Neutrophil CD64 is widely described as an accurate biomarker for the diagnosis of infection in patients with septic syndrome. We performed a systematic review and meta-analysis to evaluate the diagnostic accuracy of neutrophil CD64, comparing it with C-reactive protein (CRP) and procalcitonin (PCT) for the diagnosis of infection in adult patients with septic syndrome, based on sepsis-2 criteria. We searched the PubMed and Embase databases and Google Scholar. Original studies reporting the performance of neutrophil CD64 for sepsis diagnosis in adult patients were retained. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and hierarchical summary receiver operating characteristic (SROC) curve were calculated. We included 14 studies (2471 patients) from 2006 to 2017 in the meta-analysis. The pooled sensitivity and specificity of neutrophil CD64 for diagnosing infection in adult patients with septic syndrome were 0.87 (95% CI 0.80–0.92) and 0.89 (95% CI 0.82–0.93), respectively. The area under the SROC curve and the DOR were 0.94 (95% CI 0.92–0.96) and 53 (95% CI 22–128), respectively. There was significant heterogeneity between the studies included. Subgroup analyses showed that this heterogeneity was due to differences in sample size and the proportions of patients with sepsis included in the studies. Six studies (927 patients) compared neutrophil CD64 and CRP determinations, and six studies (744 patients) compared neutrophil CD64 and PCT determinations. The area under the SROC curve was larger for neutrophil CD64 than for CRP (0.89 [95% CI 0.87–0.92] vs. 0.84 [95% CI 0.80–0.88], P < 0.05) or PCT (0.89 [95% CI 0.84–0.95] vs. 0.84 [95% CI 0.79–0.89], P < 0.05). In adult patients with septic syndrome, neutrophil CD64 levels are an excellent biomarker with moderate accuracy outperforming both CRP and PCT determinations.

Roux-en-Y reconstructions can be divided into intact papilla of Vater and bilioenteric anastomosis (BEA) with respect to endoscopic retrograde cholangiography (ERC). Double-balloon enteroscopy-assisted ERC (DBE-ERC) may produce different results between the two populations but lacks studies. Forty-seven patients with Roux-en-Y anastomosis undergoing 73 procedures of DBE-ERC were enrolled between July 2007 and August 2013. There were 14 patients with intact papilla of Vater (group A) and 33 patients with BEA (group B). The effectiveness of DBE-ERC, including data of reaching the blind end, performance of ERC, results of endoscopic therapies, and follow-up were retrospectively analyzed and compared between the two groups. For reaching the blind end, the success rate was not different between the groups (85.7% vs. 81.8%, p = 0.7), but the mean procedure time was significantly shorter for group A (28 min vs. 52 min, p = 0.01). For ERC, the success rate was not different between the groups (91.7% vs. 96.3%, p = 0.53), but the mean procedure time was significantly longer for group A (28.4 min vs. 4 min, p < 0.001). All endoscopic therapies could be successfully performed in both groups. No group A patients and five (23.8%) group B patients developed recurrent biliary stricture/stones requiring interventions during a mean follow-up period of 26.1 months. DBE-ERC was effective for both populations with biliary disorders. Reaching the blind end was more difficult but ERC was easier for patients with BEA in terms of procedure time rather than success rates.

Since the initial cases identified in Wuhan, China, the coronavirus disease (COVID-19) has spread worldwide at an alarming rate, changing our daily lives, causing millions of deaths, and resulting in an adverse socio-economic impact worldwide [1,2]. The World Health Organization (WHO) declared COVID-19 a global pandemic in March 2020; since then, countries across the world […]

Preparation for a disaster or accident-related mass casualty events is often based on experience. The objective measures or tools for evaluating decision-making and effectiveness during such events are underdeveloped. Queueing theory has been suggested to evaluate the effectiveness of mass causality incidents (MCI) plans. Using different types of real MCI, we aimed to determine if a queueing network model could be used as a tool to assist in preparing plans to address mass causality incidents. We collected information from two types of mass casualty events: a motor vehicle accident and a dust explosion. Patient characteristics, time intervals of every working station, numbers of physicians and nurses attending, and time required by physicians and nurses during these two MCIs were collected and used for calculation in a queueing network model. Balanced efficiency was determined by calculating the numbers of server, i.e., nurses and physicians, in the two MCIs. Efficient patient flows were found in both MCIs. However, excessive medical manpower supply was revealed when the queueing network model was applied to assess the MCIs. The best fitting result, i.e., the most efficient man power utilization, can be calculated by the queueing network models. Furthermore, balanced efficiency may be a more suitable condition than the highest efficiency man power utilization when faced with MCIs. The queueing network model is a flexible tool that could be used in different types of MCIs to observe the degree of efficiency when handling MCIs.

The integration of AI in diagnosing and managing ACS shows increasing promise, yet challenges remain in translating AI-CDSS into clinical practice. This study evaluates the advancements and limitations of AI for ACS over the past three years, purpose of understanding the scope, limitations, and potential of AI-CDSS in ACS. We conducted a systematic review of recent literature, adhering to guidelines for systematic reviews. We applied QUADAS-2 and PROBAST tools for quality assessment, focusing on biases in study designs. Ten studies about AI-CDSS in ACS management underwent critical analysis, emphasizing the strength of their research methods and the thoroughness of their prospective validation to ensure theoretical integrity and practical reliability. Our research reveals that while discourse around AI-CDSS in ACS management intensifies, obstacles hinder efficacy in practical settings. These challenges include biases in tests and unrepresentative patient selection, pointing to the need for rigorous and inclusive samples. The lack of sufficient external and prospective validation in studies also raises concerns clinical utility of AI-CDSS. The result is the gap between the potential benefits of AI-CDSS and the actual impact of improving diagnostic accuracy and outcomes for ACS limitations identified. While AI-CDSS shows promise for improving diagnostic accuracy, treatment efficacy, and workflows in ACS, this study highlights the imperative to enhance model validation, including prospective validation, and address lingering diagnostic gaps. Improving study design and mitigating biases remain crucial for the acceptance and effectiveness of AI-CDSS in acute cardiac care settings.