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.
Interfacility transport of critically ill children is an important part of pre-hospital emergency care. The development of 5th generation mobile networks has brought revolutionary changes to emergency medicine, which can realize real-time sharing of information between hospitals and transfer ambulance units. In order to give full play to the advantages of superior medical institutions in diagnosis and treatment technology, equipment resources, and realize the safe and fast transfer of critically ill children, the technical specifications for the construction of interfacility transport of critically ill children’s ambulances with 5th generation mobile networks are specially formulated to standardize the team building, equipment and materials, transport process and quality control requirements for critically ill children’s ambulance transport, so as to reduce the fatality rate of critically ill children and improve the prognosis.
Objective To explore the clinical characteristics, in-hospital outcomes, and short-term survival of patients with acute kidney injury (AKI) in a large non-surgical cardiac intensive care unit (ICCU) in China. Methods Patients who had been admitted to the ICCU of the Department of Cardiology, West China Hospital of Sichuan University between June 2016 and May 2017 were retrospectively included. The diagnosis and staging of AKI were based on the Kidney Disease: Improving Global Outcomes criteria. The in-hospital outcomes were the composite of all-cause death or discharge against medical advice under extremely critical conditions. Patients without in-hospital composite outcomes were followed up to determine whether all-cause death occurred during the study period. The association of AKI with in-hospital composite outcomes or short-term survival was analyzed. Normally distributed quantitative data were expressed as mean±standard deviation, and non-normally distributed quantitative data were expressed as median (lower quartile, upper quartile). Results This study included 2083 patients, with an average age of (65.5±14.6) years old, and 681 (32.7%) were women. The prevalence rate of AKI was 15.0% (312/2083) (stage 1: 6.9%; stage 2: 4.9%; stage 3: 3.2%; respectively). Compared with patients without AKI, patients with AKI were older [(68.9±14.3) vs. (64.9±14.6) years old, P<0.001], had a higher Charles Comorbidity Index [4.0 (3.0, 6.0) vs. 2.0 (1.0, 3.0), P<0.001] and a greater Oxford Acute Illness Severity Score [32.0 (24.0, 41.2) vs. 21.0 (16.0, 26.0), P<0.001]. The incidence of in-hospital composite endpoint events was 8.4% (174/2083). Multiple logistic regression analysis showed that as the AKI stage increased, the risk of in-hospital composite endpoint events was higher [AKI stage 1 vs. no AKI: odds ratio (OR)=1.13, 95% confidence interval (CI) (0.57, 2.24); AKI stage 2 vs. no AKI: OR=2.21, 95%CI (1.08, 4.51); AKI stage 3 vs. no AKI: OR=10.88, 95%CI (4.50, 26.34); P for trend<0.001]. The patients without in-hospital composite endpoint events were followed up for a median time of 13.5 (10.7, 16.6) months, and the all-cause mortality rate was 5.5% (105/1909). Multiple Cox regression analysis showed that AKI was independently associated with all-cause death [hazard ratio=2.27, 95%CI (1.40, 3.69), P<0.001]. Conclusions AKI is common in the large ICCU in China and is more likely to occur in older patients who have more significant chronic illness complexity and acute illness severity. Moreover, AKI is independently associated with the in-hospital composite endpoint events and short-term survival.
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.