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Objective:To establish a blood consumption prediction model for emergency trauma patients based on machine learning algorithm, so as to guide blood collection and blood supply institutions to prepare for the early blood demand of mass casualties in public emergencies.Methods:A retrospective analysis was conducted on trauma patients in the emergency system database of 12 hospitals in Zhejiang Province from January 2018 to December 2020. Patients with chronic medical history such as hematological diseases and tumors, and transferred from other hospitals after external treatment were excluded. The patients were divided into the transfusion group and non-transfusion group according to whether they received blood transfusion. The differences in demographic and clinical characteristics between the two groups were compared, and the computer learning algorithm (XGBoost) was used to build the blood consumption prediction model and blood consumption volume prediction model of emergency trauma patients.Results:Totally 2025 patients were included in this study, including 1146 patients in the transfusion group and 879 patients in the non-transfusion group. The blood demand of emergency trauma patients mainly occurred within 3 days of admission (60%). The main variables affecting the blood consumption prediction model of emergency trauma patients were shock index, hematocrit, systolic blood pressure, abdominal injury, pelvic injury, ascites and hemoglobin. Compared with the traditional prediction model, XGBoost model had the highest hit rate of 59.0%. The accuracy of blood consumption prediction model was the highest when seven levels of blood volume were adopted, and the deviation fluctuated between [0~1] U. According to the prediction model, the blood consumption prediction formula was∑ nw× c. Conclusions:The preliminarily constructed prediction model of blood transfusion and blood consumption for emergency trauma patients has better performance than the traditional prediction model of blood transfusion, which provides reference for optimizing the decision-making ability of blood demand assessment of hospitals and blood supply institutions under public emergencies.
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Objective To explore the strategy of emergency medical rescue of the massive crowd in general hospital during spring festival with snow disaster. Method The clinical data of 20 966 emergency cases were analyzed retrospectively from 22 Jan,2008 to 6 Feb, 2008 with snow disaster, and concerned about the ratio of different diseases, the character of pre-hospital care and the contrast between emergency medical treatment and routine work. Results The accidence of respiratory disease ( 57.3 % ) was followed by gastrointestinal ( 25.5 % ) and trauma (6.2% )during the emergency medical treatment, and surgical trauma, syncope, coma and convulsion were the most common symptoms, also in some conditions, but empties returning was 30.3% . Similar to the above situation, the extremities (56%)and head injury (24%)were most commonly in the hospital emergency department. The incidence of falling accidents was high( 35.7 % ), and two of them were dead due to trauma on died of being trampled, and on the other was electrothermal burn and falling. Conclusions The general hospital is very important in emergency medical treatment, and it should be ready to tackle the emergency disaster, in order to reduce the loss to minimum.
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The general situation of sudden public events is introduced.Considering the characteristics and phases of massive earthquake disaster,the system of emergency medical supplies for sudden public events is set up in terms of storing and managing the emergency medical supplies by reasonable determining the amount of all kinds of emergency medical supplies such as goods reserve,market reserve,production & technology capacity reserve,etc.