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

Anand Kumar, D. Roberts, K. Wood et al.

Saad Ahmed Waqas, MBBS, Zahra Imran, MBBS, Dua Ali, MBBS et al.

William G. Underwood, Henry W. J. Reeve, Oliver Y. Feng et al.

Clinical risk prediction models are regularly updated as new data, often with additional covariates, become available. We propose CARE (Convex Aggregation of relative Risk Estimators) as a general approach for combining existing "external" estimators with a new data set in a time-to-event survival analysis setting. Our method initially employs the new data to fit a flexible family of reproducing kernel estimators via penalised partial likelihood maximisation. The final relative risk estimator is then constructed as a convex combination of the kernel and external estimators, with the convex combination coefficients and regularisation parameters selected using cross-validation. We establish high-probability bounds for the $L_2$-error of our proposed aggregated estimator, showing that it achieves a rate of convergence that is at least as good as both the optimal kernel estimator and the best external model. Empirical results from simulation studies align with the theoretical results, and we illustrate the improvements our methods provide for cardiovascular disease risk modelling. Our methodology is implemented in the Python package care-survival.

Harsha Sammangi, Aditya Jagatha, Giridhar Reddy Bojja et al.

AI Innovations in the IoT for Real-Time Patient Monitoring On one hand, the current traditional centralized healthcare architecture poses numerous issues, including data privacy, delay, and security. Here, we present an AI-enabled decentralized IoT architecture that can address such challenges during a pandemic and critical care settings. This work presents our architecture to enhance the effectiveness of the current available federated learning, blockchain, and edge computing approach, maximizing data privacy, minimizing latency, and improving other general system metrics. Experimental results demonstrate transaction latency, energy consumption, and data throughput orders of magnitude lower than competitive cloud solutions.

S. Nisula, K. Kaukonen, S. Vaara et al.

Matthew Pease, Jonathan Elmer, Arka N. Mallela et al.

Seizures are common after severe traumatic brain injury (TBI), with rates in the acute period approaching 5% with seizure prophylaxis in historical clinical trials. Post-traumatic seizures (PTS) are divided into categories: immediate PTS occur prior to resuscitation, typically in the field; early PTS occur from resuscitation to 7 days post-trauma; and late PTS occur thereafter. The relationship between immediate and early PTS, as well as their risk factors, are not well studied in modern cohorts. We performed a secondary analysis of a prospective database of severe TBI patients, defined as a post-resuscitation Glasgow Coma Scale ?8, from a single institution. For the 579 patients included, rates of immediate and early PTS were 1.6% and 3.8%, respectively. We were unable to identify any clinical correlates for immediate seizures. In contrast, early PTS were associated with age (odds ratio [OR] 1.5; 95% confidence interval [CI]: 1.1?2.0; p?<?0.01), hypoxia (3.3, 95% CI: 1.2?8.5; p?=?0.02), and subdural hematoma (SDH) (2.8, 95% CI: 1.0?2.8; p?=?0.04) in multivariable modeling. Patients with early PTS had higher rates of status epilepticus than those with immediate PTS (45% [n?=?10/22] vs. 0% [n?=?0/9]; p?=?0.03). This supports the notion of immediate PTS, which typically occur in the field and may not reliably be deciphered from pathological posturing responses, as an entity distinct from early PTS. Status epilepticus was highly morbid, associated with a 70% mortality rate. Our previously identified markers may help risk-stratify patients who may warrant longer monitoring with continuous electroencephalography to detect and treat early PTS and corresponding status epilepticus risk.

Uriya First, Ben Williams

Suppose $A$ is an Azumaya algebra over a ring $R$ and $σ$ is an involution of $A$ extending an order-$2$ automorphism $λ:R\to R$. We say $σ$ is extraordinary if there does not exist a Brauer-trivial Azumaya algebra $\mathrm{End}_R(P)$ over $R$ carrying an involution $τ$ so that $(A, σ)$ and $(\mathrm{End}_R(P), τ)$ become isomorphic over some faithfully flat extension of the fixed ring of $λ:R\to R$. We give, for the first time, an example of such an algebra and involution. We do this by finding suitable cohomological obstructions and showing they do not always vanish. We also give an example of a commutative ring $R$ with involution $λ$ so that the scheme-theoretic fixed locus $Z$ of $λ:\mathrm{Spec} R\to \mathrm{Spec} R$ is disconnected, but such that every Azumaya algebra over $R$ with involution extending $λ$ is either orthogonal at every point of $Z$, or symplectic at every point of $Z$. No examples of this kind were previously known.

Rana Imtiaz, Arshia Khan

As the aging population increases and the shortage of healthcare workers increases, the need to examine other means for caring for the aging population increases. One such means is the use of humanoid robots to care for social, emotional, and physical wellbeing of the people above 65. Understanding skilled and long term care nursing home administrators' perspectives on humanoid robots in caregiving is crucial as their insights shape the implementation of robots and their potential impact on resident well-being and quality of life. This authors surveyed two hundred and sixty nine nursing homes executives to understand their perspectives on the use of humanoid robots in their nursing home facilities. The data was coded and results revealed that the executives were keen on exploring other avenues for care such as robotics that would enhance their nursing homes abilities to care for their residents. Qualitative analysis reveals diverse perspectives on integrating humanoid robots in nursing homes. While acknowledging benefits like improved engagement and staff support, concerns persist about costs, impacts on human interaction, and doubts about robot effectiveness. This highlights complex barriers financial, technical, and human and emphasizes the need for strategic implementation. It underscores the importance of thorough training, role clarity, and showcasing technology benefits to ensure efficiency and satisfaction among staff and residents.

Soheil Hashtarkhani, David L Schwartz, Arash Shaban-Nejad

Access to health care services is a critical determinant of population health and well-being. Measuring spatial accessibility to health services is essential for understanding health care distribution and addressing potential inequities. In this study, we developed a geoprocessing toolbox including Python script tools for the ArcGIS Pro environment to measure the spatial accessibility of health services using both classic and enhanced versions of the 2-step floating catchment area method. Each of our tools incorporated both distance buffers and travel time catchments to calculate accessibility scores based on users' choices. Additionally, we developed a separate tool to create travel time catchments that is compatible with both locally available network data sets and ArcGIS Online data sources. We conducted a case study focusing on the accessibility of hemodialysis services in the state of Tennessee using the 4 versions of the accessibility tools. Notably, the calculation of the target population considered age as a significant nonspatial factor influencing hemodialysis service accessibility. Weighted populations were calculated using end-stage renal disease incidence rates in different age groups. The implemented tools are made accessible through ArcGIS Online for free use by the research community. The case study revealed disparities in the accessibility of hemodialysis services, with urban areas demonstrating higher scores compared to rural and suburban regions. These geoprocessing tools can serve as valuable decision-support resources for health care providers, organizations, and policy makers to improve equitable access to health care services. This comprehensive approach to measuring spatial accessibility can empower health care stakeholders to address health care distribution challenges effectively.

Flora M. Hammond, Jessica M. Ketchum, Vipul Vinod Patni et al.

The Extended Glasgow Outcome Scale (GOSE) is accepted as the primary outcome measure in registrational studies for traumatic brain injury (TBI). The Disability Rating Scale (DRS) is used to assess functional progress from initial acute injury, through rehabilitation and reintegration into the community and life. For these reasons, the DRS is an alternative measure for assessing meaningful global outcomes in chronic TBI. The objective of this study was to determine the minimally clinically important difference (MCID) for the DRS in chronic TBI, by determining the magnitude of DRS change associated with the MCID for the GOSE of 1 point. This study is a retrospective analysis of the multi-center, prospective, longitudinal, Traumatic Brain Injury Model Systems National Database of persons with outcomes at 1, 2, and 5 years and every 5 years thereafter post-injury. Spearman's correlations for dynamic and static relationships between the DRS and GOSE were significant. For the 1-point MCID for the GOSE, the dynamic MCID estimate for the DRS of a ?0.68-point change was calculated as the mean DRS change associated with the difference of the GOSE score between year 1 and year 2 (score range, 3?8), using all persons in the study (n?=?11,102), whereas the exploratory static MCID estimate for the DRS of ?1.28 points was calculated from the slope of the best-fit line between the DRS and GOSE at year 1 follow-up (score range, 3?8; n?=?13,415). The final MCID for the DRS was calculated by using the triangulation method (i.e., the arithmetic mean of the dynamic and exploratory static MCID estimates), which resulted in a ?1.0-point change. The significant correlation between the DRS and GOSE has allowed for the establishment of a ?1.0-point MCID for the DRS, which supports the use of the DRS as an alternative primary outcome measure for chronic TBI research studies, including clinical trials.

Zosha J. van Gelder, Annabel Snoeks, Paul P.M. van Zuijlen et al.

Over the past decades, long-term sequelae of burns have gained increasing attention. Women of childbearing age, who sustained abdominal burns earlier in life, may have unmet information needs on scar-related complications they can expect during pregnancy. We performed a review of the literature to identify abdominal, foetal, and potential other complications during pregnancy in women with abdominal burn scars. PubMed, Embase, and Scopus were searched from inception to 1 July 2020 and updated once on 23 April 2021 (PROSPERO CRD42022187883). Main search terms included pregnancy, scar, burns, and abdominal. Studies on burns obtained during pregnancy have been excluded. Screening, data extraction and bias assessment were conducted by two investigators. We included 22 studies comprising 217 patients. The time between burn injury and first pregnancy varied between 7 and 32 years. Most of the women had normal pregnancies regarding delivery mode and duration of pregnancy. The most reported abdominal burn scar complications were an increased feeling of tightness, itch, pain, and scar breakdown. In some cases, scar release surgery was performed during or prior to pregnancy. Some cases of foetal complications were described. Complications during pregnancy after abdominal burn scars may be limited. More quantitative and qualitative research is needed to assess the maternal and foetal outcomes and complications. The results may be used to inform women and contribute to personalised obstetric management.

Giulio Maria Bianco, Emanuele Raso, Luca Fiore et al.

Points-of-care (PoCs) augment healthcare systems by performing care whenever needed and are becoming increasingly crucial for the well-being of the worldwide population. Personalized medicine, chronic illness management, and cost reduction can be achieved thanks to the widespread adoption of PoCs. Significant incentives for PoCs deployment are nowadays given by wearable devices and, in particular, by RFID (RadioFrequency IDentification) and NFC (Near Field Communications), which are rising among the technological cornerstones of the healthcare internet of things (H-IoT). To fully exploit recent technological advancements, this paper proposes a system architecture for RFID- and NFC-based PoCs. The architecture comprises in a unitary framework both interfaces to benefit from their complementary features, and gathered data are shared with medical experts through secure and user-friendly interfaces that implement the Fast Health Interoperability Resource (FHIR) emerging healthcare standard. The selection of the optimal UHF and NFC components is discussed concerning the employable sensing techniques. The secure transmission of sensitive medical data is addressed by developing a user-friendly "PoC App" that is the first web app exploiting attribute-based encryption (ABE). An application example of the system for monitoring the pH and cortisol levels in sweat is implemented and preliminarily tested by a healthy volunteer.

Emily First, Markus N. Rabe, Talia Ringer et al.

Formally verifying software properties is a highly desirable but labor-intensive task. Recent work has developed methods to automate formal verification using proof assistants, such as Coq and Isabelle/HOL, e.g., by training a model to predict one proof step at a time, and using that model to search through the space of possible proofs. This paper introduces a new method to automate formal verification: We use large language models, trained on natural language text and code and fine-tuned on proofs, to generate whole proofs for theorems at once, rather than one step at a time. We combine this proof generation model with a fine-tuned repair model to repair generated proofs, further increasing proving power. As its main contributions, this paper demonstrates for the first time that: (1) Whole-proof generation using transformers is possible and is as effective as search-based techniques without requiring costly search. (2) Giving the learned model additional context, such as a prior failed proof attempt and the ensuing error message, results in proof repair and further improves automated proof generation. (3) We establish a new state of the art for fully automated proof synthesis. We reify our method in a prototype, Baldur, and evaluate it on a benchmark of 6,336 Isabelle/HOL theorems and their proofs. In addition to empirically showing the effectiveness of whole-proof generation, repair, and added context, we show that Baldur improves on the state-of-the-art tool, Thor, by automatically generating proofs for an additional 8.7% of the theorems. Together, Baldur and Thor can prove 65.7% of the theorems fully automatically. This paper paves the way for new research into using large language models for automating formal verification.

Richard Paluch, Tanja Aal, Katerina Cerna et al.

Technological development continues to advance, with consequences for the use of robots in health care. For this reason, this workshop contribution aims at consideration of how socially assistive robots can be integrated into care and what tasks they can take on. This also touches on the degree of autonomy of these robots and the balance of decision support and decision making in different situations. We want to show that decision making by robots is mediated by the balance between autonomy and safety. Our results are based on Design Fiction and Zine-Making workshops we conducted with scientific experts. Ultimately, we show that robots' actions take place in social groups. A robot does not typically decide alone, but its decision-making is embedded in group processes. The concept of heteromation, which describes the interconnection of human and machine actions, offers fruitful possibilities for exploring how robots can be integrated into caring relationships.

Ping Chang, Huayu Li, Stuart F. Quan et al.

Background and Objective: Vital sign monitoring in the Intensive Care Unit (ICU) is crucial for enabling prompt interventions for patients. This underscores the need for an accurate predictive system. Therefore, this study proposes a novel deep learning approach for forecasting Heart Rate (HR), Systolic Blood Pressure (SBP), and Diastolic Blood Pressure (DBP) in the ICU. Methods: We extracted $24,886$ ICU stays from the MIMIC-III database which contains data from over $46$ thousand patients, to train and test the model. The model proposed in this study, Transformer-based Diffusion Probabilistic Model for Sparse Time Series Forecasting (TDSTF), merges Transformer and diffusion models to forecast vital signs. The TDSTF model showed state-of-the-art performance in predicting vital signs in the ICU, outperforming other models' ability to predict distributions of vital signs and being more computationally efficient. The code is available at https://github.com/PingChang818/TDSTF. Results: The results of the study showed that TDSTF achieved a Standardized Average Continuous Ranked Probability Score (SACRPS) of $0.4438$ and a Mean Squared Error (MSE) of $0.4168$, an improvement of $18.9\%$ and $34.3\%$ over the best baseline model, respectively. The inference speed of TDSTF is more than $17$ times faster than the best baseline model. Conclusion: TDSTF is an effective and efficient solution for forecasting vital signs in the ICU, and it shows a significant improvement compared to other models in the field.

T. Geeraerts, L. Velly, L. Abdennour et al.

The latest French Guidelines for the management in the first 24hours of patients with severe traumatic brain injury (TBI) were published in 1998. Due to recent changes (intracerebral monitoring, cerebral perfusion pressure management, treatment of raised intracranial pressure), an update was required. Our objective has been to specify the significant developments since 1998. These guidelines were conducted by a group of experts for the French Society of Anesthesia and Intensive Care Medicine (Société francaise d'anesthésie et de réanimation [SFAR]) in partnership with the Association de neuro-anesthésie-réanimation de langue française (ANARLF), The French Society of Emergency Medicine (Société française de médecine d'urgence (SFMU), the Société française de neurochirurgie (SFN), the Groupe francophone de réanimation et d'urgences pédiatriques (GFRUP) and the Association des anesthésistes-réanimateurs pédiatriques d'expression française (ADARPEF). The method used to elaborate these guidelines was the Grade® method. After two Delphi rounds, 32 recommendations were formally developed by the experts focusing on the evaluation the initial severity of traumatic brain injury, the modalities of prehospital management, imaging strategies, indications for neurosurgical interventions, sedation and analgesia, indications and modalities of cerebral monitoring, medical management of raised intracranial pressure, management of multiple trauma with severe traumatic brain injury, detection and prevention of post-traumatic epilepsia, biological homeostasis (osmolarity, glycaemia, adrenal axis) and paediatric specificities.