Effect of storage duration on outcome of patients receiving red blood cell in emergency department.

The effect of the duration of red blood cell (RBC) storage on the outcomes of transfused patients remains controversial, and studies on patients in the emergency department (ED) are limited. This study aimed to determine the association between RBC storage duration and outcomes of patients receiving transfusions in the ED. For RBCs issued to patients in the ED between 2017 and 2022, the storage period of the RBC and data on the transfused patient were obtained. Patients were divided into fresh (≤ 7 days) and old (> 7 days) RBC groups, and the associations between storage duration, outcomes, and laboratory changes were evaluated. There was no significant difference in outcomes between the two groups in the 28-day mortality (adjusted odds ratio [OR] 0.91, 95% confidence interval [CI] 0.75-1.10, P = 0.320) and the length of stay (fresh 13.5 ± 18.1 vs. old 13.3 ± 19.8, P = 0.814). Regarding changes in laboratory test results, the increase in hemoglobin and hematocrit levels was not affected by the storage durations. The study revealed that transfusion of older RBCs is not associated with inferior outcomes or adverse clinical consequences when compared to that of fresh RBCs in patients in the ED.

Long-term Outcomes and Prognostic Factors of Gastric MALT Lymphoma.

PURPOSE: This study aimed to evaluate the long-term prognosis of patients with gastric mucosa-associated lymphoid tissue (MALT) lymphoma, including overall survival (OS), remission, and factors associated with an aggressive disease course. MATERIALS AND METHODS: Medical records of 153 patients diagnosed with gastric MALT lymphoma between 2013 and 2020 were retrospectively reviewed. Patients experiencing relapse, progression, high-grade transformation, or residual diseasewere included in the aggressive group and were compared with those in the indolent group. Additionally, the endoscopic findings of Helicobacter pylori-negative patients were reviewed. RESULTS: Patient characteristics were as follows: mean age (56.9±11.2 years), sex (male, 51.0%), H. pylori infection (positive, 79.7%), endoscopic location (distal, 89.5%), endoscopic feature (superficial, 89.5%), clinical stage (stage I, 92.8%), invasion depth by endoscopic ultrasound (mucosa, n=115, 75.7%), and bone marrow result (no involvement, n=77, 100.0%). The median follow-up period was 59 months (mean, 61; range, 36-124) and the continuous remission period (n=149) was 51 months (mean, 50; range, 3-112). The 5-year survival rate was 97.7% while the 5-year continuous remission was 88.3%. Factors associated with the patients in the aggressive group were old age, sex(male), and clinical stage II or higher. H. pylori-negative patients' endoscopy revealed a high incidence of atrophic gastritis in the antrum. CONCLUSIONS: The long-term prognosis of gastric MALT lymphoma appears indolent and is indicated by the 5-year OS and continuous remission rates. Aggressive disease courses are associated with old age, sex (male), and clinical stage II or higher, but are not related to OS.

Impact of a deep learning-based brain CT interpretation algorithm on clinical decision-making for intracranial hemorrhage in the emergency department.

Intracranial hemorrhage is a critical emergency that requires prompt and accurate diagnosis in the emergency department (ED). Deep learning technology can assist in interpreting non-enhanced brain CT scans, but its real-world impact on clinical decision-making is uncertain. This study assessed a deep learning-based intracranial hemorrhage detection algorithm (DLHD) in a simulated clinical environment with ten emergency medical professionals from a tertiary hospital's ED. The participants reviewed CT scans with clinical information in two steps: without and with DLHD. Diagnostic performance was measured, including sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. Consistency in clinical decision-making was evaluated using the kappa statistic. The results demonstrated that DLHD minimally affected experienced participants' diagnostic performance and decision-making. In contrast, inexperienced participants exhibited significantly increased sensitivity (59.33-72.67%, p < 0.001) and decreased specificity (65.49-53.73%, p < 0.001) with the algorithm. Clinical decision-making consistency was moderate among inexperienced professionals (k = 0.425) and higher among experienced ones (k = 0.738). Inexperienced participants changed their decisions more frequently, mainly due to the algorithm's false positives. The study highlights the need for thorough evaluation and careful integration of deep learning tools into clinical workflows, especially for less experienced professionals.

Failure Prediction of High-Flow Nasal Cannula at the Conventional Oxygen Therapy Phase in the Emergency Department.

INTRODUCTION: The use of high-flow nasal cannula (HFNC) in patients with acute hypoxemic respiratory failure has been increasing in the emergency department (ED). However, studies are lacking on the prediction of HFNC failure before therapy initiation in the ED. We investigated whether the existing indices, such as the ratio of pulse oximetry oxygen saturation/fraction of inspired oxygen to respiratory rate (ROX) and ratio of ROX index to heart rate (ROX-HR), can accurately predict HFNC failure at the conventional oxygen therapy phase in the ED. METHODS: This retrospective single-center study included patients treated with HFNC in the ED. The ROX and ROX-HR indices were calculated before initiating HFNC. An estimated fraction of inspired oxygen was used for conventional oxygen therapy. We plotted each index's receiver operating characteristics curve and calculated the area under the curve (AUC) for diagnostic capacity. The optimal cutoff values were assessed using the Youden index. The primary outcome was HFNC failure, defined as intubation in the ED. RESULTS: Among the 97 included patients, 25 (25.8%) failed HFNC therapy in the ED. The ROX and ROX-HR indices measured before initiating HFNC showed AUCs of 0.709 and 0.754, respectively. A ROX index of <5.614 and a ROX-HR index of <6.152 were associated with a high risk of intubation, even after correcting for confounding variables. CONCLUSION: The ROX and ROX-HR indices measured before initiating HFNC provide a relatively fair predictive value of HFNC failure in the ED.

The Use of Generative AI for Scientific Literature Searches for Systematic Reviews: ChatGPT and Microsoft Bing AI Performance Evaluation.

Background: A large language model is a type of artificial intelligence (AI) model that opens up great possibilities for health care practice, research, and education, although scholars have emphasized the need to proactively address the issue of unvalidated and inaccurate information regarding its use. One of the best-known large language models is ChatGPT (OpenAI). It is believed to be of great help to medical research, as it facilitates more efficient data set analysis, code generation, and literature review, allowing researchers to focus on experimental design as well as drug discovery and development. Objective: This study aims to explore the potential of ChatGPT as a real-time literature search tool for systematic reviews and clinical decision support systems, to enhance their efficiency and accuracy in health care settings. Methods: The search results of a published systematic review by human experts on the treatment of Peyronie disease were selected as a benchmark, and the literature search formula of the study was applied to ChatGPT and Microsoft Bing AI as a comparison to human researchers. Peyronie disease typically presents with discomfort, curvature, or deformity of the penis in association with palpable plaques and erectile dysfunction. To evaluate the quality of individual studies derived from AI answers, we created a structured rating system based on bibliographic information related to the publications. We classified its answers into 4 grades if the title existed: A, B, C, and F. No grade was given for a fake title or no answer. Results: From ChatGPT, 7 (0.5%) out of 1287 identified studies were directly relevant, whereas Bing AI resulted in 19 (40%) relevant studies out of 48, compared to the human benchmark of 24 studies. In the qualitative evaluation, ChatGPT had 7 grade A, 18 grade B, 167 grade C, and 211 grade F studies, and Bing AI had 19 grade A and 28 grade C studies. Conclusions: This is the first study to compare AI and conventional human systematic review methods as a real-time literature collection tool for evidence-based medicine. The results suggest that the use of ChatGPT as a tool for real-time evidence generation is not yet accurate and feasible. Therefore, researchers should be cautious about using such AI. The limitations of this study using the generative pre-trained transformer model are that the search for research topics was not diverse and that it did not prevent the hallucination of generative AI. However, this study will serve as a standard for future studies by providing an index to verify the reliability and consistency of generative AI from a user's point of view. If the reliability and consistency of AI literature search services are verified, then the use of these technologies will help medical research greatly.

Current trends in emergency airway management: a clinical review.

Airway management is a fundamental and complex process that involves a sequence of integrated tasks. Situations requiring emergency airway management may occur in the emergency department, intensive care units, and various other clinical spaces. A variety of challenges can arise during emergency airway preparation, intubation, and postintubation, which may result in significant complications for patients. Therefore, many countries are establishing step-by-step systemization and detailed guidelines and/or updating their content based on the latest research. This clinical review introduces the current trends in emergency airway management, such as emergency airway management algorithms, comparison of video and direct laryngoscopy, rapid sequence intubation, pediatric airway management, prehospital airway management, surgical airway management, and airway management education.

Predictability of the emergency department triage system during the COVID-19 pandemic.

OBJECTIVE: Emergency department (ED) triage systems are used to classify the severity and urgency of emergency patients, and Korean medical institutions use the Korean Triage and Acuity Scale (KTAS). During the COVID-19 pandemic, appropriate treatment for emergency patients was delayed due to various circumstances, such as overcrowding of EDs, lack of medical workforce resources, and increased workload on medical staff. The purpose of this study was to evaluate the accuracy of the KTAS in predicting the urgency of emergency patients during the COVID-19 pandemic. METHODS: This study retrospectively reviewed patients who were treated in the ED during the pandemic period from January 2020 to June 2021. Patients were divided into COVID-19-screening negative (SN) and COVID-19-screening positive (SP) groups. We compared the predictability of the KTAS for urgent patients between the two groups. RESULTS: From a total of 107,480 patients, 62,776 patients (58.4%) were included in the SN group and 44,704 (41.6%) were included in the SP group. The odds ratios for severity variables at each KTAS level revealed a more evident discriminatory power of the KTAS for severity variables in the SN group (P<0.001). The predictability of the KTAS for severity variables was higher in the SN group than in the SP group (area under the curve, P<0.001). CONCLUSION: During the pandemic, the KTAS had low accuracy in predicting patients in critical condition in the ED. Therefore, in future pandemic periods, supplementation of the current ED triage system should be considered in order to accurately classify the severity of patients.

Mortality after cardiac arrest in children less than 2 years: relevant factors.

BACKGROUND: There are only scant studies of predicting outcomes of pediatric resuscitation due to lack of population-based data. This study aimed to determine variable factors that may impact the survival of resuscitated children aged under 24 months. METHODS: This is a retrospective study of 66 children under 24 months. Cardiopulmonary resuscitation (CPR) with pediatric advanced life support guideline was performed uniformly for all children. Linear regression analysis with variable factors was conducted to determine impacts on mortality. RESULT: Factors with statistically significant increases in mortality were the number of administered epinephrine (p value < 0.001), total CPR duration (p value < 0.001), in-hospital CPR duration of out-hospital cardiac arrest (p value < 0.001), and changes in cardiac rhythm (p value < 0.040). However, there is no statistically significant association between patient outcomes and remaining factors such as age, sex, underlying disease, etiology, time between last normal to CPR, initial CPR location, initial cardiac rhythm, venous access time, or inotropic usage. CONCLUSION: More than 10 times of epinephrine administration and CPR duration longer than 30 minutes were associated with a higher mortality rate, while each epinephrine administration and prolonged CPR time increased mortality. IMPACT STATEMENT: This study analyzed various factors influencing mortality after cardiac arrest in patients under 24 months. Increased number of administered epinephrine and prolonged cardiopulmonary resuscitation duration do not increase survival rate in patients under 24 months. In patients with electrocardiogram rhythm changes during CPR, mortality increased when the rhythm changed into asystole in comparison to no changes occurring in the rhythm.

Improved patient mortality predictions in emergency departments with deep learning data-synthesis and ensemble models.

The triage process in emergency departments (EDs) relies on the subjective assessment of medical practitioners, making it unreliable in certain aspects. There is a need for a more accurate and objective algorithm to determine the urgency of patients. This paper explores the application of advanced data-synthesis algorithms, machine learning (ML) algorithms, and ensemble models to predict patient mortality. Patients predicted to be at risk of mortality are in a highly critical condition, signifying an urgent need for immediate medical intervention. This paper aims to determine the most effective method for predicting mortality by enhancing the F1 score while maintaining high area under the receiver operating characteristic curve (AUC) score. This study used a dataset of 7325 patients who visited the Yonsei Severance Hospital's ED, located in Seoul, South Korea. The patients were divided into two groups: patients who deceased in the ED and patients who didn't. Various data-synthesis techniques, such as SMOTE, ADASYN, CTGAN, TVAE, CopulaGAN, and Gaussian Copula, were deployed to generate synthetic patient data. Twenty two ML models were then utilized, including tree-based algorithms like Decision tree, AdaBoost, LightGBM, CatBoost, XGBoost, NGBoost, TabNet, which are deep neural network algorithms, and statistical algorithms such as Support Vector Machine, Logistic Regression, Random Forest, k-nearest neighbors, and Gaussian Naive Bayes, as well as Ensemble Models which use the results from the ML models. Based on 21 patient information features used in the pandemic influenza triage algorithm (PITA), the models explained previously were applied to aim for the prediction of patient mortality. In evaluating ML algorithms using an imbalanced medical dataset, conventional metrics like accuracy scores or AUC can be misleading. This paper emphasizes the importance of using the F1 score as the primary performance measure, focusing on recall and specificity in detecting patient mortality. The highest-ranked model for predicting mortality utilized the Gaussian Copula data-synthesis technique and the CatBoost classifier, achieving an AUC of 0.9731 and an F1 score of 0.7059. These findings highlight the effectiveness of machine learning algorithms and data-synthesis techniques in improving the prediction performance of mortality in EDs.

Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department.

This study aimed to develop a machine learning-based clinical decision support system for emergency departments based on the decision-making framework of physicians. We extracted 27 fixed and 93 observation features using data on vital signs, mental status, laboratory results, and electrocardiograms during emergency department stay. Outcomes included intubation, admission to the intensive care unit, inotrope or vasopressor administration, and in-hospital cardiac arrest. eXtreme gradient boosting algorithm was used to learn and predict each outcome. Specificity, sensitivity, precision, F1 score, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve were assessed. We analyzed 303,345 patients with 4,787,121 input data, resampled into 24,148,958 1 h-units. The models displayed a discriminative ability to predict outcomes (AUROC > 0.9), and the model with lagging 6 and leading 0 displayed the highest value. The AUROC curve of in-hospital cardiac arrest had the smallest change, with increased lagging for all outcomes. With inotropic use, intubation, and intensive care unit admission, the range of AUROC curve change with the leading 6 was the highest according to different amounts of previous information (lagging). In this study, a human-centered approach to emulate the clinical decision-making process of emergency physicians has been adopted to enhance the use of the system. Machine learning-based clinical decision support systems customized according to clinical situations can help improve the quality of care.

Severe Systemic Reactions Following Bee Sting Injuries in Korea.

PURPOSE: Most bee sting injuries are benign, although sometimes they can result in life threatening outcomes, such as anaphylaxis and death. The purpose of this study was to investigate the epidemiologic status of bee sting injuries in Korea and to identify risk factors associated with severe systemic reactions (SSRs). MATERIALS AND METHODS: Cases were extracted from a multicenter retrospective registry for patients who had visited emergency departments (EDs) for bee sting injuries. SSRs were defined as hypotension or altered mental status upon ED arrival, hospitalization, or death. Patient demographics and injury characteristics were compared between SSR and non-SSR groups. Logistic regression was performed to identify risk factors for bee sting-associated SSRs, and the characteristics of fatality cases were summarized. RESULTS: Among the 9673 patients with bee sting injuries, 537 had an SSR and 38 died. The most frequent injury sites included the hands and head/face. Logistic regression analysis revealed that the occurrence of SSRs was associated with male sex [odds ratio (95% confidence interval); 1.634 (1.133-2.357)] and age [1.030 (1.020-1.041)]. Additionally, the risk of SSRs from trunk and head/face stings was high [2.858 (1.405-5.815) and 2.123 (1.333-3.382), respectively]. Bee venom acupuncture [3.685 (1.408-9.641)] and stings in the winter [4.573 (1.420-14.723)] were factors that increased the risk of SSRs. CONCLUSION: Our findings emphasize the need for implementing safety policies and education on bee sting-related incidents to protect high-risk groups.

Predictive indicators for determining red blood cell transfusion strategies in the emergency department.

BACKGROUND AND IMPORTANCE: Appropriate decision-making is critical for transfusions to prevent unnecessary adverse outcomes; however, transfusion in the emergency department (ED) can only be decided based on sparse evidence in a limited time window. OBJECTIVES: This study aimed to identify factors associated with appropriate red blood cell (RBC) transfusion in the ED by analyzing retrospective data of patients who received transfusions at a single center. OUTCOME MEASURES AND ANALYSIS: This study analyzed associations between transfusion appropriateness and sex, age, initial vital signs, an ED triage score [the Korean Triage and Acuity Scale (KTAS)], the length of stay, and the hemoglobin (Hb) concentration. MAIN RESULTS: Of 10 490 transfusions, 10 109 were deemed appropriate, and 381 were considered inappropriate. A younger age ( P  < 0.001) and a KTAS level of 3-5 ( P  = 0.028) were associated with inappropriate transfusions, after adjusting for O 2 saturation and the Hb level. CONCLUSIONS: In this single-center retrospective study, younger age and higher ED triage scores were associated with the appropriateness of RBC transfusions.

Effects of Emergency Transfer Coordination Center on Length of Stay of Critically Ill Patients in the Emergency Department.

INTRODUCTION: Critically ill patients are frequently transferred from other hospitals to the emergency departments (ED) of tertiary hospitals. Due to the unforeseen transfer, the ED length of stay (LOS) of the patient is likely to be prolonged in addition to other potentially adverse effects. In this study we sought to confirm whether the establishment of an organized unit - the Emergency Transfer Coordination Center (ETCC) - to systematically coordinate emergency transfers would be effective in reducing the ED LOS of transferred, critically ill patients. METHODS: The present study is a retrospective observational study focusing on patients who were transferred from other hospitals and admitted to the intensive care unit (ICU) of the ED in a tertiary hospital located in northwestern Seoul, the capital city of South Korea, from January 2019 - December 2020. The exposure variable of the study was ETCC approval before transfer, and ED LOS was the primary outcome. We used propensity score matching for comparison between the group with ETCC approval and the control group. RESULTS: Included in the study were 1,097 patients admitted to the ICU after being transferred from other hospitals, of whom 306 (27.9%) were transferred with ETCC approval. The median ED LOS in the ETCC-approved group was significantly reduced to 277 minutes compared to 385 minutes in the group without ETCC approval. The ETCC had a greater effect on reducing evaluation time than boarding time, which was the same for populations with different clinical features. CONCLUSION: An ETCC can be effective in systematically reducing the ED LOS of critically ill patients who are transferred from other hospitals to tertiary hospitals that are experiencing severe crowding.

Effect of Applying a Real-Time Medical Record Input Assistance System With Voice Artificial Intelligence on Triage Task Performance in the Emergency Department: Prospective Interventional Study.

BACKGROUND: Natural language processing has been established as an important tool when using unstructured text data; however, most studies in the medical field have been limited to a retrospective analysis of text entered manually by humans. Little research has focused on applying natural language processing to the conversion of raw voice data generated in the clinical field into text using speech-to-text algorithms. OBJECTIVE: In this study, we investigated the promptness and reliability of a real-time medical record input assistance system with voice artificial intelligence (RMIS-AI) and compared it to the manual method for triage tasks in the emergency department. METHODS: From June 4, 2021, to September 12, 2021, RMIS-AI, using a machine learning engine trained with 1717 triage cases over 6 months, was prospectively applied in clinical practice in a triage unit. We analyzed a total of 1063 triage tasks performed by 19 triage nurses who agreed to participate. The primary outcome was the time for participants to perform the triage task. RESULTS: The median time for participants to perform the triage task was 204 (IQR 155, 277) seconds by RMIS-AI and 231 (IQR 180, 313) seconds using manual method; this difference was statistically significant (P<.001). Most variables required for entry in the triage note showed a higher record completion rate by the manual method, but in the recording of additional chief concerns and past medical history, RMIS-AI showed a higher record completion rate than the manual method. Categorical variables entered by RMIS-AI showed less accuracy compared with continuous variables, such as vital signs. CONCLUSIONS: RMIS-AI improves the promptness in performing triage tasks as compared to using the manual input method. However, to make it a reliable alternative to the conventional method, technical supplementation and additional research should be pursued.

Thrombotic Microangiopathy Score as a New Predictor of Neurologic Outcomes in Patients after Out-of-Hospital Cardiac Arrest.

PURPOSE: Given the morphological characteristics of schistocytes, thrombotic microangiopathy (TMA) score can be beneficial as it can be automatically and accurately measured. This study aimed to investigate whether serial TMA scores until 48 h post admission are associated with clinical outcomes in patients undergoing targeted temperature management (TTM) after out-of-hospital cardiac arrest (OHCA). MATERIALS AND METHODS: We retrospectively evaluated a cohort of 185 patients using a prospective registry. We analyzed TMA scores at admission and after 12, 24, and 48 hours. The primary outcome measures were poor neurological outcome at discharge and 30-day mortality. RESULTS: Increased TMA scores at all measured time points were independent predictors of poor neurological outcomes and 30-day mortality, with TMA score at time-12 showing the strongest correlation [odds ratio (OR), 3.008; 95% confidence interval (CI), 1.707-5.300; p<0.001 and hazard ratio (HR), 1.517; 95% CI, 1.196-1.925; p<0.001]. Specifically, a TMA score ≥2 at time-12 was closely associated with an increased predictability of poor neurological outcomes (OR, 6.302; 95% CI, 2.841-13.976; p<0.001) and 30-day mortality (HR, 2.656; 95% CI, 1.675-4.211; p<0.001). CONCLUSION: Increased TMA scores predicted neurological outcomes and 30-day mortality in patients undergoing TTM after OHCA. In addition to the benefit of being serially measured using an automated hematology analyzer, TMA score may be a helpful tool for rapid risk stratification and identification of the need for intensive care in patients with return of spontaneous circulation after OHCA.

Clinical Frailty Scale as a predictor of short-term mortality: A systematic review and meta-analysis of studies on diagnostic test accuracy.

BACKGROUND: The Clinical Frailty Scale (CFS) is a representative frailty assessment tool in medicine. This systematic review and meta-analysis aimed to examine whether frailty defined based on the CFS could adequately predict short-term mortality in emergency department (ED) patients. METHODS: The PubMed, EMBASE, and Cochrane libraries were searched for eligible studies until December 23, 2021. We included studies in which frailty was measured by the CFS and short-term mortality was reported for ED patients. All studies were screened by two independent researchers. Sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) values were calculated based on the data extracted from each study. Additionally, the diagnostic odds ratio (DOR) was calculated for effect size analysis, and the area under the curve (AUC) of summary receiver operating characteristics was calculated. Outcomes were in-hospital and 1-month mortality rate for patients with the CFS scores of ≥5, ≥6, and ≥7. RESULTS: Overall, 17 studies (n = 45,022) were included. Although there was no evidence of publication bias, a high degree of heterogeneity was observed. For the CFS score of ≥5, the PLR, NLR, and DOR values for in-hospital mortality were 1.446 (95% confidence interval [CI] 1.325-1.578), 0.563 (95% CI 0.355-0.893), and 2.728 (95% CI 1.872-3.976), respectively. In addition, the pooled statistics for 1-month mortality were 1.566 (95% CI 1.241-1.976), 0.582 (95% CI 0.430-0.789), and 2.696 (95% CI 1.673-4.345), respectively. Subgroup analysis of trauma patients revealed that the CFS score of ≥5 could adequately predict in-hospital mortality (PLR 1.641, 95% CI 1.242-2.170; NLR 0.580, 95% CI 0.461-0.729; DOR 2.883, 95% CI 1.994-4.168). The AUC values represented sufficient to good diagnostic accuracy. CONCLUSIONS: Evidence that is published to date suggests that the CFS is an accurate and reliable tool for predicting short-term mortality in emergency patients.

Characteristics of crashes and injuries on delivery motorcycles: A retrospective cohort study.

Objectives: The food delivery market is growing rapidly. As most delivery riders use motorcycles, motorcycle crashes will increase along with the growing delivery market size. This study aimed at examining the proportions of motorcycle crashes and characteristics of injuries incurred while using motorcycles for occupational purposes.Methods: This retrospective analysis included motorcycle crash patients aged 16 years or older, who were treated in 23 emergency rooms in Korea, between 2014 and 2018. Patients were divided into two groups: delivery riders (delivery group) and others (nondelivery group). Crash and injury characteristics were compared between the two groups. In addition, trends of patients in the delivery group were compared from 2014 to 2018.Results: This study examined 26,982 motorcycle crash patients, including 3894 (14.43%) patients in the delivery group and 23,088 (85.57%) in the nondelivery group. The number of patients in the delivery group increased drastically from 583 in 2014 to 1029 in 2018, whereas the number of patients in the nondelivery group did not considerably increase (4411 in 2014 and 4462 in 2018). The delivery group had a higher proportion of crashes caused by collisions with cars or other motorcycles (p < 0.001); however, injury severity was lower. The delivery group had a lower proportion of head and face injuries but a higher proportion of extremity injuries. Furthermore, 39.9% of all crashes in this group occurred between 17:00 and 21:00. Over time, there were neither any changes in the injury severities, nor any changes in the characteristics of the delivery group, with the exception of increases in both the proportion of motorist insurance and the proportion of wearing a helmet.Conclusions: The results indicated differences in characteristics between delivery motorcycle crashes and other motorcycle crashes. Although delivery motorcycle crash severity was low compared to other motorcycle crashes, the number of patients increased significantly. Therefore, to prevent crashes, it is necessary to improve the working environment. In addition, to prevent the extremity injuries of delivery riders, the policy of wearing extremity protective gears should be considered.

Delta neutrophil index and shock index can stratify risk for the requirement for massive transfusion in patients with primary postpartum hemorrhage in the emergency department.

BACKGROUND: Postpartum hemorrhage (PPH) constitutes a major risk for maternal mortality and morbidity. Unfortunately, the severity of PPH can be underestimated because it is difficult to accurately measure blood loss by visual estimation. The delta neutrophil index (DNI), which reflects circulating immature granulocytes, is automatically calculated in hematological analyzers. We evaluated the significance of the DNI in predicting hemorrhage severity based on the requirement for massive transfusion (MT) in patients with PPH. METHODS: We retrospectively analyzed data from a prospective registry to evaluate the association between the DNI and MT. Moreover, we assessed the predictive ability of the combination of DNI and shock index (SI) for the requirement for MT. MT was defined as a transfusion of ≥10 units of red blood cells within 24 h of PPH. In total, 278 patients were enrolled in this study and 60 required MT. RESULTS: Multivariable logistic regression revealed that the DNI and SI were independent predictors of MT. The optimal cut-off values of ≥3.3% and ≥1.0 for the DNI and SI, respectively, were significantly associated with an increased risk of MT (DNI: positive likelihood ratio [PLR] 3.54, 95% confidence interval [CI] 2.5-5.1 and negative likelihood ratio [NLR] 0.48, 95% CI 0.4-0.7; SI: PLR 3.21, 95% CI 2.4-4.2 and NLR 0.31, 95% CI 0.19-0.49). The optimal cut-off point for predicted probability was calculated for combining the DNI value and SI value with the equation derived from logistic regression analysis. Compared with DNI or SI alone, the combination of DNI and SI significantly improved the specificity, accuracy, and positive likelihood ratio of the MT risk. CONCLUSION: The DNI and SI can be routinely and easily measured in the ED without additional costs or time and can therefore, be considered suitable parameters for the early risk stratification of patients with primary PPH.

Paediatric Trauma Score as a non-imaging tool for predicting intracranial haemorrhage in patients with traumatic brain injury.

To identify a useful non-imaging tool to screen paediatric patients with traumatic brain injury for intracranial haemorrhage (ICH). We retrospectively analysed patients aged < 15 years who visited the emergency department with head trauma between January 2015 and September 2020. We divided patients into two groups (ICH and non-ICH) and compared their demographic and clinical factors. Among 85 patients, 21 and 64 were in the ICH and non-ICH groups, respectively. Age (p = 0.002), Pediatric trauma score (PTS; p < 0.001), seizure (p = 0.042), and fracture (p < 0.001) differed significantly between the two groups. Factors differing significantly between the groups were as follows: age (odds ratio, 0.84, p = 0.004), seizure (4.83, p = 0.013), PTS (0.15, p < 0.001), and fracture (69.3, p < 0.001). Factors with meaningful cut-off values were age (cut-off [sensitivity, specificity], 6.5 [0.688, 0.714], p = 0.003) and PTS [10.5 (0.906, 0.81), p < 0.001]. Based on the previously known value for critical injury (≤ 8 points) and the cut-off value of the PTS identified in this study (≤ 10 points), we divided patients into low-risk, medium-risk, and high-risk groups; their probabilities of ICH (95% confidence intervals) were 0.16-12.74%, 35.86-89.14%, and 100%, respectively. PTS was the only factor that differed significantly between mild and severe ICH cases (p = 0.012). PTS is a useful screening tool with a high predictability for ICH and can help reduce radiation exposure when used to screen patient groups before performing imaging studies.