Objectives To explore the characteristics of thrombosis in critically ill patients with Omicron infection and the therapeutic value of prophylactic low molecular weight heparin (LMWH) treatment. MethodsA single center, retrospective cohort study included critically ill adult patients with Omicron variant of SARS-CoV-2 admitted to Peking University Third Hospital from December 7, 2022, to February 8, 2023. The patients were categorized into two groups based prophylactic LMWH. Propensity score (PS) matching was used to match patients (1: 1 ratio) based on the predefined criteria. General clinical information and laboratory parameters were compared. This study was retrospectively registered at Chinese Clinical Trail Registry (ChiCTR2300067434). ResultsFour hundred and fifty-two patients and 360 patients were included before and after PS matching. There were no statistical differences in mortality, the incidence of pulmonary embolism, arterial thrombosis or bleeding between the anticoagulation group and non-coagulation group before and after PS matching. There were 91 thrombotic events in 82 patients (18.14%), of which 54 cases (59.34%) were lower limb intermuscular vein thrombosis, 3 cases (3.30%) were pulmonary embolism, 14 cases (15.38%) were acute myocardial infarction and 3 cases (3.30%) were acute cerebral infarction. The thrombotic event resulted in the death of 5 patients. D-dimer increased in 385 cases (85.56%). On the 1st, 3rd, 6th and 9th day, the concentration of D-dimer in the anticoagulant group was higher than that in the non-anticoagulant group (P=0.006, 0.001, 0.024 and 0.006, respectively). ConclusionsAlthough thrombosis and coagulation disorders are still common complications of COVID-19, it is not the direct cause of most death in COVID-19 patients caused by Omicron. The role of prophylactic anticoagulation treatment for Omicron-infected patients needs further study.
Objective To analyze the current situation and demand of emergency and critical care training for medical staff in plateau areas, and to provide a reference for further emergency and critical care training for medical staff in plateau areas. Methods From July 1, 2018 to July 30, 2020, medical staff (including physicians, nursing staff, and other medical staff) from hospitals in various regions of Tibet were surveyed anonymously, to investigate the content and demand of medical staff in plateau areas receiving emergency and critical care training. The content and demand of medical staff from different levels of hospitals receiving emergency and critical care training were further compared. Results A total of 45 questionnaires were distributed in this study, and a total of 43 valid questionnaires were collected, with an effective response rate of 95.6%. The average age of medical staff was (35.67±9.17) years old, with a male to female ratio of 1∶1.5. The proportion of tertiary, secondary, and lower level hospitals to which medical staff belong were 23.3%, 27.9%, and 48.8%, respectively. The number and proportion of medical staff receiving training on chest pain, heart failure, stroke, gastrointestinal bleeding, respiratory failure, metabolic crisis, and sepsis diseases were 25 (58.1%), 25 (58.1%), 24 (55.8%), 23 (53.5%), 20 (46.5%), 14 (32.6%), and 12 (27.9%), respectively. The number and proportion of medical staff who believed that training in the heart failure, respiratory failure, metabolic diseases, stroke, gastrointestinal bleeding, chest pain, and sepsis needed to be strengthened were 38 (88.4%), 36 (83.7%), 35 (81.4%), 34 (79.1%), 34 (79.1%), 33 (76.7%), and 29 (67.4%), respectively. Thirteen medical staff (30.2%) hoped to acquire knowledge and skills through teaching. There were no statistically significant differences in gender, age, job type, professional title, and department type among medical staff from tertiary, secondary, and lower level hospitals participating in the survey (P>0.05). The proportion of medical staff in hospitals below secondary receiving training on chest pain was lower than that in second level hospitals (38.1% vs. 91.7%). The proportion of medical staff in hospitals below secondary receiving training on heart failure was lower than that in secondary and tertiary hospitals (38.1% vs. 75.0% vs. 80.0%). The proportion of medical staff in hospitals below secondary receiving training on respiratory failure was lower than that in tertiary hospitals (28.6% vs. 80.0%). The demand for sepsis training among medical staff in hospitals below secondary was higher than that in tertiary hospitals (85.7% vs. 30.0%). There was no statistically significant difference in the other training contents and demands (P>0.05). Conclusion The content of critical care training for medical staff in plateau areas cannot meet their demands, especially for medical staff in hospitals below secondary. In the future, it is necessary to strengthen training support, allocate advantageous resources to different levels of hospitals, expand the scope of training coverage, and enrich training methods to better improve the ability of medical personnel in plateau areas to diagnose and treat related diseases.
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.
The analysis of big data in medical field cannot be isolated from the high quality clinical database, and the construction of first aid database in our country is still in the early stage of exploration. This paper introduces the idea and key technology of the construction of multi-parameter first aid database. By combining emergency business flow with information flow, an emergency data integration model was designed with reference to the architecture of the Medical Information Mart for Intensive Care III (MIMIC-III), created by Computational Physiology Laboratory of Massachusetts Institute of Technology (MIT), and a high-quality first-aid database was built. The database currently covers 22 941 medical records for 19 814 different patients from May 2015 to October 2017, including relatively complete information on physiology, biochemistry, treatment, examination, nursing, etc. And based on the database, the first First-Aid Big Data Datathon event, which 13 teams from all over the country participated in, was launched. The First-Aid database provides a reference for the construction and application of clinical database in China. And it could provide powerful data support for scientific research, clinical decision making and the improvement of medical quality, which will further promote secondary analysis of clinical data in our country.
ObjectiveTo compare the indirect calorimetry (IC) measured resting energy expenditure (MREE) with adjusted Harris-Benedict formula calculating resting energy expenditure (CREE) in the mechanically ventilated surgical critically ill patients and to evaluate the relationship between the resting energy expenditure (REE) with the severity of illness. MethodsTwenty-one patients undergonging mechanical ventilation for critical illness in the intensive care unit of general surgery between August 2008 and February 2010 were included in this study. Data during the study period of nutrition support were collected for computation of the severity of critical illness by acute physiology and chronic health evaluation Ⅱ scores (APACHE Ⅱ scores) and organ dysfunction scores (Marshall scores). MREE was measured by using IC of the MedGraphics CCM/D System within the first 7 d after nutrition therapy. CREE was calculated by using the HarrisBenedict formula adjusted with correction factors for illness at the same time. According to APACHE Ⅱ scores on admission, the enrolled patients were divided into two groups: APACHEⅡ score ≥20 scores group (n=8) and APACHE Ⅱ score lt;20 scores group (n=13), and the differences between MREE and CREE of patients in two groups were determined. ResultsThe reduction of variation tendency in CREE other than MREE in the enrolled patients within the first week of nutritional support was statistical significance (Plt;0.001). The CREE of patients 〔(1 984.49±461.83) kcal/d〕 was significantly higher than the MREE 〔(1 563.88±496.93) kcal/d〕 during the first week of nutritional support (Plt;0.001). The MREE on the 0, 1, 2, and 4 d after nutrition therapy were statistically significant lower than CREE at the same time interval in these patients (Plt;0.01), and the differences at the other time points were not significant (Pgt;0.05). There was a trend towards a reduction in APACHE Ⅱ and Marshall scores within the first week of nutrition therapy that reached statistical significance (Plt;0.001). During the first week of nutrition therapy, APACHEⅡ and Marshall scores of patients in ≥20 scores group were significantly higher than those in lt;20 scores group, respectively (Plt;0.05 or Plt;0.01), and the reductions of APACHE Ⅱ scores and Marshall scores were significant in patients of two groups (Plt;0.001). A significant positive correlation was found between CREE with APACHE Ⅱ scores (r=0.656, Plt;0.001) and Marshall scores (r=0.608，Plt;0.001) in patients within the first week after nutrition support. Although no statistically significant correlation was observed between MREE and APACHEⅡ scores (r=-0.045， P=0.563), a significant positive correlation was observed between MREE and Marshall scores (r=0.263, P=0.001) within the first week after nutrition therapy. There was no correlation between MREE and CREE (r=0.064, P=0.408) in patients at the same time interval. The reduction of MREE of patients in ≥20 scores group other than in lt;20 scores group was statistically significant within the first week after nutrition therapy (P=0.034). In addition, the MREE of patients in ≥20 scores group were not significantly different from those in lt;20 scores group (Pgt;0.05), and the mean CREE was not different in two groups patients within the first week of nutritional therapy 〔(1 999.55±372.73) kcal/d vs. (1 918.39±375.27) kcal/d, P=0.887〕. CREE was significantly higher than MREE of patients in ≥20 scores group within the first week except the 3 d and 5 d after nutrition therapy (Plt;0.05), while in lt;20 scores group CREE was significantly higher than MREE in patients only within the first 3 d after nutrition therapy (Plt;0.05 or Plt;0.01). MREE and CREE of patients in ≥20 scores group were not different from those in lt;20 scores group, respectively (Pgt;0.05).
【摘要】 目的 〖JP2〗研究质子泵抑制剂（PPI）是否为危重患者发生医院获得性肺炎的危险因素。 方法 收集2002年6月-2009年6月收治的198例重症患者资料，分为使用PPI组（96例）和未使用PPI组（102例）。采用logistic回归分析PPI使用情况和医院获得性肺炎的关系。 结果 使用PPI组肺炎的发生率较高（26.9%），尤其是PPI使用时间超过7 d者（37.5%）。在不同的多变量logistic回归模型中，分别用APACHE Ⅱ评分和入住重症监护室原因校正后，使用PPI以及使用天数均是医院获得性肺炎发生的危险因素（P=0.031，OR＝2.230，95%CI：1.957~2.947；P=0.002，OR＝1.824，95%CI：1.457~2.242）。 结论 长时间应用PPI可能是增加ICU患者发生医院获得性肺炎的一种风险因素。【Abstract】 Objective To identify whether proton pump inhibitors (PPI) is a risk factor of hospital-acquired pneumonia (HAP) in critical patients. Methods The clinical data of the critical patients admitted to ICU from June 2002 to June 2009 were retrospectively analyzed. A total of 198 patients were divided into two groups: 96 in PPI group and 102 in non-PPI group. The relationship between PPI and HAP was analyzed by logistic regression. Results The patients in PPI group had a higher risk of HAP (26.9%), especially who were treated with PPI more than 7 days (37.5%). Adjusted by APACHE Ⅱ score and reason for admission to ICU, PPI therapy and the using duration of PPI were both the risk factors of HAP in different multiple logistic models (P=0.031, OR=2.230, 95%CI: 1.957-2.947; P=0.002, OR=1.824, 95%CI: 1.457-2.242). Conclusion Long-term use of PPI is a risk factor of HAP.