ObjectiveTo investigate the etiologic feature and prognosis of adult patients with severe community-acquired pneumonia (SCAP). MethodsAccording to the guideline on the diagnosis and treatment of community-acquired pneumonia in 2006, 105 patients with SCAP were included in the study. The proportion of pathogens (including multiple resistant bacteria) and mortality rate were recorded. Appropriate statistical methods were selected and all data were analyzed by using SPSS Version 18.0 computerized program. ResultsThe predominant pathogen with SCAP was Pseudomonas aeruginosa, followed by Klebsiella pneumoniae, Staphylococcus aureus, and Legionella pneumophila. In death cases, Klebsiella pneumoniae was the most common pathogen, followed by Staphylococcus aureus. It was showed in the drug sensitivity test that most pathogens were drug-sensitive strains. The patients with tumor had higher risks to get infected with Gram-negative bacillus. ConclusionsThe etiology of patients with SCAP in our emergency department is given priority to Gram-negative bacillus and sensitive strains, of which Pseudomonas aeruginosa and Klebsiella pneumoniae are predominant. As for the Gram-positive cocci, Staphylococcus aureus is the most common pathogen. Legionella pneumophila is the most common pathogen in atypical pathogens, which only account for a small proportion of the aetiology of SCAP. Patients with Klebsiella pneumoniae and Staphylococcus aureus infections are associated with poor prognosis.
Objective To investigate the core competencies of emergency specialized nurses and the influencing factors in Sichuan Province so as to provide a basis for improving the training systems. Methods The trainees who received specialized training in West China Hospital every March and September between 2012 and 2014 were investigated with questionnaire survey. Results A total of 270 questionnaires were given out, and 246 valid questionnaires were retrieved. The scores of emergency specialized nurses’ core competencies ranged from 165 to 258, with an average of 214.55±22.56. According to the scores, 4.88% of the emergency specialized nurses’ core competencies were at a low level, 67.07% were at a middle level and 28.5% were at a high level. The influencing factors of core competencies included education, professional title, position, level of hospitals and years of working experience in the emergency department. Conclusion Core competencies of emergency specialized nurses need to be further improved and the training systems need to be improved consistently.
Objective To analyze the characteristics of patients transferred by ambulances to emergency department before and after coronavirus disease 2019 epidemic, in order to improve the efficiency of emergency triage, optimize the utilization of emergency resources, and provide a reference for standardized tiered medical services in different situation. Methods The patients’ information collected through Wenjuanxing questionnaire was extracted, who were transferred by ambulances to the Emergency Department of West China Hospital of Sichuan University between December 27th, 2018 and April 28th, 2019 (before epidemic), or between December 27th, 2019 and April 28th, 2020 (during epidemic), or between December 27th, 2020 and April 28th, 2021 [in regular epidemic prevention and control period (REPCP)]. The general information, sources, reasons for referral, disease spectrum and triage levels of patients in the three periods were compared. Results There were 3993, 2252 and 1851 cases before epidemic, during epidemic, and in REPCP, respectively. The differences in gender and age among the three periods were not statistically significant (P>0.05). The percentage of referrals from tertiary hospitals in each period was 74.00%, 72.65%, and 76.12%, respectively, which was higher in REPCP than that during epidemic (P<0.05). The percentage of direct referrals from emergency department in each period was 41.00%, 42.14%, and 44.46%, respectively, which was higher in REPCP than that before epidemic (P<0.05). The percentage of two-way referrals in each period was 37.79%, 36.63%, and 34.36%, respectively, which was lower in REPCP than that before epidemic (P<0.05). During epidemic and in REPCP, the proportions of referrals due to “need for surgery” (24.72%, 27.84%, and 28.74%, respectively) and “request by family members” (49.64%, 53.33%, and 56.24%, respectively) increased compared with those before epidemic (P<0.05), while the proportion of referrals due to “critical illness” decreased compared with that before epidemic (40.20%, 35.21%, and 33.17%, respectively; P<0.05); the proportion of referrals due to “diagnosis unknown” decreased in REPCP compared with that before epidemic (15.50%, 13.90%, and 11.89%, respectively; P<0.05). The proportion of acute aortic syndromes in REPCP increased compared with that during epidemic (3.46%, 2.98%, and 4.65%, respectively; P<0.05), the proportion of trauma in REPCP increased compared with that before epidemic (13.72%, 15.76%, and 17.77%, respectively; P<0.05), and the proportion of pneumonia/acute exacerbation of chronic obstructive pulmonary disease during epidemic and in REPCP decreased compared with that before epidemic (8.44%, 3.73%, and 3.84%, respectively; P<0.05). The proportion of critically ill patients referred in each period was 72.88%, 75.58%, and 79.15%, respectively, which was the highest in REPCP (P<0.05). Conclusions The epidemic has a significant impact on emergency ambulance referrals, and emergency triage needs to be continuously optimised and improved in staff, facilities, processes and management. It is necessary to further improve the implementation of hierarchical diagnosis and treatment, strengthen information communication between referral and emergency departments of receiving hospitals, and improve referral efficiency.
Objective To establish and verify the early prediction model of critical illness patients with influenza. Methods Critical illness patients with influenza who diagnosed with influenza in the emergency departments from West China Hospital of Sichuan University, Shangjin Hospital of West China Hospital of Sichuan University, and Panzhihua Central Hospital between January 1, 2017 and June 30, 2020 were selected. According to K-fold cross validation method, 70% of patients were randomly assigned to the model group, and 30% of patients were assigned to the model verification group. The patients in the model group and the model verification group were divided into the critical illness group and the non-critical illness group, respectively. Based on the modified National Early Warning Score (MEWS) and the Simplified British Thoracic Society Score (confusion, uremia, respiratory, BP, age 65 years, CRB-65 score), a critical illness influenza early prediction model was constructed and its accuracy was evaluated. Results A total of 612 patients were included. Among them, there were 427 cases in the model group and 185 cases in the model verification group. In the model group, there were 304 cases of non-critical illness and 123 cases of critical illness. In the model verification group, there were 152 cases of non-critical illness and 33 cases of critical illness. The results of binary logistic regression analysis showed that age, hypertension, the number of days between the onset of symptoms and presentation at the emergency department, consciousness state, white blood cell count, and lymphocyte count, oxygen saturation of blood were the independent risk factors for critical illness influenza. Based on these 7 risk factors, an early prediction model for critical illness influenza was established. The correct percentages of the model for non-critical illness and critical illness patients were 95.4% and 77.2%, respectively, with an overall correct prediction percentage of 90.2%. The results of the receiver operator characteristic curve showed that the sensitivity and specificity of the early prediction model for critical illness influenza in predicting critical illness patients were 0.909, 0.921, and the area under the curve and its 95% confidence interval were 0.931 (0.860, 0.999). The sensitivity, specificity, and area under the curve (0.935, 0.865, 0.942) of the early prediction model for critical illness influenza were higher than those of MEWS (0.642, 0.595, 0.536) and CRB-65 (0.628, 0.862, 0.703). Conclusions The conclusion is that age, hypertension, the number of days between the onset of symptoms and presentation at the emergency department, consciousness, oxygen saturation, white blood cell count, and absolute lymphocyte count are independent risk factors for predicting severe influenza cases. The early prediction model for critical illness patients with influenza has high accuracy in predicting severe influenza cases, and its predictive value and accuracy are superior to those of the MEWS score and CRB-65 score.