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

Tong Zhang, MS, Sai Rahul Ponnana, MS, Santosh Kumar Sirasapalli, MS et al.

Background: Composite mobility patterns may better model built environment exposures, yet are rarely implemented to understand the prevalence of cardiometabolic disease (CMD). Objectives: The purpose of this study was to investigate the association between a novel Wireless Movement Index (WMI) and prevalent CMD redlined U.S. census tracts. Methods: The Homeowners Loan Corporation (HOLC) was used to identify census tracts graded A-D and their age-adjusted prevalence (2019) for systolic hypertension, coronary heart disease, diabetes, obesity, chronic kidney disease, and stroke were collected. From nationally representative cell-phone tracking data, the WMI was constructed to identify population-level movement patterns and visits to points of interest. The association between WMI and disease prevalence was investigated using multivariable linear regression models across HOLC grades. Results: Among 16,352 tracts, 4,458 were classified as HOLC grades A-B, 7,572 as grade C, and 4,322 as grade D. Grade D tract residents reported 55% of their visits to other grade D census tracts, with only 9% to grade A/B census tracts. The WMI was negatively associated with CMD prevalence across all HOLC grades, but this protective association was most pronounced in redlined areas. In grade D tracts, each unit increase in WMI was associated with a −2.33 (95% CI: −2.79 to −1.86), −2.93 (95% CI: −3.42 to −2.45), and −0.99 (95% CI: −1.30 to −0.68) decrease in prevalent hypertension, obesity, and diabetes. Conclusions: Even among redlined census tracts, those that reported a higher WMI (indicative of more frequent and diverse mobility) were likely to have better population-level cardiometabolic health. WMI may serve as a scalable, dynamic proxy for environmental opportunity and structural inequity.

J. Devlin, Russel J. Roberts, Jeffrey J. Fong et al.

Jiahua Xu, Dawei Zhou, Lei Hu et al.

Multimodal medical images play a crucial role in the precise and comprehensive clinical diagnosis. Diffusion model is a powerful strategy to synthesize the required medical images. However, existing approaches still suffer from the problem of anatomical structure distortion due to the overfitting of high-frequency information and the weakening of low-frequency information. Thus, we propose a novel method based on dynamic frequency balance and knowledge guidance. Specifically, we first extract the low-frequency and high-frequency components by decomposing the critical features of the model using wavelet transform. Then, a dynamic frequency balance module is designed to adaptively adjust frequency for enhancing global low-frequency features and effective high-frequency details as well as suppressing high-frequency noise. To further overcome the challenges posed by the large differences between different medical modalities, we construct a knowledge-guided mechanism that fuses the prior clinical knowledge from a visual language model with visual features, to facilitate the generation of accurate anatomical structures. Experimental evaluations on multiple datasets show the proposed method achieves significant improvements in qualitative and quantitative assessments, verifying its effectiveness and superiority.

Alicia, Lee, Mai Lee Chang et al.

The increasing number of older adults who experience cognitive decline places a burden on informal caregivers, whose support with tasks of daily living determines whether older adults can remain in their homes. To explore how agents might help lower-SES older adults to age-in-place, we interviewed ten pairs of older adults experiencing cognitive decline and their informal caregivers. We explored how they coordinate care, manage burdens, and sustain autonomy and privacy. Older adults exercised control by delegating tasks to specific caregivers, keeping information about all the care they received from their adult children. Many abandoned some tasks of daily living, lowering their quality of life to ease caregiver burden. One effective strategy, piggybacking, uses spontaneous overlaps in errands to get more work done with less caregiver effort. This raises the questions: (i) Can agents help with piggyback coordination? (ii) Would it keep older adults in their homes longer, while not increasing caregiver burden?

Kevin Zhang, Yonghan Jung, Divyat Mahajan et al.

Identifying and measuring biases associated with sensitive attributes is a crucial consideration in healthcare to prevent treatment disparities. One prominent issue is inaccurate pulse oximeter readings, which tend to overestimate oxygen saturation for dark-skinned patients and misrepresent supplemental oxygen needs. Most existing research has revealed statistical disparities linking device measurement errors to patient outcomes in intensive care units (ICUs) without causal formalization. This study causally investigates how racial discrepancies in oximetry measurements affect invasive ventilation in ICU settings. We employ a causal inference-based approach using path-specific effects to isolate the impact of bias by race on clinical decision-making. To estimate these effects, we leverage a doubly robust estimator, propose its self-normalized variant for improved sample efficiency, and provide novel finite-sample guarantees. Our methodology is validated on semi-synthetic data and applied to two large real-world health datasets: MIMIC-IV and eICU. Contrary to prior work, our analysis reveals minimal impact of racial discrepancies on invasive ventilation rates. However, path-specific effects mediated by oxygen saturation disparity are more pronounced on ventilation duration, and the severity differs across datasets. Our work provides a novel pipeline for investigating potential disparities in clinical decision-making and, more importantly, highlights the necessity of causal methods to robustly assess fairness in healthcare.

Suman Kunwar, Prabesh Rai

The increasing number of Health Care facilities in Nepal has added up the challenges on managing health care waste (HCW). Improper segregation and disposal of HCW leads to contamination, spreading of infectious diseases and risk for waste handlers. This study benchmarks the state of the art waste classification models: ResNeXt-50, EfficientNet-B0, MobileNetV3-S, YOLOv8-n and YOLOv5-s using stratified 5-fold cross-validation technique on combined HCW data. YOLOv5-s achieved the highest accuracy (95.06%) but fell short with the YOLOv8-n model in inference speed with few milliseconds. The EfficientNet-B0 showed promising results of 93.22% accuracy but took the highest inference time. Following a repetitive ANOVA test to confirm the statistical significance, the best performing model (YOLOv5-s) was deployed to the web with bin color mapped using Nepal's HCW management standards. Further work is suggested to address data limitation and ensure localized context.

G. Apicella, A. Molent, M. Gaudenzi

The state-of-the-art proposes Life Care Annuities, that have been recently designed as variable annuity contracts with Long-Term Care payouts and Guaranteed Lifelong Withdrawal Benefits. In this paper, we propose more general features for these insurance products and refine their pricing methods. We name our proposed product ``GLWB-LTC''. In particular, as to the product features, we allow dynamic withdrawal strategies, including the surrender option. Furthermore, we consider stochastic interest rates, described by a Cox-Ingersoll-Ross process. As to the numerical methods, we solve the stochastic control problem involved by the selection of the optimal withdrawal strategy through a robust tree method, which outperforms the Monte Carlo approach. We name this method ``Tree-LTC'', and we use it to estimate the fair price of the product, as some relevant parameters vary, such as, for instance, the entry age of the policyholder. Furthermore, our numerical results show how the optimal withdrawal strategy varies over time with the health status of the policyholder. Our findings stress the important advantage of flexible withdrawal strategies in relation to insurance policies offering protection from health risks. Indeed, the policyholder is given more choice about how much to save for protection from the possible disability states at future times.

Zhiyuan Wang, Fangxu Yuan, Virginia LeBaron et al.

Effective patient-provider communication is crucial in clinical care, directly impacting patient outcomes and quality of life. Traditional evaluation methods, such as human ratings, patient feedback, and provider self-assessments, are often limited by high costs and scalability issues. Although existing natural language processing (NLP) techniques show promise, they struggle with the nuances of clinical communication and require sensitive clinical data for training, reducing their effectiveness in real-world applications. Emerging large language models (LLMs) offer a new approach to assessing complex communication metrics, with the potential to advance the field through integration into passive sensing and just-in-time intervention systems. This study explores LLMs as evaluators of palliative care communication quality, leveraging their linguistic, in-context learning, and reasoning capabilities. Specifically, using simulated scripts crafted and labeled by healthcare professionals, we test proprietary models (e.g., GPT-4) and fine-tune open-source LLMs (e.g., LLaMA2) with a synthetic dataset generated by GPT-4 to evaluate clinical conversations, to identify key metrics such as `understanding' and `empathy'. Our findings demonstrated LLMs' superior performance in evaluating clinical communication, providing actionable feedback with reasoning, and demonstrating the feasibility and practical viability of developing in-house LLMs. This research highlights LLMs' potential to enhance patient-provider interactions and lays the groundwork for downstream steps in developing LLM-empowered clinical health systems.

Vincent Guigues, Anton Kleywegt, Victor Hugo Nascimento et al.

This paper describes an online tool for the visualization of medical emergency locations, randomly generated sample paths of medical emergencies, and the animation of ambulance movements under the control of various dispatch methods in response to these emergencies. The tool incorporates statistical models for forecasting emergency locations and call arrival times, the simulation of emergency arrivals and ambulance movement trajectories, and the computation and visualization of performance metrics such as ambulance response time distributions. Data for the Rio de Janeiro Emergency Medical Service are available on the website. A user can upload emergency data for any Emergency Medical Service, and can then use the visualization tool to explore the uploaded data. A user can also use the statistical tools and/or the simulation tool with any of the dispatch methods provided, and can then use the visualization tool to explore the computational output. Future enhancements include the ability of a user to embed additional dispatch algorithms into the simulation; the tool can then be used to visualize the simulation results obtained with the newly embedded algorithms.

Wei Liu, Mengshi Lu, Pengyi Shi

In this paper, we study a queueing model that incorporates patient reentrance to reflect patients' recurring requests for nurse care and their rest periods between these requests. Within this framework, we address two levels of decision-making: the priority discipline decision for each nurse and the nurse-patient assignment problem. We introduce the shortest-first and longest-first rules in the priority discipline decision problem and show the condition under which each policy excels through theoretical analysis and comprehensive simulations. For the nurse-patient assignment problem, we propose two heuristic policies. We show that the policy maximizing the immediate decrease in holding costs outperforms the alternative policy, which considers the long-term aggregate holding cost. Additionally, both proposed policies significantly surpass the benchmark policy, which does not utilize queue length information.

Pedram Hosseini, Jessica M. Sin, Bing Ren et al.

There is a lack of benchmarks for evaluating large language models (LLMs) in long-form medical question answering (QA). Most existing medical QA evaluation benchmarks focus on automatic metrics and multiple-choice questions. While valuable, these benchmarks fail to fully capture or assess the complexities of real-world clinical applications where LLMs are being deployed. Furthermore, existing studies on evaluating long-form answer generation in medical QA are primarily closed-source, lacking access to human medical expert annotations, which makes it difficult to reproduce results and enhance existing baselines. In this work, we introduce a new publicly available benchmark featuring real-world consumer medical questions with long-form answer evaluations annotated by medical doctors. We performed pairwise comparisons of responses from various open and closed-source medical and general-purpose LLMs based on criteria such as correctness, helpfulness, harmfulness, and bias. Additionally, we performed a comprehensive LLM-as-a-judge analysis to study the alignment between human judgments and LLMs. Our preliminary results highlight the strong potential of open LLMs in medical QA compared to leading closed models. Code & Data: https://github.com/lavita-ai/medical-eval-sphere

William Cagas, Chan Ko, Blake Hsiao et al.

The proliferation of machine learning models in diverse clinical applications has led to a growing need for high-fidelity, medical image training data. Such data is often scarce due to cost constraints and privacy concerns. Alleviating this burden, medical image synthesis via generative adversarial networks (GANs) emerged as a powerful method for synthetically generating photo-realistic images based on existing sets of real medical images. However, the exact image set size required to efficiently train such a GAN is unclear. In this work, we experimentally establish benchmarks that measure the relationship between a sample dataset size and the fidelity of the generated images, given the dataset's distribution of image complexities. We analyze statistical metrics based on delentropy, an image complexity measure rooted in Shannon's entropy in information theory. For our pipeline, we conduct experiments with two state-of-the-art GANs, StyleGAN 3 and SPADE-GAN, trained on multiple medical imaging datasets with variable sample sizes. Across both GANs, general performance improved with increasing training set size but suffered with increasing complexity.

Qixiang Zhang, Haonan Wang, Xiaomeng Li

Semi-supervised medical image segmentation (SSMIS) has emerged as a promising solution to tackle the challenges of time-consuming manual labeling in the medical field. However, in practical scenarios, there are often domain variations within the datasets, leading to derivative scenarios like semi-supervised medical domain generalization (Semi-MDG) and unsupervised medical domain adaptation (UMDA). In this paper, we aim to develop a generic framework that masters all three tasks. We notice a critical shared challenge across three scenarios: the explicit semantic knowledge for segmentation performance and rich domain knowledge for generalizability exclusively exist in the labeled set and unlabeled set respectively. Such discrepancy hinders existing methods from effectively comprehending both types of knowledge under semi-supervised settings. To tackle this challenge, we develop a Semantic & Domain Knowledge Messenger (S&D Messenger) which facilitates direct knowledge delivery between the labeled and unlabeled set, and thus allowing the model to comprehend both of them in each individual learning flow. Equipped with our S&D Messenger, a naive pseudo-labeling method can achieve huge improvement on six benchmark datasets for SSMIS (+7.5%), UMDA (+5.6%), and Semi-MDG tasks (+1.14%), compared with state-of-the-art methods designed for specific tasks.

Carmen B. Rodríguez, Stephanie M. Wu, Stephanie Alimena et al.

Many studies have examined social determinants of health (SDoH) independently, overlooking their interconnected nature. Our study uses a multidimensional approach to construct a neighborhood-level measure that explores how multiple SDoH jointly impact care received for endometrial cancer (EC) patients in Massachusetts (MA). Using 2015-2019 American Community Survey data, we implemented a Bayesian multivariate Bernoulli mixture model to identify neighborhoods with similar SDoH features in MA. Five neighborhood SDoH (NSDoH) profiles were derived and characterized: (1) advantaged non-Hispanic White; (2) disadvantaged racially/ethnically diverse, more renter-occupied housing with limited English proficiency; (3) working class, lower educational attainment; (4) racially/ethnically diverse and greater economic security and educational attainment; and (5) racially/ethnically diverse, more renter-occupied housing with limited English proficiency. Based on residential information, we assigned these profiles to EC patients in the Massachusetts Cancer Registry. We used these profile assignments as the primary exposure in a Bayesian logistic regression to estimate the odds of receiving optimal EC care, adjusting for patient-level sociodemographic and clinical characteristics. NSDoH profiles were not significantly associated with receiving optimal EC care. However, compared to patients assigned to Profile 1, patients in all other profiles had lower odds of receiving optimal care. Our findings demonstrate how a flexible model-based clustering approach can account for the interconnected and multidimensional nature of NSDoH in a practical and interpretable way. Deriving and geospatially mapping NSDoH profiles may allow for identifying areas of need and inform targeted public health interventions tailored to each neighborhood's specific social determinants to improve healthcare delivery.

T. Hayasaka, Kazuharu Kawano, Kazuki Kurihara et al.

Background Tracheal intubation is the gold standard for securing the airway, and it is not uncommon to encounter intubation difficulties in intensive care units and emergency rooms. Currently, there is a need for an objective measure to assess intubation difficulties in emergency situations by physicians, residents, and paramedics who are unfamiliar with tracheal intubation. Artificial intelligence (AI) is currently used in medical imaging owing to advanced performance. We aimed to create an AI model to classify intubation difficulties from the patient’s facial image using a convolutional neural network (CNN), which links the facial image with the actual difficulty of intubation. Methods Patients scheduled for surgery at Yamagata University Hospital between April and August 2020 were enrolled. Patients who underwent surgery with altered facial appearance, surgery with altered range of motion in the neck, or intubation performed by a physician with less than 3 years of anesthesia experience were excluded. Sixteen different facial images were obtained from the patients since the day after surgery. All images were judged as “Easy”/“Difficult” by an anesthesiologist, and an AI classification model was created using deep learning by linking the patient’s facial image and the intubation difficulty. Receiver operating characteristic curves of actual intubation difficulty and AI model were developed, and sensitivity, specificity, and area under the curve (AUC) were calculated; median AUC was used as the result. Class activation heat maps were used to visualize how the AI model classifies intubation difficulties. Results The best AI model for classifying intubation difficulties from 16 different images was generated in the supine-side-closed mouth-base position. The accuracy was 80.5%; sensitivity, 81.8%; specificity, 83.3%; AUC, 0.864; and 95% confidence interval, [0.731-0.969], indicating that the class activation heat map was concentrated around the neck regardless of the background; the AI model recognized facial contours and identified intubation difficulties. Conclusion This is the first study to apply deep learning (CNN) to classify intubation difficulties using an AI model. We could create an AI model with an AUC of 0.864. Our AI model may be useful for tracheal intubation performed by inexperienced medical staff in emergency situations or under general anesthesia.

Mei Fong Liew, W. Siow, Y. Yau et al.

Dear Editor, Although COVID-19 has not been officially labelled as a pandemic yet, the global burden of disease is significant and continues to rise. The virus has a high humanto-human transmissibility via airborne, droplet and contact routes [1]. Patient numbers can surge, and hospitals should be ready not just with the infrastructure, but also staff to be familiar with workflows. Kain and Fowler [2] have eloquently detailed influenza pandemic preparations for hospitals and intensive care units, and we feel the principles described in the article are relevant to COVID-19. Staff must consider patient transfers in between wards, as COVID-19 patients are admitted in isolation facilities to contain infected cases and to avoid nosocomial spread [1]. Infectious cases may be intentionally brought out of isolation rooms for various reasons. Intra-hospital transfer may be required from emergency departments to the wards, from the general floor to the intensive care unit and from the wards to radiology suites. Inter-hospital transfer may be required for extracorporeal membrane oxygenation (ECMO) if patients with COVID-19 develop severe acute respiratory distress syndrome within hospitals with only basic ventilation facilities. During episodes of patient transport outside of isolation, potential breaches of infection control can occur. At the same time, when COVID-19 patients turn ill during transport, their management is exceptionally challenging as accompanying staff would be wearing cumbersome personal protective equipment (PPE) [3]. Mitigating the spread of COVID-19 is a national priority in Singapore [4], and part of this effort involves planning and conducting safe patient transport for suspected or confirmed cases. HCWs who handle the transport of COVID-19 patients must consider the following principles (see Table 1): firstly, early recognition of the deteriorating patient; secondly, HCW safety; thirdly, bystander safety; fourthly, contingency plans for medical emergencies during transport; fifthly, post-transport decontamination. Specific action steps require designated zones for transport [5], sufficient supplies of PPE, staff training and support personnel like security officers and cleaning crews. Powered air-purifying respirators add a layer of safety on top of N95 respirators [3] and should be used if possible for high-risk cases, such as those requiring ambulance transport to ECMO centres. Given the continued global spread of COVID-19, we expect that more hospitals will need to deal with this disease. Haphazard transport of infected cases leading to nosocomial spread can stymie efforts to break the chains of transmission. We hope that our suggestions can aid others in ensuring safe patient transport for COVID-19 and reduce nosocomial spread.

Hirokazu Taguchi

Ygiyeva D, Pivina L, Messova A et al.

Diana Ygiyeva,1 Lyudmila Pivina,1 Assylzhan Messova,1 Zhanar Urazalina,1 Yuliya Semenova,1 Almas Dyussupov,1 Altay Dyussupov,1 Tatyana Belikhina,2 Marat Syzdykbayev,1 Gulnara Batenova,1 Ayaulym Akhmetova,1 Amber Adams3 1Department of Emergency Medicine, Semey Medical University, Semey, Abay Region, Kazakhstan; 2Nuclear Medicine Department, Center of Nuclear Medicine and Oncology, Semey, Abay Region, Kazakhstan; 3JoAnne Gay Dishman School of Nursing, Lamar University, Beaumont, TX, USACorrespondence: Assylzhan Messova; Lyudmila Pivina, Department of Emergency Medicine, Semey Medical University, Abay str. 103, Semey, Abay Region, Kazakhstan, Tel +7 777 2138307 ; +77055227300, Email assylzhan2006@mail.ru; semskluda@rambler.ruObjective: Consultation is an important and necessary aspect of patient care in the emergency department. We prospectively examined difficulties during the consultation process between paramedics and providers in emergency departments in Kazakhstan.Methods: The paramedics were interviewed using various platforms and face-to-face meetings. Questionnaires were administered to paramedics to gather feedback on the current consultation process. In our survey, 202 paramedics of ambulance from the cities of Semey, Pavlodar, and Ust-Kamenogorsk, located in the North and East of Kazakhstan took part.Results: Serious barriers to effective consultation were identified during consultations with cardiologists, pediatricians, and traumatologists. Weekends, as well as nighttime, are associated with more consultation difficulties. The most common problems for paramedics are non-attendance of the consultant, refusal of hospitalization by the consultant, and referral to other specialists and departments. More than 40% of the respondents noted the desire to share responsibility for the patient with medical consultants, which indicates uncertainty in their own knowledge due to the limited work experience of the majority of respondents.Conclusion: Barriers that arise during the consultation process of patients with emergency conditions can lead to unfavorable outcomes. Strategies to address these barriers are needed to improve the quality of patient care. This review aims to understand and evaluate the issues that arise during the consultation process.Keywords: emergency department, consultation, paramedics, prehospital medical care

Laura Hertner, Panagiotis Stylianopoulos, Andreas Heinz et al.

Abstract Background Previous research concluded that substance (mis)use is increasing among forcibly displaced populations. Nevertheless, little research has been conducted within a social ecological framework aimed at identifying and understanding the factors affecting substance (mis)use embedded in the post-migration context in high-income countries. The present study aims to develop an understanding of the links and underlying mechanisms between refugees’ social ecological determinants and substance (mis)using behavior. Methods Rapid assessments (RAs), including 108 semi-structured interviews and 10 focus group discussions with key persons from various professional, and personal backgrounds, were carried out in German urban and rural areas. The RA approach of interviewing key persons and not solely refugees that (mis)use substances allowed us to gather multi-perspective knowledge on this sensitive topic. Qualitative content analysis was applied, aiming at identifying determinants of substance (mis)use embedded in the post-migration context of refugees and understanding the underlying mechanisms. Results One main result of the data suggests that the link between refugees’ countries of origin and their post-migration substance (mis)use is not as direct as often assumed. It is observed that refugees’ prospects and opportunities in receiving countries (e.g., work permits) undermine this commonly reproduced link. Further determinants are related to living conditions in German refugee shelters and social relations with peers and families. The influence of refugees’ living conditions can be summarized as potentially increasing substance availability and distress, whereas family separation produces a loss of control and responsibility, increasing the risk for substance (mis)use. Peers’ influence on substance (mis)use was reported to reflect a search for a sense of belonging. Conclusions Given that refugees who (mis)use substances have limited to no control over the factors identified in our study to be associated with substance (mis)use, common treatment and prevention approaches are challenged. Furthermore, we recommend aiming for a holistic comprehension of refugees’ substance (mis)use by expanding the focus beyond individuals to the social ecological context in any attempt, including prevention, treatment, research, and policy.

Kazuto Miyata, Sayaka Shigematsu, Naoki Miyayama

Abstract Background Cardiac herniation has been reported in thoracic trauma and after pneumonectomy; however, it is sporadic in cardiac surgery. Case presentation A 35-year-old male patient underwent an elective totally endoscopic robotic-assisted mitral valve repair (TERMVR). His hemodynamics were stable after weaning from cardiopulmonary bypass, and no residual mitral valve regurgitation was observed. However, during suturing of the port wound, the patient developed hypotension, which improved with phenylephrine administration. Four-chamber transesophageal echocardiography (TEE) images showed cardiac deformity, and postoperative chest radiography confirmed the dextrocardia. The cardiac herniation was repaired by deflating the left lung and over-inflating the right lung using a double-lumen tube, allowing selective ventilation without re-thoracotomy. The patient was discharged on the sixth postoperative day without complications. Conclusions This was a very unusual case of cardiac herniation during TERMVR visualized using distinct TEE images. The cardiac herniation was successfully repaired using a double-lumen tube without re-thoracotomy.