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

Y. Raita, T. Goto, M. Faridi et al.

BackgroundDevelopment of emergency department (ED) triage systems that accurately differentiate and prioritize critically ill from stable patients remains challenging. We used machine learning models to predict clinical outcomes, and then compared their performance with that of a conventional approach—the Emergency Severity Index (ESI).MethodsUsing National Hospital and Ambulatory Medical Care Survey (NHAMCS) ED data, from 2007 through 2015, we identified all adult patients (aged ≥ 18 years). In the randomly sampled training set (70%), using routinely available triage data as predictors (e.g., demographics, triage vital signs, chief complaints, comorbidities), we developed four machine learning models: Lasso regression, random forest, gradient boosted decision tree, and deep neural network. As the reference model, we constructed a logistic regression model using the five-level ESI data. The clinical outcomes were critical care (admission to intensive care unit or in-hospital death) and hospitalization (direct hospital admission or transfer). In the test set (the remaining 30%), we measured the predictive performance, including area under the receiver-operating-characteristics curve (AUC) and net benefit (decision curves) for each model.ResultsOf 135,470 eligible ED visits, 2.1% had critical care outcome and 16.2% had hospitalization outcome. In the critical care outcome prediction, all four machine learning models outperformed the reference model (e.g., AUC, 0.86 [95%CI 0.85–0.87] in the deep neural network vs 0.74 [95%CI 0.72–0.75] in the reference model), with less under-triaged patients in ESI triage levels 3 to 5 (urgent to non-urgent). Likewise, in the hospitalization outcome prediction, all machine learning models outperformed the reference model (e.g., AUC, 0.82 [95%CI 0.82–0.83] in the deep neural network vs 0.69 [95%CI 0.68–0.69] in the reference model) with less over-triages in ESI triage levels 1 to 3 (immediate to urgent). In the decision curve analysis, all machine learning models consistently achieved a greater net benefit—a larger number of appropriate triages considering a trade-off with over-triages—across the range of clinical thresholds.ConclusionsCompared to the conventional approach, the machine learning models demonstrated a superior performance to predict critical care and hospitalization outcomes. The application of modern machine learning models may enhance clinicians’ triage decision making, thereby achieving better clinical care and optimal resource utilization.

Thomas M Scalea, Deborah M Stein, Gregory J Jurkovich et al.

Objectives Liver trauma management has evolved over recent decades, shifting from primarily operative to algorithmic approaches incorporating initial non-operative management alongside operative intervention for severe injuries. Inconsistent core outcome measures between studies hamper meaningful clinical research and evidence synthesis. This study aimed to develop a core outcome set (COS) for liver trauma research to standardize outcome reporting and improve study comparability.Methods A modified Delphi consensus methodology after COMET (Core Outcome Measures in Effectiveness Trials) and COS-STAD (Core Outcome Set-Standards for Development) guidelines was employed and twenty trauma surgery experts were recruited through purposive sampling of high-impact publications and professional networks. Round 1 collected free-text outcome suggestions. Round 2 used a 9-point Likert scale rating of those previously identified outcomes. Consensus was defined as ≥70% rating outcomes as critically important (7–9) and ≤15% as unimportant (1–3). Intraclass correlation (ICC) assessed agreement. Round 3 involved reprioritization of non-consensus outcomes.Results All 20 experts completed three rounds (95% response rate). From 102 initial suggestions, 41 unique outcomes were identified. By consensus, 12 outcomes were prioritized spanning four domains: operative decision-making, non-operative management success, hepatic complications, and healthcare utilization-related outcomes. The ICC was 0.89 (95% CI 0.84 to 0.94), indicating strong inter-rater reliability.Conclusions This rigorously developed COS for liver trauma provides standardized outcomes to guide future research and improve cross-study comparability. Adoption of these outcomes may enhance reporting consistency and facilitate evidence synthesis in liver trauma research.Study type Consensus development study (modified Delphi)Level of evidence V.

Minh Triet Pham, Quynh Chi Dang, Le Nhat Tan

Indoor localization systems in care facilities enable optimization of staff allocation, workload management, and quality of care delivery. Traditional machine learning approaches to Bluetooth Low Energy (BLE)-based localization treat each temporal measurement as an independent observation, fundamentally limiting their performance. To address this limitation, this paper introduces Deep Attention-based Sequential Ensemble Learning (DASEL), a novel framework that reconceptualizes indoor localization as a sequential learning problem. The framework integrates frequency-based feature engineering, bidirectional GRU networks with attention mechanisms, multi-directional sliding windows, and confidence-weighted temporal smoothing to capture human movement trajectories. Evaluated on real-world data from a care facility using 4-fold temporal cross-validation, DASEL achieves a macro F1 score of 0.4438, representing a 53.1% improvement over the best traditional baseline (0.2898).

Amanda H. Bjornstad, Natalie Oberhauser-Lim, Tammy Phan et al.

Introduction: Patients with psychiatric concerns often present to an emergency department (ED) for medical evaluation prior to inpatient psychiatry placement. One diagnosis to consider prior to disposition is autoimmune encephalitis (AIE). This report describes a pediatric patient who presented with psychiatric symptoms that required inpatient admission and workup to diagnose a rare form of AIE. Case Report: A 16-year-old female with no known past medical history presented as a transfer from an outside hospital for medical evaluation of two days of auditory and visual hallucinations. Initial labs and imaging were unremarkable. Due to the acuity of her symptoms and abnormal vital signs, she was admitted to the hospital for further medical workup. After almost three weeks inpatient and multiple specialist consultations, she was diagnosed with anti-heavy chain neuronal intermediate filament AIE. The next month of admission included treatment with immunomodulators, antibiotics for associated infections, and malignancy evaluation. Symptoms resolved, and the patient was discharged. The patient remained asymptomatic on immunotherapies, and without psychiatric medications, the following year. Conclusion: During evaluation of psychiatric concerns in the ED, it is essential to consider organic causes of behavioral changes, which can be difficult to discern. Autoimmune encephalitis can be subtle. Features such as autonomic dysregulation, acute or subacute symptom onset, recent infection, autoimmune or malignancy history, cognitive deficits, or focal neurologic findings should raise clinical suspicion. For patients with psychiatric symptoms, the role of an emergency physician is not to diagnose autoimmune encephalitis, but to recognize nuances in patient presentations to best direct proper workup, treatment, and disposition.

Asma Ahmadinejad, Amir Saeed, Marzieh Davoodi

Abstract Objective Tricyclic antidepressants (TCAs) are used to treat depression, but if abused or misused, they can cause poisoning, which can be fatal. The main treatment for TCA poisoning is administering sodium bicarbonate. Methods We report a 16-year-old girl diagnosed with nortriptyline poisoning with a profound neurologic and cardiovascular manifestations, successfully treated using sodium bicarbonate, intralipid emulsion, and plasmapheresis. Conclusions Plasmapheresis can be a good treatment modality for patients with TCA poisoning who do not respond well to classic treatments.

Muttaqin MZ

M Zaenul Muttaqin Public Administration Study Program, Cenderawasih University, Jayapura City, IndonesiaCorrespondence: M Zaenul Muttaqin, Email mzaenul69@gmail.com

Geyang Guo, Tarek Naous, Hiromi Wakaki et al.

Language Models (LMs) are typically tuned with human preferences to produce helpful responses, but the impact of preference tuning on the ability to handle culturally diverse queries remains understudied. In this paper, we systematically analyze how native human cultural preferences can be incorporated into the preference learning process to train more culturally aware LMs. We introduce \textbf{CARE}, a multilingual resource containing 3,490 culturally specific questions and 31.7k responses with human judgments. We demonstrate how a modest amount of high-quality native preferences improves cultural awareness across various LMs, outperforming larger generic preference data. Our analyses reveal that models with stronger initial cultural performance benefit more from alignment, leading to gaps among models developed in different regions with varying access to culturally relevant data. CARE is publicly available at https://github.com/Guochry/CARE.

Marta Sánchez Mora, Beatriz Lázaro Álvarez, Amaya Arboníes Cabodevilla et al.

P. Bay, É. Audureau, S. Preau et al.

Background During the first COVID-19 pandemic wave, COVID-19-associated pulmonary aspergillosis (CAPA) has been reported in up to 11–28% of critically ill COVID-19 patients and associated with increased mortality. As new SARS-CoV-2 variants emerged, the characteristics of critically ill COVID-19 patients have evolved, particularly in the era of Omicron. The purpose of this study is to investigate the characteristics of CAPA in the era of new variants. Methods This is a prospective multicenter observational cohort study conducted in France in 36 participating intensive care units (ICU), between December 7th, 2021 and April 26th 2023. Diagnosis criteria of CAPA relied on European Confederation of Medical Mycology (ECMM)/International Society for Human & Animal Mycology (ISHAM) consensus criteria. Results 566 patients were included over the study period. The prevalence of CAPA was 5.1% [95% CI 3.4–7.3], and rose to 9.1% among patients who required invasive mechanical ventilation (IMV). Univariable analysis showed that CAPA patients were more frequently immunosuppressed and required more frequently IMV support, vasopressors and renal replacement therapy during ICU stay than non-CAPA patients. SAPS II score at ICU admission, immunosuppression, and a SARS-CoV-2 Delta variant were independently associated with CAPA in multivariable logistic regression analysis. Although CAPA was not significantly associated with day-28 mortality, patients with CAPA experienced a longer duration of mechanical ventilation and ICU stay. Conclusion This study contributes valuable insights into the prevalence, characteristics, and outcomes of CAPA in the era of Delta and Omicron variants. We report a lower prevalence of CAPA (5.1%) among critically-ill COVID-19 patients than previously reported, mainly affecting intubated-patients. Duration of mechanical ventilation and ICU stay were significantly longer in CAPA patients.

Paul Peng, Alex F. Manini

Abstract Background Capnography is a quantitative and reliable method of determining the ventilatory status of patients. We describe the test characteristics of capnography obtained during Emergency Department triage for screening acidemia. Results We performed an observational, pilot study of adult patients presenting to Emergency Department (ED) triage. The primary outcome was acidemia, as determined by the basic metabolic panel and/or blood gas during the ED visit. Secondary outcomes include comparison of estimated and measured respiratory rates (RR), relationships between end-tidal CO2 (EtCO 2 ) and venous partial pressure of CO 2 , admission disposition, in-hospital mortality during admission, and capnogram waveform analysis. A total of 100 adult ED encounters were included in the study and acidemia ( $$ \left[HC{O}_{3}^{-}\right]\le 22 \text{mEq/L}$$ or $$ pH< 7.35$$ ) was identified in 28 patients. The measured respiratory rate (20.3 ± 6.4 breaths/min) was significantly different from the estimated rate (18.4 ± 1.6 breaths/min), and its area under the receiver operating curve (c-statistic) to predict acidemia was only 0.60 (95% CI 0.51–0.75, p = 0.03). A low end-tidal CO2 (EtCO 2   < 32 mmHg) had positive (LR+) and negative (LR−) likelihood ratios of 4.68 (95% CI 2.59–8.45) and 0.34 (95% CI 0.19–0.61) for acidemia, respectively—corresponding to sensitivity 71.4% (95% CI 51.3–86.8) and specificity 84.7% (95% CI 74.3–92.1). The c-statistic for EtCO 2 was 0.849 (95% CI 0.76–0.94, p = 0.00). Waveform analysis further revealed characteristically abnormal capnograms that were associated with underlying pathophysiology. Conclusions Capnography is a quantitative method of screening acidemia in patients and can be implemented feasibly in Emergency Department triage as an adjunct to vital signs. While it was shown to have only modest ability to predict acidemia, triage capnography has wide generalizability to screen other life-threatening disease processes such as sepsis or can serve as an early indicator of clinical deterioration.

Ianis Siriopol, Ioana Grigoras, Daniel Rusu et al.

Yin Bun Cheung, Xiangmei Ma, Isha Chaudhry et al.

Background: Incidence of adverse outcome events rises as patients with advanced illness approach end-of-life. Exposures that tend to occur near end-of-life, e.g., use of wheelchair, oxygen therapy and palliative care, may therefore be found associated with the incidence of the adverse outcomes. We propose a strategy for time-to-event analysis to mitigate the time-varying confounding. Methods: We propose a concept of reverse time-to-death (rTTD) and its use for the time-scale in time-to-event analysis. We used data on community-based palliative care uptake (exposure) and emergency department visits (outcome) among patients with advanced cancer in Singapore to illustrate. We compare the results against that of the common practice of using time-on-study (TOS) as time-scale. Results: Graphical analysis demonstrated that cancer patients receiving palliative care had higher rate of emergency department visits than non-recipients mainly because they were closer to end-of-life, and that rTTD analysis made comparison between patients at the same time-to-death. Analysis of emergency department visits in relation to palliative care using TOS time-scale showed significant increase in hazard ratio estimate when observed time-varying covariates were omitted from statistical adjustment (change-in-estimate=0.38; 95% CI 0.15 to 0.60). There was no such change in otherwise the same analysis using rTTD (change-in-estimate=0.04; 95% CI -0.02 to 0.11), demonstrating the ability of rTTD time-scale to mitigate confounding that intensifies in relation to time-to-death. Conclusion: Use of rTTD as time-scale in time-to-event analysis provides a simple and robust approach to control time-varying confounding in studies of advanced illness, even if the confounders are unmeasured.

Omer Cantor, Uriya A. First

Let $A$ be a finite dimensional algebra (possibly with some extra structure) over an infinite field $K$ and let $r\in\mathbb{N}$. The $r$-tuples $(a_1,\dots,a_r)\in A^r$ which fail to generate $A$ are the $K$-points of a closed subvariety $Z_r$ of the affine space underlying $A^r$, the codimension of which may be thought of as quantifying how well a generic $r$-tuple in $A^r$ generates $A$. Taking this intuition one step further, the second author, Reichstein and Williams showed that lower bounds on the codimension of $Z_r$ in $A^r$ (for every $r$) imply upper bounds on the number of generators of \emph{forms} of the $K$-algebra $A$ over finitely generated $K$-rings. That work also demonstrates how finer information on $Z_r$ may be used to construct forms of $A$ which require many elements to generate. The dimension and irreducible components of $Z_r$ are known in a few cases, which in particular lead to upper bounds on the number of generators of Azumaya algebras and Azumaya algebras with involution of the first kind (orthogonal or symplectic). This paper treats the case of Azumaya algebras with a unitary involution by finding the dimension and irreducible components of $Z_r$ when $A$ is the $K$-algebra with involution $(\mathrm{M}_n(K)\times \mathrm{M}_n(K), (a,b)\mapsto (b^{\mathrm{t}},a^{\mathrm{t}}))$. Our analysis implies that every Azumaya algebra with a unitary involution over a finitely generated $K$-ring of Krull dimension $d$ can be generated by $\lfloor \frac{d}{2n-2}+\frac{3}{2} \rfloor$ elements. We also give examples which require at least half that many elements to generate, by building on the work of the second author, Reichstein and Williams. Our method of finding the dimension and irreducible components of $Z_r$ actually applies to all $K$-algebras $A$ satisfying a mild assumption.

Xiangmei Ma, Grace Meijuan Yang, Qingyuan Zhuang et al.

Objectives: Prior event rate ratio (PERR) is a method shown to perform well in mitigating confounding in real-world evidence research but it depends on several model assumptions. We propose an analytic strategy to correct biases arising from violation of two model assumptions, namely, population homogeneity and event-independent treatment. Study Design and Setting: We reformulate PERR estimation by embedding a treatment-by-period interaction term in an analytic model for recurrent event data, which is robust to bias arising from unobserved heterogeneity. Based on this model, we propose a set of methods to examine the presence of event-dependent treatment and to correct the resultant bias. We evaluate the proposed methods by simulation and apply it to a de-identified dataset on palliative care and emergency department visits in patients with advanced cancer. Results: Simulation results showed that the proposed method could mitigate the two sources of bias in PERR. In the palliative care study, analysis by the Cox model showed that patients who had started receiving palliative care had higher incidence of emergency department visits than their match controls (hazard ratio 3.31; 95% confidence interval 2.78 to 3.94). Using PERR without the proposed bias control strategy indicated a 19% reduction of the incidence (0.81; 0.64 to 1.02). However, there was evidence of event-dependent treatment. The proposed correction method showed no effect of palliative care on ED visits (1.00; 0.79 to 1.26). Conclusions: The proposed analytic strategy can control two sources of biases in the PERR approach. It enriches the armamentarium for real-world evidence research.

S. Millington, N. Aissaoui, E. Bowcock et al.

W. Knaus, D. Wagner, J. Zimmerman et al.

Jacqueline Twamley, R. Monks, K. Beaver

OBJECTIVES Critical illness recovery is a journey; from intensive care unit to hospital ward to home. However, evidence is limited on how best to enable recovery from critical illness. This study aimed to prioritise areas for improvement in care and services for patients recovering from critical illness. RESEARCH DESIGN This study used experience-based co-design. Service users and providers worked in partnership to identify and prioritise service improvements for patients who had survived an episode of critical illness. METHOD Qualitative interviews were carried out with patients (n = 10) who had experienced critical illness, and staff (n = 9) who had experienced caring for patients in the intensive care unit. Key patient touchpoints were identified and used to produce a film, reflecting the critical illness journey. A patient feedback event incorporated an emotional mapping exercise, to identify key points during the recovery journey. A joint patient/family (n = 10) and staff (n = 10) event was held to view the film and identify priorities for improvements. FINDINGS Emotional mapping highlighted areas where services were not synchronised with patients' needs. Four patient-focussed priorities for service improvement emerged 1. Improving the critical care experience, 2. Addressing patients' emotional and psychological needs, 3. Positioning patients at the centre of services and 4. Building a supportive framework for recovery. CONCLUSION Evidence-based co-design was used successfully in this study to identify priorities for improvements for patients recovering from critical illness. This approach positions patients at the centre of service improvements and realigns care delivery around what matters most to patients. Person-centred care provision underpins all identified priorities. IMPLICATIONS FOR CLINICAL PRACTICE Intensive care unit staff should get to know patients and their families by talking more to patients and families about their care and engaging in more non-medical conversations. Emotional and psychological support should be provided to aid rehabilitation and recovery from critical illness in the intensive care unit, on general wards, and in the community. Information and services should be available when patients need them, rather than at fixed time points or settings. Recovery services should focus on enabling and building the self-efficacy of patients to empower them to be in control of their recovery journey.

Freda M. Warner, Bobo Tong, Jessie McDougall et al.

Open data sharing of clinical research aims to improve transparency and support novel scientific discoveries. There are also risks, including participant identification and the potential for stigmatization. The perspectives of persons participating in research are needed to inform open data-sharing policies. The aim of the current study was to determine perspectives on data sharing in persons with spinal cord injury (SCI), including risks and benefits, and types of data people are most willing to share. A secondary aim was to examine predictors of willingness to share data. Persons with SCIs in the United States and Canada completed a survey developed and disseminated through various channels, including our community partner, the North American Spinal Cord Injury Consortium. The study collected data from 232 participants, with 52.2% from Canada and 42.2% from the United States, and the majority completed the survey in English. Most participants had previously participated in research and had been living with an SCI for ?5 years. Overall, most participants reported that the potential benefits of data sharing outweighed the negatives, with persons with SCI seen as the most trustworthy partners for data sharing. The highest levels of concern were that information could be stolen and companies might use the information for marketing purposes. Persons with SCI were generally supportive of data sharing for research purposes. Clinical trials should consider including a statement on open data sharing in informed consents to better acknowledge the contribution of research participants in future studies.