Parsa Mirhaji

Coordination and management of multicenter clinical studies in trauma: Experience from the PRospective Observational Multicenter Major Trauma Transfusion (PROMMTT) Study☆

AIM Early death due to hemorrhage is a major consequence of traumatic injury. Transfusion practices differ among hospitals and it is unknown which transfusion practices improve survival. This report describes the experience of the PRospective Observational Multicenter Major Trauma Transfusion (PROMMTT) Study Data Coordination Center in designing and coordinating a study to examine transfusion practices at ten Level 1 trauma centers in the US. METHODS PROMMTT was a multisite prospective observational study of severely injured transfused trauma patients. The clinical sites collected real-time information on the timing and amounts of blood product infusions as well as colloids and crystalloids, vital signs, initial diagnostic and clinical laboratory tests, life saving interventions and other clinical care data. RESULTS Between July 2009 and October 2010, PROMMTT screened 12,561 trauma admissions and enrolled 1245 patients who received one or more blood transfusions within 6h of Emergency Department (ED) admission. A total of 297 massive transfusions were observed over the course of the study at a combined rate of 5.0 massive transfusion patients/week. CONCLUSION PROMMTT is the first multisite study to collect real-time prospective data on trauma patients requiring transfusion. Support from the Department of Defense and collaborative expertise from the ten participating centers helped to demonstrate the feasibility of prospective trauma transfusion studies. The observational data collected from this study will be an invaluable resource for research in trauma surgery and it will guide the design and conduct of future randomized trials.

Ontology driven integration platform for clinical and translational research

Semantic Web technologies offer a promising framework for integration of disparate biomedical data. In this paper we present the semantic information integration platform under development at the Center for Clinical and Translational Sciences (CCTS) at the University of Texas Health Science Center at Houston (UTHSC-H) as part of our Clinical and Translational Science Award (CTSA) program. We utilize the Semantic Web technologies not only for integrating, repurposing and classification of multi-source clinical data, but also to construct a distributed environment for information sharing, and collaboration online. Service Oriented Architecture (SOA) is used to modularize and distribute reusable services in a dynamic and distributed environment. Components of the semantic solution and its overall architecture are described.

Public Health Surveillance Meets Translational Informatics: A Desiderata:

“Public health surveillance (PHS) is the ongoing and systematic collection, analysis, interpretation, and dissemination of data regarding a health-related event for use in public health action to reduce morbidity and mortality and to improve health.” As information technology gains acceptance as a core element of public health practice, many approaches to the design of PHS systems have been proposed, much has been spent implementing them, and expectations have been high. Unfortunately, the systems implemented so far have been criticized as having not met expectations, especially in the domain of early detection and bioterrorism readiness, or so-called syndromic surveillance (The term “syndromic surveillance” applies to monitoring health-related data that precede diagnosis to signal a sufficient probability of a case or an outbreak that warrants public health response.). There are no fully established frameworks to enable seamless interoperability, information sharing, and collaboration among PHS stakeholders and the technological and infrastructural requirements to fulfill the grand vision of initiatives such as the Public Health Information Network and National Health Information Network are poorly investigated and documented. In this article, we examine the current state of the conceptualization, design, analysis, and implementation of PHS systems from a translational informatics perspective. Although most examples in this article are informed by the needs of public health preparedness (syndromic and bioterrorism detection and response), we believe the framework we introduce is generalizable and applicable to the broader context of PHS systems. We also apply concepts from cognitive science and knowledge engineering to suggest directions for improvement and further research.

Informatics Critical to Public Health Surveillance

Public health surveillance is the ongoing, systematic collection, analysis, interpretation, and dissemination of data regarding a health-related event for use in public health action to reduce morbidity and mortality and to improve health by effective response management and coordination. As new pressures for early detection of disease outbreaks have arisen, particularly for outbreaks of possible bioterrorism (BT) origin, and as electronic health data have become increasingly available, so has the demand for public health situation awareness systems. Although these systems are valuable for early warning of public health emergencies, there remains the cost of developing and managing such large and complex systems and of investigating inevitable false alarms. Whether these systems are dependable and cost effective enough and can demonstrate a significant and indispensable role in detection or prevention of mass casualty events of BT origin remains to be proven. This article will focus on the complexities of design, analysis, implementation and evaluation of public health surveillance and situation awareness systems and, in some cases, will discuss the key technologies being studied in Center for Biosecurity Informatics Research at University of Texas, Health Science Center at Houston.

Public health situation awareness: toward a semantic approach

We propose a knowledge-based public health situation awareness system. The basis for this system is an explicit representation of public health situation awareness concepts and their interrelationships. This representation is based upon the users’ (public health decision makers) cognitive model of the world, and optimized towards the efficacy of performance and relevance to the public health situation awareness processes and tasks. In our approach, explicit domain knowledge is the foundation for interpretation of public health data, as apposed to conventional systems where the statistical methods are the essence of the processes. Objectives: To develop a prototype knowledge-based system for public health situation awareness and to demonstrate the utility of knowledge intensive approaches in integration of heterogeneous information, eliminating the effects of incomplete and poor quality surveillance data, uncertainty in syndrome and aberration detection and visualization of complex information structures in public health surveillance settings, particularly in the context of bioterrorism (BT) preparedness. The system employs the Resource Definition Framework (RDF) and additional layers of more expressive languages to explicate the knowledge of domain experts into machine interpretable and computable problem-solving modules that can then guide users and computer systems in sifting through the most “relevant” data for syndrome and outbreak detection and investigation of root cause of the event. The Center for Biosecurity and Public Health Informatics Research is developing a prototype knowledge-based system around influenza, which has complex natural disease patterns, many public health implications, and is a potential agent for bioterrorism. The preliminary data from this effort may demonstrate superior performance in information integration, syndrome and aberration detection, information access through information visualization, and cross-domain investigation of the root causes of public health events.