Impact of integrative care on cardiovascular disease risk in newly diagnosed type 2 diabetes mellitus patients: A BI-VitalLife Cohort study.

INTRODUCTION: Type 2 diabetes mellitus (T2DM), a chronic metabolic disorder, significantly increases cardiovascular disease (CVD) risk. Integrative care (IC) offers a personalized health management approach, utilizing various interventions to mitigate this risk. However, the impact of IC on CVD risk in newly diagnosed T2Dm patients remains unclear. This study aims to assess the differences in CVD risk development within 120 months following a new diagnosis of T2DM, using real-world data from Bumrungrad International Hospital and Vitallife Scientific Wellness Center. METHODS: This study utilized the BI-VitalLife Cohort dataset that contains de-identified demographics, vitals, diagnoses and clinical information, laboratory and radiological data, medications, and treatments of more than 2.8 million patients who visited Bumrungrad International Hospital and/or VitalLife Scientific Wellness Center from June 1, 1999, to May 31, 2022. This study focused on newly diagnosed T2DM patients, defined according to American Diabetes Association criteria. We compared CVD risk between the IC and conventional care (CC) groups using the Kaplan-Meier curve and Cox proportional hazard model, adjusted for age, sex, and laboratory values. Propensity score matching was employed to enhance comparability. RESULTS: Of the 5,687 patients included, 236 were in the IC group and 5,451 in the CC group. The IC group, characterized by a lower age at T2DM diagnosis, showed favorable hematological and metabolic profiles. The Cox proportional hazard ratios revealed a significantly lower CVD risk in the IC group within 120 months post-T2DM diagnosis compared to the CC group, consistent even after adjusting for confounding factors. Propensity score-matched analysis supported these findings. CONCLUSION: Personalized integrative care may offer a significant advantage in reducing CVD risk among newly diagnosed T2DM patients compared to conventional care, even when considering various confounding factors. This study sheds light on the potential of integrative care in informing treatment strategies for T2DM patients at risk of developing CVD.

Improved detection of congestive heart failure via probabilistic symbolic pattern recognition and heart rate variability metrics.

OBJECTIVE: A timely diagnosis of congestive heart failure (CHF) is crucial to evade a life-threatening event. This paper presents a novel probabilistic symbol pattern recognition (PSPR) approach to detect CHF in subjects from their cardiac interbeat (R-R) intervals. METHOD: PSPR discretizes each continuous R-R interval time series by mapping them onto an eight-symbol alphabet and then models the pattern transition behavior in the symbolic representation of the series. The PSPR-based analysis of the discretized series from 107 subjects (69 normal and 38 CHF subjects) yielded discernible features to distinguish normal subjects and subjects with CHF. In addition to PSPR features, we also extracted features using the time-domain heart rate variability measures such as average and standard deviation of R-R intervals. RESULTS: An ensemble of bagged decision trees was used to classify two groups resulting in a five-fold cross-validation accuracy, specificity, and sensitivity of 98.1%, 100%, and 94.7%, respectively. However, a 20% holdout validation yielded an accuracy, specificity, and sensitivity of 99.5%, 100%, and 98.57%, respectively. Results from this study suggest that features obtained with the combination of PSPR and long-term heart rate variability measures can be used in developing automated CHF diagnosis tools.

Multisite Semiautomated Clinical Data Repository for Duplication 15q Syndrome: Study Protocol and Early Uses.

BACKGROUND: Chromosome 15q11.2-q13.1 duplication syndrome (Dup15q syndrome) is a rare disorder caused by duplications of chromosome 15q11.2-q13.1, resulting in a wide range of developmental disabilities in affected individuals. The Dup15q Alliance is an organization that provides family support and promotes research to improve the quality of life of patients living with Dup15q syndrome. Because of the low prevalence of this condition, the establishment of a single research repository would have been difficult and more time consuming without collaboration across multiple institutions. OBJECTIVE: The goal of this project is to establish a national deidentified database with clinical and survey information on individuals diagnosed with Dup15q syndrome. METHODS: The development of a multiclinic site repository for clinical and survey data on individuals with Dup15q syndrome was initiated and supported by the Dup15q Alliance. Using collaborative workflows, communication protocols, and stakeholder engagement tools, a comprehensive database of patient-centered information was built. RESULTS: We successfully established a self-report populating, centralized repository for Dup15q syndrome research. This repository also resulted in the development of standardized instruments that can be used for other studies relating to developmental disorders. By standardizing the data collection instruments, it allows us integrate our data with other national databases, such as the National Database for Autism Research. A substantial portion of the data collected from the questionnaires was facilitated through direct engagement of participants and their families. This allowed for a more complete set of information to be collected with a minimal turnaround time. CONCLUSIONS: We developed a repository that can efficiently be mined for shared clinical phenotypes observed at multiple clinic sites and used as a springboard for future clinical and basic research studies.

Analyzing Electronic Medical Records to Predict Risk of DIT (Death, Intubation, or Transfer to ICU) in Pediatric Respiratory Failure or Related Conditions.

Large volumes of data are generated in hospital settings, including clinical and physiological data generated during the course of patient care. Our goal, as proof of concept, was to identify early clinical factors or traits useful for predicting the outcome, of death, intubation, or transfer to ICU, for children with pediatric respiratory failure. We implemented both supervised and unsupervised methods to extend our understanding on statistical relationships in clinical and physiological data. As a supervised learning method, we use binary logistic regression to predict the risk of developing DIT outcome. Next, we implemented unsupervised k-means algorithm on principal components of clinical and physiological data to further explore the contribution of clinical and physiological data on developing DIT outcome. Our results show that early signals of DIT can be detected in physiological data, and two risk factors, blood pressure and oxygen level, are the most important determinant of developing DIT.

Giving raw data a chance to talk: a demonstration of exploratory visual analytics with a pediatric research database using Microsoft Live Labs Pivot to promote cohort discovery, research, and quality assessment.

Secondary use of large and open data sets provides researchers with an opportunity to address high-impact questions that would otherwise be prohibitively expensive and time consuming to study. Despite the availability of data, generating hypotheses from huge data sets is often challenging, and the lack of complex analysis of data might lead to weak hypotheses. To overcome these issues and to assist researchers in building hypotheses from raw data, we are working on a visual and analytical platform called PRD Pivot. PRD Pivot is a de-identified pediatric research database designed to make secondary use of rich data sources, such as the electronic health record (EHR). The development of visual analytics using Microsoft Live Labs Pivot makes the process of data elaboration, information gathering, knowledge generation, and complex information exploration transparent to tool users and provides researchers with the ability to sort and filter by various criteria, which can lead to strong, novel hypotheses.

Potential identification of pediatric asthma patients within pediatric research database using low rank matrix decomposition.

Asthma is a prevalent disease in pediatric patients and most of the cases begin at very early years of life in children. Early identification of patients at high risk of developing the disease can alert us to provide them the best treatment to manage asthma symptoms. Often evaluating patients with high risk of developing asthma from huge data sets (e.g., electronic medical record) is challenging and very time consuming, and lack of complex analysis of data or proper clinical logic determination might produce invalid results and irrelevant treatments. In this article, we used data from the Pediatric Research Database (PRD) to develop an asthma prediction model from past All Patient Refined Diagnosis Related Groupings (APR-DRGs) coding assignments. The knowledge gleamed in this asthma prediction model, from both routinely use by physicians and experimental findings, will become fused into a knowledge-based database for dissemination to those involved with asthma patients. Success with this model may lead to expansion with other diseases.

Clinical data integration of distributed data sources using Health Level Seven (HL7) v3-RIM mapping.

BACKGROUND: Health information exchange and health information integration has become one of the top priorities for healthcare systems across institutions and hospitals. Most organizations and establishments implement health information exchange and integration in order to support meaningful information retrieval among their disparate healthcare systems. The challenges that prevent efficient health information integration for heterogeneous data sources are the lack of a common standard to support mapping across distributed data sources and the numerous and diverse healthcare domains. Health Level Seven (HL7) is a standards development organization which creates standards, but is itself not the standard. They create the Reference Information Model. RIM is developed by HL7's technical committees. It is a standardized abstract representation of HL7 data across all the domains of health care. In this article, we aim to present a design and a prototype implementation of HL7 v3-RIM mapping for information integration of distributed clinical data sources. The implementation enables the user to retrieve and search information that has been integrated using HL7 v3-RIM technology from disparate health care systems. METHOD AND RESULTS: We designed and developed a prototype implementation of HL7 v3-RIM mapping function to integrate distributed clinical data sources using R-MIM classes from HL7 v3-RIM as a global view along with a collaborative centralized web-based mapping tool to tackle the evolution of both global and local schemas. Our prototype was implemented and integrated with a Clinical Database management Systems CDMS as a plug-in module. We tested the prototype system with some use case scenarios for distributed clinical data sources across several legacy CDMS. The results have been effective in improving information delivery, completing tasks that would have been otherwise difficult to accomplish, and reducing the time required to finish tasks which are used in collaborative information retrieval and sharing with other systems. CONCLUSIONS: We created a prototype implementation of HL7 v3-RIM mapping for information integration between distributed clinical data sources to promote collaborative healthcare and translational research. The prototype has effectively and efficiently ensured the accuracy of the information and knowledge extractions for systems that have been integrated.

Automated generation of massive image knowledge collections using Microsoft Live Labs Pivot to promote neuroimaging and translational research.

BACKGROUND: Massive datasets comprising high-resolution images, generated in neuro-imaging studies and in clinical imaging research, are increasingly challenging our ability to analyze, share, and filter such images in clinical and basic translational research. Pivot collection exploratory analysis provides each user the ability to fully interact with the massive amounts of visual data to fully facilitate sufficient sorting, flexibility and speed to fluidly access, explore or analyze the massive image data sets of high-resolution images and their associated meta information, such as neuro-imaging databases from the Allen Brain Atlas. It is used in clustering, filtering, data sharing and classifying of the visual data into various deep zoom levels and meta information categories to detect the underlying hidden pattern within the data set that has been used. METHOD: We deployed prototype Pivot collections using the Linux CentOS running on the Apache web server. We also tested the prototype Pivot collections on other operating systems like Windows (the most common variants) and UNIX, etc. It is demonstrated that the approach yields very good results when compared with other approaches used by some researchers for generation, creation, and clustering of massive image collections such as the coronal and horizontal sections of the mouse brain from the Allen Brain Atlas. RESULTS: Pivot visual analytics was used to analyze a prototype of dataset Dab2 co-expressed genes from the Allen Brain Atlas. The metadata along with high-resolution images were automatically extracted using the Allen Brain Atlas API. It is then used to identify the hidden information based on the various categories and conditions applied by using options generated from automated collection. A metadata category like chromosome, as well as data for individual cases like sex, age, and plan attributes of a particular gene, is used to filter, sort and to determine if there exist other genes with a similar characteristics to Dab2. And online access to the mouse brain pivot collection can be viewed using the link http://edtech-dev.uthsc.edu/CTSI/teeDev1/unittest/PaPa/collection.html (user name: tviangte and password: demome) CONCLUSIONS: Our proposed algorithm has automated the creation of large image Pivot collections; this will enable investigators of clinical research projects to easily and quickly analyse the image collections through a perspective that is useful for making critical decisions about the image patterns discovered.

Web GIS in practice IX: a demonstration of geospatial visual analytics using Microsoft Live Labs Pivot technology and WHO mortality data.

The goal of visual analytics is to facilitate the discourse between the user and the data by providing dynamic displays and versatile visual interaction opportunities with the data that can support analytical reasoning and the exploration of data from multiple user-customisable aspects. This paper introduces geospatial visual analytics, a specialised subtype of visual analytics, and provides pointers to a number of learning resources about the subject, as well as some examples of human health, surveillance, emergency management and epidemiology-related geospatial visual analytics applications and examples of free software tools that readers can experiment with, such as Google Public Data Explorer. The authors also present a practical demonstration of geospatial visual analytics using partial data for 35 countries from a publicly available World Health Organization (WHO) mortality dataset and Microsoft Live Labs Pivot technology, a free, general purpose visual analytics tool that offers a fresh way to visually browse and arrange massive amounts of data and images online and also supports geographic and temporal classifications of datasets featuring geospatial and temporal components. Interested readers can download a Zip archive (included with the manuscript as an additional file) containing all files, modules and library functions used to deploy the WHO mortality data Pivot collection described in this paper.

Slim-prim: a biomedical informatics database to promote translational research.

With the current national emphasis on translational research, data-exchange systems that can bridge the basic and clinical sciences are vital. To meet this challenge, we have developed Slim-Prim, an integrated data system (IDS) for collecting, processing, archiving, and distributing basic and clinical research data. Slim-Prim is accessed via user-friendly Web-based applications, thus increasing data accessibility and eliminating the security risks inherent with office or laboratory servers. Slim-Prim serves as a laboratory management interface and archival data repository for institutional projects. Importantly, multiple levels of controlled access allow HIPAA-compliant sharing of de-identified information to facilitate data sharing and analysis across research domains; thus Slim-Prim encourages collaboration between researchers and clinicians, an essential factor in the development of translational research. Slim-Prim is an example of utilizing an IDS to improve organizational efficiency and to bridge the gap between laboratory discovery and practice.

Biomedical Informatics Unit (BMIU): Slim-prim system bridges the gap between laboratory discovery and practice.

With current national emphasis on translational research, data exchange systems are needed that bridge basic science and clinical research. To meet this challenge, an electronic system was developed by the Biomedical Informatics Unit (BMIU) of the University of Tennessee Clinical Translation Science Institute (UT CTSI). This integrated data system collects, processes, archives, and distributes basic, clinical, and translational research data. The system provides information via web-based applications in a secure and Health Insurance Portability and Accountability Act (HIPAA)-compliant manner to facilitate data sharing and analysis across domains. The system is currently in use by a number of studies and has proven to be an effective tool for data collection and processing in clinical studies.