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

Mehmet Yortanlı, Ramazan Köylü, Öznur Köylü et al.

Aim: This study aimed to evaluate the diagnostic and prognostic significance of serum beta-trace protein (BTP) levels in patients diagnosed with sepsis in the emergency department. Materials and Methods: This prospective, single-center, observational clinical study was conducted in the emergency department and intensive care unit of a tertiary hospital. A total of 104 sepsis patients and 48 healthy adult volunteers who presented to the emergency department between April 2015 and October 2015 were included. Blood samples were collected on days 1 and 3, and BTP levels were measured using the ELISA method. Statistical analyses were performed using SPSS 22.0. Results: BTP levels were significantly higher in sepsis patients compared to the control group (p=0.013). However, no significant difference was observed between day 1 and day 3 BTP levels (p=0.119). When categorized by sepsis severity, BTP levels did not correlate with disease severity (p>0.05). Additionally, no significant association was found between BTP levels and mortality (p=0.651). Conclusion: BTP may serve as a potential biomarker for sepsis diagnosis, but it is not a reliable indicator of disease severity or prognosis. Further large-scale studies are needed.

Vijay Nagappa, M.C. Shamantha

Rationale: Although traditionally linked to myocardial dysfunction, left ventricular (LV) thrombus with preserved systolic function presents a distinct diagnostic challenge, necessitating its consideration in all cardioembolic strokes. Patient’s Concern: A 50-year-old woman with diabetes experienced sudden unresponsiveness accompanied by significant right upper limb weakness. Remarkably, her clinical history was devoid of symptoms suggestive of ischemic or arrhythmic cardiac events. Diagnosis: Advanced neuroimaging identified acute bilateral medial thalamic and paramedian midbrain infarctions. Transthoracic echocardiography revealed an unexpectedly large, mobile LV thrombus, coexisting with normal systolic function (ejection fraction: 60%). Interventions: Immediate anticoagulation with low molecular weight heparin was initiated, followed by oral warfarin titrated to an international normalized ratio of 2.0-3.0. Physical therapy was implemented to address residual motor deficits. Outcomes: Three months post-intervention, the patient demonstrated marked neurological improvement (right upper limb power: 3/5) and complete thrombus resolution, confirmed by follow-up echocardiography. Lessons: This case highlights that LV thrombus is a rare but critical consideration in embolic stroke of uncertain origin, even with normal cardiac function. It underscores the necessity of prompt echocardiography and early anticoagulation to achieve optimal outcomes.

Abdurrahman Oral, Ekim Sağlam Gürmen, Mustafa Yorgancıoğlu

Aim: In this study, we aimed to determine the role of BAP-65, DECAF and DECAF-L scores in predicting morbidity and mortality in chronic obstructive pulmonary disease patients. These scores offer a potential standardized approach for evaluating chronic obstructive pulmonary disease (COPD) exacerbations in the emergency department. Materials and Methods: This is a prospective observational study including COPD patients admitted to the emergency department. BAP-65, DECAF and DECAF-L scores were calculated. Initial outcomes including discharge, hospitalization or transfer to the intensive care unit, 30-day readmission and 30-day mortality were recorded. Results: A total of 200 patients were included. BAP-65, DECAF and DECAF-L scores were significantly associated with the type of initial outcomes (discharge, hospital admission, or intensive care unit admission) and (p<0.001 for each). Lactate values were higher in deceased patients than in survived patients (p=0.004). When the lactate value increased by 1 unit, the risk of 30-day mortality increased by 35.8%. A significant difference was found between 30-day mortality and the DECAF-L score obtained by adding lactate to the DECAF score (area under the curve=0.653; p=0.039). This risk increased by 29.6% when the DECAF-L value increased by 1 unit. Conclusion: Increasing the use of BAP-65, DECAF, and DECAF-L scores in the decision for discharge or hospitalization in COPD patients admitted to emergency departments will provide great convenience. In addition, we believe that it would be beneficial to increase the use of the DECAF-L score, which was found to be effective in predicting mortality in emergency departments.

Sherif F. Nagueh, MD, Payam Pournazari, MD, Priscilla Wessly, MD et al.

Background: Mitral transcatheter edge-to-edge repair (M-TEER) is an effective treatment for mitral regurgitation (MR) patients. Objectives: The aim of this research was to study M-TEER effects on left atrial (LA) and left ventricular (LV) functions. Methods: LV function was evaluated using conductance catheters for pressure volume loops in 22 patients with primary MR and a control group of 17 heart transplant recipients with normal function. LA pressures and LA operating chamber stiffness were obtained using fluid-filled catheters. MR severity was assessed by echocardiography. Results: Compared to the control group, primary MR patients had increased LV volumes, diastolic pressures, tau, and LV chamber stiffness constant (all P ≤ 0.01). After M-TEER, LV and LA volumes and pressures and MR regurgitant volume decreased (all P < 0.05), without significant change in LV preload recruitable stroke work (50.7 ± 14 vs 47 ± 17 mm Hg), end systolic pressure/volume (1.67 ± 0.69 vs 1.66 ± 0.62 mm Hg/mL), pressure volume area (11,493 ± 3,428 vs 11,898 ± 5,256 mm Hg. mL), and chamber stiffness constant (0.05 ± 0.07 vs 0.03 ± 0.02, all P > 0.20). Post-M-TEER, patients with LA V wave pressure ≥20 mm Hg had significantly higher LA operating chamber stiffness, tau, and LV chamber stiffness constant vs patients with V wave pressure <20 mm Hg all (P < 0.05). Conclusions: After M-TEER, there is no significant change in invasive indices of LV contractility. Tau is significantly prolonged after M-TEER, whereas LV chamber stiffness constant is abnormally increased before M-TEER without a significant change afterward. Patients with LA “V” wave pressure ≥20 mm Hg after M-TEER have worse LV diastolic function and higher LA operating chamber stiffness.

Shihan Fu, Bingsheng Yao, Smit Desai et al.

Older adult patients constitute a rapidly growing subgroup of Intensive Care Unit (ICU) patients. In these situations, their family caregivers are expected to represent the unconscious patients to access and interpret patients' medical information. However, caregivers currently have to rely on overloaded clinicians for information updates and typically lack the health literacy to understand complex medical information. Our project aims to explore the information needs of caregivers of ICU older adult patients, from which we can propose design opportunities to guide future AI systems. The project begins with formative interviews with 11 caregivers to identify their challenges in accessing and interpreting medical information; From these findings, we then synthesize design requirements and propose an AI system prototype to cope with caregivers' challenges. The system prototype has two key features: a timeline visualization to show the AI extracted and summarized older adult patients' key medical events; and an LLM-based chatbot to provide context-aware informational support. We conclude our paper by reporting on the follow-up user evaluation of the system and discussing future AI-based systems for ICU caregivers of older adults.

Robert Korom, Sarah Kiptinness, Najib Adan et al.

We evaluate the impact of large language model-based clinical decision support in live care. In partnership with Penda Health, a network of primary care clinics in Nairobi, Kenya, we studied AI Consult, a tool that serves as a safety net for clinicians by identifying potential documentation and clinical decision-making errors. AI Consult integrates into clinician workflows, activating only when needed and preserving clinician autonomy. We conducted a quality improvement study, comparing outcomes for 39,849 patient visits performed by clinicians with or without access to AI Consult across 15 clinics. Visits were rated by independent physicians to identify clinical errors. Clinicians with access to AI Consult made relatively fewer errors: 16% fewer diagnostic errors and 13% fewer treatment errors. In absolute terms, the introduction of AI Consult would avert diagnostic errors in 22,000 visits and treatment errors in 29,000 visits annually at Penda alone. In a survey of clinicians with AI Consult, all clinicians said that AI Consult improved the quality of care they delivered, with 75% saying the effect was "substantial". These results required a clinical workflow-aligned AI Consult implementation and active deployment to encourage clinician uptake. We hope this study demonstrates the potential for LLM-based clinical decision support tools to reduce errors in real-world settings and provides a practical framework for advancing responsible adoption.

Siteng Ma, Honghui Du, Yu An et al.

Deep learning has achieved significant breakthroughs in medical imaging, but these advancements are often dependent on large, well-annotated datasets. However, obtaining such datasets poses a significant challenge, as it requires time-consuming and labor-intensive annotations from medical experts. Consequently, there is growing interest in learning paradigms such as incomplete, inexact, and absent supervision, which are designed to operate under limited, inexact, or missing labels. This survey categorizes and reviews the evolving research in these areas, analyzing around 600 notable contributions since 2018. It covers tasks such as image classification, segmentation, and detection across various medical application areas, including but not limited to brain, chest, and cardiac imaging. We attempt to establish the relationships among existing research studies in related areas. We provide formal definitions of different learning paradigms and offer a comprehensive summary and interpretation of various learning mechanisms and strategies, aiding readers in better understanding the current research landscape and ideas. We also discuss potential future research challenges.

V. V. Evreinov

Introduction. Valproic acid is one of the commonly prescribed basic anticonvulsants for the treatment of epileptic seizures in children with cerebral palsy. Its active metabolites can cause hematological and coagulation disorders, cause valproate-induced steatohepatitis.The objective was to assess the level of hematological, biochemical and coagulation blood parameters in the perioperative period in children with severe forms of cerebral palsy during the treatment of concomitant epilepsy with valproic acid.Materials and methods.  A prospective cohort study included 72 patients with cerebral palsy, spastic hip dislocations, who underwent reconstructive interventions on hip joints. Depending on the presence of concomitant epilepsy, the patients were divided into two groups. Perioperative laboratory blood parameters, complication incidence, the duration of stay in the intensive care unit and hospitalization were assessed.Results.  In patients with epilepsy, the number of platelets in the blood was lower compared to the control group. The level of alkaline phosphatase before and after surgery in 34 % of children who took anticonvulsants exceeded the maximum value of the norm. Groups differed in terms of peri -operative coagulogram and thromboelastography. The frequency of complications in patients with epilepsy ranged from 0.08 % to 16.2 %.Conclusions.  Basic therapy with valproic acid in children with severe forms of cerebral palsy and concomitant epilepsy is associated with a tendency to hypocoagulation, but was not accompanied by clinically significant thrombocytopenia or coagulopathy during hip surgery. Taking valproic acid drugs in patients with cerebral palsy and epilepsy was not accompanied by an increase in serum liver enzymes at all stages of  observation, which minimized the likelihood of valproate-induced hepatotoxicity. Anticonvulsant therapy with valproate in children with severe forms of cerebral palsy and concomitant epilepsy did not increase the potential risk of complications in the perioperative period, did not affect the duration of stay of patients in the intensive care unit and hospitalization.

Arketa Pllumi Guli, Zamira Shabani, Emiljano Pjetri

Introduction: Dextrocardia is rare in the general population and may be associated with significant additional cardiac malformations. It is commonly associated with additional cardiac malformations. In this report, we have described the follow-up of a patient with Situs inversus dextrocardia and cyanogen complex cardiopathy in a 16-year-old Albanian male. The male patient born on 2007 in Albania, was referred to our ambulatory at 6 months of life by pediatrician cause of cyanosis and cardiac murmur. It was performed the echo Color Doppler examination, with the conclusion: situs inversus dextrocardia, unique ventricle, pulmonary arterial atresia. On 2008, a diagnostic catheterization was performed. The medico-chirurgical consultation has decided to leave the boy in natural history with a periodic follow–up. On 06.2009 in one of the routine examinations, there was make evidence of hypertrophy of the unique ventricle associated with arteria hypertension. From that time the patient is under medical treatment with periodic monitoring. Conclusions: The regular follow up of complex cyanogen congenital heart disease improve health care towards risqué target group. In heave desaturations patient the hypertension must be valuated as secondary complication of primary problem.   

Nasser A. AlJoaib, Faisal A. AlGhamdi, Annas Ghafoor et al.

Introduction: Hemorrhagic shock demands swift intervention. Management involves the rapid infusion of blood products to restore circulation and uphold tissue perfusion. The aim of this study was to evaluate the effectiveness of prehospital plasma administration in trauma patients, comparing outcomes with normal saline. This was a meta-analysis of randomized controlled trials. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline, searches were conducted in PubMed, MEDLINE, and the Cochrane Central Register of Controlled Trials from August 1, 2018, to April 4, 2023. The PubMed search string included terms related to blood plasma, prehospital care, emergency medical services, and hemorrhagic shock: (Blood Plasma [MeSH Terms] OR fresh frozen plasma [MeSH Terms] OR plasma OR fresh frozen plasma OR FFP) AND (Prehospital OR emergency care, prehospital [MeSH Terms] OR prehospital emergency care [MeSH Terms] OR prehospital OR prehospital OR EMS OR emergency medical service [MeSH Terms]) AND (hemorrhagic shock [MeSH Terms] OR hemorrhage OR hemorrhage OR hemorrhagic shock OR hemorrhagic shock). Results from the trials were pooled using a random effects model, presented as risk ratios with 95% confidence intervals. Results: In the analysis of 760 patients from three studies, outcomes included mortality at 24 h and 28 days, multi-organ failure (MOF), acute lung injury, and vasopressor use within 24 h. Patients were divided into plasma (363) and normal saline (397) groups. Conclusion: There is no distinction between prehospital plasma administration and normal saline concerning mortality at 24 and 28 days or the need for vasopressors within 24 h. Moreover, plasma administration did not appear to influence rates of acute lung injury or MOF.

Emmanuel Parada-Huerta, Graciela Merinos-Sánchez, Luis A. Gorordo-Delsol

Antecedentes: La piedra angular del tratamiento del síndrome de supresión etílica son las benzodiazepinas. Objetivo: Estudiar e implementar el uso de dexmedetomidina como adyuvante a la terapia estándar, por su poco efecto sobre la hemodinámica y la respiración, aunado a la disminución en la estancia hospitalaria. Método: Se seleccionaron estudios para su revisión mediante una búsqueda sistemática, según la metodología PRISMA, en las bases de datos PubMed, Science- Direct, Epistemonikos y Europe PMC. Resultados: Se identificaron 14 estudios. Después de aplicar los criterios de selección, se agruparon 5 estudios de cohorte y 2 estudios clínicos aleatorizados para el análisis estadístico, y se encontró que la duración de la estadía tenía una diferencia media de 41.65 (26.60-56.70), I2 = 3.2%, p < 0.001, para el subgrupo de cohorte control; no así en los estudios clínicos aleatorizados, en los que se encontró que la diferencia de media fue de −21.00 (−29.28 a −12.73), I2 = 0%, p < 0.001, que favorece la terapia con dexmedetomidina. El uso de dexmedetomidina se asoció con una reducción significativa en la estancia en la unidad de cuidados intensivos (UCI) en comparación con la terapia estándar en el grupo de estudios clínicos aleatorizados. Conclusiones: El uso de dexmedetomidina en la terapia estándar en los estudios clínicos aleatorizados mostró un beneficio significativo en la reducción de la estancia en la UCI en comparación con benzodiazepinas solas.

Wentao Chen, Xichen Xu, Jie Luo et al.

Image-to-image translation is a common task in computer vision and has been rapidly increasing the impact on the field of medical imaging. Deep learning-based methods that employ conditional generative adversarial networks (cGANs), such as Pix2PixGAN, have been extensively explored to perform image-to-image translation tasks. However, when noisy medical image data are considered, such methods cannot be directly applied to produce clean images. Recently, an augmented GAN architecture named AmbientGAN has been proposed that can be trained on noisy measurement data to synthesize high-quality clean medical images. Inspired by AmbientGAN, in this work, we propose a new cGAN architecture, Ambient-Pix2PixGAN, for performing medical image-to-image translation tasks by use of noisy measurement data. Numerical studies that consider MRI-to-PET translation are conducted. Both traditional image quality metrics and task-based image quality metrics are employed to assess the proposed Ambient-Pix2PixGAN. It is demonstrated that our proposed Ambient-Pix2PixGAN can be successfully trained on noisy measurement data to produce high-quality translated images in target imaging modality.

Yinchi Zhou, Tianqi Chen, Jun Hou et al.

Image-to-image translation is a vital component in medical imaging processing, with many uses in a wide range of imaging modalities and clinical scenarios. Previous methods include Generative Adversarial Networks (GANs) and Diffusion Models (DMs), which offer realism but suffer from instability and lack uncertainty estimation. Even though both GAN and DM methods have individually exhibited their capability in medical image translation tasks, the potential of combining a GAN and DM to further improve translation performance and to enable uncertainty estimation remains largely unexplored. In this work, we address these challenges by proposing a Cascade Multi-path Shortcut Diffusion Model (CMDM) for high-quality medical image translation and uncertainty estimation. To reduce the required number of iterations and ensure robust performance, our method first obtains a conditional GAN-generated prior image that will be used for the efficient reverse translation with a DM in the subsequent step. Additionally, a multi-path shortcut diffusion strategy is employed to refine translation results and estimate uncertainty. A cascaded pipeline further enhances translation quality, incorporating residual averaging between cascades. We collected three different medical image datasets with two sub-tasks for each dataset to test the generalizability of our approach. Our experimental results found that CMDM can produce high-quality translations comparable to state-of-the-art methods while providing reasonable uncertainty estimations that correlate well with the translation error.

Danial Khorasanian, Jonathan Patrick, Antoine Sauré

Despite the rapid growth of the home care industry, research on the scheduling and routing of home care visits in the presence of uncertainty is still limited. This paper investigates a dynamic version of this problem in which the number of referrals and their required number of visits are uncertain. We develop a Markov decision process (MDP) model for the single-nurse problem to minimize the expected weighted sum of the rejection, diversion, overtime, and travel time costs. Since optimally solving the MDP is intractable, we employ an approximate linear program (ALP) to obtain a feasible policy. The typical ALP approach can only solve very small-scale instances of the problem. We derive an intuitively explainable closed-form solution for the optimal ALP parameters in a special case of the problem. Inspired by this form, we provide two heuristic reduction techniques for the ALP model in the general problem to solve large-scale instances in an acceptable time. Numerical results show that the ALP policy outperforms a myopic policy that reflects current practice, and is better than a scenario-based policy in most instances considered.

Akram Yoosoofsah

Predicting extubation failure in intensive care is challenging due to complex data and the severe consequences of inaccurate predictions. Machine learning shows promise in improving clinical decision-making but often fails to account for temporal patient trajectories and model interpretability, highlighting the need for innovative solutions. This study aimed to develop an actionable, interpretable prediction system for extubation failure using temporal modelling approaches such as Long Short-Term Memory (LSTM) and Temporal Convolutional Networks (TCN). A retrospective cohort study of 4,701 mechanically ventilated patients from the MIMIC-IV database was conducted. Data from the 6 hours before extubation, including static and dynamic features, were processed through novel techniques addressing data inconsistency and synthetic data challenges. Feature selection was guided by clinical relevance and literature benchmarks. Iterative experimentation involved training LSTM, TCN, and LightGBM models. Initial results showed a strong bias toward predicting extubation success, despite advanced hyperparameter tuning and static data inclusion. Data was stratified by sampling frequency to reduce synthetic data impacts, leading to a fused decision system with improved performance. However, all architectures yielded modest predictive power (AUC-ROC ~0.6; F1 <0.5) with no clear advantage in incorporating static data or additional features. Ablation analysis indicated minimal impact of individual features on model performance. This thesis highlights the challenges of synthetic data in extubation failure prediction and introduces strategies to mitigate bias, including clinician-informed preprocessing and novel feature subsetting. While performance was limited, the study provides a foundation for future work, emphasising the need for reliable, interpretable models to optimise ICU outcomes.

Yining Hua, Hongbin Na, Zehan Li et al.

Large language models (LLMs) are emerging as promising tools for mental health care, offering scalable support through their ability to generate human-like responses. However, the effectiveness of these models in clinical settings remains unclear. This scoping review aimed to assess the current generative applications of LLMs in mental health care, focusing on studies where these models were tested with human participants in real-world scenarios. A systematic search across APA PsycNet, Scopus, PubMed, and Web of Science identified 726 unique articles, of which 17 met the inclusion criteria. These studies encompassed applications such as clinical assistance, counseling, therapy, and emotional support. However, the evaluation methods were often non-standardized, with most studies relying on ad hoc scales that limit comparability and robustness. Privacy, safety, and fairness were also frequently underexplored. Moreover, reliance on proprietary models, such as OpenAI's GPT series, raises concerns about transparency and reproducibility. While LLMs show potential in expanding mental health care access, especially in underserved areas, the current evidence does not fully support their use as standalone interventions. More rigorous, standardized evaluations and ethical oversight are needed to ensure these tools can be safely and effectively integrated into clinical practice.

Aniket Deroy, Subhankar Maity

Gastrointestinal (GI) tract cancers account for a substantial portion of the global cancer burden, where early diagnosis is critical for improved management and patient outcomes. The complex aetiologies and overlapping symptoms across GI cancers often delay diagnosis, leading to suboptimal treatment strategies. Cancer-related queries are crucial for timely diagnosis, treatment, and patient education, as access to accurate, comprehensive information can significantly influence outcomes. However, the complexity of cancer as a disease, combined with the vast amount of available data, makes it difficult for clinicians and patients to quickly find precise answers. To address these challenges, we leverage large language models (LLMs) such as GPT-3.5 Turbo to generate accurate, contextually relevant responses to cancer-related queries. Pre-trained with medical data, these models provide timely, actionable insights that support informed decision-making in cancer diagnosis and care, ultimately improving patient outcomes. We calculate two metrics: A1 (which represents the fraction of entities present in the model-generated answer compared to the gold standard) and A2 (which represents the linguistic correctness and meaningfulness of the model-generated answer with respect to the gold standard), achieving maximum values of 0.546 and 0.881, respectively.

Md Abir Hossen, Sonam Kharade, Bradley Schmerl et al.

Robotic systems have subsystems with a combinatorially large configuration space and hundreds or thousands of possible software and hardware configuration options interacting non-trivially. The configurable parameters are set to target specific objectives, but they can cause functional faults when incorrectly configured. Finding the root cause of such faults is challenging due to the exponentially large configuration space and the dependencies between the robot's configuration settings and performance. This paper proposes CaRE -- a method for diagnosing the root cause of functional faults through the lens of causality. CaRE abstracts the causal relationships between various configuration options and the robot's performance objectives by learning a causal structure and estimating the causal effects of options on robot performance indicators. We demonstrate CaRE's efficacy by finding the root cause of the observed functional faults and validating the diagnosed root cause by conducting experiments in both physical robots (Husky and Turtlebot 3) and in simulation (Gazebo). Furthermore, we demonstrate that the causal models learned from robots in simulation (e.g., Husky in Gazebo) are transferable to physical robots across different platforms (e.g., Husky and Turtlebot 3).

Cameron Zachreson, Ruarai Tobin, Camelia Walker et al.

Residential aged-care facilities (RACFs, also called long-term care facilities, aged care homes, or nursing homes) have elevated risks of respiratory infection outbreaks and associated disease burden. During the COVID-19 pandemic, social isolation policies were commonly used in these facilities to prevent and mitigate outbreaks. We refer specifically to general isolation policies that were intended to reduce contact between residents, without regard to confirmed infection status. Such policies are controversial because of their association with adverse mental and physical health indicators and there is a lack of modelling that assesses their effectiveness. We developed an agent-based model of COVID-19 transmission in a structured population, intended to represent the salient characteristics of a residential care environment. Using our model, we generated stochastic ensembles of simulated outbreaks and compared summary statistics of outbreaks simulated} under different mitigation conditions. Our study focuses on the marginal impact of general isolation (reducing social contact between residents), regardless of confirmed infection. In the absence of any asymptomatic screening, general isolation of residents to their rooms reduced median cumulative cases by approximately 27%. However, when conducted concurrently with asymptomatic screening and isolation of confirmed cases, general isolation reduced the median number of cumulative infections by only 12% in our simulations. Our simulations showed that general isolation of residents did not provide substantial benefits beyond those achieved through screening, isolation of confirmed cases, and deployment of PPE. Our conclusions are sensitive to assumptions about the proportion of total contacts in a facility accounted for by casual interactions between residents.

Melissa J. Bloomer, Chloe Woszczeiko, Alison M. Hutchinson