Liberal versus restrictive red blood cell transfusion strategy in sepsis or septic shock: a systematic review and meta-analysis of randomized trials.

BACKGROUND: We assessed the effect of liberal versus restrictive red blood cell transfusion strategy on survival outcome in sepsis or septic shock by systematically reviewing the literature and synthesizing evidence from randomized controlled trials (RCTs). METHODS: We searched the MEDLINE, Cochrane Central Register of Controlled Trials, and Web of Science databases. We included RCTs that compared mortality between a liberal transfusion strategy with a hemoglobin threshold of 9 or 10 g/dL and a restrictive transfusion strategy with a hemoglobin threshold of 7 g/dL in adults with sepsis or septic shock. Two investigators independently screened citations and conducted data extraction. The primary outcome was 28- or 30-day mortality. Secondary outcomes were 60- and 90-day mortality, use of life support at 28 days of admission, and number of patients transfused during their intensive care unit stay. DerSimonian-Laird random-effects models were used to report pooled odds ratios (ORs). RESULTS: A total of 1516 patients from three RCTs were included; 749 were randomly assigned to the liberal transfusion group and 767 to the restrictive strategy group. Within 28-30 days, 273 patients (36.4%) died in the liberal transfusion group, while 278 (36.2%) died in the restrictive transfusion group (pooled OR, 0.99; 95% confidence interval [CI], 0.67-1.46). For the primary outcome, heterogeneity was observed among the studies (I2 = 61.0%, χ2 = 5.13, p = 0.08). For secondary outcomes, only two RCTs were included. There were no significant differences in secondary outcomes between the two groups. CONCLUSIONS: We could not show any difference in 28- or 30-day mortality between the liberal and restrictive transfusion strategies in sepsis or septic shock patients by meta-analysis of RCTs. Our results should be interpreted with caution due to the existence of heterogeneity. As sepsis complicates a potentially wide range of underlying diseases, further trials in carefully selected populations are anticipated. TRIAL REGISTRATION: This present study was registered in the PROSPERO database (CRD42018108578).

Efficacy of the presence of an emergency physician in prehospital major trauma care: A nationwide cohort study in Japan.

PURPOSE: The beneficial effect of the presence of an emergency physician in prehospital major trauma care is controversial. The aim in this study is to assess whether an emergency physician on scene can improve survival outcome of critical trauma patients. METHODS: This retrospective cohort study was conducted by using nationwide trauma registry data between 2004 and 2013 in Japan. Severe trauma patients (injury severity score (ISS) ≥ 16) who were transported directly to the hospital from the injury site were included in our analysis. Patients who were predicted to be untreatable (abbreviated injury score (AIS) = 6 and/or cardiopulmonary arrest at least one time before hospital arrival) were excluded. Participants were divided into either a physician or paramedics group based on the prehospital practitioner. The primary outcome was survival rate at discharge. Multivariable logistic regression analysis was performed to compare the outcome with adjustment for age, gender, ISS, cause of injury, and pre-hospital vital signs. RESULTS: A total of 30,283 patients were eligible for the selection criteria (physician: 1222, paramedics: 29,061). Overall, 172 patients (14.1%) died in the physician group compared to 3508 patients (12.1%) in the paramedics group. Patients in the physician group had higher ISSs than those in the paramedics group. In multivariable logistic regression, the physician group had an odds ratio (OR) of 1.16 (95% confidence interval (CI) = 0.97 to 1.40, p = 0.11) for in-hospital survival. CONCLUSIONS: Our results failed to show a difference in survival at discharge between non-physician-staffed ambulances and physician-staffed ambulances.

Role of reverse transendothelial migration of neutrophils in inflammation.

Transmigration of neutrophils through vascular endothelial walls into the inflamed tissues is a critical defense mechanism of innate immune system against infection and injury caused by sepsis, trauma, ischemia-reperfusion, and other acute or chronic inflammatory diseases. However, their excessive infiltration and uncontrolled activation may lead to the destruction of normal tissue architecture and unrestrained inflammation. Transendothelial migration (TEM) in a luminal-to-abluminal direction is widely known as the final step of neutrophil migration cascade into the inflamed tissues. Recent studies have shown that neutrophils not necessarily move from the vascular lumen to the extravascular tissues in a one way direction; they also proceed in an opposite direction, known as reverse transendothelial migration (rTEM) to get back into the vascular lumen again. This novel paradigm of neutrophil round trip is currently on the spotlight due to its possible interaction with immune system. Current review highlighting the growing demand of this newly identified neutrophil migratory event will not only rewrite the disease pathophysiology, but also help scientists design novel therapeutic strategy leading to the remission of inflammatory diseases in which controlling exaggerated neutrophil infiltration is a major challenge.

Neutralization of osteopontin attenuates neutrophil migration in sepsis-induced acute lung injury.

INTRODUCTION: Sepsis refers to severe systemic inflammation leading to acute lung injury (ALI) and death. Introducing novel therapies can reduce the mortality in ALI. Osteopontin (OPN), a secretory glycoprotein produced by immune reactive cells, plays a deleterious role in various inflammatory diseases. However, its role in ALI caused by sepsis remains unexplored. We hypothesize that treatment with an OPN-neutralizing antibody (anti-OPN Ab) protects mice against ALI during sepsis. METHODS: Sepsis was induced in 8-week-old male C57BL/6 mice by cecal ligation and puncture (CLP). Anti-OPN Ab or non-immunized IgG as control, at a dose of 50 µg/mouse, was intravenously injected at the time of CLP. After 20 hours, the expression of OPN and proinflammatory cytokines in tissues and plasma was examined by real-time PCR, Western blot, and ELISA. Plasma levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST) and lactate dehydrogenase (LDH) and the lung myeloperoxidase (MPO) levels were determined by colorimetric assays. Lung damage and neutrophil infiltrations were determined by histological H&E and Gr-1 staining, respectively. The effect of recombinant mouse OPN (rmOPN) on human neutrophil-like cell (HL-60) migration was performed by Boyden chamber assays and the involvement of intracellular signaling molecules in HL-60 cells was revealed by Western blot. RESULTS: After 20 hours of sepsis, mRNA and protein levels of OPN were significantly induced in lungs, spleen, and plasma. Treatment with an anti-OPN Ab in septic mice significantly reduced the plasma levels of ALT, AST, and LDH, and the proinflammatory cytokines IL-6, IL-1ß and the chemokine MIP-2, compared with the vehicle group. Similarly, the lung mRNA and protein expressions of proinflammatory cytokines and chemokine were greatly reduced in anti-OPN Ab-treated animals. The lung histological architecture, MPO and neutrophil infiltration were significantly improved in anti-OPN Ab-treated mice compared with the vehicle animals. Treatment of rmOPN in HL-60 cells significantly increased their migration, in vitro. The neutrophils treated with rmOPN remarkably increased the levels of phospho focal adhesion kinase (pFAK), phospho extracellular signal-regulated kinase (pERK) and phospho p38. CONCLUSIONS: Our findings clearly demonstrate the beneficial outcomes of anti-OPN Ab treatment in protecting against ALI, implicating a novel therapeutic strategy in sepsis.

Hospital disaster response using business impact analysis.

INTRODUCTION: The catastrophic Great East Japan Earthquake in 2011 created a crisis in a university-affiliated hospital by disrupting the water supply for 10 days. In response, this study was conducted to analyze water use and prioritize water consumption in each department of the hospital by applying a business impact analysis (BIA). Identifying the minimum amount of water necessary for continuing operations during a disaster was an additional goal. PROBLEM: Water is essential for many hospital operations and disaster-ready policies must be in place for the safety and continued care of patients. METHODS: A team of doctors, nurses, and office workers in the hospital devised a BIA questionnaire to examine all operations using water. The questionnaire included department name, operation name, suggested substitutes for water, and the estimated daily amount of water consumption. Operations were placed in one of three ranks (S, A, or B) depending on the impact on patients and the need for operational continuity. Recovery time objective (RTO), which is equivalent to the maximum tolerable period of disruption, was determined. Furthermore, the actual use of water and the efficiency of substitute methods, practiced during the water-disrupted periods, were verified in each operation. RESULTS: There were 24 activities using water in eight departments, and the estimated water consumption in the hospital was 326 (SD = 17) m³ per day: 64 (SD = 3) m³ for S (20%), 167 (SD = 8) m³ for A (51%), and 95 (SD = 5) m³ for B operations (29%). During the disruption, the hospital had about 520 m³ of available water. When the RTO was set to four days, the amount of water available would have been 130 m³ per day. During the crisis, 81% of the substitute methods were used for the S and A operations. CONCLUSION: This is the first study to identify and prioritize hospital operations necessary for the efficient continuation of medical treatment during suspension of the water supply by applying a BIA. Understanding the priority of operations and the minimum daily water requirement for each operation is important for a hospital in the event of an unexpected adverse situation, such as a major disaster.

Characteristics and clinical outcomes of patients with combined burns and trauma in Japan: Analysis of a nationwide trauma registry database.

INTRODUCTION: Patients with combined burns and trauma are often seen in the United States. The combination of trauma with burns increases mortality. In contrast, the characteristics and outcomes of these cases remain unknown in Japan. This study investigated the characteristics and outcomes of trauma associated with burns in Japan. METHODS: This multicenter retrospective cohort study was conducted by utilizing data from the Japan Trauma Data Bank for the period between 2004 and 2017. We evaluated the characteristics of burn patients (n = 5783) divided into two groups: burns only (n = 5537) and combined burns and trauma (n = 246). Clinical characteristics, including patient background, severity of trauma, injury mechanism, total body surface area affected, injury location, treatments, and clinical outcomes, were examined. RESULTS: Most patients in both the groups were injured by flames. The number proportion of patients with 40-89% of the total body surface area affected was 1069/5537 (19.3%) in the burn-only group and 23/246 (9.3%) in the combined burn and trauma group. The in-hospital mortality was 1006/5537 (18.2%) in the burn-only group and 17/246 (6.9%) in the combined burn and trauma group. CONCLUSIONS: We demonstrated the characteristics of Japanese patients with burns only compared with those with combined burns and trauma. Flames were the main cause of burns, and in-hospital mortality was lower in the combined burn and trauma group associated with a smaller burn area.

[Case of dyspnea due to toilet cleaner containing nitric acid for household use].

A 40-year-old male tried to clean a urinal at his home storing 900 mL of a toilet cleaner containing 9.8% nitric acid to remove calcium deposit, and clean the toilet floor for twenty minutes. Immediately after using the cleaner, he experienced eye irritation. He washed out the toilet cleaner. However, he thereafter experienced dyspnea, a compressive sensation in his chest, and chest and back pain about 40 minutes after the cleaning the toilet. He monitored his symptoms overnight and found them to gradually improve. However, the symptoms still remained the next morning and therefore he came to our department on foot. He had no particular past or family history. On arrival, his physiological findings and chest computed tomography scan were negative for any abnormalities. His arterial blood gas analysis revealed a mild abnormality of oxygenation. Observation without any drugs revealed that a complete remission of his symptoms occurred after approximately 4 weeks. Based on the results of the experiments, contact with the mucosal membrane and nitric acid gas produced by any accidentally coexisting metals or contact with moisture, including nitric acid produced by a reaction between CaCO3 and cleaner, may have been the mechanism of occurrence for the symptoms observed in this case. This is the first reported case of nitric acid poisoning due to the use of a toilet cleanser intended for household use.

A Machine Learning Prediction Model for Non-cardiogenic Out-of-hospital Cardiac Arrest with Initial Non-shockable Rhythm.

Objectives: The purpose of this study was to develop and validate a machine learning prediction model for the prognosis of non-cardiogenic out-of-hospital cardiac arrest (OHCA) with an initial non-shockable rhythm. Design: Data were obtained from a nationwide OHCA registry in Japan. Overall, 222,056 patients with OHCA and an initial non-shockable rhythm were identified from the registry in 2016 and 2017. Patients aged <18 years and OHCA caused by cardiogenic origin, cancer, and external factors were excluded. Finally, 58,854 participants were included. Methods: Patients were classified into the training dataset (n=29,304, data from 2016) and the test dataset (n=29,550, data from 2017). The training dataset was used to train and develop the machine learning model, and the test dataset was used for internal validation. We selected XGBoost as the machine learning classifier. The primary outcome was the poor prognosis defined as cerebral performance category of 3-5 at 1 month. Eleven prehospital variables were selected as outcome predictors. Results: In validation, the machine learning model predicted the primary outcome with an accuracy of 90.8% [95% confidence interval (CI): 90.5-91.2], a sensitivity of 91.4% [CI: 90.7-91.4], a specificity of 74.1% [CI: 69.2-78.6], and an area under the receiver operating characteristic value of 0.89 [0.87-0.92]. The important features for model development were the prehospital return of spontaneous circulation, prehospital adrenaline administration, and initial electrical rhythm. Conclusions: We developed a favorable machine learning model to predict the prognosis of non-cardiogenic OHCA with an initial non-shockable rhythm in the early stage of resuscitation.

Complement Activation in Patients With Heat-Related Illnesses: Soluble CD59 Is a Novel Biomarker Indicating Severity of Heat-Related Illnesses.

OBJECTIVES: Although multiple organ dysfunction syndrome (MODS) is the main cause of death in patients with heat-related illnesses, its underlying pathophysiological mechanism remains elusive. Complement activation is considered one of the main causes of MODS in patients with sepsis and trauma. Considering the pathophysiological similarity of heat related-illnesses with sepsis and trauma, the complement system might be activated in patients with heat-related illnesses as well. Our aim was to investigate whether excessive complement activation occurs in patients with heat-related illnesses. DESIGN: Prospective observational study. SETTING: Emergency department in the university hospital. PATIENTS: Thirty-two patients with heat-related illnesses and 15 age-matched healthy controls were enrolled in this study. INTERVENTIONS: Blood samples were collected from the study subjects for the measurement of complement factors. MEASUREMENTS AND MAIN RESULTS: Complement component 3a (C3a), complement component 5a (C5a), C5b-9, complement factor B (Ba), Factor H, and soluble CD59 in plasma were measured. The levels of C3a, C5a, C5b-9, and Ba significantly increased in patients with heat-related illnesses on day 0 compared with those in the healthy controls. Soluble CD59 was significantly high in patients with heat-related illnesses on day 0 and showed a correlation with the severity of the condition (Acute Physiology and Chronic Health Evaluation II, Sequential Organ Failure Assessment, and staging scores), Japanese Association for Acute Medicine disseminated intravascular coagulation scores, and the coagulation system (prothrombin time and fibrin degradation products). CONCLUSIONS: The complement system was activated in patients with heat-related illnesses, suggesting that it is one of the causes of MODS. Soluble CD59 may be a potent biomarker for the severity of heat-related illnesses.

An international, stepped wedge, cluster-randomized trial investigating the 0/1-h algorithm in suspected acute coronary syndrome in Asia: the rational of the DROP-Asian ACS study.

BACKGROUND: More than half of the world's population lives in Asia. With current life expectancies in Asian countries, the burden of cardiovascular disease is increasing exponentially. Overcrowding in the emergency departments (ED) has become a public health problem. Since 2015, the European Society of Cardiology recommends the use of a 0/1-h algorithm based on high-sensitivity cardiac troponin (hs-cTn) for rapid triage of patients with suspected non-ST elevation acute coronary syndrome (NSTE-ACS). However, these algorithms are currently not recommended by Asian guidelines due to the lack of suitable data. METHODS: The DROP-Asian ACS is a prospective, stepped wedge, cluster-randomized trial enrolling 4260 participants presenting with chest pain to the ED of 12 acute care hospitals in five Asian countries (UMIN; 000042461). Consecutive patients presenting with suspected acute coronary syndrome between July 2022 and Apr 2024 were included. Initially, all clusters will apply "usual care" according to local standard operating procedures including hs-cTnT but not the 0/1-h algorithm. The primary outcome is the incidence of major adverse cardiac events (MACE), the composite of all-cause death, myocardial infarction, unstable angina, or unplanned revascularization within 30 days. The difference in MACE (with one-sided 95% CI) was estimated to evaluate non-inferiority. The non-inferiority margin was prespecified at 1.5%. Secondary efficacy outcomes include costs for healthcare resources and duration of stay in ED. CONCLUSIONS: This study provides important evidence concerning the safety and efficacy of the 0/1-h algorithm in Asian countries and may help to reduce congestion of the ED as well as medical costs.

Early outcome prediction for out-of-hospital cardiac arrest with initial shockable rhythm using machine learning models.

AIM: Early outcome prediction for out-of-hospital cardiac arrest with initial shockable rhythm is useful in selecting the choice of resuscitative treatment by clinicians. This study aimed to develop and validate a machine learning-based outcome prediction model for out-of-hospital cardiac arrest with initial shockable rhythm, which can be used on patient's arrival at the hospital. METHODS: Data were obtained from a nationwide out-of-hospital cardiac arrest registry in Japan. Of 43,350 out-of-hospital cardiac arrest patients with initial shockable rhythm registered between 2013 and 2017, patients aged <18 years and those with cardiac arrest caused by external factors were excluded. Subjects were classified into training (n = 23,668, 2013-2016 data) and test (n = 6381, data from 2017) sets for validation. Only 19 prehospital variables were used for the outcome prediction. The primary outcome was death at 1 month or survival with poor neurological function (cerebral performance category 3-5; "poor" outcome). Several machine learning models, including those based on logistic regression, support vector machine, random forest, and multilayer perceptron classifiers were compared. RESULTS: In validation analyses, all machine learning models performed satisfactorily with area under the receiver operating characteristic curve values of 0.882 [95% confidence interval [CI]: 0.869-0.894] for logistic regression, 0.866 [95% CI: 0.853-0.879] for support vector machine, 0.877 [95% CI: 0.865-0.890] for random forest, and 0.888 [95% CI: 0.876-0.900] for multilayer perceptron classifiers. CONCLUSIONS: A favourable machine learning-based prognostic model available to use on patient arrival at the hospital was developed for out-of-hospital cardiac arrest with initial shockable rhythm.

Machine Learning Approach to Predict Positive Screening of Methicillin-Resistant Staphylococcus aureus During Mechanical Ventilation Using Synthetic Dataset From MIMIC-IV Database.

Background: Mechanically ventilated patients are susceptible to nosocomial infections such as ventilator-associated pneumonia. To treat ventilated patients with suspected infection, clinicians select appropriate antibiotics. However, decision-making regarding the use of antibiotics for methicillin-resistant Staphylococcus aureus (MRSA) is challenging, because of the lack of evidence-supported criteria. This study aims to derive a machine learning model to predict MRSA as a possible pathogen responsible for infection in mechanically ventilated patients. Methods: Data were collected from the Medical Information Mart for Intensive Care (MIMIC)-IV database (an openly available database of patients treated at the Beth Israel Deaconess Medical Center in the period 2008-2019). Of 26,409 mechanically ventilated patients, 809 were screened for MRSA during the mechanical ventilation period and included in the study. The outcome was positivity to MRSA on screening, which was highly imbalanced in the dataset, with 93.9% positive outcomes. Therefore, after dividing the dataset into a training set (n = 566) and a test set (n = 243) for validation by stratified random sampling with a 7:3 allocation ratio, synthetic datasets with 50% positive outcomes were created by synthetic minority over-sampling for both sets individually (synthetic training set: n = 1,064; synthetic test set: n = 456). Using these synthetic datasets, we trained and validated an XGBoost machine learning model using 28 predictor variables for outcome prediction. Model performance was evaluated by area under the receiver operating characteristic (AUROC), sensitivity, specificity, and other statistical measurements. Feature importance was computed by the Gini method. Results: In validation, the XGBoost model demonstrated reliable outcome prediction with an AUROC value of 0.89 [95% confidence interval (CI): 0.83-0.95]. The model showed a high sensitivity of 0.98 [CI: 0.95-0.99], but a low specificity of 0.47 [CI: 0.41-0.54] and a positive predictive value of 0.65 [CI: 0.62-0.68]. Important predictor variables included admission from the emergency department, insertion of arterial lines, prior quinolone use, hemodialysis, and admission to a surgical intensive care unit. Conclusions: We were able to develop an effective machine learning model to predict positive MRSA screening during mechanical ventilation using synthetic datasets, thus encouraging further research to develop a clinically relevant machine learning model for antibiotics stewardship.

Risk factors for cancellation after dispatch of rapid response cars for prehospital emergency care: a single-center, case-control study.

AIM: The objective of this study is to identify the risk factors for cancellation after dispatch of rapid response cars (RRC) for prehospital emergency care. METHODS: We retrospectively extracted data from all RRC cases dispatched from our hospital between April 2017 and March 2019. A total of 1,440 cases were included in our study and divided into either the "cancelled" group (n = 723) or the "treated" group (n = 717), based on the occurrence of cancellation. The variables obtained from the request calls for RRC included patient characteristics, distance from the hospital to the scene, and reasons for RRC request. The variables were compared between the two groups and logistic regression analysis was carried out to identify the risk factors for RRC cancellation. RESULTS: Multivariable analysis showed that distance from the hospital to the scene (odds ratio [OR] 1.25; 95% confidence interval (CI), 1.21-1.28), suspicion of cardiopulmonary arrest with no witness information (OR 7.61; 95% CI, 4.13-14.00), dyspnea (OR 2.22; 95% CI, 1.19-4.11), and suicide by hanging (OR 3.49; 95% CI, 1.37-8.89) were independent risk factors for cancellation. CONCLUSIONS: In our study, a greater distance from the hospital to the scene, suspicion of cardiopulmonary arrest with no witness information, dyspnea, and suicide by hanging were identified as independent risk factors for cancellation after dispatch of RRC. Evaluating the risk factors for cancellation at individual facilities could help hospitals adjust their dispatch criteria to allocate limited medical resources more effectively.

Machine learning-based mortality prediction model for heat-related illness.

In this study, we aimed to develop and validate a machine learning-based mortality prediction model for hospitalized heat-related illness patients. After 2393 hospitalized patients were extracted from a multicentered heat-related illness registry in Japan, subjects were divided into the training set for development (n = 1516, data from 2014, 2017-2019) and the test set (n = 877, data from 2020) for validation. Twenty-four variables including characteristics of patients, vital signs, and laboratory test data at hospital arrival were trained as predictor features for machine learning. The outcome was death during hospital stay. In validation, the developed machine learning models (logistic regression, support vector machine, random forest, XGBoost) demonstrated favorable performance for outcome prediction with significantly increased values of the area under the precision-recall curve (AUPR) of 0.415 [95% confidence interval (CI) 0.336-0.494], 0.395 [CI 0.318-0.472], 0.426 [CI 0.346-0.506], and 0.528 [CI 0.442-0.614], respectively, compared to that of the conventional acute physiology and chronic health evaluation (APACHE)-II score of 0.287 [CI 0.222-0.351] as a reference standard. The area under the receiver operating characteristic curve (AUROC) values were also high over 0.92 in all models, although there were no statistical differences compared to APACHE-II. This is the first demonstration of the potential of machine learning-based mortality prediction models for heat-related illnesses.

Corticosteroid treatment for early acute respiratory distress syndrome: a systematic review and meta-analysis of randomized trials.

BACKGROUND: The effect of corticosteroid treatment on survival outcome in early acute respiratory distress syndrome (ARDS) is still debated. We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) to assess the efficacy of prolonged corticosteroid therapy in early ARDS. METHODS: We assessed the MEDLINE, Cochrane Central Register of Controlled Trials, and Web of Science databases from inception to August 1, 2020. We included RCTs that compared prolonged corticosteroid therapy with control treatment wherein the intervention was started within 72 h of ARDS diagnosis. Two investigators independently screened the citations and conducted the data extraction. The primary outcomes were all-cause 28- or 30-day mortality and 60-day mortality. Several endpoints such as ventilator-free days and adverse events were set as the secondary outcomes. DerSimonian-Laird random-effects models were used to report pooled odds ratios (ORs). RESULTS: Among the 4 RCTs included, all referred to the all-cause 28- or 30-day mortality. In the corticosteroid group, 108 of 385 patients (28.1%) died, while 139 of 357 (38.9%) died in the control group (pooled OR, 0.61; 95% confidence interval [CI], 0.44-0.85). Three RCTs mentioned the all-cause 60-day mortality. In the corticosteroid group, 78 of 300 patients (26.0%) died, while 101 of 265 (38.1%) died in the control group (pooled OR, 0.57; 95% CI, 0.40-0.83). For secondary outcomes, corticosteroid treatment versus control significantly prolonged the ventilator-free days (4 RCTs: mean difference, 3.74; 95% CI, 1.53-5.95) but caused hyperglycemia (3 RCTs: pooled OR, 1.52; 95% CI, 1.04-2.21). CONCLUSIONS: Prolonged corticosteroid treatment in early ARDS improved the survival outcomes. TRIAL REGISTRATION: PROSPERO, CRD42020195969.

Characteristics, injuries, and clinical outcomes of geriatric trauma patients in Japan: an analysis of the nationwide trauma registry database.

Geriatric trauma is a major socio-economic problem, especially among the aging Japanese society. Geriatric people are more vulnerable to trauma than younger people; thus, their outcomes are often severe. This study evaluates the characteristics of geriatric trauma divided by age in the Japanese population. We evaluated trauma characteristics in patients (n = 131,088) aged ≥ 65 years by segregating them into 2 age-based cohorts: age 65-79 years (65-79 age group; n = 70,707) and age ≥ 80 years (≥ 80 age group; n = 60,381). Clinical characteristics such as patient background, injury mechanism, injury site and severity, treatment, and outcome were examined. Injuries among men were more frequent in the 65-79 age group (58.6%) than in the ≥ 80 age group (36.3%). Falls were the leading cause of trauma among the 65-79 age group (56.7%) and the ≥ 80 age group (78.9%). In-hospital mortality was 7.7% in the 65-79 age group and 6.6% in the ≥ 80 age group. High fall in the ≥ 80 age group showed 30.5% mortality. The overall in-hospital mortality was 11.8% (the 65-79 age group, 12.3%; the ≥ 80 age group, 11.2%). Most hospitalized patients were transferred to another hospital (the 65-79 age group, 52.5%; the ≥ 80 age group, 66.2%). We demonstrated the epidemiological characteristics of Japanese geriatric trauma patients. The overall in-hospital mortality was 11.8%, and fall injury in the ≥ 80 age group required caution of trauma care.

Evaluating the risks of arrhythmia following electrical injury: Two cases of electrical injuries in the upper limbs.

Electrical injuries induce ventricular arrhythmias, which are lethal. Therefore, it is important to evaluate the risk of arrhythmias at initial presentation to the emergency department in cases of electrical injuries. Here, we report two cases with electrical injuries, where current flowed between the upper limbs, requiring 24-h hospitalization for arrhythmia monitoring. The patients were 57- and 30-year-old men, who sustained separate electrical injuries (6600 V, line voltage), with current flow from one hand to the other. They did not develop any ventricular arrhythmias during hospitalization and were discharged. The risk for ventricular arrhythmias is lower for electrical injuries occurring between the upper limbs than for those occurring between the upper and lower limbs. We conclude that 24-h hospitalization for monitoring of patients with electrical injuries of the upper limbs may be sufficient.

Evaluation of appropriate indications for the use of sivelestat sodium in acute respiratory distress syndrome: a retrospective cohort study.

AIM: Sivelestat sodium, a selective neutrophil elastase inhibitor, is the only commercially available, specific therapy for acute respiratory distress syndrome (ARDS); however, its clinical efficacy is controversial. We aimed to evaluate appropriate indications for its use in ARDS. METHODS: We studied 66 patients with ARDS who were treated with sivelestat sodium. They were divided into survivors (n = 37) or non-survivors (n = 29) at 60 days, and clinical characteristics were analyzed. RESULTS: Patients' backgrounds evaluated with the Acute Physiology and Chronic Health Evaluation II (APACHE II) score and the sequential organ failure assessment (SOFA) score were significantly different between both groups (survivors versus non-survivors: APACHE II score, 14.7 ± 6.7 versus 20.5 ± 4.7, P < 0.01; SOFA, 7.25 ± 2.5 versus 9.82 ± 3.5, P < 0.01). There were no significant differences in other patients' characteristics. On receiver operator characteristic analysis of APACHE II scores before the use of sivelestat sodium, the estimated cut-off value for survival was calculated to be 18.5.On receiver operator characteristic analysis of the PaO2/FIO2 ratio, the area under the curve was the highest 3 days after the treatment, with the optimal cut-off point at 198. CONCLUSION: An APACHE II score ≤18, and a PaO2/FIO2 ratio >198 at 3 days after the use of sivelestat sodium predicted a good outcome.

Late i.v. steroid treatment for severe COVID-19-induced acute respiratory distress syndrome: a case report.

BACKGROUND: The efficacy of steroid treatment for coronavirus disease (COVID-19) is unknown. CASE PRESENTATION: A 67-year-old man was transported to our hospital due to impaired consciousness and respiratory failure. After admission, tracheal aspirate of the patient was harvested, and it tested positive for severe acute respiratory syndrome coronavirus 2 nucleic acid. He required veno-venous extracorporeal membrane oxygenation to sustain his oxygenation. However, his respiratory failure did not improve for 20 days. On day 20 of admission, we started to use i.v. steroid therapy. On day 23, lung opacity on the chest X-ray cleared and the patient's oxygen saturation improved significantly. We successfully removed extracorporeal membrane oxygenation on day 27. CONCLUSION: Our case report encourages more future trials to evaluate the therapeutic use of i.v. steroid in severe COVID-19-induced acute respiratory distress syndrome.