Hasil untuk "Medical emergencies. Critical care. Intensive care. First aid"

Giacomo Strapazzon, Oyvind Thomassen, Christopher Van Tilburg et al.

Abstract Avalanches claim the lives of around 160 winter recreationists and workers in hazardous snow-covered mountain regions worldwide. These fatalities result from asphyxia, trauma, and hypothermia. Survival for critically buried subjects relies on the speed of extrication, absence of trauma, presence of a patent airway and air pocket, immediate field treatment, and rapid transport to the most appropriate hospital by organized search and rescue (SAR) and emergency medical services (EMS). Survival may be improved with helicopter EMS, advancements in safety and rescue equipment technology, early deployment of professional SAR teams with checklists and standard operating procedures (SOPs), and the use of drones. Prehospital and in-hospital care of an avalanche accident requires medical, technical, logistical, and organizational competencies. This narrative review of historic and updated survival curves, professional society guidelines, and snow burial studies, discusses the best practices for prehospital care, triage, transport, and in-hospital management of avalanche patients.

Anqi Li, Chenxiao Wang, Yu Lu et al.

Client perceptions of the therapeutic alliance are critical for counseling effectiveness. Accurately capturing these perceptions remains challenging, as traditional post-session questionnaires are burdensome and often delayed, while existing computational approaches produce coarse scores, lack interpretable rationales, and fail to model holistic session context. We present CARE, an LLM-based framework to automatically predict multi-dimensional alliance scores and generate interpretable rationales from counseling transcripts. Built on the CounselingWAI dataset and enriched with 9,516 expert-curated rationales, CARE is fine-tuned using rationale-augmented supervision with the LLaMA-3.1-8B-Instruct backbone. Experiments show that CARE outperforms leading LLMs and substantially reduces the gap between counselor evaluations and client-perceived alliance, achieving over 70% higher Pearson correlation with client ratings. Rationale-augmented supervision further improves predictive accuracy. CARE also produces high-quality, contextually grounded rationales, validated by both automatic and human evaluations. Applied to real-world Chinese online counseling sessions, CARE uncovers common alliance-building challenges, illustrates how interaction patterns shape alliance development, and provides actionable insights, demonstrating its potential as an AI-assisted tool for supporting mental health care.

Nithya Bhasker, Fiona R. Kolbinger, Susu Hu et al.

Accurate prediction of outcomes is crucial for clinical decision-making and personalized patient care. Supervised machine learning algorithms, which are commonly used for outcome prediction in the medical domain, optimize for predictive accuracy, which can result in models latching onto spurious correlations instead of robust predictors. Causal structure learning methods on the other hand have the potential to provide robust predictors for the target, but can be too conservative because of algorithmic and data assumptions, resulting in loss of diagnostic precision. Therefore, we propose a novel model-agnostic regularization strategy, Adaptive-CaRe, for generalized outcome prediction in the medical domain. Adaptive-CaRe strikes a balance between both predictive value and causal robustness by incorporating a penalty that is proportional to the difference between the estimated statistical contribution and estimated causal contribution of the input features for model predictions. Our experiments on synthetic data establish the efficacy of the proposed Adaptive-CaRe regularizer in finding robust predictors for the target while maintaining competitive predictive accuracy. With experiments on a standard causal benchmark, we provide a blueprint for navigating the trade-off between predictive accuracy and causal robustness by tweaking the regularization strength, $λ$. Validation using real-world dataset confirms that the results translate to practical, real-domain settings. Therefore, Adaptive-CaRe provides a simple yet effective solution to the long-standing trade-off between predictive accuracy and causal robustness in the medical domain. Future work would involve studying alternate causal structure learning frameworks and complex classification models to provide deeper insights at a larger scale.

Thomas M. Scalea, Erin Major, Celina Thomas et al.

Background Despite being high risk for post-traumatic stress disorder, Black men survivors of gun violence, and particularly young men aged 18–24, seldom participate in mental health services after injury. The aim of this study was to identify barriers to participation in mental health services for this population.Methods Over a 2-year period, 1 hour-long focus group was conducted with three counselors of the local hospital-based violence intervention program and 21 individual, semistructured in-depth interviews were held with Black men who were hospitalized for a firearm-related injury. All interviews were recorded and transcribed. Transcripts were coded using open coding and grounded theory methodology and ultimately grouped into themes using MAXQDA V.2022 software.Results Median age of participants was 34 years (IQR=11). Barriers to participation revolved around competing priorities/stressors, expense, difficulty with trust and openness and the demands of street life. Motivating factors included cultural competence, persistence, availability, reliability and genuineness of the therapy staff. Most participants denied negative social stigma of therapy as a barrier but emphasized that the individual must value therapy to participate. Young, Black men were perceived as struggling with self and peer-imposed views of masculinity that conflicted with therapy participation.Conclusion Black men who have experienced violent firearm injury face strong social pressures that conflict with participation in mental health services. Programs must be integrated with other social services and be responsive to community conditions to be successful.Level of evidence IV

Christopher D. Yang, Christine K. Kim, Melissa M. Chang et al.

Objective To evaluate the current body of literature pertaining to the use of ocular point-of-care ultrasound (POCUS) in the emergency department (ED). Methods A comprehensive literature search was conducted on Scopus, Web of Science, MEDLINE, and Cochrane Central Register of Controlled Trials (CENTRAL) databases. Inclusion criteria were studies written in English and primary clinical studies involving ocular POCUS scans in an ED setting. Exclusion criteria were nonprimary studies (e.g., reviews or case reports), studies written in a non-English language, nonhuman studies, studies performed in a nonemergency setting, studies involving non-POCUS ocular ultrasound modalities, or studies published more than 10 years prior. Data extraction was guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations. Results The initial search yielded 391 results with 153 duplicates. Of the remaining 238 studies selected for retrieval and screening, 24 met the inclusion criteria. These 24 included studies encompassed 2,448 patients across prospective, retrospective, cross-sectional, and case series study designs. The majority of included studies focused on the use of POCUS in the ED to measure optic nerve sheath diameter as a proxy for papilledema and metabolic aberrations, while a minority of studies used ocular POCUS to assist in the diagnosis of orbital fractures or posterior segment pathology. Conclusion The vast majority of studies investigating the use of ocular POCUS in recent years emphasize its utility in measuring optic nerve sheath diameter and fluctuations in intracranial pressure, though additional outcomes of interest include pathology of the posterior segment, orbit, and globe.

Frida Wilske, Olof Eriksson, Rose-Marie Amini et al.

Abstract Background Neutrophil granulocytes are important parts of the defence against bacterial infections. Their action is a two-edged sword, the mediators killing the intruding bacteria are at the same time causing tissue damage. Neutrophil activation is part of the dysregulated immune response to infection defining sepsis and neutrophil elastase is one of the powerful proteases causing both effects and damage. Inhibition of neutrophil elastase has been tried in sepsis and ARDS, so far with inconclusive results. Methods We used positron emission tomography (PET) combined with computed tomography (CT) and the selective and specific neutrophil elastase inhibitor PET-tracer [11C]GW457427 ([11C]NES), in an intensive care unit porcine Escherichia coli sepsis model with the primary aim to visualise the biodistribution of neutrophil elastase in the initial acute phase of the septic reaction. Repeated PET–CT investigations were performed before and after induction of sepsis. Results At baseline [11C]NES uptake was found in the bone marrow, spleen and liver. The uptake in the bone marrow was markedly increased two hours into the sepsis, whereas in spleen and liver the uptake was not as markedly changed compared to baseline. At 4 h after the sepsis induction [11C]NES in the bone marrow decreased while the uptake increased in the spleen, liver and lungs. Conclusion The neutrophil elastase PET-tracer [11C]NES is a novel and unique instrument to study the acute innate neutrophil immune response in sepsis and associated vital organ failure. We here present images and quantitative data of the neutrophil elastase distribution the first hours of acute experimental sepsis. Surprisingly, a pronounced increase of neutrophil elastase was found in the bone marrow 2 h into the sepsis reaction followed at 4 h by increase in the liver, spleen and lungs and a concomitant reduction of the tracer uptake in bone marrow.

Shyama Sastha Krishnamoorthy Srinivasan, Harsh Pala, Mohan Kumar et al.

Current mobile health platforms are predominantly individual-centric and lack the support for coordinated, auditable multi-actor workflows. However, in many settings worldwide, health decisions are enacted by multi-actor care networks rather than single users. We present JEEVHITAA, a cross-platform mobile system enabling role-aware sharing and verifiable information flows within permissioned care circles. JEEVHITAA ingests platform and device data (Health-Connect, BLE), builds layered profiles from sensors and tiered onboarding, and enforces fine-grained, time-bounded access control across care graphs. Data are end-to-end encrypted both locally and during peer synchronization; documents can be captured or uploaded as PDFs. An integrated retrieval-augmented LLM produces structured, role-targeted summaries and action plans, offers evidence-grounded verification with provenance and confidence scores, and supports advanced insights on reports. We describe the architecture, connector abstractions, security primitives, and report robustness evaluations using synthetic ontology-driven data, as well as a feasibility study with a real-life care circle. We outline plans for longitudinal in-the-wild evaluation of access control correctness and credibility support.

Mahyar Abbasian, Ramesh Jain

Building on decades of success in digital infrastructure and biomedical innovation, we propose the Personal Care Utility (PCU) - a cybernetic system for lifelong health guidance. PCU is conceived as a global, AI-powered utility that continuously orchestrates multimodal data, knowledge, and services to assist individuals and populations alike. Drawing on multimodal agents, event-centric modeling, and contextual inference, it offers three essential capabilities: (1) trusted health information tailored to the individual, (2) proactive health navigation and behavior guidance, and (3) ongoing interpretation of recovery and treatment response after medical events. Unlike conventional episodic care, PCU functions as an ambient, adaptive companion - observing, interpreting, and guiding health in real time across daily life. By integrating personal sensing, experiential computing, and population-level analytics, PCU promises not only improved outcomes for individuals but also a new substrate for public health and scientific discovery. We describe the architecture, design principles, and implementation challenges of this emerging paradigm.

Marianne Leineweber, Clara Victoria Keusgen, Marc Bubeck et al.

Background: The use of social robotics in elderly care is increasingly discussed as one way of meeting emerging care needs due to scarce resources. While many potential benefits are associated with robotic care technologies, there is a variety of ethical challenges. To support steps towards a responsible implementation and use, this review develops an overview on ethical aspects of the use of social robots in elderly care from a decision-makers' perspective. Methods: Electronic databases were queried using a comprehensive search strategy based on the key concepts of "ethical aspects", "social robotics" and "elderly care". Abstract and title screening was conducted by two authors independently. Full-text screening was conducted by one author following a joint consolidation phase. Data was extracted using MAXQDA24 by one author, based on a consolidated coding framework. Analysis was performed through modified qualitative content analysis. Results: A total of 1,518 publications were screened, and 248 publications were included. We have organized our analysis in a scheme of ethical hazards, ethical opportunities and unsettled questions, identifying at least 60 broad ethical aspects affecting three different stakeholder groups. While some ethical issues are well-known and broadly discussed our analysis shows a plethora of potentially relevant aspects, often only marginally recognized, that are worthy of consideration from a practical perspective. Discussion: The findings highlight the need for a contextual and detailed evaluation of implementation scenarios. To make use of the vast knowledge of the ethical discourse, we hypothesize that decision-makers need to understand the specific nature of this discourse to be able to engage in careful ethical deliberation.

V Gimeno Hernan, I Duran-Muños, MR Del Pino- Jurado et al.

Objetive: In the care of renal patients, prioritising their quality of life and nursing care is essential. Research links patients' perceptions of care quality to improved outcomes such as safety, clinical efficacy, treatment adherence, and preventive practices. This study aimed to evaluate the quality of life and care perception in these patients and explore potential associations between these dimensions. Material and methods: A cross-sectional descriptive study was conducted with 43 patients attending an advanced CKD clinic. Quality of life was assessed using the KDQOL-36 questionnaire, while the IECPAX questionnaire measured perceived care quality. Sociodemographic and clinical data were collected from patient records. Participants completed the questionnaires during routine visits, with scores analysed to identify associations between variables. Results: The study included 60% men (n=28) and 32% women (n=15), with a mean age of 78 years . Among participants, 45% were diabetic, 79% hypertensive, and 58% took more than five medications daily. Mean scores were 78.76 for KDQOL-36 and 5.54 for IECPAX. Significant differences were found in the physical role domain between men and women (p=0.01) and for individuals over 65 years (p=0.04). Higher IECPAX scores were associated with taking more than five medications (p=0.05). However, no correlation was observed between KDQOL-36 and IECPAX scores. Conclusions: The findings suggest that quality of life and perceived care quality are independent in advanced CKD patients. While this study provides insights, larger multicentre studies are needed to validate these results. These findings highlight the importance of addressing both aspects separately to improve outcomes in this population.

Solha Kang, Joris Vankerschaver, Utku Ozbulak

With the advancements in self-supervised learning (SSL), transformer-based computer vision models have recently demonstrated superior results compared to convolutional neural networks (CNNs) and are poised to dominate the field of artificial intelligence (AI)-based medical imaging in the upcoming years. Nevertheless, similar to CNNs, unveiling the decision-making process of transformer-based models remains a challenge. In this work, we take a step towards demystifying the decision-making process of transformer-based medical imaging models and propose Token Insight, a novel method that identifies the critical tokens that contribute to the prediction made by the model. Our method relies on the principled approach of token discarding native to transformer-based models, requires no additional module, and can be applied to any transformer model. Using the proposed approach, we quantify the importance of each token based on its contribution to the prediction and enable a more nuanced understanding of the model's decisions. Our experimental results which are showcased on the problem of colonic polyp identification using both supervised and self-supervised pretrained vision transformers indicate that Token Insight contributes to a more transparent and interpretable transformer-based medical imaging model, fostering trust and facilitating broader adoption in clinical settings.

M. Matějovič, O. Huet, K. Dams et al.

Background Medical nutrition therapy may be associated with clinical outcomes in critically ill patients with prolonged intensive care unit (ICU) stay. We wanted to assess nutrition practices in European intensive care units (ICU) and their importance for clinical outcomes. Methods Prospective multinational cohort study in patients staying in ICU ≥ 5 days with outcome recorded until day 90. Macronutrient intake from enteral and parenteral nutrition and non-nutritional sources during the first 15 days after ICU admission was compared with targets recommended by ESPEN guidelines. We modeled associations between three categories of daily calorie and protein intake (low:  20 kcal/kg; > 1.2 g/kg) and the time-varying hazard rates of 90-day mortality or successful weaning from invasive mechanical ventilation (IMV). Results A total of 1172 patients with median [Q1;Q3] APACHE II score of 18.5 [13.0;26.0] were included, and 24% died within 90 days. Median length of ICU stay was 10.0 [7.0;16.0] days, and 74% of patients could be weaned from invasive mechanical ventilation. Patients reached on average 83% [59;107] and 65% [41;91] of ESPEN calorie and protein recommended targets, respectively. Whereas specific reasons for ICU admission (especially respiratory diseases requiring IMV) were associated with higher intakes (estimate 2.43 [95% CI: 1.60;3.25] for calorie intake, 0.14 [0.09;0.20] for protein intake), a lack of nutrition on the preceding day was associated with lower calorie and protein intakes (− 2.74 [− 3.28; − 2.21] and − 0.12 [− 0.15; − 0.09], respectively). Compared to a lower intake, a daily moderate intake was associated with higher probability of successful weaning (for calories: maximum HR 4.59 [95% CI: 1.5;14.09] on day 12; for protein: maximum HR 2.60 [1.09;6.23] on day 12), and with a lower hazard of death (for calories only: minimum HR 0.15, [0.05;0.39] on day 19). There was no evidence that a high calorie or protein intake was associated with further outcome improvements. Conclusions Calorie intake was mainly provided according to the targets recommended by the active ESPEN guideline, but protein intake was lower. In patients staying in ICU ≥ 5 days, early moderate daily calorie and protein intakes were associated with improved clinical outcomes. Trial registration NCT04143503 , registered on October 25, 2019. Graphical abstract

Maciej Niczewski, Szymon Gawęda, Paulina Kluszczyk et al.

Objective. An accurate identification of patients at the need for prioritized diagnostics and care are crucial in the emergency department (ED). Blood gas (BG) analysis is a widely available laboratory test, which allows to measure vital parameters, including markers of ventilation and perfusion. The aim of our analysis was to assess whether blood gas parameters in patients with dyspnea at an increased risk of respiratory failure admitted to the ED can predict short-term outcomes. Methods. The study group eventually consisted of 108 patients, with available BG analysis. The clinical and laboratory parameters were retrospectively evaluated, and three groups were distinguished—arterial blood gas (ABG), venous blood gas (VBG), and mixed blood gas. The primary endpoint was short-term, all-cause mortality during the follow-up of median (quartile 1–quartile 3) 2 (1–4) months. The independent risk factors for mortality that could be obtained from blood gas sampling were evaluated. Results. The short-term mortality was 35.2% (38/108). Patients who died were more frequently initially assigned to the red triage risk group, more burdened with comorbidities, and the median SpO2 on admission was significantly lower than in patients who survived the follow-up period. In the multivariable analysis, lactate was the strongest independent predictor of death, with 1 mmol/L increasing all-cause mortality by 58% in ABG (95% CI: 1.01–2.47), by 80% in VBG (95% CI: 1.13–2.88), and by 68% in the mixed blood gas analysis (95% CI: 1.22–2.31), what remained significant in VBG and mixed group after correction for base excess. In each group, pH, pO2, and pCO2 did not predict short-term mortality. Conclusions. In patients admitted to the ED due to dyspnea, at risk of respiratory failure, lactate levels in arterial, venous, and mixed blood samples are independent predictors of short-term mortality.

Tawfiq Ammari, Eunhye Ahn, Astha Lakhankar et al.

Many children in foster care experience trauma that is rooted in unstable family relationships. Other members of the foster care system like foster parents and social workers face secondary trauma. Drawing on 10 years of Reddit data, we used a mixed methods approach to analyze how different members of the foster care system find support and similar experiences at the intersection of two Reddit communities - foster care, and abuse. Users who cross this boundary focus on trauma experiences specific to different roles in foster care. While representing a small number of users, boundary crossing users contribute heavily to both communities, and, compared to matching users, receive higher scores and more replies. We explore the roles boundary crossing users have both in the online community and in the context of foster care. Finally, we present design recommendations that would support trauma survivors find communities more suited to their personal experiences.

Thomas Frost, Kezhi Li, Steve Harris

The task of predicting long-term patient outcomes using supervised machine learning is a challenging one, in part because of the high variance of each patient's trajectory, which can result in the model over-fitting to the training data. Temporal difference (TD) learning, a common reinforcement learning technique, may reduce variance by generalising learning to the pattern of state transitions rather than terminal outcomes. However, in healthcare this method requires several strong assumptions about patient states, and there appears to be limited literature evaluating the performance of TD learning against traditional supervised learning methods for long-term health outcome prediction tasks. In this study, we define a framework for applying TD learning to real-time irregularly sampled time series data using a Semi-Markov Reward Process. We evaluate the model framework in predicting intensive care mortality and show that TD learning under this framework can result in improved model robustness compared to standard supervised learning methods. and that this robustness is maintained even when validated on external datasets. This approach may offer a more reliable method when learning to predict patient outcomes using high-variance irregular time series data.

Ludmilla Penarrubia, Aude Verstraete, Maciej Orkisz et al.

Abstract Background Assessing measurement error in alveolar recruitment on computed tomography (CT) is of paramount importance to select a reliable threshold identifying patients with high potential for alveolar recruitment and to rationalize positive end-expiratory pressure (PEEP) setting in acute respiratory distress syndrome (ARDS). The aim of this study was to assess both intra- and inter-observer smallest real difference (SRD) exceeding measurement error of recruitment using both human and machine learning-made lung segmentation (i.e., delineation) on CT. This single-center observational study was performed on adult ARDS patients. CT were acquired at end-expiration and end-inspiration at the PEEP level selected by clinicians, and at end-expiration at PEEP 5 and 15 cmH2O. Two human observers and a machine learning algorithm performed lung segmentation. Recruitment was computed as the weight change of the non-aerated compartment on CT between PEEP 5 and 15 cmH2O. Results Thirteen patients were included, of whom 11 (85%) presented a severe ARDS. Intra- and inter-observer measurements of recruitment were virtually unbiased, with 95% confidence intervals (CI95%) encompassing zero. The intra-observer SRD of recruitment amounted to 3.5 [CI95% 2.4–5.2]% of lung weight. The human–human inter-observer SRD of recruitment was slightly higher amounting to 5.7 [CI95% 4.0–8.0]% of lung weight, as was the human–machine SRD (5.9 [CI95% 4.3–7.8]% of lung weight). Regarding other CT measurements, both intra-observer and inter-observer SRD were close to zero for the CT-measurements focusing on aerated lung (end-expiratory lung volume, hyperinflation), and higher for the CT-measurements relying on accurate segmentation of the non-aerated lung (lung weight, tidal recruitment…). The average symmetric surface distance between lung segmentation masks was significatively lower in intra-observer comparisons (0.8 mm [interquartile range (IQR) 0.6–0.9]) as compared to human–human (1.0 mm [IQR 0.8–1.3] and human–machine inter-observer comparisons (1.1 mm [IQR 0.9–1.3]). Conclusions The SRD exceeding intra-observer experimental error in the measurement of alveolar recruitment may be conservatively set to 5% (i.e., the upper value of the CI95%). Human–machine and human–human inter-observer measurement errors with CT are of similar magnitude, suggesting that machine learning segmentation algorithms are credible alternative to humans for quantifying alveolar recruitment on CT.