Mass-casualty incidents (MCI) are a highly important issue in disaster medicine today. In this context, professional first responders play a fundamental role as they provide preparedness and initial care to the injured. The aim of this review is to describe the form and impact of different didactic concepts in triage exercises to prepare for an MCI response.

A Scoping review search was conducted in the databases PubMed, Medline, and Psyndex as an initial examination of this topic.

Seventeen studies were included in this review. Of the reviewed studies, 52.9% followed a randomized controlled trial design with pre-post intervention measurement. The interventions implemented in the studies were associated with an increase in knowledge and/or practical skills. Of media-based interventions, 42.9% show a comparable and 57.1% greater training effect than conventional teaching methods. According to 4 studies, technical and non-technical aids increase the triage accuracy.

The benefits of media-based interventions and of technical and non-technical aids should be evaluated by a subsequent systematic review with a broader database and search terms of studies. The differences between different triage algorithms need to be investigated in future studies. It must be noted that intervention is preferable to non-intervention.

In acute ischemic stroke, a longer time from onset to endovascular treatment (EVT) is associated with worse clinical outcome. We investigated the association of clinical outcome with time from last known well to arrival at the EVT hospital and time from hospital arrival to arterial access for anterior circulation large vessel occlusion patients treated > 6 hours from last known well.

Retrospective analysis of the prospective, multicenter cohort study ESCAPE-LATE. Patients presenting > 6 hours after last known well with anterior circulation large vessel occlusion undergoing EVT were included. The primary outcome was the modified Rankin Scale (mRS) score at 90 days. Secondary outcomes were good (mRS 0–2) and poor clinical outcomes (mRS 5–6) at 90 days, as well as the National Institutes of Health Stroke Scale at 24 hours. Associations of time intervals with outcomes were assessed with univariable and multivariable logistic regression.

Two hundred patients were included in the analysis, of whom 85 (43%) were female. 90-day mRS was available for 141 patients. Of the 150 patients, 135 (90%) had moderate-to-good collaterals, and the median Alberta Stroke Program Early CT Score (ASPECTS) was 8 (IQR = 7–10). No association between ordinal mRS and time from last known well to arrival at the EVT hospital (odds ratio [OR] = 1.01, 95% CI = 1.00–1.02) or time from hospital arrival to arterial access (OR = -0.01, 95% CI = -0.02–0.00) was seen in adjusted regression models.

No relationship was observed between pre-hospital or in-hospital workflow times and clinical outcomes. Baseline ASPECTS and collateral status were favorable in the majority of patients, suggesting that physicians may have chosen to predominantly treat slow progressors in the late time window, in whom prolonged workflow times have less impact on outcomes.

In the era of cardiovascular-kidney-metabolic syndrome, thorough evaluation of medicines with multiple treatment effects/indications demands a multifaceted modeling philosophy, despite the requirement of health technology assessment (HTA) models to focus on one disease. Using Cardiff, a model previously built for type 2 diabetes (T2D), we illustrate the changes needed to capture contemporary, holistic, patient-centered decision-making, and argue that HTA bodies should revise their approach.

The upgraded model enables therapy selection and escalation determined by HbA1c thresholds, cardiovascular risk (QRISK3), comorbidities (established cardiovascular or chronic kidney disease), and weight (body mass index ≥35 kg/m2). Risk factor trajectories were updated by incorporating UKPDS-90 equations and other relevant data sources. Clinical outcomes were predicted using new risk equations incorporating cardiovascular outcomes trial data whenever possible. The updated model was applied to assess quality-adjusted life years (QALYs) and lifetime costs in newly diagnosed T2D patients in the UK, modeled via a conventional glycemic-centric approach versus a multifactorial treatment algorithm. Extrapolation to the national level utilized estimates of annual incidence.

The updated treatment algorithm captured and quantified the impact of nuanced comorbidity management called for in guidelines. In a cohort of newly diagnosed T2D patients, 81 percent initiated an SGLT2 inhibitor within five years, predominantly due to increasing cardiovascular risk, versus zero percent when escalation was dictated by HbA1c alone. Broad, early use of SGLT2 inhibitors resulted in an additional 0.73 predicted QALYs and GBP10,757 (USD13,600) in predicted lifetime cost savings per patient versus a “traditional” approach. Cost savings were primarily due to avoided renal events; extrapolation to the national level predicted cost savings to the payer of GBP2.8 billion (USD3.5 billion), which traditional models cannot capture.

The modernized Cardiff model incorporates multifactorial prescribing guidelines and contemporary evidence around cardio-renal protection and is more adept at modeling costs and outcomes of multidimensional antidiabetic treatments; traditional glucose-centric modeling methods may introduce bias. Economic modeling and HTA processes must adapt to follow the complexities of modern disease management and remain relevant as healthcare systems address the cardiovascular-kidney-metabolic syndrome epidemic.

Employment and relationship are crucial for social integration. However, individuals with major psychiatric disorders often face challenges in these domains.

We investigated employment and relationship status changes among patients across the affective and psychotic spectrum – in comparison with healthy controls, examining whether diagnostic groups or functional levels influence these transitions.

The sample from the longitudinal multicentric PsyCourse Study comprised 1260 patients with affective and psychotic spectrum disorders and 441 controls (mean age ± s.d., 39.91 ± 12.65 years; 48.9% female). Multistate models (Markov) were used to analyse transitions in employment and relationship status, focusing on transition intensities. Analyses contained multiple multistate models adjusted for age, gender, job or partner, diagnostic group and Global Assessment of Functioning (GAF) in different combinations to analyse the impact of the covariates on the hazard ratio of changing employment or relationship status.

The clinical group had a higher hazard ratio of losing partner (hazard ratio 1.46, P < 0.001) and job (hazard ratio 4.18, P < 0.001) than the control group (corrected for age/gender). Compared with controls, clinical groups had a higher hazard of losing partner (affective group, hazard ratio 2.69, P = 0.003; psychotic group, hazard ratio 3.06, P = 0.001) and job (affective group, hazard ratio 3.43, P < 0.001; psychotic group, hazard ratio 4.11, P < 0.001). Adjusting for GAF, the hazard ratio of losing partner and job decreased in both clinical groups compared with controls.

Patients face an increased hazard of job loss and relationship dissolution compared with healthy controls, and this is partially conditioned by the diagnosis and functional level. These findings underscore a high demand for destigmatisation and support for individuals in managing their functional limitations.

The Personalized Advantage Index (PAI) shows promise as a method for identifying the most effective treatment for individual patients. Previous studies have demonstrated its utility in retrospective evaluations across various settings. In this study, we explored the effect of different methodological choices in predictive modelling underlying the PAI.

Our approach involved a two-step procedure. First, we conducted a review of prior studies utilizing the PAI, evaluating each study using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). We specifically assessed whether the studies adhered to two standards of predictive modeling: refraining from using leave-one-out cross-validation (LOO CV) and preventing data leakage. Second, we examined the impact of deviating from these methodological standards in real data. We employed both a traditional approach violating these standards and an advanced approach implementing them in two large-scale datasets, PANIC-net (n = 261) and Protect-AD (n = 614).

The PROBAST-rating revealed a substantial risk of bias across studies, primarily due to inappropriate methodological choices. Most studies did not adhere to the examined prediction modeling standards, employing LOO CV and allowing data leakage. The comparison between the traditional and advanced approach revealed that ignoring these standards could systematically overestimate the utility of the PAI.

Our study cautions that violating standards in predictive modeling may strongly influence the evaluation of the PAI's utility, possibly leading to false positive results. To support an unbiased evaluation, crucial for potential clinical application, we provide a low-bias, openly accessible, and meticulously annotated script implementing the PAI.