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

M. Argenziano, S. Bruce, Cody Slater et al.

Abstract Objective To characterize patients with coronavirus disease 2019 (covid-19) in a large New York City medical center and describe their clinical course across the emergency department, hospital wards, and intensive care units. Design Retrospective manual medical record review. Setting NewYork-Presbyterian/Columbia University Irving Medical Center, a quaternary care academic medical center in New York City. Participants The first 1000 consecutive patients with a positive result on the reverse transcriptase polymerase chain reaction assay for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) who presented to the emergency department or were admitted to hospital between 1 March and 5 April 2020. Patient data were manually abstracted from electronic medical records. Main outcome measures Characterization of patients, including demographics, presenting symptoms, comorbidities on presentation, hospital course, time to intubation, complications, mortality, and disposition. Results Of the first 1000 patients, 150 presented to the emergency department, 614 were admitted to hospital (not intensive care units), and 236 were admitted or transferred to intensive care units. The most common presenting symptoms were cough (732/1000), fever (728/1000), and dyspnea (631/1000). Patients in hospital, particularly those treated in intensive care units, often had baseline comorbidities including hypertension, diabetes, and obesity. Patients admitted to intensive care units were older, predominantly male (158/236, 66.9%), and had long lengths of stay (median 23 days, interquartile range 12-32 days); 78.0% (184/236) developed acute kidney injury and 35.2% (83/236) needed dialysis. Only 4.4% (6/136) of patients who required mechanical ventilation were first intubated more than 14 days after symptom onset. Time to intubation from symptom onset had a bimodal distribution, with modes at three to four days, and at nine days. As of 30 April, 90 patients remained in hospital and 211 had died in hospital. Conclusions Patients admitted to hospital with covid-19 at this medical center faced major morbidity and mortality, with high rates of acute kidney injury and inpatient dialysis, prolonged intubations, and a bimodal distribution of time to intubation from symptom onset.

M. Devita, R. Bellomo, K. Hillman et al.

Stijn Blot, Mark van den Boogaard

Truong Thanh Hung Nguyen, Hélène Fournier, Piper Jackson et al.

Multi-agent systems (MAS) are increasingly used in healthcare to support complex decision-making through collaboration among specialized agents. Because these systems act as collective decision-makers, they raise challenges for trust, accountability, and human oversight. Existing approaches to trustworthy AI largely rely on explainability, but explainability alone is insufficient in multi-agent settings, as it does not enable care partners to challenge or correct system outputs. To address this limitation, Contestable AI (CAI) characterizes systems that support effective human challenge throughout the decision-making lifecycle by providing transparency, structured opportunities for intervention, and mechanisms for review, correction, or override. This position paper argues that contestability is a necessary design requirement for trustworthy multi-agent algorithmic care systems. We identify key limitations in current MAS and Explainable AI (XAI) research and present a human-in-the-loop framework that integrates structured argumentation and role-based contestation to preserve human agency, clinical responsibility, and trust in high-stakes care contexts.

Ray-Yuan Chung, Jaime Snyder, Zixuan Xu et al.

In pediatric chronic care, the triadic relationship among patients, caregivers, and healthcare providers introduces unique challenges for youth in managing their conditions. Diverging values, roles, and asymmetrical situational awareness across decision-maker groups often hinder collaboration and affect health outcomes, highlighting the need to support collaborative decision-making. We conducted co-design workshops with 6 youth with chronic kidney disease, 6 caregivers, and 7 healthcare providers to explore how digital technologies can be designed to support collaborative decision-making. Findings identify barriers across all levels of situational awareness, ranging from individual cognitive and emotional constraints, misaligned mental models, to relational conflicts regarding care goals. We propose design implications that support continuous decision-making practice, align mental models, balance caregiver support and youth autonomy development, and surface potential care challenges. This work advances the design of collaborative decision-making technologies that promote shared understanding and empower families in pediatric chronic care.

Mohamed Khalil Ben Salah, Philippe Jouvet, Rita Noumeir

Continuous monitoring of vital signs in Pediatric Intensive Care Units (PICUs) is essential for early detection of clinical deterioration and effective clinical decision-making. However, contact-based sensors such as pulse oximeters may cause skin irritation, increase infection risk, and lead to patient discomfort. Remote photoplethysmography (rPPG) offers a contactless alternative to monitor heart rate using facial video, but remains underutilized in PICUs due to motion artifacts, occlusions, variable lighting, and domain shifts between laboratory and clinical data. We introduce a self-supervised pretraining framework for rPPG estimation in the PICU setting, based on a progressive curriculum strategy. The approach leverages the VisionMamba architecture and integrates an adaptive masking mechanism, where a lightweight Mamba-based controller assigns spatiotemporal importance scores to guide probabilistic patch sampling. This strategy dynamically increases reconstruction difficulty while preserving physiological relevance. To address the lack of labeled clinical data, we adopt a teacher-student distillation setup. A supervised expert model, trained on public datasets, provides latent physiological guidance to the student. The curriculum progresses through three stages: clean public videos, synthetic occlusion scenarios, and unlabeled videos from 500 pediatric patients. Our framework achieves a 42% reduction in mean absolute error relative to standard masked autoencoders and outperforms PhysFormer by 31%, reaching a final MAE of 3.2 bpm. Without explicit region-of-interest extraction, the model consistently attends to pulse-rich areas and demonstrates robustness under clinical occlusions and noise.

Joel Wester, Samuel Rhys Cox, Henning Pohl et al.

Despite growing recognition that responsible AI requires domain knowledge, current work on conversational AI primarily draws on clinical expertise that prioritises diagnosis and intervention. However, much of everyday emotional support needs occur in non-clinical contexts, and therefore requires different conversational approaches. We examine how chaplains, who guide individuals through personal crises, grief, and reflection, perceive and engage with conversational AI. We recruited eighteen chaplains to build AI chatbots. While some chaplains viewed chatbots with cautious optimism, the majority expressed limitations of chatbots' ability to support everyday well-being. Our analysis reveals how chaplains perceive their pastoral care duties and areas where AI chatbots fall short, along the themes of Listening, Connecting, Carrying, and Wanting. These themes resonate with the idea of attunement, recently highlighted as a relational lens for understanding the delicate experiences care technologies provide. This perspective informs chatbot design aimed at supporting well-being in non-clinical contexts.

E. I. Zinchenko, A. S. Petrov, D. L. Fateeva et al.

When surgical treatment is limited in patients with stage IIIA-IIIB non–small cell lung cancer (NSCLC), multimodality treatment can improve long-term outcomes: neoadjuvant chemotherapy to reduce the tumor size followed by radical surgery.In recent years, the combination of standard induction chemotherapy with immune checkpoint inhibitors has been the subject of active debate in the literature. Some studies demonstrate the efficacy of neoadjuvant chemoimmunotherapy, whereas other authors note additional difficulties of surgery after it.In this case report we demonstrate features of surgery in a male patient with stage IIIB central NSCLC after chemoimmunotherapy and discuss difficulties of surgery associated with a minimally invasive video-assisted thoracoscopic surgery approach in complex anatomical lung resection and methods for preventing intraoperative and postoperative complications.

Christoffer Ericsson, Veronica Lindström, Jeanette Viggen Andersen et al.

Abstract Political efforts in the Nordic countries aim to promote gender equality. However, an assumption is that patriarchal structures remain embedded in EMS organizations, often leading to a ‘glass ceiling’ effect for women. The Emergency Medical Services (EMS), generally positioned at the intersection of safety authorities and healthcare, operates within environments often shaped by masculine values and norms. Concurrently, the service also connects strongly to compassion, caring and nursing, which have been historically female-dominant professions and working environments. In recent decades, more females have entered the EMS. Despite the growing number of female paramedics, challenges persist, particularly in relation to gender inequality and workplace culture. Females in EMS field continue to face gender stereotypes, which may contribute to inequality. Gender stereotypes, combined with research describing sexual harassment and bias, underscore the need for further discussions and research on the impact of gender on paramedic work environments and career pathways for women working in the service.

Fengpei Yuan, Nehal Hasnaeen, Ran Zhang et al.

This study explores a novel approach to advancing dementia care by integrating socially assistive robotics, reinforcement learning (RL), large language models (LLMs), and clinical domain expertise within a simulated environment. This integration addresses the critical challenge of limited experimental data in socially assistive robotics for dementia care, providing a dynamic simulation environment that realistically models interactions between persons living with dementia (PLWDs) and robotic caregivers. The proposed framework introduces a probabilistic model to represent the cognitive and emotional states of PLWDs, combined with an LLM-based behavior simulation to emulate their responses. We further develop and train an adaptive RL system enabling humanoid robots, such as Pepper, to deliver context-aware and personalized interactions and assistance based on PLWDs' cognitive and emotional states. The framework also generalizes to computer-based agents, highlighting its versatility. Results demonstrate that the RL system, enhanced by LLMs, effectively interprets and responds to the complex needs of PLWDs, providing tailored caregiving strategies. This research contributes to human-computer and human-robot interaction by offering a customizable AI-driven caregiving platform, advancing understanding of dementia-related challenges, and fostering collaborative innovation in assistive technologies. The proposed approach has the potential to enhance the independence and quality of life for PLWDs while alleviating caregiver burden, underscoring the transformative role of interaction-focused AI systems in dementia care.

Jeffrey Zhang, Zhe Chen, Katherine R. Courtright et al.

While palliative care is increasingly commonly delivered to hospitalized patients with serious illnesses, few studies have estimated its causal effects. Courtright et al. (2016) adopted a cluster-randomized stepped-wedge design to assess the effect of palliative care on a patient-centered outcome. The randomized intervention was a nudge to administer palliative care but did not guarantee receipt of palliative care, resulting in noncompliance (compliance rate ~30%). A subsequent analysis using methods suited for standard trial designs produced statistically anomalous results, as an intention-to-treat analysis found no effect while an instrumental variable analysis did (Courtright et al., 2024). This highlights the need for a more principled approach to address noncompliance in stepped-wedge designs. We provide a formal causal inference framework for the stepped-wedge design with noncompliance by introducing a relevant causal estimand and corresponding estimators and inferential procedures. Through simulation, we compare an array of estimators across a range of stepped-wedge designs and provide practical guidance in choosing an analysis method. Finally, we apply our recommended methods to reanalyze the trial of Courtright et al. (2016), producing point estimates suggesting a larger effect than the original analysis of (Courtright et al., 2024), but intervals that did not reach statistical significance.

Koki Suenaga, Tomohiro Furuta, Satoshi Ono

Technologies for automatically generating work schedules have been extensively studied; however, in long-term care facilities, the conditions vary between facilities, making it essential to interview the managers who create shift schedules to design facility-specific constraint conditions. The proposed method utilizes constraint templates to extract combinations of various components, such as shift patterns for consecutive days or staff combinations. The templates can extract a variety of constraints by changing the number of days and the number of staff members to focus on and changing the extraction focus to patterns or frequency. In addition, unlike existing constraint extraction techniques, this study incorporates mechanisms to exclude exceptional constraints. The extracted constraints can be employed by a constraint programming solver to create care worker schedules. Experiments demonstrated that our proposed method successfully created schedules that satisfied all hard constraints and reduced the number of violations for soft constraints by circumventing the extraction of exceptional constraints.

Wanqiu Xie, Yue Li, Xianglin Meng et al.

AIM To establish a prediction model and assess the risk factors for severe diabetic ketoacidosis (DKA) in adult patients during the ICU. INTRODUCTION With DKA hospitalization rates consistently increasing, in-hospital mortality has become a growing concern. METHODS DKA patients aged >18 years old in the US-based critical care database (Medical Information Mart for Intensive Care (MIMIC-IV)) were considered. Independent risk factors for in-hospital mortality were screened using extreme gradient boosting (XGBoost) and the Bayesian information criterion (BIC) optimal subset regression. One predictive model was developed using machine learning extreme gradient boosting (XGBoost), and the other one was a nomogram based on logistic regression to estimate risks of in-hospital mortality with severe DKA. Established models were assessed by using internal validation and external validation. The MIMIC-IV was split into training and testing samples in a 7:3 ratio. The eICU Collaborative Research Database and admissions data from the department of critical care medicine of the first affiliated hospital of Harbin medical university were used for independent validation. The discriminatory ability of the model was determined by illustrating a receiver operating curve (ROC) and calculating the C-index. Meanwhile, the calibration plot and Hosmer-Lemeshow goodness-of-fit test (HL test) was conducted to evaluate the performance of our new build model. Decision curve analysis (DCA) was performed to assess the clinical net benefit. Net Reclassification Improvement (NRI) was used to compare the predictive power of the two models. RESULTS A multivariable model that included acute physiology score III (APS III), the highest levels of blood plasma osmolality (osmolarity_max), minimum osmolarity (osmolarity_min)/osmolarity _max, vasopressor, and the highest levels of blood lactate was represented as the nomogram. The C- index of the nomogram model was 0.915 (95% CI: 0.966-0.864) in the training dataset and 0.971 (95% CI: 0.992-0.950) in the internal validation. The nomogram's sensitivity was well according to all data's HL test (P > 0.05). DCA showed that our model was clinically valuable. The XGB (extreme gradient boosting) model achieved an AUC (area under the curve) of 0.950 (95% CI, 0.920-0.980); however, the nomogram model made was more effective than XGB based on NRI. CONCLUSION The predictive XGB and nomogram models for predicting in-hospital patient deaths with DKA were effective. The forecast models can help clinical physicians promptly identify patients at high risk of DKA, prevent in-hospital deaths, and promptly intervene.

Angelia D. Gibson, PhD, Zaida Bayrón-Marrero, MS, Benjamin Nieves-Lopez, BS et al.

OBJECTIVES:. To identify triggering receptor expressed in myeloid cells-like transcript-1 positive (TLT-1+) microparticles (MPs) and evaluate if their presence is associated with clinical outcomes and/or disease severity in acute respiratory distress syndrome (ARDS). DESIGN:. Retrospective cohort study. SETTING:. ARDS Network clinical trials. PATIENTS:. A total of 564 patients were diagnosed with ARDS. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. Using flow cytometry, we demonstrated the presence of TLT-1+ platelet-derived microparticles (PMP) that bind fibrinogen in plasma samples from fresh donors. We retrospectively quantified TLT-1, glycoprotein (Gp) 1b, or αIIbβIIIa immunopositive microparticles in plasma samples from patients with ARDS enrolled in the ARMA, KARMA, and LARMA (Studies 01 and 03 lower versus higher tidal volume, ketoconazole treatment, and lisofylline treatment Clincial Trials) ARDS Network clinical trials and evaluated the relationship between these measures and clinical outcomes. No associations were found between Gp1b+ MPs and clinical outcomes for any of the cohorts. When stratified by quartile, associations were found for survival, ventilation-free breathing, and thrombocytopenia with αIIbβIIIa+ and TLT-1+ MPs (χ2 p < 0.001). Notably, 63 of 64 patients in this study who failed to achieve unassisted breathing had TLT+ PMP in the 75th percentile. In all three cohorts, patients whose TLT+ MP counts were higher than the median had higher Acute Physiology and Chronic Health Evaluation III scores, were more likely to present with thrombocytopenia and were 3.7 times (p < 0.001) more likely to die than patients with lower TLT+ PMP after adjusting for other risk factors. CONCLUSIONS:. Although both αIIbβIIIa+ and TLT+ microparticles (αIIbβIIIa, TLT-1) were associated with mortality, TLT-1+ MPs demonstrated stronger correlations with Acute Physiology and Chronic Health Evaluation III scores, unassisted breathing, and multiple system organ failure. These findings warrant further exploration of the mechanistic role of TLT-1+ PMP in ARDS or acute lung injury progression.

Gavin G. Calpin, Sandra Hembrecht, Katie Giblin et al.

Abstract Introduction Appendicectomy remains the standard treatment for appendicitis. There is a lack of clarity on the timeframe in which surgery should be performed to avoid unfavourable outcomes. Aim To perform a systematic review and network meta-analysis to evaluate the impact the (1)time-of-day surgery is performed (2), time elapsed from symptom onset to hospital presentation (patient time) (3), time elapsed from hospital presentation to surgery (hospital time), and (4)time elapsed from symptom onset to surgery (total time) have on appendicectomy outcomes. Methods A systematic review was performed as per PRISMA-NMA guidelines. The time-of-day which surgery was done was divided into day, evening and night. The other groups were divided into < 24 h, 24–48 h and > 48 h. The rate of complicated appendicitis, operative time, perforation, post-operative complications, surgical site infection (SSI), length of stay (LOS), readmission and mortality rates were analysed. Results Sixteen studies were included with a total of 232,678 patients. The time of day at which surgery was performed had no impact on outcomes. The incidence of complicated appendicitis, post-operative complications and LOS were significantly better when the hospital time and total time were < 24 h. Readmission and mortality rates were significantly better when the hospital time was < 48 h. SSI, operative time, and the rate of perforation were comparable in all groups. Conclusion Appendicectomy within 24 h of hospital admission is associated with improved outcomes compared to patients having surgery 24–48 and > 48 h after admission. The time-of-day which surgery is performed does not impact outcomes.

Jamilla Goedegebuur, MD, Floor E. Smits, MSc, Jacob W. M. Snoep, BSc et al.

IMPORTANCE:. Mechanical power (MP) could serve as a valuable parameter in clinical practice to estimate the likelihood of adverse outcomes. However, the safety thresholds for MP in mechanical ventilation remain underexplored and contentious. OBJECTIVES:. This study aims to investigate the association between MP and hospital mortality across varying degrees of lung disease severity, classified by Pao2/Fio2 ratios. DESIGN, SETTING, AND PARTICIPANTS:. This is a retrospective cohort study using automatically extracted data. Patients admitted to the ICU of a tertiary referral hospital in The Netherlands between 2018 and 2024 and ventilated in pressure-controlled mode were included. MAIN OUTCOMES AND MEASURES:. Logistic regression, adjusted for age, sex, Acute Physiology and Chronic Health Evaluation-IV score, and Pao2/Fio2 ratio, was used to calculate the odds ratio (OR) for all-cause in-hospital mortality. RESULTS:. A total of 2184 patients were analyzed, with a mean age of 62.5 ± 13.8 years, of whom 1508 (70.2%) were male. The mean MP was highest in patients with the lowest Pao2/Fio2 ratios (21.5 ± 6.5 J/min) compared with those with the highest ratios (12.0 ± 3.8 J/min; p < 0.001). Adjusted analyses revealed that increased MP was associated with higher mortality (OR, 1.06; 95% CI, 1.03–1.09 per J/min increase). Similarly, MP normalized for body weight showed a stronger association with mortality (OR, 1.004; 95% CI, 1.002–1.006 per J/min/kg increase). An increase in mortality was seen when MP exceeded 16–18 J/min. CONCLUSIONS AND RELEVANCE:. Our findings demonstrate a significant association between MP and hospital mortality, even after adjusting for key confounders. Mortality increases notably when MP exceeds 16–18 J/min. Normalized MP presents an even stronger association with mortality. These results underscore the need for further research into ventilation strategies that consider MP adjustments.

Jiayi Wen, Xiaoqing Yu

Does public insurance reduce uninsured long-term care (LTC) risks in developing countries, where informal insurance predominates? This paper exploits the rollout of LTC insurance in China around 2016 to examine the impact of public LTC insurance on healthy workers' labor supply, a critical self-insurance channel. We find that workers eligible for public LTC insurance were less likely to engage in labor work and worked fewer weeks annually following the policy change, suggesting a mitigation of uninsured risks. However, these impacts were insignificant among those with strong informal insurance coverage. Parallel changes in anticipated formal care use corroborate these findings. While our results reveal that public LTC insurance provides limited additional risk-sharing when informal insurance predominates, they also underscore its growing importance.

Nur Yildirim, Susanna Zlotnikov, Deniz Sayar et al.

Advances in artificial intelligence (AI) have enabled unprecedented capabilities, yet innovation teams struggle when envisioning AI concepts. Data science teams think of innovations users do not want, while domain experts think of innovations that cannot be built. A lack of effective ideation seems to be a breakdown point. How might multidisciplinary teams identify buildable and desirable use cases? This paper presents a first hand account of ideating AI concepts to improve critical care medicine. As a team of data scientists, clinicians, and HCI researchers, we conducted a series of design workshops to explore more effective approaches to AI concept ideation and problem formulation. We detail our process, the challenges we encountered, and practices and artifacts that proved effective. We discuss the research implications for improved collaboration and stakeholder engagement, and discuss the role HCI might play in reducing the high failure rate experienced in AI innovation.

Sara Eklind, Karin Olby, Eva Åkerman

Onlak Ruangsomboon, Supawich Jirathanavichai, Nutthida Phanprasert et al.

Introduction: High-flow nasal cannula (HFNC) is a respiratory support measure for coronavirus 2019 (COVID-19) patients that has been increasingly used in the emergency department (ED). Although the respiratory rate oxygenation (ROX) index can predict HFNC success, its utility in emergency COVID-19 patients has not been well-established. Also, no studies have compared it to its simpler component, the oxygen saturation to fraction of inspired oxygen (SpO2/FiO2 [SF]) ratio, or its modified version incorporating heart rate. Therefore, we aimed to compare the utility of the SF ratio, the ROX index (SF ratio/respiratory rate), and the modified ROX index (ROX index/heart rate) in predicting HFNC success in emergency COVID-19 patients. Methods: We conducted this multicenter retrospective study at five EDs in Thailand between January–December 2021. Adult patients with COVID-19 treated with HFNC in the ED were included. The three study parameters were recorded at 0 and 2 hours. The primary outcome was HFNC success, defined as no requirement of mechanical ventilation at HFNC termination. Results: A total of 173 patients were recruited; 55 (31.8%) had successful treatment. The two-hour SF ratio yielded the highest discrimination capacity (AUROC 0.651, 95% CI 0.558–0.744), followed by two-hour ROX and modified ROX indices (AUROC 0.612 and 0.606, respectively). The two-hour SF ratio also had the best calibration and overall model performance. At its optimal cut-point of 128.19, it gave a balanced sensitivity (65.3%) and specificity (61.8%). The two-hour SF≥128.19 was also significantly and independently associated with HFNC failure (adjusted odds ratio 0.29, 95% CI 0.13–0.65; P=0.003). Conclusion: The SF ratio predicted HFNC success better than the ROX and modified ROX indices in ED patients with COVID-19. With its simplicity and efficiency, it may be the appropriate tool to guide management and ED disposition for COVID-19 patients receiving HFNC in the ED.