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Purpose of Review: Several innovative digital technologies have begun to be applied to diagnosing and treating migraine. We reviewed the potential benefits and opportunities from delivering migraine care through comprehensive digital clinics., Recent Findings: There are increasing applications of digitization to migraine diagnosis and management, including e-diaries, and patient self-management, especially after the COVID-19 pandemic. Digital care delivery appears to better engage chronic migraine sufferers who may struggle to present to physical clinics., Summary: Digital clinics appear to be a promising treatment modality for patients with chronic migraine. They potentially minimize travel time, shorten waiting periods, improve usability, and increase access to neurologists. Additionally, they have the potential to provide care at a much lower cost than traditional physical clinics. However, the current state of evidence mostly draws on case-reports, suggesting a need for future randomized trials comparing digital interventions with standard care pathways., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Exercise interventions show promise in the treatment of anxiety disorders, but effects on health-related quality of life (HR-QoL), work ability, and sick leave are little studied. We investigated these outcomes in a 12-week randomized controlled trial with a 1-year follow-up., Methods: Patients aged 18-65 (n = 222) with anxiety disorders from primary care centers in Gothenburg were randomized to a control group or one of two 12-week exercise intervention groups (low-intensity, [LI] and moderate/high-intensity, [HI]); 148 were evaluated at 12-weeks and 113 completed the 1-year follow-up. The EuroQol 5D (EQ5D; index and the visual analogue scale [VAS]), work ability score (WAS), presenteeism, and self-reported sick leave were assessed at baseline, 12 weeks, and 1 year. Improvements were defined by binary cut-offs for each scale. Binary logistic regression with odds ratios (OR) and 95 % confidence intervals (CI) were reported., Results: There were improved scores for EQ5D and WAS in the HI group compared to controls after 12 weeks (EQ5D index: 4.74 [1.91-11.7], EQ5D-VAS 4.00, [1.65-9.72], WAS 3.41 [1.24-7.37]) and 1 year (EQ5D index: 3.05 [1.05-8.81], EQ5D-VAS 3.20 [1.16-8.84], WAS 5.50 [1.85-16.3]). Post-hoc analysis showed higher ORs in participants on antidepressants (n = 75) (12-week EQ5D index: OR 9.95 [2.85-34.8]) and significant improvements in EQ5D scores for both intervention groups after 1 year. There were no between-group differences for presenteeism or sick leave., Limitations: Discontinuation was high, mostly early after randomization (n = 74), as is common for anxiety interventions., Conclusions: HI Exercise improves HR-QoL and work ability in anxiety patients, especially when combined with antidepressants., Competing Interests: Declaration of competing interest The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper to disclose., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The full random-effects model (FREM) is an innovative and relatively novel covariate modeling technique. It differs from other covariate modeling approaches in that it treats covariates as observations and captures their impact on model parameters using their covariances. These unique characteristics mean that FREM is insensitive to correlations between covariates and implicitly handles missing covariate data. In practice, this implies that covariates are less likely to be excluded from the modeling scope in light of the observed data. FREM has been shown to be a useful modeling method for small datasets, but its pre-specification properties make it a very compelling modeling choice for late-stage phases of drug development. The present tutorial aims to explain what FREM models are and how they can be used in practice., (© 2024 Pharmetheus AB. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Introduction: High cardiorespiratory fitness (CRF) has been associated with a lower risk of depression, anxiety, and cerebrovascular disease. The aim was to explore CRF changes over-time associated with these outcomes., Methods: This large-scale prospective cohort study, using data from Swedish population-wide registries and databases (during 1972-2020), included men (n=131,431), with measures of estimated CRF (estCRF) in late adolescence (maximal cycle test) and adulthood (submaximal cycle test) (mean years between 24.6, SD 8.8). The study explored how change in estCRF was associated with incident depression, anxiety, and cerebrovascular disease using Cox proportional hazards models. Analyses were performed in 2023., Results: Higher estCRF in late adolescence and adulthood were associated with a lower risk of incident depression, anxiety, and cerebrovascular disease later in life. For all three outcomes, an increase in estCRF (mL/min/kg and z-score) between the two-time points was associated with a lower risk. Further, decreasing from moderate or high estCRF in adolescence to low estCRF in adulthood, compared to staying at a moderate or high level, was associated with a higher risk of depression and anxiety (HR: 1.24 95% CI 1.07-1.45 and 1.25 95% CI 1.06-1.49, respectively). Conversely, increasing from moderate to high estCRF was associated with a lower risk of incident anxiety (HR: 0.84 95% CI 0.71-0.99)., Conclusions: The findings indicate that there is a longitudinal association between negative change in estCRF and increased risk of depression, anxiety, and cerebrovascular disease later in life. Decreasing levels of estCRF could be a helpful indicator when identifying these disorders at a population level., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

The inclusion of covariates in pharmacometric models is important due to their ability to explain variability in drug exposure and response. Clear communication of the impact of covariates is needed to support informed decision making in clinical practice and in drug development. However, effectively conveying these effects to key stakeholders and decision makers can be challenging. Forest plots have been proposed to meet these communication needs. However, forest plots for the illustration of covariate effects in pharmacometrics are complex combinations of model predictions, uncertainty estimates, tabulated results, and reference lines and intervals. The purpose of this tutorial is to outline the aspects that influence the interpretation of forest plots, recommend best practices, and offer specific guidance for a clear and transparent communication of covariate effects., (© 2024 Pharmetheus AB. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Cardiac valve event timing plays a crucial role when conducting clinical measurements using echocardiography. However, established automated approaches are limited by the need of external electrocardiogram sensors, and manual measurements often rely on timing from different cardiac cycles. Recent methods have applied deep learning to cardiac timing, but they have mainly been restricted to only detecting two key time points, namely end-diastole (ED) and end-systole (ES). In this work, we propose a deep learning approach that leverages triplane recordings to enhance detection of valve events in echocardiography. Our method demonstrates improved performance detecting six different events, including valve events conventionally associated with ED and ES. Of all events, we achieve an average absolute frame difference (aFD) of maximum 1.4 frames (29 ms) for start of diastasis, down to 0.6 frames (12 ms) for mitral valve opening when performing a ten-fold cross-validation with test splits on triplane data from 240 patients. On an external independent test consisting of apical long-axis data from 180 other patients, the worst performing event detection had an aFD of 1.8 (30 ms). The proposed approach has the potential to significantly impact clinical practice by enabling more accurate, rapid and comprehensive event detection, leading to improved clinical measurements.

Objective: The right ventricle receives less attention than its left counterpart in echocardiography research, practice and development of automated solutions. In the work described here, we sought to determine that the deep learning methods for automated segmentation of the left ventricle in 2-D echocardiograms are also valid for the right ventricle. Additionally, here we describe and explore a keypoint detection approach to segmentation that guards against erratic behavior often displayed by segmentation models., Methods: We used a data set of echo images focused on the right ventricle from 250 participants to train and evaluate several deep learning models for segmentation and keypoint detection. We propose a compact architecture (U-Net KP) employing the latter approach. The architecture is designed to balance high speed with accuracy and robustness., Results: All featured models achieved segmentation accuracy close to the inter-observer variability. When computing the metrics of right ventricular systolic function from contour predictions of U-Net KP, we obtained the bias and 95% limits of agreement of 0.8 ± 10.8% for the right ventricular fractional area change measurements, -0.04 ± 0.54 cm for the tricuspid annular plane systolic excursion measurements and 0.2 ± 6.6% for the right ventricular free wall strain measurements. These results were also comparable to the semi-automatically derived inter-observer discrepancies of 0.4 ± 11.8%, -0.37 ± 0.58 cm and -1.0 ± 7.7% for the aforementioned metrics, respectively., Conclusion: Given the appropriate data, automated segmentation and quantification of the right ventricle in 2-D echocardiography are feasible with existing methods. However, keypoint detection architectures may offer higher robustness and information density for the same computational cost., Competing Interests: Conflict of interest S.A.A. holds a full-time position at GE Vingmed Ultrasound AS, Horten, Norway. L.L. is a part-time consultant at GE Vingmed Ultrasound AS, Horten, Norway., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Objectives: While suicidal behaviour has become less prevalent in non-manual workers in recent decades, rates have increased in manual workers. We aimed to identify occupations within the construction industry with excess risk of suicide and non-fatal self-harm., Methods: This cohort of Swedish construction workers comprises 389 132 individuals examined 1971-1993 and followed 1987-2018 using national hospital and cause of death registers. More than 200 job titles were merged into 22 occupational groups. For 296 891 men alive in 1987 and active in the construction sector, survival was calculated from baseline to first event of non-fatal self-harm or suicide and censored for emigration, long-term unemployment, disability pension, retirement, death from other causes or end of follow-up. HRs with 95% CIs were obtained from multiple Cox proportional hazard regression., Results: Overall, 1618 cases of suicide and 4774 events of non-fatal self-harm were registered. Self-harm before baseline was the single largest risk factor for suicide, HR 9.3 (95% CI 7.5 to 11.6). Compared with the overall mean, labourers and rock workers had excess risk for suicide, HR 1.4 (95% CI 1.1 to 1.7) and 1.5 (95% CI 1.0 to 2.3), respectively, while electricians, clerks and foremen had reduced risk. Labourers, concrete workers, sheet metal workers, painters, glaziers and the group 'other construction workers' were at increased risk for non-fatal self-harm. Almost all categories of manual workers were at increased risk for suicidal behaviour relative to clerks and foremen., Conclusions: Specific occupations within the construction sector were associated with excess risk for suicidal behaviour. Future studies should identify underlying risk factors to inform tailored interventions., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Both Alzheimer's disease and vascular dementia are the result of disease processes that typically develop over several decades. Population studies have estimated that more than half of the risk for dementia is preventable or at least modifiable through behavioral adaptations. The association between these lifestyle factors and the risk of dementia is most evident for exposure in midlife. However, habits formed in middle age often reflect a lifetime of behavior patterns and living conditions. Therefore, individuals who, for example, are able to maintain healthy diets and regular exercise during their middle years are likely to benefit from these cognition-protective habits they have practiced throughout their lives. For numerous adult diseases, significant risks can often be traced back to early childhood. Suboptimal conditions during the perinatal period, childhood and adolescence can increase the risk of adult diseases, including stroke, heart disease, insulin resistance, hypertension and dementia. This review aims at summarizing some of the evidence for dementia risks from a life-time perspective with the goal of raising awareness for early dementia prevention and successful aging., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

Marzeptacog alfa (MarzAA) is under development for subcutaneous treatment of episodic bleeds in patients with hemophilia A/B and was studied in a phase III trial evaluating MarzAA compared with standard-of-care (SoC) for on-demand use. The work presented here aimed to evaluate MarzAA and SoC treatment of bleeding events on a standardized four-point efficacy scale (poor, fair, good, and excellent). Two continuous-time Markov modeling approaches were explored; a four-state model analyzing all four categories of bleeding improvement and a two-state model analyzing a binarized outcome (treatment failure (poor/fair), and treatment success (good/excellent)). Different covariates impacting improvement of bleeding episodes as well as a putative relationship between MarzAA exposure and improvement of bleeding episodes were evaluated. In the final four-state model, higher baseline diastolic blood pressure and higher age (> 33 years of age) were found to negatively and positively impact improvement of bleeding condition, respectively. Bleeding events occurring in knees and ankles were found to improve faster than bleeding events at other locations. The covariate effects had most impact on early treatment success (≤ 3 hours) whereas at later timepoints (> 12 hours), treatment success was similar for all patients indicating that these covariates might be clinically relevant for early treatment response. A statistically significant relationship between MarzAA zero-order absorption and improvement of bleedings (P < 0.05) were identified albeit with low precision. No statistically significant difference in treatment response between MarzAA and intravenous SoC was identified, indicating the potential of MarzAA for treatment of episodic bleeding events with a favorable subcutaneous administration route., (© 2024 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

During drug development, a key step is the identification of relevant covariates predicting between-subject variations in drug response. The full random effects model (FREM) is one of the full-covariate approaches used to identify relevant covariates in nonlinear mixed effects models. Here we explore the ability of FREM to handle missing (both missing completely at random (MCAR) and missing at random (MAR)) covariate data and compare it to the full fixed-effects model (FFEM) approach, applied either with complete case analysis or mean imputation. A global health dataset (20 421 children) was used to develop a FREM describing the changes of height for age Z-score (HAZ) over time. Simulated datasets (n = 1000) were generated with variable rates of missing (MCAR) covariate data (0%-90%) and different proportions of missing (MAR) data condition on either observed covariates or predicted HAZ. The three methods were used to re-estimate model and compared in terms of bias and precision which showed that FREM had only minor increases in bias and minor loss of precision at increasing percentages of missing (MCAR) covariate data and performed similarly in the MAR scenarios. Conversely, the FFEM approaches either collapsed at ≥ $$ \ge $$ 70% of missing (MCAR) covariate data (FFEM complete case analysis) or had large bias increases and loss of precision (FFEM with mean imputation). Our results suggest that FREM is an appropriate approach to covariate modeling for datasets with missing (MCAR and MAR) covariate data, such as in global health studies., (© 2023 Pharmetheus AB and The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)