Improve Patient Outcomes Research Articles

Unveiling the Hidden Impact: Hematoma Volumes Unravel Circuit Disruptions in Intracerebral Hemorrhage.

Intracerebral hemorrhage (ICH) imposes a significant burden on patients, and the volume of hematoma plays a crucial role in determining the severity and prognosis of ICH. Although significant recent progress has been made in understanding the cellular and molecular mechanisms of surrounding brain tissue in ICH, our current knowledge regarding the precise impact of hematoma volumes on neural circuit damage remains limited. Here, using a viral tracing technique in a mouse model of striatum ICH, two distinct patterns of injury response were observed in upstream connectivity, characterized by both linear and nonlinear trends in specific brain areas. Notably, even low-volume hematomas had a substantial impact on downstream connectivity. Neurons in the striatum-ICH region exhibited heightened excitability, evidenced by electrophysiological measurements and changes in metabolic markers. Furthermore, a strong linear relationship (R2 = 0.91) was observed between hematoma volumes and NFL damage, suggesting a novel biochemical index for evaluating changes in neural injury. RNA sequencing analysis revealed the activation of the MAPK signaling pathway following hematoma, and the addition of MAPK inhibitor revealed a decrease in neuronal circuit damage, leading to alleviation of motor dysfunction in mice. Taken together, our study highlights the crucial role of hematoma size as a determinant of circuit injury in ICH. These findings have important implications for clinical evaluations and treatment strategies, offering opportunities for precise therapeutic approaches to mitigate the detrimental effects of ICH and improve patient outcomes.

Development of a novel, concentric micro-ECoG array enabling simultaneous detection of a single location by multiple electrode sizes.

Detection of the epileptogenic zone is critical, especially for patients with
drug-resistant epilepsy. Accurately mapping cortical regions exhibiting high activity during
spontaneous seizure events while detecting neural activity up to 500 Hz can assist clinicians'
surgical decisions and improve patient outcomes. We designed, fabricated,
and tested a novel hybrid, multi-scale micro-electrocorticography (micro-ECoG) array with
a unique embedded configuration. This array was compared to a commercially available
microelectrode array (Neuronexus) for recording neural activity in rodent sensory cortex
elicited by somatosensory evoked potentials and pilocarpine-induced seizures. Main results
Evoked potentials and spatial maps recorded by the multi-scale array ("micros", "mesos", and
"macros" refering to the relative electrode sizes, 40 micron, 1 mm, and 4 mm respectively)
were comparable to the Neuronexus array. The SSEPs recorded with the micros had higher
peak amplitudes and greater signal power than those recorded by the larger mesos and macro.
Seizure onset events and high-frequency oscillations (∼450 Hz) were detected on the multi-
scale, similar to the commercially available array. The micros had greater SNR than the mesos
and macro over the 5-1000 Hz frequency range during seizure monitoring. During cortical
stimulation experimentation, the mesos successfully elicited motor effects. 
Previous studies have compared macro- and microelectrodes for localizing seizure activity in
adjacent regions. The multi-scale design validated here is the first to simultaneously measure
macro- and microelectrode signals from the same overlapping cortical area. This enables direct
comparison of microelectrode recordings to the macroelectrode recordings used in standard
neurosurgical practice. Previous studies have also shown that cortical regions generating
high-frequency oscillations are at an increased risk for becoming epileptogenic zones. More
accurate mapping of these micro seizures may improve surgical outcomes for epilepsy patients.

Perioperative anaphylaxis: updates on pathophysiology.

Perioperative anaphylaxis has historically been attributed to IgE/FcεRI-mediated reactions; there is now recognition of allergic and nonallergic triggers encompassing various reactions beyond IgE-mediated responses. This review aims to present recent advancements in knowledge regarding the mechanisms and pathophysiology of perioperative anaphylaxis. Emerging evidence highlights the role of the mast-cell related G-coupled protein receptor X2 pathway in direct mast cell degranulation, shedding light on previously unknown mechanisms. This pathway, alongside traditional IgE/FcεRI-mediated reactions, contributes to the complex nature of anaphylactic reactions. Investigations into the microbiota-anaphylaxis connection are ongoing, with potential implications for future treatment strategies. While serum tryptase levels serve as mast cell activation indicators, identifying triggers remains challenging. A range of mediators have been associated with anaphylaxis, including vasoactive peptides, proteases, lipid molecules, cytokines, chemokines, interleukins, complement components, and coagulation factors. Further understanding of clinical endotypes and the microenvironment where anaphylactic reactions unfold is essential for standardizing mediator testing and characterization in perioperative anaphylaxis. Ongoing research aims to elucidate the mechanisms, pathways, and mediators involved across multiple organ systems, including the cardiovascular, respiratory, and integumentary systems, which will be crucial for improving patient outcomes.

Implementation of a multi-site neonatal simulation improvement program: a cost analysis

BackgroundTo improve patient outcomes and provider team practice, the California Perinatal Quality Care Collaborative (CPQCC) created the Simulating Success quality improvement program to assist hospitals in implementing a neonatal resuscitation training curriculum. This study aimed to examine the costs associated with the design and implementation of the Simulating Success program.MethodsFrom 2017–2020, a total of 14 sites participated in the Simulating Success program and 4 of them systematically collected resource utilization data. Using a micro-costing approach, we examined costs for the design and implementation of the program occurring at CPQCC and the 4 study sites. Data collection forms were used to track personnel time, equipment/supplies, space use, and travel (including transportation, food, and lodging). Cost analysis was conducted from the healthcare sector perspective. Costs incurred by CPQCC were allocated to participant sites and then combined with site-specific costs to estimate the mean cost per site, along with its 95% confidence interval (CI). Cost estimates were inflation-adjusted to 2022 U.S. dollars.ResultsDesigning and implementing the Simulating Success program cost $228,148.36 at CPQCC, with personnel cost accounting for the largest share (92.2%), followed by program-related travel (6.1%), equipment/supplies (1.5%), and space use (0.2%). Allocating these costs across participant sites and accounting for site-specific resource utilizations resulted in a mean cost of $39,210.69 per participant site (95% CI: $34,094.52-$44,326.86). In sensitivity analysis varying several study assumptions (e.g., number of participant sites, exclusion of design costs, and useful life span of manikins), the mean cost per site changed from $35,645.22 to $39,935.73. At all four sites, monthly cost of other neonatal resuscitation training was lower during the program implementation period (mean = $1,112.52 per site) than pre-implementation period (mean = $2,504.01 per site). In the 3 months after the Simulating Success program ended, monthly cost of neonatal resuscitation training was also lower than the pre-implementation period at two of the four sites.ConclusionsEstablishing a multi-site neonatal in situ simulation program requires investment of sufficient resources. However, such programs may have financial and non-financial benefits in the long run by offsetting the need for other neonatal resuscitation training and improving practice.

Augmentation of hepatocellular carcinoma malignancy by annexin A5 through modulation of invasion and angiogenesis

ABSTRUCT Background Hepatocellular carcinoma (HCC) continues to play a substantial role in cancer-related morbidity and mortality, largely owing to its pronounced tumor heterogeneity and propensity for recurrence. This underscores the pressing need for in-depth examination of its highly malignant mechanisms. Annexin A5 (ANXA5), recognized as a hallmark tumor protein, has emerged as a focal point of interest because of its ambiguous function and mechanism in HCC prognosis. This study aimed to provide a comprehensive understanding of the role of ANXA5 in the malignant progression of human HCC cells by employing an integrative approach that combines conventional experimental methods with RNA sequencing. Methods Differences in ANXA5 expression between HCC tissues and corresponding nontumor tissues were evaluated using immunofluorescence (n = 25). Correlation analysis was subsequently performed to assess the association between ANXA5 expression and clinicopathological features (n = 65). The role of ANXA5 in human HCC cell lines with ANXA5 gene knockout and overexpression was explored in vitro using migration and invasion assays and Ki-67 indices and in vivo based on node mice xenograft model. A tube formation assay using human umbilical vein endothelial cells (HUVECs) was conducted to demonstrate the angiogenic effects of ANXA5 in HCC. Single-cell and bulk RNA sequencing was used to further investigate the underlying mechanisms involved. Results This study revealed that ANXA5 is highly expressed in patients with HCC and correlates with poor prognosis. Assays for migration, invasion, and proliferation based on ANXA5 gene knockout and overexpression systems in human HCC cell lines have demonstrated that ANXA5 enhances HCC malignancy in vitro and in vivo. Tube formation assays of HUVECs indicated that ANXA5 facilitates angiogenesis and recruits endothelial cells to HCC cells. Single-cell and bulk RNA sequencing data analysis further confirmed that ANXA5 expression in HCC is associated with hepatocyte metabolism, immune response activation, and various oncogenic signaling pathways. Conclusions This study revealed a meaningful association between elevated ANXA5 expression in tumor tissues and an unfavorable prognosis in patients with HCC. In addition, ANXA5 promotes HCC malignancy by promoting invasion and angiogenesis. Thus, ANXA5 has emerged as a promising therapeutic target for HCC and has the potential to improve patient outcomes.

Novel Endocrine Therapeutic Opportunities for Estrogen Receptor-Positive Ovarian Cancer—What Can We Learn from Breast Cancer?

Low-grade serous ovarian cancer (LGSOC) is a rare ovarian malignancy primarily affecting younger women and is characterized by an indolent growth pattern. It exhibits indolent growth and high estrogen/progesterone receptor expression, suggesting potential responsiveness to endocrine therapy. However, treatment efficacy remains limited due to the development of endocrine resistance. The mechanisms of resistance, whether primary or acquired, are still largely unknown and present a significant hurdle in achieving favorable treatment outcomes with endocrine therapy in these patients. In estrogen receptor-positive breast cancer, mechanisms of endocrine resistance have been largely explored and novel treatment strategies to overcome resistance have emerged. Considering the shared estrogen receptor positivity in LGSOC and breast cancer, we wanted to explore whether there are any parallel mechanisms of resistance and whether we can extend endocrine breast cancer treatments to LGSOC. This review aims to highlight the underlying molecular mechanisms possibly driving endocrine resistance in ovarian cancer, while also exploring the available therapeutic opportunities to overcome this resistance. By unraveling the potential pathways involved and examining emerging strategies, this review explores valuable insights for advancing treatment options and improving patient outcomes in LGSOC, which has limited therapeutic options available.

Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins.

Digital twins have emerged as a groundbreaking concept in personalized medicine, offering immense potential to transform health care delivery and improve patient outcomes. It is important to highlight the impact of digital twins on personalized medicine across the understanding of patient health, risk assessment, clinical trials and drug development, and patient monitoring. By mirroring individual health profiles, digital twins offer unparalleled insights into patient-specific conditions, enabling more accurate risk assessments and tailored interventions. However, their application extends beyond clinical benefits, prompting significant ethical debates over data privacy, consent, and potential biases in health care. The rapid evolution of this technology necessitates a careful balancing act between innovation and ethical responsibility. As the field of personalized medicine continues to evolve, digital twins hold tremendous promise in transforming health care delivery and revolutionizing patient care. While challenges exist, the continued development and integration of digital twins hold the potential to revolutionize personalized medicine, ushering in an era of tailored treatments and improved patient well-being. Digital twins can assist in recognizing trends and indicators that might signal the presence of diseases or forecast the likelihood of developing specific medical conditions, along with the progression of such diseases. Nevertheless, the use of human digital twins gives rise to ethical dilemmas related to informed consent, data ownership, and the potential for discrimination based on health profiles. There is a critical need for robust guidelines and regulations to navigate these challenges, ensuring that the pursuit of advanced health care solutions does not compromise patient rights and well-being. This viewpoint aims to ignite a comprehensive dialogue on the responsible integration of digital twins in medicine, advocating for a future where technology serves as a cornerstone for personalized, ethical, and effective patient care.

Removal of Complete Spontaneous Migration of an Intrauterine Contraceptive Device to the Bladder by Cystoscopy

Intrauterine contraceptive devices (IUCD) are widely used as a reversible method of contraception. Although uterine perforation caused by an IUCD is rare, in some cases, the device may migrate to the pelvic/abdominal cavity or nearby structures. When the IUCD migrate to the bladder, it can lead to various symptoms. These include pain or discomfort in the lower abdomen, difficulties or pain during urination, frequent urinary tract infections, and the development of bladder stones. This article presents a case report of a 24-year-old woman with an intrauterine contraceptive device (IUCD) that had migrated completely into the bladder. She had an IUCD inserted by a midwife four years earlier and became pregnant a year later, unaware of the IUCD’s presence. She later presented with lower abdominal pain, hematuria, and dysuria three months before being admitted to our hospital. Imaging confirmed the intravesical location of the IUCD. She underwent successful cystoscopy treatment to remove the migrated IUCD. Prompt diagnosis and appropriate management are crucial in preventing complications and improving patient outcomes. Clinicians should be aware of this potential complication and consider it when patients present with symptoms or complications after IUCD insertion. Regular monitoring and timely intervention can help detect and address IUCD migration effectively.

Updates in the treatment of eating disorders in 2023: a year in review in eating disorders: the Journal of treatment & Prevention

ABSTRACT Empirical evidence is unequivocal in illustrating that the majority of patients with eating disorders will not fully recover during treatment. To that end, the need for optimized treatment approaches and improved patient outcomes cannot be overstated. While empirical efforts are underway to optimize outcomes, this article reviews treatment-related research findings published in Eating Disorders: The Journal of Treatment & Prevention during 2023. Importantly, this review encapsulates research addressing (i) between-session patient behaviors, (ii) the integration of technology into treatment approaches, (iii) methods to augment emotional regulation in the context of eating disorder treatment, (iv) methods to measure progress, and potentially risk markers for patient dropout, during treatment, (v) optimizing treatment approaches for inpatient settings, and (vi) augmenting family therapy-based approaches. Incorporating novel technological advances may be critical in enhancing the scalability of eating disorder treatments, since treatment uptake remains an ongoing challenge for the field. Moreover, expanding the scope of non-outpatient eating disorder treatment settings, while ensuring fidelity to theoretical models developed in outpatient settings, is critical as treatment is effectively administer across the spectrum of levels of patient care.

Artificial intelligence in surgery.

Artificial intelligence (AI) is rapidly emerging in healthcare, yet applications in surgery remain relatively nascent. Here we review the integration of AI in the field of surgery, centering our discussion on multifaceted improvements in surgical care in the preoperative, intraoperative and postoperative space. The emergence of foundation model architectures, wearable technologies and improving surgical data infrastructures is enabling rapid advances in AI interventions and utility. We discuss how maturing AI methods hold the potential to improve patient outcomes, facilitate surgical education and optimize surgical care. We review the current applications of deep learning approaches and outline a vision for future advances through multimodal foundation models.

Segmentation of mediastinal lymph nodes in CT with anatomical priors.

Lymph nodes (LNs) in the chest have a tendency to enlarge due to various pathologies, such as lung cancer or pneumonia. Clinicians routinely measure nodal size to monitor disease progression, confirm metastatic cancer, and assess treatment response. However, variations in their shapes and appearances make it cumbersome to identify LNs, which reside outside of most organs. We propose to segment LNs in the mediastinum by leveraging the anatomical priors of 28 different structures (e.g., lung, trachea etc.) generated by the public TotalSegmentator tool. The CT volumes from 89 patients available in the public NIH CT Lymph Node dataset were used to train three 3D off-the-shelf nnUNet models to segment LNs. The public St. Olavs dataset containing 15 patients (out-of-training-distribution) was used to evaluate the segmentation performance. For LNs with short axis diameter 8mm, the 3D cascade nnUNet model obtained the highest Dice score of 67.9 ± 23.4 and lowest Hausdorff distance error of 22.8 ± 20.2. For LNs of all sizes, the Dice score was 58.7 ± 21.3 and this represented a 10% improvement over a recently published approach evaluated on the same test dataset. To our knowledge, we are the first to harness 28 distinct anatomical priors to segment mediastinal LNs, and our work can be extended to other nodal zones in the body. The proposed method has the potential for improved patient outcomes through the identification of enlarged nodes in initial staging CT scans.

Clinical Presentation and Risk Stratification of Pulmonary Embolism

AbstractPulmonary embolism (PE) presents with a spectrum of symptoms, ranging from asymptomatic cases to life-threatening events. Common symptoms include sudden dyspnea, chest pain, limb swelling, syncope, and hemoptysis. Clinical presentation varies based on thrombus burden, demographics, and time to presentation. Diagnostic evaluation involves assessing symptoms, physical examination findings, and utilizing laboratory tests, including D-dimer. Risk stratification using tools like Wells score, Pulmonary Embolism Severity Index, and Hestia criteria aids in determining the severity of PE. PE is categorized based on hemodynamic status, temporal patterns, and anatomic locations of emboli to guide in making treatment decisions. Risk stratification plays a crucial role in directing management strategies, with elderly and comorbid individuals at higher risk. Early identification and appropriate risk stratification are essential for effective management of PE. As we delve into this review article, we aim to enhance the knowledge base surrounding PE, contributing to improved patient outcomes through informed decision-making in clinical practice.

A Predictive Noninvasive Single-Nucleotide Variation-Based Biomarker Signature for Resectable Pancreatic Cancer: Protocol for a Prospective Validation Study.

Single-nucleotide variations (SNVs; formerly SNPs) are inherited genetic variants that can be easily determined in routine clinical practice using a simple blood or saliva test. SNVs have potential to serve as noninvasive biomarkers for predicting cancer-specific patient outcomes after resection of pancreatic ductal adenocarcinoma (PDAC). Two recent analyses led to the identification and validation of three SNVs in the CD44 and CHI3L2 genes (rs187115, rs353630, and rs684559), which can be used as predictive biomarkers to help select patients most likely to benefit from pancreatic resection. These variants were associated with an over 2-fold increased risk for tumor-related death in three independent PDAC study cohorts from Europe and the United States, including The Cancer Genome Atlas cohorts (reaching a P value of 1×10-8). However, these analyses were limited by the inherent biases of a retrospective study design, such as selection and publication biases, thereby limiting the clinical use of these promising biomarkers in guiding PDAC therapy. To overcome the limitations of previous retrospectively designed studies and translate the findings into clinical practice, we aim to validate the association of the identified SNVs with survival in a controlled setting using a prospective cohort of patients with PDAC following pancreatic resection. All patients with PDAC who will undergo pancreatic resection at three participating hospitals in Switzerland and fulfill the inclusion criteria will be included in the study consecutively. The SNV genotypes will be determined using standard genotyping techniques from patient blood samples. For each genotyped locus, log-rank and Cox multivariate regression tests will be performed, accounting for the relevant covariates American Joint Committee on Cancer stage and resection status. Clinical follow-up data will be collected for at least 3 years. Sample size calculation resulted in a required sample of 150 patients to sufficiently power the analysis. The follow-up data collection started in August 2019 and the estimated end of data collection will be in May 2027. The study is still recruiting participants and 142 patients have been recruited as of November 2023. The DNA extraction and genotyping of the SNVs will be performed after inclusion of the last patient. Since no SNV genotypes have been determined, no data analysis has been performed to date. The results are expected to be published in 2027. This is the first prospective study of the CD44 and CHI3L2 SNV-based biomarker signature in PDAC. A prospective validation of this signature would enable its clinical use as a noninvasive predictive biomarker of survival after pancreatic resection that is readily available at the time of diagnosis and can assist in guiding PDAC therapy. The results of this study may help to individualize treatment decisions and potentially improve patient outcomes. DERR1-10.2196/54042.

Smart Health Monitoring with Power-Aware IoT Using a Wireless Body Area Network

Smart health monitoring systems have become increasingly popular in recent years due to the growth of the Internet of Things (IoT) technology and its applications in healthcare. This project proposes an IoT-based smart health monitoring system that utilises an ESP32 Microcontroller, MAX30100 Pulse Oximeter Heart Rate Sensor Module, MLX90614 ESF Non-Contact Human Body Infrared Temperature Measurement Module, LCD 16*2, 3.7v 2000mAh Lithium Battery, TP4056 1A Li-Ion Lithium Battery charging Module, and some buttons. The system is designed to monitor the user's heart rate, blood oxygen level, and body temperature in real-time and display the readings on an LCD screen as well as an IoT web page. The project aims to provide an affordable, portable, and user-friendly smart health monitoring solution that can be used at home, in hospitals, or other healthcare settings. The project methodology involved a combination of hardware and software components, including circuit design and programming using the Arduino IDE. The system was tested and evaluated for its performance, accuracy, and usability using a sample population. The results showed that the system was effective in monitoring the user's vital signs and displaying the readings in real-time. The system was found to be accurate and reliable, with a high level of user satisfaction and ease of use. Overall, the IoT-based smart health monitoring system proposed in this project has significant implications for healthcare, particularly in terms of remote monitoring, telemedicine, and personalised healthcare. The system has the potential to improve patient outcomes, reduce healthcare costs, and increase access to healthcare services. The project also highlights the potential of IoT technology in healthcare and the need for further research and development in this area

Portal Vein Doppler tracks volume status in patients with severe tricuspid regurgitation: A Proof-of-Concept Study.

Renal and liver congestion are associated with adverse outcomes in patients with tricuspid regurgitation (TR). Currently, there are no valid sonographic indicators of fluid status in this population. Intra-renal venous Doppler (IRVD) is a novel method for quantifying renal congestion but its interpretation can be challenging in severe TR due to altered hemodynamics. This study explores the potential of portal vein Doppler (PVD) as an alternative marker for decongestion during volume removal in patients with severe TR. 42 patients with severe TR undergoing decongestive therapy were prospectively enrolled. Inferior vena cava diameter (IVCd), PVD and IRVD were sequentially assessed during volume removal. Improvement criteria were Portal Vein Pulsatility Fraction (PVPF) < 70% and Renal Venous Stasis Index (RVSI) < 0.5 for partial improvement, and PVPF <30% and RVSI <0.2 for complete improvement. After volume removal, PVPF significantly improved from 130 ± 39% to 47 ± 44% (p < 0.001), while IRVD improved from 0.72 ± 0.08 to 0.54 ± 0.22 (p < 0.001). A higher proportion of patients displayed improvement in PVD compared to IRVD (partial: 38% vs. 29%, complete: 41% vs. 7%) (p < 0.001). IRVD only improved in patients with concomitant improvement in severe TR. PVD was the only predictor of achieving ≥5 litres of negative fluid balance (AUC 0.83 p = 0.001). This proof-of-concept study suggests that PVD is the only sonographic marker that can track volume removal in severe TR, offering a potential indicator for decongestion in this population. Further intervention trials are warranted to determine if PVD-guided decongestion improves patient outcomes in severe TR.

Renal Hydatid Cyst

Renal hydatid cysts are a rare manifestation of echinococcosis, especially in pediatric cases. Diagnosis can be challenging, and delayed diagnosis may lead to larger cysts, increasing the risk of complications and the potential need for nephrectomy. Here, we present a case of a renal hydatid cyst in a pediatric patient, emphasizing the rarity of such cases in this age group. We discuss the diagnostic approach, highlighting the importance of early recognition and treatment to avoid the need for nephrectomy and to improve patient outcomes

Metabolic Acidosis in CKD: Pathogenesis, Adverse Effects, and Treatment Effects

Metabolic acidosis is a frequent complication of chronic kidney disease and is associated with a number of adverse outcomes, including worsening kidney function, poor musculoskeletal health, cardiovascular events, and death. Mechanisms that prevent metabolic acidosis detrimentally promote further kidney damage, creating a cycle between acid accumulation and acid-mediated kidney injury. Disrupting this cycle through the provision of alkali, most commonly using sodium bicarbonate, is hypothesized to preserve kidney function while also mitigating adverse effects of excess acid on bone and muscle. However, results from clinical trials have been conflicting. There is also significant interest to determine whether sodium bicarbonate might improve patient outcomes for those who do not have overt metabolic acidosis. Such individuals are hypothesized to be experiencing acid-mediated organ damage despite having a normal serum bicarbonate concentration, a state often referred to as subclinical metabolic acidosis. Results from small- to medium-sized trials in individuals with subclinical metabolic acidosis have also been inconclusive. Well-powered clinical trials to determine the efficacy and safety of sodium bicarbonate are necessary to determine if this intervention improves patient outcomes.

The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review.

Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM. Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs' potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field. Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs' use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data. A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency; (2) efficiency, workflow, and information management demonstrated the capacity of LLMs to significantly boost operational efficiency, particularly through the automation of patient record synthesis, which could reduce administrative burden and enhance patient-centric care; (3) risks, ethics, and transparency were identified as areas of concern, especially regarding the reliability of LLMs' outputs, and specific studies highlighted the challenges of ensuring unbiased decision-making amidst potentially flawed training data sets, stressing the importance of thorough validation and ethical oversight; and (4) education and communication possibilities included LLMs' capacity to enrich medical training, such as through using simulated patient interactions that enhance communication skills. LLMs have the potential to fundamentally transform EM, enhancing clinical decision-making, optimizing workflows, and improving patient outcomes. This review sets the stage for future advancements by identifying key research areas: prospective validation of LLM applications, establishing standards for responsible use, understanding provider and patient perceptions, and improving physicians' AI literacy. Effective integration of LLMs into EM will require collaborative efforts and thorough evaluation to ensure these technologies can be safely and effectively applied.

Implementation of Artificial Intelligence in Personalized Prognostic Assessment of Lung Cancer: A Narrative Review

Artificial Intelligence (AI) has revolutionized the management of non-small-cell lung cancer (NSCLC) by enhancing different aspects, including staging, prognosis assessment, treatment prediction, response evaluation, recurrence/prognosis prediction, and personalized prognostic assessment. AI algorithms may accurately classify NSCLC stages using machine learning techniques and deep imaging data analysis. This could potentially improve precision and efficiency in staging, facilitating personalized treatment decisions. Furthermore, there are data suggesting the potential application of AI-based models in predicting prognosis in terms of survival rates and disease progression by integrating clinical, imaging and molecular data. In the present narrative review, we will analyze the preliminary studies reporting on how AI algorithms could predict responses to various treatment modalities, such as surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. There is robust evidence suggesting that AI also plays a crucial role in predicting the likelihood of tumor recurrence after surgery and the pattern of failure, which has significant implications for tailoring adjuvant treatments. The successful implementation of AI in personalized prognostic assessment requires the integration of different data sources, including clinical, molecular, and imaging data. Machine learning (ML) and deep learning (DL) techniques enable AI models to analyze these data and generate personalized prognostic predictions, allowing for a precise and individualized approach to patient care. However, challenges relating to data quality, interpretability, and the ability of AI models to generalize need to be addressed. Collaboration among clinicians, data scientists, and regulators is critical for the responsible implementation of AI and for maximizing its benefits in providing a more personalized prognostic assessment. Continued research, validation, and collaboration are essential to fully exploit the potential of AI in NSCLC management and improve patient outcomes. Herein, we have summarized the state of the art of applications of AI in lung cancer for predicting staging, prognosis, and pattern of recurrence after treatment in order to provide to the readers a large comprehensive overview of this challenging issue.

Adherence to and clinical utility of "quality indicators" for Staphylococcus aureus bacteremia: a retrospective, multicenter study.

We aimed to improve the prognosis, treatment, and management of Staphylococcus aureus bacteremia (SAB) by evaluating the association between adherence to quality indicators (QIs) and clinical outcomes in patients with their clinical outcomes. We retrospectively collected clinical and microbiological data on hospitalized patients with SAB from 14 hospitals (three with > 600, two with 401-600, five with 201-400, and four with ≤ 200 beds) in Japan from January to December 2022. The SAB management quality was evaluated using the SAB-QI score (ranging from 0 to 13 points), which consists of 13 QIs (grouped into five categories) based on previous literature. Of the 4,448 positive blood culture episodes, 289 patients with SAB (6.5%) were enrolled. The SAB-QI scores ranged from 3 to 13, with a median score of 9 points. The SAB-QI score was highest in middle-sized hospitals with 401-600 beds. Adherence to each of the four QI categories (blood culture, echocardiography, source control, and antibiotic treatment) was significantly higher in survived cases than in fatal cases. Kaplan-Meier curves with log-rank tests demonstrated that higher adherence to SAB-QIs indicated a better prognosis. Logistic regression analysis revealed that age, methicillin resistance, multiple comorbidities (≥2), and low SAB-QI score were significantly associated with 30-day mortality in patients with SAB. Our study highlights that greater adherence to the SAB-QIs correlates with improved patient outcomes. Management of patients with SAB should follow these recommended indicators to maintain the quality of care, especially for patients with poor prognosticators.