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Background: Predictive factors associated with independent ambulation post-stroke are less commonly reported for patients during the acute phase of stroke. This study aimed to identify the clinical variables predicting ambulation independence in the acute phase of stroke and test the superiority of their prediction accuracy. Methods: Sixty-nine patients, hospitalized in the acute phase for an initial unilateral, supratentorial stroke lesion, were divided into independent (n = 24) and dependent ambulation (n = 45) groups, with functional ambulation category scores of 4–5 and ≤ 3, respectively. They were evaluated upon admission using the modified Rankin scale (mRS), Stroke Impairment Assessment Set (SIAS) concerning the motor function of the lower extremities, Ability for Basic Movement Scale modified version 2 (ABMS2), and Functional Independence Measure (FIM). Results: The scores of the four measures were significantly different between the groups. A univariate logistic regression analysis identified these variables as prognostic factors for independent ambulation. A receiver operating characteristic curve analysis identified the cutoff values (area under the curve) for the mRS, SIAS, FIM, and ABMS2 as 3 (0.74), 12 (0.73), 55 (0.85), and 23 (0.84), respectively. Conclusion: In summary, the FIM and ABMS2 may be more accurate in predicting ambulation independence in patients with stroke during the acute phase.

Background: Survival analysis is a cornerstone of medical research, enabling the assessment of clinical outcomes for disease progression and treatment efficiency. Despite its central importance, no commonly used spreadsheet software can handle survival analysis and there is no web server available for its computation. Objective: Here, we introduce a web-based tool capable of performing univariate and multivariate Cox proportional hazards survival analysis using data generated by genomic, transcriptomic, proteomic, or metabolomic studies. Methods: We implemented different methods to establish cut-off values for the trichotomization or dichotomization of continuous data. The false discovery rate is computed to correct for multiple hypothesis testing. A multivariate analysis option enables comparing omics data with clinical variables. Results: We established a registration-free web-based survival analysis tool capable of performing univariate and multivariate survival analysis using any custom-generated data. Conclusions: This tool fills a gap and will be an invaluable contribution to basic medical and clinical research.

The symptoms of obsessive-compulsive disorder (OCD) are highly heterogeneous and it is unclear what is the optimal way to conceptualize this heterogeneity. This study aimed to establish a comprehensive symptom structure model of OCD across the lifespan using factor and network analytic techniques. A large multinational cohort of well-characterized children, adolescents, and adults diagnosed with OCD ( = 1366) participated in the study. All completed the Dimensional Yale-Brown Obsessive-Compulsive Scale, which contains an expanded checklist of 87 distinct OCD symptoms. Exploratory and confirmatory factor analysis were used to outline empirically supported symptom dimensions, and interconnections among the resulting dimensions were established using network analysis. Associations between dimensions and sociodemographic and clinical variables were explored using structural equation modeling (SEM). Thirteen first-order symptom dimensions emerged that could be parsimoniously reduced to eight broad dimensions, which were valid across the lifespan: Disturbing Thoughts, Incompleteness, Contamination, Hoarding, Transformation, Body Focus, Superstition, and Loss/Separation. A general OCD factor could be included in the final factor model without a significant decline in model fit according to most fit indices. Network analysis showed that Incompleteness and Disturbing Thoughts were most central (i.e. had most unique interconnections with other dimensions). SEM showed that the eight broad dimensions were differentially related to sociodemographic and clinical variables. Future research will need to establish if this expanded hierarchical and multidimensional model can help improve our understanding of the etiology, neurobiology and treatment of OCD.

Background Tau tangle neuropathology can be visualized in living individuals using radioligands that bind specifically to aggregated tau. MTBR243 is a biomarker that has been found to be strongly associated with the presence of tau tangles in the brain. In this analysis we compared MTBR243 measured in CSF to Tau PET and clinical variables. Method MTBR243 was analyzed in 200 baseline CSF samples from study participants from two clinical trials in early and mild‐to‐moderate Alzheimer’s Disease (AD) (Tauriel, NCT03289143 and Lauriet, NCT03828747), including 182 participants who also had Tau PET data using [18F]GTP1 tau PET tracer. All subjects were amyloid positive per study inclusion criteria. Tau PET results were analyzed as continuous variable using the Temporal Meta ROI SUVR and dichotomized as low/high based on Temporal Meta ROI SUVR of 1.33. Clinical measures at baseline included CDR‐SB, ADAS‐Cog11, ADCS‐ADL, and MMSE. Result The CSF MTBR243 concentration had high correlation with Tau PET SUVR (Spearman’s ρ = 0.69, 95% CI 0.60‐0.76). 69.8% were classified as high Tau PET in the participants who were analyzed for MTBR243 in CSF. The AUC‐ROC of CSF MTBR243 for identifying Tau PET positivity was 0.89 (95% CI: 0.84‐0.94). We found that both Tau PET and CSF MTBR243 correlated with all 4 clinical measures at baseline as well as change from baseline. The correlations for the two biomarkers with clinical measures were very similar. Conclusion CSF MTBR243 correlates well with Tau PET in this amyloid positive population and discriminates well between low and high Tau PET patients. Tau PET and CSF MTBR243 have similar correlations to clinical variables, suggesting that CSF MTBR243 could be a suitable candidate as a fluid surrogate biomarker for Tau PET.

Substantial empirical evidence has indicated impairment in the cognitive functioning of patients with obsessive-compulsive disorder (OCD) despite inconsistencies. Although several confounding factors have been investigated to explain the conflicting results, the findings remain mixed. This study aimed to investigate cognitive dysfunction in patients with OCD using a meta-analytic approach. The PubMed database was searched between 1980 and October 2012, and reference lists of review papers were examined. A total of 221 studies were identified, of which 88 studies met inclusion criteria. Neuropsychological performance and demographic and clinical variables were extracted from each study. Patients with OCD were significantly impaired in tasks that measured visuospatial memory, executive function, verbal memory and verbal fluency, whereas auditory attention was preserved in these individuals. The largest effect size was found in the ability to recall complex visual stimuli. Overall effect estimates were in the small to medium ranges for executive function, verbal memory and verbal fluency. The effects of potentially confounding factors including educational level, symptom severity, medication status and co-morbid disorders were not significant. Patients with OCD appear to have wide-ranging cognitive deficits, although their impairment is not so large in general. The different test forms and methods of testing may have influenced the performance of patients with OCD, indicating the need to select carefully the test forms and methods of testing used in future research. The effects of various confounding variables on cognitive functioning need to be investigated further and to be controlled before a definite conclusion can be made.

Background The significance of the regional distribution of Aβ‐PET signal is not well established. We used data‐driven approaches to identify Aβ‐PET patterns and their clinicopathological associations in four independent cohorts (12,379 cognitively impaired patients). Methods We analysed multi‐tracer template‐space Aβ‐PET SUVR images to assign clinically impaired participants (MCI/Dementia) into groups based on their topography of Aβ‐PET binding. Using the IDEAS cohort, we applied independent component analysis to extract grey matter (GM) components of cortical and subcortical binding we then clustered patients by their score from these GM components using k‐means clustering. We then fit data from three independent cohorts to this model assessing associations between Aβ‐PET patterns and clinical impairment, APOE‐ε4, tau‐PET, and neuropathology (Table 1). Results We retained 11 GM components of Aβ‐PET and following k‐means clustering uncovered three clusters of participants in IDEAS (Figure 1a). One cluster had low GM Aβ‐PET binding (i.e., Aβ‐, n = 4729, mean CL=2), while the other two were Aβ+ with differences along a posterior‐anterior gradient: Aβ+(posterior) (n = 2484, mean CL=76), with predominant occipital binding, and Aβ+(typical) (n = 3148, mean CL=86). Applying new data to this model replicated this gradient in each independent dataset (Figure 1b). Contrasting clinical variables for the two Aβ+ clusters showed Aβ+(posterior) patients were more clinically impaired than Aβ+(typical) patients, had a lower proportion of APOE‐ε4 carriership, and had higher posterior tau‐PET (Figure 2a‐c). At autopsy, the Aβ+(posterior) cluster presented with more severe cerebral amyloid angiopathy (CAA) (Figure 2d), but similar Thal and CERAD staging. There were no reliable differences in CL, age, and hippocampal volume (Figure 2e‐g). Conclusions We reliably assign patients into an Aβ+(posterior) PET binding group uncovering consistent associations that suggests occipital regions should not be neglected in the appraisal of Aβ‐PET. Specifically, occipital Aβ+ PET binding may be a marker of CAA and more severe cognitive impairment.

Background The significance of the regional distribution of Aβ‐PET signal is not well established. We used data‐driven approaches to identify Aβ‐PET patterns and their clinicopathological associations in four independent cohorts (12,379 cognitively impaired patients). Methods We analysed multi‐tracer template‐space Aβ‐PET SUVR images to assign clinically impaired participants (MCI/Dementia) into groups based on their topography of Aβ‐PET binding. Using the IDEAS cohort, we applied independent component analysis to extract grey matter (GM) components of cortical and subcortical binding we then clustered patients by their score from these GM components using k‐means clustering. We then fit data from three independent cohorts to this model assessing associations between Aβ‐PET patterns and clinical impairment, APOE‐ε4, tau‐PET, and neuropathology (Table 1). Results We retained 11 GM components of Aβ‐PET and following k‐means clustering uncovered three clusters of participants in IDEAS (Figure 1a). One cluster had low GM Aβ‐PET binding (i.e., Aβ‐, n = 4729, mean CL=2), while the other two were Aβ+ with differences along a posterior‐anterior gradient: Aβ+(posterior) (n = 2484, mean CL=76), with predominant occipital binding, and Aβ+(typical) (n = 3148, mean CL=86). Applying new data to this model replicated this gradient in each independent dataset (Figure 1b). Contrasting clinical variables for the two Aβ+ clusters showed Aβ+(posterior) patients were more clinically impaired than Aβ+(typical) patients, had a lower proportion of APOE‐ε4 carriership, and had higher posterior tau‐PET (Figure 2a‐c). At autopsy, the Aβ+(posterior) cluster presented with more severe cerebral amyloid angiopathy (CAA) (Figure 2d), but similar Thal and CERAD staging. There were no reliable differences in CL, age, and hippocampal volume (Figure 2e‐g). Conclusions We reliably assign patients into an Aβ+(posterior) PET binding group uncovering consistent associations that suggests occipital regions should not be neglected in the appraisal of Aβ‐PET. Specifically, occipital Aβ+ PET binding may be a marker of CAA and more severe cognitive impairment.

For many years, biofeedback and neurofeedback have been implemented in the treatment of depression. However, the effectiveness of these techniques on depressive symptomatology is still controversial. Hence, we conducted a meta-analysis of studies extracted from PubMed, Scopus, Web of Science and Embase. Two different strings were considered for each of the two objectives of the study: A first group comprising studies patients with major depressive disorder (MDD) and a second group including studies targeting depressive symptomatology reduction in other mental or medical conditions. In the first group of studies including patients with MDD, the within-group analyses yielded an effect size of Hedges' g = 0.717, while the between-group analysis an effect size of Hedges' g = 1.050. Moderator analyses indicate that treatment efficacy is only significant when accounting for experimental design, in favor of randomized controlled trials (RCTs) in comparison to non RCTs, whereas the type of neurofeedback, trial design, year of publication, number of sessions, age, sex and quality of study did not influence treatment efficacy. In the second group of studies, a small but significant effect between groups was found (Hedges' g = 0.303) in favor of bio- and neurofeedback against control groups. Moderator analyses revealed that treatment efficacy was not moderated by any of the sociodemographic and clinical variables. Heart rate variability (HRV) biofeedback and neurofeedback are associated with a reduction in self-reported depression. Despite the fact that the field has still a large room for improvement in terms of research quality, the results presented in this study suggests that both modalities may become relevant complementary strategies for the treatment of MDD and depressive symptomatology in the coming years.

Telemonitoring (TM) can improve heart failure (HF) outcomes by facilitating patient self-care and clinical decisions. The Medly program enables patients to use a mobile phone to record daily HF readings and receive personalized self-care messages generated by a clinically validated algorithm. The TM system also generates alerts, which are immediately acted upon by the patients' existing care team. This program has been operating for 3 years as part of the standard of care in an outpatient heart function clinic in Toronto, Canada. This study aimed to evaluate the 6-month impact of this TM program on health service utilization, clinical outcomes, quality of life (QoL), and patient self-care. This pragmatic quality improvement study employed a pretest-posttest design to compare 6-month outcome measures with those at program enrollment. The primary outcome was the number of HF-related hospitalizations. Secondary outcomes included all-cause hospitalizations, emergency department visits (HF related and all cause), length of stay (HF related and all cause), and visits to the outpatient clinic. Clinical outcomes included bloodwork (B-type natriuretic peptide [BNP], creatinine, and sodium), left ventricular ejection fraction, and predicted survival score using the Seattle Heart Failure Model. QoL was measured using the Minnesota Living with Heart Failure Questionnaire (MLHFQ) and the 5-level EuroQol 5-dimensional questionnaire. Self-care was measured using the Self-Care of Heart Failure Index (SCHFI). The difference in outcome scores was analyzed using negative binomial distribution and Poisson regressions for the health service utilization outcomes and linear regressions for all other outcomes to control for key demographic and clinical variables. Available data for 315 patients enrolled in the TM program between August 2016 and January 2019 were analyzed. A 50% decrease in HF-related hospitalizations (incidence rate ratio [IRR]=0.50; P<.001) and a 24% decrease in the number of all-cause hospitalizations (IRR=0.76; P=.02) were found when comparing the number of events 6 months after program enrollment with the number of events 6 months before enrollment. With regard to clinical outcomes at 6 months, a 59% decrease in BNP values was found after adjusting for control variables. Moreover, 6-month MLHFQ total scores were 9.8 points lower than baseline scores (P<.001), representing a clinically meaningful improvement in HF-related QoL. Similarly, the MLHFQ physical and emotional subscales showed a decrease of 5.4 points (P<.001) and 1.5 points (P=.04), respectively. Finally, patient self-care after 6 months improved as demonstrated by a 7.8-point (P<.001) and 8.5-point (P=.01) increase in the SCHFI maintenance and management scores, respectively. No significant changes were observed in the remaining secondary outcomes. This study suggests that an HF TM program, which provides patients with self-care support and active monitoring by their existing care team, can reduce health service utilization and improve clinical, QoL, and patient self-care outcomes.

Functional impairment is a defining feature of psychotic disorders. A range of factors has been shown to influence functioning, including negative symptoms, cognitive performance and cognitive reserve (CR). However, it is not clear how these variables may affect functioning in first-episode psychosis (FEP) patients. This 2-year follow-up study aimed to explore the possible mediating effects of CR on the relationship between cognitive performance or specific clinical symptoms and functional outcome. A prospective study of non-affective FEP patients was performed (211 at baseline and 139 at follow-up). CR was entered in a path analysis model as potential mediators between cognitive domains or clinical symptoms and functioning. At baseline, the relationship between clinical variables or cognitive performance and functioning was not mediated by CR. At follow-up, the effect of attention (p = 0.003) and negative symptoms (p = 0.012) assessed at baseline on functioning was partially mediated by CR (p = 0.032 and 0.016), whereas the relationship between verbal memory (p = 0.057) and functioning was mediated by CR (p = 0.014). Verbal memory and positive and total subscales of PANSS assessed at follow-up were partially mediated by CR and the effect of working memory on functioning was totally mediated by CR. Our results showed the influence of CR in mediating the relationship between cognitive domains or clinical symptoms and functioning in FEP. In particular, CR partially mediated the relationship between some cognitive domains or clinical symptoms and functioning at follow-up. Therefore, CR could improve our understanding of the long-term functioning of patients with a non-affective FEP.