Neonicotinoids are a new class of insecticides widely applied for crop protection. These insecticides act as agonists at nicotinic acetylcholine receptors, which cause insect paralysis and death. The high specificity for receptors in insects was considered to possess highly selective toxicity to insects and relative sparing of mammals. However, an increasing number of cases of acute neonicotinoid poisoning have been reported in recent years. We reported a man who developed respiratory failure and shock after ingestion of neonicotinoid insecticide. A detailed literature review found that respiratory, cardiovascular and certain neurological presentations are warning signs of severe neonicotinoid intoxication. The amounts of ingested neonicotinoid insecticide and the plasma neonicotinoid concentration are not useful guides for the management of intoxicated patients. Supportive treatment and decontamination are the practical methods for the management of all neonicotinoid‐poisoned patients.

Traumatic brain injury (TBI) remains a critical public health challenge. Although studies have found several prognostic factors for TBI, a useful early predictive tool for mortality has yet to be developed in the triage of the emergency room. This study aimed to use machine learning algorithms of artificial intelligence (AI) to develop predictive models for TBI patients in the emergency room triage. We retrospectively enrolled 18,249 adult TBI patients in the electronic medical records of three hospitals of Chi Mei Medical Group from January 2010 to December 2019, and undertook the 12 potentially predictive feature variables for predicting mortality during hospitalization. Six machine learning algorithms including logistical regression (LR) random forest (RF), support vector machines (SVM), LightGBM, XGBoost, and multilayer perceptron (MLP) were used to build the predictive model. The results showed that all six predictive models had high AUC from 0.851 to 0.925. Among these models, the LR-based model was the best model for mortality risk prediction with the highest AUC of 0.925; thus, we integrated the best model into the existed hospital information system for assisting clinical decision-making. These results revealed that the LR-based model was the best model to predict the mortality risk in patients with TBI in the emergency room. Since the developed prediction system can easily obtain the 12 feature variables during the initial triage, it can provide quick and early mortality prediction to clinicians for guiding deciding further treatment as well as helping explain the patient’s condition to family members.

Accurate stratification of sepsis can effectively guide the triage of patient care and shared decision making in the emergency department (ED). However, previous research on sepsis identification models focused mainly on ICU patients, and discrepancies in model performance between the development and external validation datasets are rarely evaluated. The aim of our study was to develop and externally validate a machine learning model to stratify sepsis patients in the ED. We retrospectively collected clinical data from two geographically separate institutes that provided a different level of care at different time periods. The Sepsis-3 criteria were used as the reference standard in both datasets for identifying true sepsis cases. An eXtreme Gradient Boosting (XGBoost) algorithm was developed to stratify sepsis patients and the performance of the model was compared with traditional clinical sepsis tools; quick Sequential Organ Failure Assessment (qSOFA) and Systemic Inflammatory Response Syndrome (SIRS). There were 8296 patients (1752 (21%) being septic) in the development and 1744 patients (506 (29%) being septic) in the external validation datasets. The mortality of septic patients in the development and validation datasets was 13.5% and 17%, respectively. In the internal validation, XGBoost achieved an area under the receiver operating characteristic curve (AUROC) of 0.86, exceeding SIRS (0.68) and qSOFA (0.56). The performance of XGBoost deteriorated in the external validation (the AUROC of XGBoost, SIRS and qSOFA was 0.75, 0.57 and 0.66, respectively). Heterogeneity in patient characteristics, such as sepsis prevalence, severity, age, comorbidity and infection focus, could reduce model performance. Our model showed good discriminative capabilities for the identification of sepsis patients and outperformed the existing sepsis identification tools. Implementation of the ML model in the ED can facilitate timely sepsis identification and treatment. However, dataset discrepancies should be carefully evaluated before implementing the ML approach in clinical practice. This finding reinforces the necessity for future studies to perform external validation to ensure the generalisability of any developed ML approaches.

Background: One of the most severe complications of liposuction and fat grafting is pulmonary fat embolism (PFE). However, most healthcare workers are not familiar with PFE. We performed a systematic review to describe the details of PFE. Methods: PubMed, EMBASE, and Google Scholar were searched up to October 2022. Further analysis focused on clinical, diagnostic, and outcome parameters. Results: A total of 40 patients from 19 countries were included. Chest computed tomography (CT) yielded 100% accuracy in the diagnosis of PFE. More than 90% of the deceased died within 5 days after surgery, and in 69% of patients, onset of symptoms occurred within 24 h after surgery. The proportions of patients who required mechanical ventilation, had a cardiac arrest event, or died among all patients and among those whose onset of symptoms occurred within 24 h after surgery were 76%, 38%, and 34% versus 86%, 56%, and 54%, respectively. Conclusions: The earlier the onset of symptoms was, the more severe the clinical course was. Once a patient presents with PFE-related symptoms, surgery should be halted, supportive care initiated, and chest CT used to diagnose PFE. According to our review results, if a patient with PFE survives the initial episode without permanent sequelae, a complete recovery can be anticipated.

People sometimes siphon fuel to fill their tanks. However, this is a potentially dangerous procedure and may cause hydrocarbon pneumonitis. We present the case of a patient with severe hydrocarbon pneumonitis after siphoning fuel. The patient underwent artificial ventilation and was admitted to hospital for 97 days. We review the relevant literature for a better understanding of clinical features and management strategies for hydrocarbon pneumonitis following fuel siphonage. We reviewed 15 articles, which included 3 original articles and 12 case reports that reported the clinical features of fuel siphonage. In addition, we added our presented case for data analysis. A total of 40 cases were included in this review. The literature review found that hydrocarbon pneumonitis caused by fuel siphonage occurs worldwide and that most patients (80%) became symptomatic within 1 day of aspiration. Cough (70%), chest pain (62.5%), dyspnoea (55%), and fever (52.5%) presented in more than half of all patients. The right middle lobe (80%) was the predominantly involved lung field; more than one-third of patients (36.7%) showed the involvement of two lobes. Patient history, computed tomographic scans of the chest, and bronchoalveolar lavage are the commonly used diagnostic tools. Supportive care remains the foundation of treatment, whereas antibiotics, steroids, and bronchoalveolar lavage are practical therapies. Patients' clinical improvement precedes the resolution of lesions on chest X-ray. Most complications arise from pulmonary lesions. The prognosis of patients suffering from hydrocarbon pneumonitis following fuel siphonage might be improved by accurate diagnosis and appropriate care.

The flood after a typhoon may lead to increase in patients with cellulitis of lower limbs. However, the microbiological features of these cases are rarely reported. We conducted a study of patients with lower extremity cellulitis after a typhoon followed in southern Taiwan to study the risk factors of cellulitis and the bacteriological features of the patients. We reviewed all the medical records of cellulitis at emergency departments of two teaching hospitals in southern Taiwan 30 days before and after the landing of Typhoon Morakot and collected data on the demographic and bacteriological characteristics. In addition, we evaluated the relationship between the daily number of patients and the rainfall in the Tainan area. The number of cellulitis patients increased from 183 to 344 during the 30-day period after the typhoon. The number peaked in the third and fourth days and lasted for 3 weeks. The proportion of patients with water immersion of the affected limb was higher after the typhoon (6% vs. 37%, odds ratio [OR]: 9.0, 95% Confidence interval [CI]: 4.7–17.2). We found cultures from the infected limbs with immersion had more polymicrobial (73% vs. 26%, OR: 7.8, 95% CI: 3.2–19.2) and Gram-negative bacilli infection (86% vs. 34%, OR: 11.8, 95% CI: 4.1–34.5). Flood arose from Typhoon Morakot caused increases in cellulitis patients, which lasted for 3 weeks. Antibiotic treatment that were effective to both Gram-positive cocci and Gram-negative bacilli are recommended for patients with limbs emerged in the water.