Chronic diseases spectrum and multimorbidity in elderly inpatients based on a 12-year epidemiological survey in China.

BACKGROUND: The number and proportion of the elderly population have been continuously increasing in China, leading to the elevated prevalence of chronic diseases and multimorbidity, which ultimately brings heavy burden to society and families. Meanwhile, the status of multimorbidity tends to be more complex in elderly inpatients than community population. In view of the above concerns, this study was designed to investigate the health status of elderly inpatients by analyzing clinical data in Chinese People's Liberation Army (PLA) General Hospital from 2008 to 2019, including the constitution of common diseases, comorbidities, the status of multimorbidity, in-hospital death and polypharmacy among elderly inpatients, so as to better understand the diseases spectrum and multimorbidity of elderly inpatients and also to provide supporting evidence for targeted management of chronic diseases in the elderly. METHODS: A clinical inpatients database was set up by collecting medical records of elderly inpatients from 2008 to 2019 in Chinese PLA General Hospital, focusing on diseases spectrum and characteristics of elderly inpatients. In this study, we collected data of inpatients aged ≥ 65 years old, and further analyzed the constitution of diseases, multimorbidity rates and mortality causes in the past decade. In addition, the prescriptions were also analyzed to investigate the status of polypharmacy in elderly inpatients. RESULTS: A total of 210,169 elderly patients were hospitalized from January 1st, 2008 to December 31st, 2019. The corresponding number of hospitalizations was 290,833. The average age of the study population was 72.67 years old. Of the total population, 73,493 elderly patients were re-admitted within one year, with the re-hospitalization rate of 25.27%. Malignant tumor, hypertension, ischemic heart disease, diabetes mellitus and cerebrovascular disease were the top 5 diseases. Among the study population, the number of patients with two or more long-term health conditions was 267,259, accounting for 91.89%, with an average of 4.68 diseases. In addition, the average number of medications taken by the study population was 5.4, among which, the proportion of patients taking more than 5 types of medications accounted for 55.42%. CONCLUSIONS: By analyzing the constitution of diseases and multimorbidity, we found that multimorbidity has turned out to be a prominent problem in elderly inpatients, greatly affecting the process of healthy aging and increasing the burden on families and society. Therefore, multidisciplinary treatment should be strengthened to make reasonable preventive and therapeutic strategies to improve the life quality of the elderly. Meanwhile, more attention should be paid to reasonable medications for elderly patients with multimorbidity to avoid preventable side effects caused by irrational medication therapy.

Matrix Metallopeptidase 14: A Candidate Prognostic Biomarker for Diffuse Large B-Cell Lymphoma.

BACKGROUND: Matrix metallopeptidase 14 (MMP14) is an important gene in the regulation of T-cell function. However, the correlation between MMP14 expression, prognosis, and immune cell infiltration in diffuse large B-cell lymphoma (DLBCL) remains unclear. METHODS: We investigated the influence of MMP14 on clinical prognosis using data obtained from three Gene Expression Omnibus (GEO) database sets (GSE98588, GSE10846, and GSE4475). The expression of MMP14 was analyzed using the Gene Expression Profiling Interactive Analysis (GEPIA). The correlation between MMP14 and immune cell infiltration was investigated using the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and Tumor Immune Estimation Resource (TIMER) tools. In addition, the correlation between MMP14 expression and immune gene markers was analyzed by TIMER and GEPIA. RESULTS: MMP14 expression positively correlated with favorable progression-free survival (PFS; GSE98588, P = 0.02) and overall survival (OS; GSE98588, P = 0.003; GSE10846, P = 5.517e-05; and GSE4475, P = 9.85e-04). Moreover, MMP14 expression was higher in DLBCL tumors than in normal tissues. Regarding clinical characteristics, high MMP14 expression was found to be correlated with race. MMP14 expression was also correlated with immune cell infiltration and had a remarkable correlation with various immune marker sets. It was found that M0 macrophages were the immune cells most related to survival, decreasing with the increase in Ann Arbor clinical stage. The results especially showed that MMP14 was a prognostic biomarker and related to the macrophages M0. CONCLUSION: The results suggest that MMP14 is a novel prognostic molecular marker for DLBCL and is related to the immune cell infiltration, especially related to the macrophages M0. Our study provides insights for understanding the potential roles of MMP14 in tumor immunology and its suitability as a prognosis biomarker in DLBCL.

Hypomethylation of 111 Probes Predicts Poor Prognosis for Glioblastoma.

Glioblastoma (GBM) is a complicated brain tumor with heterogeneous outcome. Identification of effective biomarkers is an urgent need for the treatment decision-making and precise evaluation of prognosis. Based on a relatively large dataset of genome-wide methylation (138 glioblastoma patients), a joint-score of 111 methyl-probes was found to be of statistical significance for prognostic evaluation. Low joint-score were significantly associated with adverse outcomes (OS: P < 0.001, PFS: P = 0.03). Multivariable analyses adjusted for known risk factors confirmed the low joint-score of 111 methyl-probes as a high risk factor. The prognostic value of the methylated joint-score was further validated in another dataset of glioblastoma patients (OS: P = 0.006). Additionally, variance analysis revealed that aberrant genetic and epigenetic alterations were significantly associated with the joint-score of those methyl-probes. In conclusion, our results supported the joint-score of 111 methyl-probes as a potential prognosticator for the precision treatment of glioblastoma.

[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.

[Big data in emergency medcine and Datathon event].

OBJECTIVE: A detailed, high-scale clinical data can be generated in the process of diagnosis and treatment of emergency critically ill patients. The integration and analysis and utilization of these data are of great value for improving the treatment level and efficiency and developing the data-driven clinical assistant decision support. China has large volume of health information resources, however, the construction of healthcare databases and subsequent secondary analysis has just started. With the effort of the Chinese PLA General Hospital in building an emergency database and promoting data sharing, the first emergency database was published in China and a health Datathon was organized utilizing this database, providing experience for clinical data integration, database construction, cross-disciplinary collaboration and data sharing. Referring to the development at home and abroad, this review discussed work in this area and further proposed establishing a big data cooperation for emergency medicine and building a learning healthcare system to integrate more clinical resources and form a closed loop of "clinical database construction-analysis-applications", and enhance the effectiveness of medical big data in reducing medical costs and improving healthcare delivery.

Promoting Secondary Analysis of Electronic Medical Records in China: Summary of the PLAGH-MIT Critical Data Conference and Health Datathon.

Electronic health records (EHRs) have been widely adopted among modern hospitals to collect and track clinical data. Secondary analysis of EHRs could complement the traditional randomized control trial (RCT) research model. However, most researchers in China lack either the technical expertise or the resources needed to utilize EHRs as a resource. In addition, a climate of cross-disciplinary collaboration to gain insights from EHRs, a crucial component of a learning healthcare system, is not prevalent. To address these issues, members from the Massachusetts Institute of Technology (MIT) and the People's Liberation Army General Hospital (PLAGH) organized the first clinical data conference and health datathon in China, which provided a platform for clinicians, statisticians, and data scientists to team up and address information gaps in the intensive care unit (ICU).

[Severity classification of chronic obstructive pulmonary disease based on deep learning].

In this paper, a deep learning method has been raised to build an automatic classification algorithm of severity of chronic obstructive pulmonary disease. Large sample clinical data as input feature were analyzed for their weights in classification. Through feature selection, model training, parameter optimization and model testing, a classification prediction model based on deep belief network was built to predict severity classification criteria raised by the Global Initiative for Chronic Obstructive Lung Disease (GOLD). We get accuracy over 90% in prediction for two different standardized versions of severity criteria raised in 2007 and 2011 respectively. Moreover, we also got the contribution ranking of different input features through analyzing the model coefficient matrix and confirmed that there was a certain degree of agreement between the more contributive input features and the clinical diagnostic knowledge. The validity of the deep belief network model was proved by this result. This study provides an effective solution for the application of deep learning method in automatic diagnostic decision making.

[Development of medical image database aiming at subject service of clinic research].

In order to optimize the data flow of subject datasets and to establish the service platform of medical image data, we developed a medical image database aiming at subject service of clinic research. Firstly, a novel integrated infrastructure was designed, which was based on the requirements of database system and the survey of data resource. Then, several standards and technologies had been used in the construction of this novel system, including "Subject dataset-Sample data-Image files" three-ties image information framework, DICOM-based data processing, Index & file hybrid structure of file management strategy, etc. The new system has been successfully deployed in our test-bed and has got satisfactory results.

E-health-oriented community health information system in china: our challenges, solution, and experience.

China has been implementing regional collaborative medical service (also known as e-health) for >5 years, but is still facing the challenges of bridging different community health information systems (CHISs). The fact that different communities have different systems makes it difficult to share information and data between different CHISs. To explore a solution for addressing this problem, we constructed a demonstration CHIS in Beijing's Dongcheng District. This system is based on the Software-as-a-Service model, in which a central data center is used to store users' health records and to provide different services. This system provides a comprehensive platform combining disease prevention, health protection, medical care, rehabilitation, health education, and family planning. In this article, we first show the challenge of implementing e-health-oriented CHIS in China, then we briefly introduce our solution, and finally we share our experience learned from the modern CHIS implementation practice.

[Research on information extraction of electronic medical records in Chinese].

This is a research to enhance the application of natural language understanding and ontology in the Chinese medical text semantic annotation and content analysis, and so to provide technology support for the computer-readable electronic medical records (EMR). The Chinese EMR information extraction and statistical analysis of related subjects in accordance to the user's demands were performed through building the named entity rules, the classified word list and field ontology by using GATE platform on the basis of EMR text set's construction and pre-processing. The automatic and artificial semantic annotation of EMR text set was implemented. The situation of drugs used in medicinal treatment and the distribution of patients' age and sex were obtained. The ontology-based semantic information extraction can improve the function of computer for text understanding, and the discovery of knowledge in EMR through field ontology is feasible.

A study on an information security system of a regional collaborative medical platform.

The objective of this study was to share the experience of building an information security system for a regional collaborative medical platform (RCMP) and discuss the lessons learned from practical projects. Safety measures are analyzed from the perspective of system engineering. We present the essential requirements, critical architectures, and policies for system security of regional collaborative medical platforms.

E-health in China: challenges, initial directions, and experience.

China started to pay more attention to the construction of a regional and national health information network after the outbreak of the severe acute respiratory syndrome (SARS) epidemic in 2003. The construction of a public health system is considered the most important part of national medical reform, with information and computer technology serving as the key to deploying regional collaborative medical service, also known as e-health. In this paper, we analyze the difficulties in carrying out e-health projects in China, a discussion of ongoing projects, and a case presentation representing current progress.

A comparison of two data storage strategies for implementing e-health in China.

Data sharing and information exchange among medical institutions is a requirement for convenient and effective data availability for both healthcare professionals and patients. In this paper, the characteristics of medical data are studied; two mainstream technologies of data storage for medical information are compared, and three strategies of medical documents storage are described with detailed advantages and disadvantages. Semi-structured storage technology is easier to deploy and much more promising to promote in a wider range than all-structured methods. The combination of central and distributed data storage is more practical for regional data sharing. This analysis suggests that semi-structural data storage technology and the combination of central and distributed data storage are efficient and fit well the current situation in China.