 patients from the Swedish Väststroke and Riksstroke Registers

We aimed to identify determinants in acute stroke that are associated with falls during the stroke unit stay. In order to enable individualized preventive actions, this knowledge is fundamental. Based on local and national quality register data on an unselected sample of 5065 stroke patients admitted to a stroke unit at a Swedish university hospital, univariable and multivariable logistic regression analyses were performed. The dependent variable was any fall during stroke unit stay. The independent variables related to function, activity, personal factors, time to assessment, comorbidities and treatments. Determinants of falls were: being male (odds ratio (OR) 2.25, 95% confidence interval (95% CI) 1.79–2.84), haemorrhagic stroke (OR 1.39, 95% CI 1.05–1.86), moderate stroke symptoms according to the National Institutes of Health Stroke Scale (NIHSS score 2–5 vs. NIHSS score 0–1) (OR 1.43, 95% CI 1.08–1.90), smoking (OR 1.70, 95% CI 1.29–2.25), impaired postural control in walking (OR 4.61, 95% CI 3.29–6.46), impaired postural control in standing (OR 1.60, 95% CI 1.25–2.05), stroke-related arm- and hand problems, OR 1.45, 95% CI 1.11–1.91), impaired cognition (OR 1.43, 95% CI 1.04–1.95), and urinary tract infection (OR 1.91, 95% CI 1.43–2.56). The findings from this study are useful in clinical practice and might help to improve patient safety after stroke.

A network approach to the five-facet model of mindfulness

Despite the large-scale dissemination of mindfulness-based interventions, debates persist about the very nature of mindfulness. To date, one of the dominant views is the five-facet approach, which suggests that mindfulness includes five facets (i.e., Observing, Describing, Nonjudging, Nonreactivity, and Acting with Awareness). However, uncertainty remains regarding the potential interplay between these facets. In this study, we investigated the five-facet model via network analysis in an unselected sample (n = 1704). We used two distinct computational network approaches: a Gaussian graphical model (i.e., undirected) and a directed acyclic graph, with each model determining the relations between the facets and their relative importance in the network. Both computational approaches pointed to the facet denoting Acting with Awareness as playing an especially potent role in the network system. Altogether, our findings offer novel data-driven clues for the field's larger quest to ascertain the very foundations of mindfulness.

Sudden cardiac death risk in hypertrophic cardiomyopathy: comparison between echocardiography and magnetic resonance imaging

In hypertrophic cardiomyopathy (HCM) patients, left ventricular (LV) maximal wall thickness (MWT) is one of the most important factors determining sudden cardiac death (SCD) risk. In a large unselected sample of HCM patients, we aimed to simulate what changes would occur in the calculated SCD risk according to the European HCM Risk-SCD calculator when MWT measured using echocardiography was changed to MWT measured using MRI. All consecutive patients with HCM who underwent cardiac MRI were included. MWT measured with echocardiography and MRI were compared, and 5-year SCD risk according to the HCM Risk-SCD calculator was computed using four different models. The final population included 673 patients [389 (57.8%) males, median age 50 years, interquartile range (36–60)]. The median MWT was lower measured by echocardiography than by MRI [20 (17–24) mm vs 21 (18–24) mm; p < 0.0001]. There was agreement between echocardiography and MRI in the measurement of maximal LV wall thickness in 96 patients (14.3%). The largest differences between echo and MRI were − 13 mm and + 9 mm. The differences in MWT by echocardiography and MRI translated to a maximal difference of 8.33% in the absolute 5-year risk of SCD, i.e., the echocardiography-based risk was 8.33% lower than the MRI-based estimates. Interestingly, 13.7% of patients would have been reclassified into different SCD risk categories if MRI had been used to measure MWT instead of echocardiography. In conclusion, although there was high general intermodality agreement between echocardiography and MRI in the MWT measurements, the differences in MWT translated to significant differences in the 5-year risk of SCD.

A Network Approach to the Five-Facet Model of Mindfulness

Despite the extensive dissemination of mindfulness-based interventions, debates persist about the very definition of mindfulness. For decades, the ontological discourse on mindfulness has mainly been confined to the development of operational definitions. To date, the dominant paradigm is the five-facet approach that suggests that mindfulness includes five facets (i.e., Observing, Describing, Nonjudging, Nonreactivity, and Acting with Awareness). However, uncertainty still abounds regarding the potential interplay between the facets. In this preregistered study, we investigated the five-facet approach via network analysis in an unselected sample (N=1,704). To do so, we used two distinct computational network approaches: a graphical Gaussian model (GGM) and a directed acyclic graph (DAG). Each model estimates edges (i.e., the relations between the facets) and the importance of nodes (i.e., the facets) in different ways. Our results indicate that the five facets can be conceptualized as a single, coherent network system of interacting elements. Moreover, both GGM and DAG pointed to the acting with awareness facet as playing an especially potent role in the network system. Altogether, our findings offer viable data-driven heuristics for the field's larger quest to ascertain the foundations of mindfulness.

The impact of anodal tDCS on attentional networks as a function of trait anxiety and depressive symptoms: A preregistered double-blind sham-controlled experiment

Attention is a multifaceted construct, including three distinct attentional networks: the alerting, orienting, and executive conflict networks. Recently, researchers have started to envision strategies to enhance the attentional networks, and transcranial Direct Current Stimulation (tDCS) has emerged as a promising tool to do so, especially regarding the executive conflict network. On the other hand, other research lines have suggested that anodal tDCS might yield more substantial impacts among depressive and anxious participants. In this preregistered study, we thus examined two questions. First, we wanted to replicate previous observations and tested whether anodal tDCS does improve the executive conflict network's efficiency. Second, we set out to clarify the impact of anxiety and depressive symptoms on this effect. To do so, we adopted a double-blind within-subject protocol in an unselected sample (n = 50) and delivered a single session of anodal— applied over the dorsolateral part of the left prefrontal cortex—versus sham tDCS during the completion of a task assessing the attentional networks. We assessed anxiety and depressive symptoms at baseline. Although there were no significant direct effects of tDCS on the attentional networks, we found that the higher the levels of depression and trait anxiety, the larger the executive conflict network's enhancement during tDCS. By highlighting the importance of trait anxiety and depression when considering the impact of tDCS on the attentional networks, this study fulfills a valuable niche in clinical neuroscience, wherein preclinical data provide critical clues for larger, more definitive future translational efforts.

Social Media Coverage of Scientific Articles Immediately After Publication Predicts Subsequent Citations - #SoME_Impact Score: Observational Analysis

Background Social media coverage is increasingly used to spread the message of scientific publications. Traditionally, the scientific impact of an article is measured by the number of citations. At a journal level, this conventionally matures over a 2-year period, and it is challenging to gauge impact around the time of publication. Objective We, therefore, aimed to assess whether Web-based attention is associated with citations and to develop a predictive model that assigns relative importance to different elements of social media coverage: the #SoME_Impact score. Methods We included all original articles published in 2015 in a selection of the highest impact journals: The New England Journal of Medicine, The Lancet, the Journal of the American Medical Association, Nature, Cell, and Science. We first characterized the change in Altmetric score over time by taking a single month’s sample of recently published articles from the same journals and gathered Altmetric data daily from the time of publication to create a mixed effects spline model. We then obtained the overall weighted Altmetric score for all articles from 2015, the unweighted data for each Altmetric component, and the 2-year citation count from Scopus for each of these articles from 2016 to 2017. We created a stepwise multivariable linear regression model to develop a #SoME_Score that was predictive of 2-year citations. The score was validated using a dataset of articles from the same journals published in 2016. Results In our unselected sample of 145 recently published articles, social media coverage appeared to plateau approximately 14 days after publication. A total of 3150 articles with a median citation count of 16 (IQR 5-33) and Altmetric score of 72 (IQR 28-169) were included for analysis. On multivariable regression, compared with articles in the lowest quantile of #SoME_Score, articles in the second, third, and upper quantiles had 0.81, 15.20, and 87.67 more citations, respectively. On the validation dataset, #SoME_Score model outperformed the Altmetric score (adjusted R2 0.19 vs 0.09; P<.001). Articles in the upper quantile of #SoME_Score were more than 5 times more likely to be among the upper quantile of those cites (odds ratio 5.61, 95% CI 4.70-6.73). Conclusions Social media attention predicts citations and could be used as an early surrogate measure of scientific impact. Owing to the cross-sectional study design, we cannot determine whether correlation relates to causation.

Obsessive-Compulsive Symptom Dimensions and Insomnia: Associations Among a Treatment-Seeking Veteran Sample

Sleep disturbances are a prevalent and pernicious correlate of most emotional disorders. A growing body of literature has recently found evidence for an association between sleep disturbances and obsessive-compulsive disorder (OCD). Though informative, this link has yet to be explored in a veteran population. Further, the degree to which this relationship is accounted for by relevant third variables is limited. The current study investigated the relationship between self-reported insomnia and OCD symptoms after controlling for probable depression and posttraumatic stress disorder (PTSD) using an unselected sample of veterans (N = 57). Most of the sample reported clinically significant OCD (61%) and insomnia symptoms (58%). Results revealed associations between insomnia and OCD unacceptable thoughts/neutralizing compulsions, but not contamination obsessions/washing compulsions, responsibility for harm obsessions/checking compulsions, or symmetry obsessions/ordering compulsions. Findings highlight the need for more research on OCD and sleep problems and clinical work focused on sleep for patients reporting increased OCD symptoms, particularly veterans.

Biomarker-based patient selection improves stroke rehabilitation trial efficiency

Background and PurposeInter-subject variability complicates trials of novel stroke rehabilitation therapies, particularly in the sub-acute phase after stroke. We tested whether selecting patients using motor evoked potential (MEP) status, a physiological biomarker of motor system function, could improve trial efficiency.MethodsA retrospective analysis of data from 207 patients (103 women, mean (SD) 70.6 (15.1) years) was used to estimate sample sizes and recruitment rates required to detect a 7-point difference between hypothetical control and treatment groups in upper-limb Fugl-Meyer and Action Research Arm Test scores at 90 days post-stroke. Analyses were carried out for the full sample and for subsets defined by motor evoked potential (MEP) status.ResultsSelecting patients according to MEP status reduced the required sample size by 75% compared to an unselected sample. The estimated time needed to recruit the required sample was also reduced by 72% for patients with MEPs, and was increased by 2-3-fold for patients without MEPs.ConclusionsUsing biomarkers to select patients can improve stroke rehabilitation trial efficiency by reducing the sample size and recruitment time needed to detect a clinically meaningful effect of the tested intervention.

Social Media Coverage of Scientific Articles Immediately After Publication Predicts Subsequent Citations - #SoME_Impact Score: Observational Analysis (Preprint)

BACKGROUND Social media coverage is increasingly used to spread the message of scientific publications. Traditionally, the scientific impact of an article is measured by the number of citations. At a journal level, this conventionally matures over a 2-year period, and it is challenging to gauge impact around the time of publication. OBJECTIVE We, therefore, aimed to assess whether Web-based attention is associated with citations and to develop a predictive model that assigns relative importance to different elements of social media coverage: the #SoME_Impact score. METHODS We included all original articles published in 2015 in a selection of the highest impact journals: The New England Journal of Medicine, The Lancet, the Journal of the American Medical Association, Nature, Cell, and Science. We first characterized the change in Altmetric score over time by taking a single month’s sample of recently published articles from the same journals and gathered Altmetric data daily from the time of publication to create a mixed effects spline model. We then obtained the overall weighted Altmetric score for all articles from 2015, the unweighted data for each Altmetric component, and the 2-year citation count from Scopus for each of these articles from 2016 to 2017. We created a stepwise multivariable linear regression model to develop a #SoME_Score that was predictive of 2-year citations. The score was validated using a dataset of articles from the same journals published in 2016. RESULTS In our unselected sample of 145 recently published articles, social media coverage appeared to plateau approximately 14 days after publication. A total of 3150 articles with a median citation count of 16 (IQR 5-33) and Altmetric score of 72 (IQR 28-169) were included for analysis. On multivariable regression, compared with articles in the lowest quantile of #SoME_Score, articles in the second, third, and upper quantiles had 0.81, 15.20, and 87.67 more citations, respectively. On the validation dataset, #SoME_Score model outperformed the Altmetric score (adjusted R² 0.19 vs 0.09; <i>P</i>&lt;.001). Articles in the upper quantile of #SoME_Score were more than 5 times more likely to be among the upper quantile of those cites (odds ratio 5.61, 95% CI 4.70-6.73). CONCLUSIONS Social media attention predicts citations and could be used as an early surrogate measure of scientific impact. Owing to the cross-sectional study design, we cannot determine whether correlation relates to causation.