[Construction of multi-parameter emergency database and preliminary application research].

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.

Ultrasound molecular imaging for early detection of acute renal ischemia-reperfusion injury.

Background: Microcirculatory perfusion disorder and inflammatory response are critical links in acute kidney injury (AKI). We aim to construct anti-vascular cell adhesion molecule-1(VCAM-1) targeted microbubbles (TM) to monitor renal microcirculatory perfusion and inflammatory response. Methods: TM carrying VCAM-1 polypeptide was constructed by biological coupling. The binding ability of TM to human umbilical vein endothelial cells (HUVECs) was detected. Bilateral renal ischemia-reperfusion injury (IRI) models of mice were established to evaluate microcirculatory perfusion and inflammatory response using TM. Thirty-six mice were randomly divided into six groups according to the different reperfusion time (0.5, 2, 6, 12, and 24 h) and sham-operated group (Sham group). The correlation of TM imaging with serum and histopathological biomarkers was investigated. Results: TM has advantages such as uniform distribution, regular shape, high stability, and good biosafety. TM could bind specifically to VCAM-1 molecule expressed by tumor necrosis factor-alpha (TNF-α)-treated HUVECs. In the renal IRI-AKI model, the area under the curve (AUC) of TM significantly decreased both in the renal cortical and medullary after 2 h of reperfusion compared with the Sham group (p < 0.05). Normalized intensity difference (NID) of TM at different reperfusion time was all higher than that of blank microbubbles (BM) and the Sham group (p < 0.05). Ultrasound molecular imaging of TM could detect AKI early before commonly used renal function markers, histopathological biomarkers, and BM imaging. AUC of TM was negatively correlated with serum creatinine (Scr), blood urea nitrogen (BUN), and Cystatin C (Cys-C) levels, and NID of TM was linearly correlated with VCAM-1, TNF-α, and interleukin-6 (IL-6) expression (p < 0.05). Conclusions: Ultrasound molecular imaging based on TM carrying VCAM-1 polypeptide can accurately evaluate the changes in renal microcirculatory perfusion and inflammatory response, which might be a promising modality for early diagnosis of AKI.

The therapeutic effect of Picroside II in renal ischemia-reperfusion induced acute kidney injury: An experimental study.

The microcirculation hemodynamics change and inflammatory response are the two main pathophysiological mechanisms of renal ischemia-reperfusion injury (IRI) induced acute kidney injury (AKI). The treatment of microcirculation hemodynamics and inflammatory response can effectively alleviate renal injury and correct renal function. Picroside II (P II) has a wide range of pharmacological effects. Still, there are few studies on protecting IRI-AKI, and whether P II can improve renal microcirculation perfusion is still being determined. This study aims to explore the protective effect of P II on IRI-AKI and evaluate its ability to enhance renal microcirculation perfusion. In this study, a bilateral renal IRI-AKI model in mice was established, and the changes in renal microcirculation and inflammatory response were quantitatively evaluated before and after P II intervention by contrast-enhanced ultrasound (CEUS). At the same time, serum and tissue markers were measured to assess the changes in renal function. The results showed that after P II intervention, the levels of serum creatinine (Scr), blood urea nitrogen (BUN), serum cystatin C (Cys-C), kidney injury molecule-1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL), tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), malondialdehyde (MDA), and superoxide dismutase (SOD), as well as the time-to-peak (TTP), peak intensity (PI) and area under the curve (AUC), and the normalized intensity difference (NID) were all alleviated. In conclusion, P II can improve renal microcirculation perfusion changes caused by IRI-AKI, reduce inflammatory reactions during AKI, and enhance renal antioxidant stress capacity. P II may be a new and promising drug for treating IRI-AKI.

Contrast-enhanced ultrasound in evaluating the severity of acute kidney injury: An animal experimental study.

PURPOSE: Early assessment of the severity of acute kidney injury (AKI) is critical to the prognosis of patients. Renal microcirculation hemodynamic changes and inflammatory response are the essential links of AKI induced by ischemia-reperfusion injury (IRI). This study aims to explore the value of contrast-enhanced ultrasound (CEUS) based on vascular cell adhesion molecule-1 (VCAM-1) targeted microbubbles (TM) in evaluating the renal microcirculation hemodynamics and inflammatory response of different severity of AKI. METHODS: Eighteen male C57BL/6J mice were randomly divided into three groups (n = 6): sham operation (sham) group, mild IRI-AKI (m-AKI) group, and severe IRI-AKI (s-AKI) group. CEUS based on VCAM-1 TM was used to evaluate renal microcirculation perfusion and inflammatory response. Pearson's correlation was used to analyze the correlation between ultrasonic variables and pro-inflammatory factors. RESULTS: Compared with the sham group, AUC in m-AKI and s-AKI groups was significantly decreased, and s-AKI group was lower than m-AKI group (P < 0.05). NID of m-AKI and s-AKI groups was significantly higher than that of the sham group, and s-AKI group was higher than that of m-AKI group (P < 0.05). There was a linear positive correlation between NID and VCAM-1 protein expression (r = 0.7384, P < 0.05). NID and AUC were correlated with TNF-α and IL-6 levels (P < 0.05). Compared with early AKI biomarkers, CEUS based on VCAM-1 TM has higher sensitivity in evaluating the severity of AKI. CONCLUSIONS: CEUS based on VCAM-1 TM can evaluate renal microcirculation perfusion and inflammatory response in mild and severe AKI, which may provide helpful information for assessing the severity of AKI.

Synthesis and Characterization of Cardanol-Based Non-Isocyanate Polyurethane.

This paper describes the synthesis of NIPU by using cardanol as starting material. A cardanol formaldehyde oligomer was first prepared through the reaction of cardanol and formaldehyde, catalyzed by citric acid. The resulting oligomer was then subjected to epoxidation with m-chloroperbenzoic acid to obtain an epoxide compound, which was subsequently used to fix carbon dioxide (CO2) and form a cyclic carbonate. Using this cyclic carbonate, along with an amine, cardanol-based isocyanate polyurethane (NIPU) was prepared. Different characterization methods, such as Fourier transform infrared spectroscopy (FTIR), proton nuclear magnetic resonance (NMR), gel permeation chromatography (GPC), and thermogravimetric analysis (TGA), were used to confirm the synthesis of the four intermediate products and NIPU in the reaction process. This study highlights the promise of bio-based NIPU as a sustainable alternative in a number of applications while offering insightful information on the synthesis and characterization of the material.

A New Time-Window Prediction Model For Traumatic Hemorrhagic Shock Based on Interpretable Machine Learning.

ABSTRACT: Early warning prediction of traumatic hemorrhagic shock (THS) can greatly reduce patient mortality and morbidity. We aimed to develop and validate models with different stepped feature sets to predict THS in advance. From the PLA General Hospital Emergency Rescue Database and Medical Information Mart for Intensive Care III, we identified 604 and 1,614 patients, respectively. Two popular machine learning algorithms (i.e., extreme gradient boosting [XGBoost] and logistic regression) were applied. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the performance of the models. By analyzing the feature importance based on XGBoost, we found that features in vital signs (VS), routine blood (RB), and blood gas analysis (BG) were the most relevant to THS (0.292, 0.249, and 0.225, respectively). Thus, the stepped relationships existing in them were revealed. Furthermore, the three stepped feature sets (i.e., VS, VS + RB, and VS + RB + sBG) were passed to the two machine learning algorithms to predict THS in the subsequent T hours (where T = 3, 2, 1, or 0.5), respectively. Results showed that the XGBoost model performance was significantly better than the logistic regression. The model using vital signs alone achieved good performance at the half-hour time window (AUROC = 0.935), and the performance was increased when laboratory results were added, especially when the time window was 1 h (AUROC = 0.950 and 0.968, respectively). These good-performing interpretable models demonstrated acceptable generalization ability in external validation, which could flexibly and rollingly predict THS T hours (where T = 0.5, 1) prior to clinical recognition. A prospective study is necessary to determine the clinical utility of the proposed THS prediction models.

Prediction of fatal adverse prognosis in patients with fever-related diseases based on machine learning: A retrospective study.

BACKGROUND: Fever is the most common chief complaint of emergency patients. Early identification of patients at an increasing risk of death may avert adverse outcomes. The aim of this study was to establish an early prediction model of fatal adverse prognosis of fever patients by extracting key indicators using big data technology. METHODS: A retrospective study of patients' data was conducted using the Emergency Rescue Database of Chinese People's Liberation Army General Hospital. Patients were divided into the fatal adverse prognosis group and the good prognosis group. The commonly used clinical indicators were compared. Recursive feature elimination (RFE) method was used to determine the optimal number of the included variables. In the training model, logistic regression, random forest, adaboost and bagging were selected. We also collected the emergency room data from December 2018 to December 2019 with the same inclusion and exclusion criterion. The performance of the model was evaluated by accuracy, F1-score, precision, sensitivity and the areas under receiver operator characteristic curves (ROC-AUC). RESULTS: The accuracy of logistic regression, decision tree, adaboost and bagging was 0.951, 0.928, 0.924, and 0.924, F1-scores were 0.938, 0.933, 0.930, and 0.930, the precision was 0.943, 0.938, 0.937, and 0.937, ROC-AUC were 0.808, 0.738, 0.736, and 0.885, respectively. ROC-AUC of ten-fold cross-validation in logistic and bagging models were 0.80 and 0.87, respectively. The top six coefficients and odds ratio (OR) values of the variables in the Logistic regression were cardiac troponin T (CTnT) (coefficient=0.346, OR = 1.413), temperature (T) (coefficient=0.235, OR = 1.265), respiratory rate (RR) (coefficient= -0.206,OR = 0.814), serum kalium (K) (coefficient=0.137, OR = 1.146), pulse oxygen saturation (SPO2) (coefficient= -0.101, OR = 0.904), and albumin (ALB) (coefficient= -0.043, OR = 0.958). The weights of the top six variables in the bagging model were: CTnT, RR, lactate dehydrogenase, serum amylase, heartrate, and systolic blood pressure. CONCLUSIONS: The main clinical indicators of concern included CTnT, RR, SPO2, T, ALB and K. The bagging model and logistic regression model had better diagnostic performance comprehesively. Those may be conducive to the early identification of critical patients with fever by physicians.

[Exploration of design and practice of disaster medical rescue monitoring system with privacy protection mechanism].

OBJECTIVE: On the premise of fully studying the disaster medical rescue monitoring mechanism in emergencies at home and abroad, the functional requirements of the domestic disaster medical rescue monitoring system was analyzed in this paper, the logical framework and data structure of disaster medical rescue monitoring system with privacy protection mechanism was designed by department of emergency in Chinese PLA General Hospital, department of information management in School of Economics and Management of Beijing Jiaotong University, the School of Information Management of Nanjing University. Three major functional modules were realized in the system: reporter information management, disaster medical rescue data upload, and disaster medical rescue data search. Android client and Web client were developed for easy access to the system. The system also had the function of privacy protection. Based on symmetric searchable encryption algorithm, the system realized the encryption storage of untrusted servers and ensured the security of medical and health data. It is beneficial for the further development and improvement of disaster medical rescue data collection in China.

[Prediction and feature selection for fatal gastrointestinal bleeding recurrence in hospital via machine learning].

OBJECTIVE: To propose a method of prediction for fatal gastrointestinal bleeding recurrence in hospital and a method of feature selection via machine learning models. METHODS: 728 digestive tract hemorrhage samples were extracted from the first aid database of PLA General Hospital, and 343 patients among them were diagnosed as fatal gastrointestinal bleeding recurrence in hospital. A total of 64 physiological or laboratory indicators were extracted and screened. Based on the ten-fold cross-validation, Logistic regression, AdaBoost and XGBoost were used for classification prediction and comparison. XGBoost was used to search sequence features, and the key indicators for predicting fatal gastrointestinal bleeding recurrence in hospital were screened out according to the importance of the indicators during training. RESULTS: Logistic regression, AdaBoost and XGBoost all get better F1.5 score under each feature input dimension, among which XGBoost had the best effect and the highest score, which was able to identify as many patients as possible who might have fatal gastrointestinal bleeding recurrence in hospital. Through XGBoost iteration results, the Top 30 indicators with high importance for predicting fatal gastrointestinal bleeding recurrence in hospital were ranked. The F1.5 scores of the first 12 key indicators peaked at iteration (0.893), including hemoglobin (Hb), calcium (CA), red blood cell count (RBC), mean platelet volume (MPV), mean erythrocyte hemoglobin concentration (MCH), systolic blood pressure (SBP), platelet count (PLT), magnesium (MG), lymphocyte (LYM), glucose (GLU, blood gas analysis), glucose (GLU, blood biochemistry) and diastolic blood pressure (DBP). CONCLUSIONS: Logistic regression, AdaBoost and XGBoost could achieve the purpose of early warning for predicting fatal gastrointestinal bleeding recurrence in hospital, and XGBoost is the most suitable. The 12 most important indicators were screened out by sequential forward selection.

[Big data in emergency and clinical decision support system].

OBJECTIVE: Medical big data is a hot research topic in China, and it is also the main research direction in the field of emergency medicine. The current situation of the construction of the first-aid big data platform and the construction of the first-aid clinical decision support system were analyzed, the problems existing in the development of the first-aid big data research field were enumerated, to explore the theoretical methods for promoting the development of domestic first-aid big data, so as to provide references for the research in related fields.

[Enlightenment from the surveillance in post extreme emergencies and disasters].

OBJECTIVE: The detailed analysis of the surveillance in post extreme emergencies and disasters (SPEED) provides practical reference for China to establish a disaster medical rescue information monitoring system with Chinese characteristics. METHODS: The SPEED system under the scene of disaster medical rescue information monitoring is analyzed in detail. The SPEED system design, work flows, system implementation and other aspects are analyzed and summarized in this paper, and suggests the enlightenment of SPEED system for Chinese disaster medical rescue information monitoring work. RESULTS: The SPEED system is an information monitoring system for the early stages of disasters. It provides monitoring for diseases caused by disasters, and life and health trends. It has a complete data collection mechanism, a comprehensive personnel training system, a complete system function, and an implementation strategy involving multi-layer, multi-region, and multi -sector. It is a powerful tool for disaster medical rescue and management personnel to obtain information in time. In the field of disaster medical rescue, a similar public-facing information monitoring system in China is still not perfect. CONCLUSIONS: Learning the design flows and establishment mode of the SPEED system can provide reference for China to establish a disaster medical rescue information monitoring system with Chinese characteristics.

[Analysis of core indicators of fatal gastrointestinal rebleeding in emergency from the perspective of big data].

OBJECTIVE: To explore a method of screening the core indicators in the emergency database that can be used to evaluate the in-hospital fatal gastrointestinal rebleeding by using the big data algorithm. METHODS: Based on the emergency database of the Chinese PLA General Hospital, through the big data retrieval technology, all the 647 patients diagnosed as gastrointestinal bleeding in the emergency database were enrolled, except those who were admitted to the hospital for the first time and whose hemoglobin (Hb) was less than 90 g/L or did not undergo Hb test. Among them, there were 313 in the rebleeding group (fatal rebleeding in the hospital) and 334 in the non-rebleeding group (no fatal rebleeding in the hospital). General data of patients were collected, including gender, age, physical signs, blood gas, test index collection data, and the identification of gastrointestinal rebleeding. The fusion algorithm of rough set algorithm, genetic algorithm, and cellular automaton algorithm were used to calculate the key indicators that affect gastrointestinal rebleeding. RESULTS: A total of 499 indicators were calculated by machine fusion algorithm, after screening 5 times repeatedly, 24 key indicators were screened out, 3 of which were vital signs, including systolic blood pressure (SBP), diastolic blood pressure (DBP), temperature (T); 7 key indicators of blood routine, including white blood cell count (WBC), eosinophil (EOS), monocyte (MONO), Hb, hematocrit (HCT), red cell distribution width (RDW), mean corpuscular hemoglobin (MCH); 3 key indicators of coagulation, including prothrombin time (PT), plasma fibrinogen (FIB), activated partial thromboplastin time (APTT); 5 key indicators of biochemical, including myoglobin (MYO), chloride, glucose (GLU), serum albumin (ALB), total bilirubin (TBil); and 6 key indicators of blood gas, including pH, lactate (Lac), oxygen saturation (SO2), base excess (BE), bicarbonate (HCO3-), partial pressure of carbon dioxide (PaCO2). CONCLUSIONS: Using big data technology, 24 core indicators for evaluating the fatal gastrointestinal rebleeding in hospitals can be screened out from the emergency database, providing new ideas and methods for clinical diagnosis of the disease.

[A cross-disciplinary collaborative "Datathon" model to promote the application of medical big data].

OBJECTIVE: Medical practice generates and stores immense amounts of clinical process data, while integrating and utilization of these data requires interdisciplinary cooperation together with novel models and methods to further promote applications of medical big data and research of artificial intelligence. A "Datathon" model is a novel event of data analysis and is typically organized as intense, short-duration, competitions in which participants with various knowledge and skills cooperate to address clinical questions based on "real world" data. This article introduces the origin of Datathon, organization of the events and relevant practice. The Datathon approach provides innovative solutions to promote cross-disciplinary collaboration and new methods for conducting research of big data in healthcare. It also offers insight into teaming up multi-expertise experts to investigate relevant clinical questions and further accelerate the application of medical big data.

[Analysis of diseases distribution in Medical Information Mart for Intensive Care III database].

OBJECTIVE: To study the distribution of diseases in Medical Information Mart for Intensive Care (MIMIC-III) database in order to provide reference for clinicians and engineers who use MIMIC-III database to solve clinical research problems. METHODS: The exploratory data analysis technologies were used to explore the distribution characteristics of diseases and emergencies of patients (excluding newborns) in MIMIC-III database were explored; then, neonatal gestational age, weight, length of hospital stay in intensive care unit (ICU) were analyzed with the same method. RESULTS: In the MIMIC-III database, 46 428 patients were admitted for the first time, and 49 214 ICU records were recorded. There were 26 076 males and 20 352 females; the median age was 60.5 (38.6, 75.6) years, and most patients were between 60 and 80 years old. The first diagnosis in the disease spectrum analysis was firstly ranked by circulatory diseases (32%), followed by injury and poisoning (14%), digestive system disease (8%), tumor (7%), respiratory disease (6%) and so on. Patients with ischemic heart disease accounted for the largest proportion of circulatory disease (42%), the proportion of these patients gradually increased with age of 60-70 years old, then decreased. However, the proportion of patients with cerebrovascular disease declined first and then increased with age, which was the main cause of death of circulatory system disease (ICU mortality was 22.5%). Injury and poisoning patients showed a significant decrease with age. Digestive system diseases were younger than the general population (most people aged between 50 to 60 years), and non-infectious enteritis and colitis were the main causes of death (ICU mortality was 18.3%). Respiratory infections were predominant in infected patients (34%), but circulatory system infections were the main cause of death (ICU mortality was 25.6%). Secondly, in the neonatal care unit, premature infants accounted for the vast majority (82%). As the gestational age increased, the duration of ICU was decreased, and the mortality was decreased. CONCLUSIONS: The diseases distribution of patients can be provided by MIMIC-III database, which helps to grasp the overview of the volume and age distribution of the target patients in advance, and carry out the next step of research. Meanwhile, it points out the important role of exploratory data analysis in electronic health records analysis.

[Pilot research: construction of emergency rescue database].

OBJECTIVE: To construct a database containing multiple kinds of diseases that can provide "real world" data for first-aid clinical research. METHODS: Structured or non-structured information from hospital information system, laboratory information system, emergency medical system, emergency nursing system and bedside monitoring instruments of patients who visited department of emergency in PLA General Hospital from January 2014 to January 2018 were extracted. Database was created by forms, code writing, and data process. RESULTS: Emergency Rescue Database is a single center database established by PLA General Hospital. The information was collected from the patients who had visited the emergency department in PLA General Hospital since January 2014 to January 2018. The database included 530 585 patients' information of triage and 22 941 patients' information of treatment in critical rescue room, including information related to human demography, triage, medical records, vital signs, lab tests, image and biological examinations and so on. There were 12 tables (PATIENTS, TRIAGE_PATIENTS, EMG_PATIENTS_VISIT, VITAL_SIGNS, CHARTEVENTS, MEDICAL_ORDER, MEDICAL_RECORD, NURSING_RECORD, LAB_TEST_MASTER, LAB_RESULT, MEDICAL_EXAMINATION, EMG_INOUT_RECORD) that containing different kinds of patients' information. CONCLUSIONS: The setup of high quality emergency databases lay solid ground for scientific researches based on data. The model of constructing Emergency Rescue Database could be the reference for other medical institutions to build multiple-diseases databases.