Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective To reveal the characteristics of Tibetan human body composition in Gannan.Methods The body composition of 410 Tibetan adults(221 males and 189 females)in Gannan was measured by bioelectrical impedance method.The data were processed by SPSS 20.0 statistical software.Results Related analysis results showed that the total body muscle mass,estimated fat level and trunk muscle mass of the male decreased with age,while the percent body water,visceral fat level and percent trunk fat increased with age.The stature and left lower limb muscle mass of the female decreased with age,while body weight,percent body fat,body mass index,visceral fat level,percent right upper limb fat,right upper limb muscle mass,percent left upper limb,left upper limb muscle mass,percent right lower limb fat,percent left lower limb fat,left lower limb muscle mass and percent trunk fat increased with age.Analysis of single factor variance showed that there were statistically significant differences in the four indexes of male percent body water,visceral fat level,percent trunk fat and trunk muscle mass,while there were no statistically significant differences in the other 14 indexes among age groups.Females weight,stature,percent body fat,total body muscle mass,estimated fat level,body mass index,visceral fat level,percent right upper limb fat,right upper limb muscle mass,percent left upper limb fat,left upper limb muscle mass,percent right lower limb fat,percent left lower limb fat,left lower limb muscle mass,percent trunk fat,trunk muscle mass 16 index values were statistically significant differences between age groups,the remaining two index values were not statistically significant differences between age groups.The results of u test showed that the body weight,total body muscle mass,estimated fat level,percent body water,limb muscle mass and trunk muscle mass of males were higher than those of females,and percent body fat,limb fat rate,visceral fat rate and trunk fat rate of females were higher than those of males.According to the body mass index,Gannan Tibetan male and female were overweight;according to the percent body fat,Gannan Tibetan males were non-obesity and females were obesity.Conclusion The body mass index and overall fat content of Tibetan males and females in Gannan are relatively high among the 13 ethnic groups.

Objective To explore the common features of Chinese ethnic groups.Methods Eight body indexes of 62 ethnic groups in China were analyzed.Results The cluster analysis showed that 52 males and 59 females ethnic groups were grouped into the mixed group dominated by the northern ethnic group and the mixed group dominated by the southern ethnic group.Eight Han ethnic groups were grouped into each group,but no Han group was aggregated.The result of body index classification showed that the main body types of Chinese male population were long trunk,middle chest,wide shoulder,wide pelvis and middle leg.Middle body,wide chest,wide shoulder,wide pelvis and middle leg were the main body types of Chinese female population.This showed that the characteristics of Chinese ethnic groups had obvious consistency.The consistency of Chinese group features was related to its close origin.It should be said that Han nationality played an important role in the process of communication and integration of various ethnic groups in China.In the history of the Han nationality,there had been many large-scale population migration.The southern movement of the northern ethnic minorities into the northern Han and the southward movement of the northern Han into the south promoted the formation of the Southern Han,which made the southern Han and the northern Han had similar body features,and also promoted the southern ethnic minorities into the southern Han.In addition,the Han nationality who moved into minority areas also gradually integrated into minority areas.Conclusion There are obvious commonalities in Chinese ethnic groups.

Objective: To develop and validate a deep learning model based on fundus photos for the identification of coronary heart disease (CHD) and associated risk factors. Methods: Subjects aged>18 years with complete clinical examination data from 149 hospitals and medical examination centers in China were included in this retrospective study. Two radiologists, who were not aware of the study design, independently evaluated the coronary angiography images of each subject to make CHD diagnosis. A deep learning model using convolutional neural networks (CNN) was used to label the fundus images according to the presence or absence of CHD, and the model was proportionally divided into training and test sets for model training. The prediction performance of the model was evaluated in the test set using monocular and binocular fundus images respectively. Prediction efficacy of the algorithm for cardiovascular risk factors (e.g., age, systolic blood pressure, gender) and coronary events were evaluated by regression analysis using the area under the receiver operating characteristic curve (AUC) and R² correlation coefficient. Results: The study retrospectively collected 51 765 fundus images from 25 222 subjects, including 10 255 patients with CHD, and there were 14 419 male subjects in this cohort. Of these, 46 603 fundus images from 22 701 subjects were included in the training set and 5 162 fundus images from 2 521 subjects were included in the test set. In the test set, the deep learning model could accurately predict patients' age with an R² value of 0.931 (95%CI 0.929-0.933) for monocular photos and 0.938 (95%CI 0.936-0.940) for binocular photos. The AUC values for sex identification from single eye and binocular retinal fundus images were 0.983 (95%CI 0.982-0.984) and 0.988 (95%CI 0.987-0.989), respectively. The AUC value of the model was 0.876 (95%CI 0.874-0.877) with either monocular fundus photographs and AUC value was 0.885 (95%CI 0.884-0.888) with binocular fundus photographs to predict CHD, the sensitivity of the model was 0.894 and specificity was 0.755 with accuracy of 0.714 using binocular fundus photographs for the prediction of CHD. Conclusion: The deep learning model based on fundus photographs performs well in identifying coronary heart disease and assessing related risk factors such as age and sex.
Humans ; Male ; Retrospective Studies ; Deep Learning ; Fundus Oculi ; ROC Curve ; Algorithms ; Risk Factors ; Coronary Disease/diagnostic imaging*

BACKGROUND: It has been a global trend that increasing complications related to pelvic floor surgeries have been reported over time. The current study aimed to outline the development of Chinese pelvic floor surgeries related to pelvic organ prolapse (POP) over the past 14 years and investigate the potential influence of enhanced monitoring conducted by the Chinese Association of Urogynecology since 2011. METHODS: A total of 44,594 women with POP who underwent pelvic floor surgeries between October 1, 2004 and September 30, 2018 were included from 22 tertiary academic medical centers. The data were reported voluntarily and obtained from a database. We compared the proportion of each procedure in the 7 years before and 7 years after September 30, 2011. The data were analyzed by performing Z test (one-sided). RESULTS: The number of different procedures during October 1, 2011-September 30, 2018 was more than twice that during October 1, 2004-September 30, 2011. Regarding pelvic floor surgeries related to POP, the rate of synthetic mesh procedures increased from 38.1% (5298/13,906) during October 1, 2004-September 30, 2011 to 46.0% (14,107/30,688) during October 1, 2011-September 30, 2018, whereas the rate of non-mesh procedures decreased from 61.9% (8608/13,906) to 54.0% (16,581/30,688) (Z = 15.53, P < 0.001). Regarding synthetic mesh surgeries related to POP, the rates of transvaginal placement of surgical mesh (TVM) procedures decreased from 94.1% (4983/5298) to 82.2% (11,603/14,107) (Z = 20.79, P < 0.001), but the rate of laparoscopic sacrocolpopexy (LSC) procedures increased from 5.9% (315/5298) to 17.8% (2504/14,107). CONCLUSIONS: The rate of synthetic mesh procedures increased while that of non-mesh procedures decreased significantly. The rate of TVM procedures decreased while the rate of LSC procedures increased significantly. TRIAL REGISTRATION NUMBER NCT03620565, https://register.clinicaltrials.gov.
China ; Female ; Gynecologic Surgical Procedures/adverse effects* ; Humans ; Pelvic Floor/surgery* ; Pelvic Organ Prolapse/surgery* ; Surgical Mesh/adverse effects* ; Treatment Outcome ; Vagina

PURPOSE: The increasing number of deaths due to road traffic accidents (RTAs) has attracted global attention. However, the influence of road types is rarely considered in the study of RTAs. This study evaluates the influence of different road types in RTAs in northern Guizhou to provide a basis for the formulation of evidence-based policies and measures. METHODS: We obtained the data from the Zunyi Traffic Management Data Platform for the years 2009-2018. The mortality rates of RTAs were calculated. Descriptive methods and Chi-square tests were used to analyze the characteristics of road traffic collisions on different road types. We also examined the associations between the mortality rate per 10,000 vehicles and the growth of per capital gross domestic product (GDP) with Spearman's rank correlation analysis. According to the passing volume and the infrastructure, we defined different types of roads, like administrative road, functional road, general urban road and urban expressway. RESULTS: In 2012, the traffic mortality rate of administrative roads was 8.9 per 100,000 people, and the mortality rate of functional roads was 7.4 per 100,000 people, which decreased in 2018 to 6.1 deaths per 100,000 people and 5.2 deaths per 100,000 people, respectively. The mortality rate per 10,000 vehicles reached the highest level in 2011 (28.8 per 10,000 vehicles and 22.5 per 10,000 vehicles on administrative and functional roads, respectively). The death rate of county roads was the highest among administrative roads (χ CONCLUSION Although our research shows that RTAs in northern Guizhou have steadily declined in recent years, the range of decline is relatively small. Many measures and sustainable efforts are needed to control road traffic death and accelerate the progress in road traffic safety in northern Guizhou.

OBJECTIVE: To develop machine learning models for predicting intensive care unit (ICU) readmission using ensemble learning algorithms. METHODS: A publicly accessible American ICU database, medical information mart for intensive care (MIMIC)-Ⅲ as the data source was used, and the patients were selected by the inclusion and exclusion criteria. A set of variables that had the predictive ability of outcome including demographics, vital signs, laboratory tests, and comorbidities of patients were extracted from the dataset. We built the ICU readmission prediction models based on ensemble learning methods including random forest, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT), and compared the prediction performance of the machine learning models with a conventional Logistic regression model. Five-fold cross validation was used to train and validate the prediction models. Average sensitivity, positive prediction value, negative prediction value, false positive rate, false negative rate, area under the receiver operating characteristic curve (AUROC) and Brier score were used as performance measures. After constructing the prediction models, top 10 predictive variables based on importance ranking were identified by the model with the best discrimination. RESULTS: Among these ICU readmission prediction models, GBDT (AUROC=0.858) had better performance than random forest (AUROC=0.827), and was slightly superior to AdaBoost (AUROC=0.851) in terms of AUROC. Compared with Logistic regression (AUROC=0.810), the discrimination of the three ensemble learning models was much better. The feature importance provided by GBDT showed that the top ranking variables included vital signs and laboratory tests. The patients with ICU readmission had higher mean arterial pressure, systolic blood pressure, diastolic blood pressure, and heart rate than the patients without ICU readmission. Meanwhile, the patients readmitted to ICU experienced lower urine output and higher serum creatinine. Overall, the patients having repeated admissions during their hospitalization showed worse heart function and renal function compared with the patients without ICU readmission. CONCLUSION The ensemble learning based ICU readmission prediction models had better performance than Logistic regression model. Such ensemble learning models have the potential to aid ICU physicians in identifying those patients with high risk of ICU readmission and thus help improve overall clinical outcomes.
Critical Illness ; Humans ; Intensive Care Units ; Machine Learning ; Patient Readmission ; ROC Curve

OBJECTIVE: To construct length of intensive care unit (ICU) stay (LOS-ICU) prediction models for ICU patients, based on three machine learning models support vector machine (SVM), classification and regression tree (CART), and random forest (RF), and to compare the prediction perfor-mance of the three machine learning models with the customized simplified acute physiology score Ⅱ(SAPS-Ⅱ) model. METHODS: We used medical information mart for intensive care (MIMIC)-Ⅲ database for model development and validation. The primary outcome was prolonged LOS-ICU(pLOS-ICU), defined as longer than the third quartile of patients' LOS-ICU in the studied dataset. The recursive feature elimination method was used to do feature selection for three machine learning models. We utilized 5-fold cross validation to evaluate model prediction performance. The Brier value, area under the receiver operation characteristic curve (AUROC), and estimated calibration index (ECI) were used as perfor-mance measures. Performances of the four models were compared, and performance differences between the models were assessed using two-sided t test. The model with the best prediction performance was employed to generate variable importance ranking, and the identified top five important predictors were pre-sented. RESULTS: The final cohort in our study consisted of 40 200 eligible ICU patients, of whom 23.7% were with pLOS-ICU. The proportion of the male patients was 57.6%, and the age of all the ICU patients was (61.9±16.5) years.Results showed that the three machine learning models outperformed the customized SAPS-Ⅱ model in terms of all the performance measures with statistical significance (P < 0.01). Among the three machine learning models, the RF model achieved the best overall performance (Brier value, 0.145), discrimination (AUROC, 0.770) and calibration (ECI, 7.259). The calibration curve showed that the RF model slightly overestimated the risk of pLOS-ICU in high-risk ICU patients, but underestimated the risk of pLOS-ICU in low-risk ICU patients. Top five important predictors for pLOS-ICU identified by the RF model included age, heart rate, systolic blood pressure, body tempe-rature, and ratio of arterial oxygen tension to the fraction of inspired oxygen(PaO₂/FiO₂). CONCLUSION The RF algorithm-based pLOS-ICU prediction model had a best prediction performance in this study. It lays a foundation for future application of the RF-based pLOS-ICU prediction model in ICU clinical practice.
Aged ; Humans ; Intensive Care Units ; Machine Learning ; Male ; Middle Aged ; Research Design