Incorporating emerging knowledge into Emergency Medical Service (EMS) competency assessments is critical to reflect current evidence-based out-of-hospital care. However, a standardized approach is needed to incorporate new evidence into EMS competency assessments because of the rapid pace of knowledge generation.

The objective was to develop a framework to evaluate and integrate new source material into EMS competency assessments.

The National Registry of Emergency Medical Technicians (National Registry) and the Prehospital Guidelines Consortium (PGC) convened a panel of experts. A Delphi method, consisting of virtual meetings and electronic surveys, was used to develop a Table of Evidence matrix that defines sources of EMS evidence. In Round One, participants listed all potential sources of evidence available to inform EMS education. In Round Two, participants categorized these sources into: (a) levels of evidence quality; and (b) type of source material. In Round Three, the panel revised a proposed Table of Evidence. Finally, in Round Four, participants provided recommendations on how each source should be incorporated into competency assessments depending on type and quality. Descriptive statistics were calculated with qualitative analyses conducted by two independent reviewers and a third arbitrator.

In Round One, 24 sources of evidence were identified. In Round Two, these were classified into high- (n = 4), medium- (n = 15), and low-quality (n = 5) of evidence, followed by categorization by purpose into providing recommendations (n = 10), primary research (n = 7), and educational content (n = 7). In Round Three, the Table of Evidence was revised based on participant feedback. In Round Four, the panel developed a tiered system of evidence integration from immediate incorporation of high-quality sources to more stringent requirements for lower-quality sources.

The Table of Evidence provides a framework for the rapid and standardized incorporation of new source material into EMS competency assessments. Future goals are to evaluate the application of the Table of Evidence framework in initial and continued competency assessments.

To examine the costs and cost-effectiveness of mirtazapine compared to placebo over 12-week follow-up.

Economic evaluation in a double-blind randomized controlled trial of mirtazapine vs. placebo.

Community settings and care homes in 26 UK centers.

People with probable or possible Alzheimer’s disease and agitation.

Primary outcome included incremental cost of participants’ health and social care per 6-point difference in CMAI score at 12 weeks. Secondary cost-utility analyses examined participants’ and unpaid carers’ gain in quality-adjusted life years (derived from EQ-5D-5L, DEMQOL-Proxy-U, and DEMQOL-U) from the health and social care and societal perspectives.

One hundred and two participants were allocated to each group; 81 mirtazapine and 90 placebo participants completed a 12-week assessment (87 and 95, respectively, completed a 6-week assessment). Mirtazapine and placebo groups did not differ on mean CMAI scores or health and social care costs over the study period, before or after adjustment for center and living arrangement (independent living/care home). On the primary outcome, neither mirtazapine nor placebo could be considered a cost-effective strategy with a high level of confidence. Groups did not differ in terms of participant self- or proxy-rated or carer self-rated quality of life scores, health and social care or societal costs, before or after adjustment.

On cost-effectiveness grounds, the use of mirtazapine cannot be recommended for agitated behaviors in people living with dementia. Effective and cost-effective medications for agitation in dementia remain to be identified in cases where non-pharmacological strategies for managing agitation have been unsuccessful.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

This study examines the relationship of serum total tau, neurofilament light (NFL), ubiquitin carboxyl-terminal hydrolase L1 (UCH-L1), and glial fibrillary acidic protein (GFAP) with neurocognitive performance in service members and veterans with a history of traumatic brain injury (TBI).

Service members (n = 488) with a history of uncomplicated mild (n = 172), complicated mild, moderate, severe, or penetrating TBI (sTBI; n = 126), injured controls (n = 116), and non-injured controls (n = 74) prospectively enrolled from Military Treatment Facilities. Participants completed a blood draw and neuropsychological assessment a year or more post-injury. Six neuropsychological composite scores and presence/absence of mild neurocognitive disorder (MNCD) were evaluated. Within each group, stepwise hierarchical regression models were conducted.

Within the sTBI group, increased serum UCH-L1 was related to worse immediate memory and delayed memory (R2Δ = .065–.084, ps < .05) performance, while increased GFAP was related to worse perceptual reasoning (R2Δ = .030, p = .036). Unexpectedly, within injured controls, UCH-L1 and GFAP were inversely related to working memory (R2Δ = .052–.071, ps < .05), and NFL was related to executive functioning (R2Δ = .039, p = .021) and MNCD (Exp(B) = 1.119, p = .029).

Results suggest GFAP and UCH-L1 could play a role in predicting poor cognitive outcome following complicated mild and more severe TBI. Further investigation of blood biomarkers and cognition is warranted.

In clinical and translational research, data science is often and fortuitously integrated with data collection. This contrasts to the typical position of data scientists in other settings, where they are isolated from data collectors. Because of this, effective use of data science techniques to resolve translational questions requires innovation in the organization and management of these data.

We propose an operational framework that respects this important difference in how research teams are organized. To maximize the accuracy and speed of the clinical and translational data science enterprise under this framework, we define a set of eight best practices for data management.

In our own work at the University of Rochester, we have strived to utilize these practices in a customized version of the open source LabKey platform for integrated data management and collaboration. We have applied this platform to cohorts that longitudinally track multidomain data from over 3000 subjects.

We argue that this has made analytical datasets more readily available and lowered the bar to interdisciplinary collaboration, enabling a team-based data science that is unique to the clinical and translational setting.

Depression and anxiety disorders show a high comorbidity based on common pathophysiological mechanisms. Childhood environmental and life stressors are leading factors in both disorders and result in stable changes of genetic expression mediated by epigenetics, which has been found to impact in the transcription of genes, influence neurogenesis, neuroplasticity and neuronal connectivity.

To provide an overview about functional neuroimaging genetics in MDD and anxiety disorders.

Functional MRI, epigenetic and genetic information was obtained in a cohort of patients with MDD with high and low levels of anxiety and healthy controls. Associations between methylation of SLC6A4, genetic variants and brain function and connectivity was analysed.

Higher methylation of SLC6A4 gene was associated with higher BOLD response during emotion processing and lower BOLD response during higher order cognitive processes. Specific asociation with anxiety and depression are further analysed.

Our study provides further support to the hypothesis that particular DNA methylation states that are associated with brain function during emotion processing are detectable in the periphery. The influence of anxiety or depression on this association is discussed.