Daniele Segagni

An ICT infrastructure to integrate clinical and molecular data in oncology research

BackgroundThe ONCO-i2b2 platform is a bioinformatics tool designed to integrate clinical and research data and support translational research in oncology. It is implemented by the University of Pavia and the IRCCS Fondazione Maugeri hospital (FSM), and grounded on the software developed by the Informatics for Integrating Biology and the Bedside (i2b2) research center. I2b2 has delivered an open source suite based on a data warehouse, which is efficiently interrogated to find sets of interesting patients through a query tool interface.MethodsOnco-i2b2 integrates data coming from multiple sources and allows the users to jointly query them. I2b2 data are then stored in a data warehouse, where facts are hierarchically structured as ontologies. Onco-i2b2 gathers data from the FSM pathology unit (PU) database and from the hospital biobank and merges them with the clinical information from the hospital information system.Our main effort was to provide a robust integrated research environment, giving a particular emphasis to the integration process and facing different challenges, consecutively listed: biospecimen samples privacy and anonymization; synchronization of the biobank database with the i2b2 data warehouse through a series of Extract, Transform, Load (ETL) operations; development and integration of a Natural Language Processing (NLP) module, to retrieve coded information, such as SNOMED terms and malignant tumors (TNM) classifications, and clinical tests results from unstructured medical records. Furthermore, we have developed an internal SNOMED ontology rested on the NCBO BioPortal web services.ResultsOnco-i2b2 manages data of more than 6,500 patients with breast cancer diagnosis collected between 2001 and 2011 (over 390 of them have at least one biological sample in the cancer biobank), more than 47,000 visits and 96,000 observations over 960 medical concepts.ConclusionsOnco-i2b2 is a concrete example of how integrated Information and Communication Technology architecture can be implemented to support translational research. The next steps of our project will involve the extension of its capabilities by implementing new plug-in devoted to bioinformatics data analysis as well as a temporal query module.

R engine cell: integrating R into the i2b2 software infrastructure.

Informatics for Integrating Biology and the Bedside (i2b2) is an initiative funded by the NIH that aims at building an informatics infrastructure to support biomedical research. The University of Pavia has recently integrated i2b2 infrastructure with a registry of inherited arrhythmogenic diseases. Within this project, the authors created a novel i2b2 cell, named R Engine Cell, which allows the communication between i2b2 and the R statistical software. As survival analyses are routinely performed by cardiology researchers, the authors have first concentrated on making Kaplan-Meier analyses available within the i2b2 web interface. To this aim, the authors developed a web-client plug-in to select the patient set on which to perform the analysis and to display the results in a graphical, intuitive way. R Engine Cell has been designed to easily support the integration of other R-based statistical analyses into i2b2.

The ONCO-I2b2 project: integrating biobank information and clinical data to support translational research in oncology.

The University of Pavia and the IRCCS Fondazione Salvatore Maugeri of Pavia (FSM), has recently started an IT initiative to support clinical research in oncology, called ONCO-i2b2. ONCO-i2b2, funded by the Lombardia region, grounds on the software developed by the Informatics for Integrating Biology and the Bedside (i2b2) NIH project. Using i2b2 and new software modules purposely designed, data coming from multiple sources are integrated and jointly queried. The core of the integration process stands in retrieving and merging data from the biobank management software and from the FSM hospital information system. The integration process is based on a ontology of the problem domain and on open-source software integration modules. A Natural Language Processing module has been implemented, too. This module automatically extracts clinical information of oncology patients from unstructured medical records. The system currently manages more than two thousands patients and will be further implemented and improved in the next two years.

Big Data Technologies: New Opportunities for Diabetes Management

The so-called big data revolution provides substantial opportunities to diabetes management. At least 3 important directions are currently of great interest. First, the integration of different sources of information, from primary and secondary care to administrative information, may allow depicting a novel view of patient’s care processes and of single patient’s behaviors, taking into account the multifaceted nature of chronic care. Second, the availability of novel diabetes technologies, able to gather large amounts of real-time data, requires the implementation of distributed platforms for data analysis and decision support. Finally, the inclusion of geographical and environmental information into such complex IT systems may further increase the capability of interpreting the data gathered and extract new knowledge from them. This article reviews the main concepts and definitions related to big data, it presents some efforts in health care, and discusses the potential role of big data in diabetes care. Finally, as an example, it describes the research efforts carried on in the MOSAIC project, funded by the European Commission.

BigQ: a NoSQL based framework to handle genomic variants in i2b2

BackgroundPrecision medicine requires the tight integration of clinical and molecular data. To this end, it is mandatory to define proper technological solutions able to manage the overwhelming amount of high throughput genomic data needed to test associations between genomic signatures and human phenotypes. The i2b2 Center (Informatics for Integrating Biology and the Bedside) has developed a widely internationally adopted framework to use existing clinical data for discovery research that can help the definition of precision medicine interventions when coupled with genetic data. i2b2 can be significantly advanced by designing efficient management solutions of Next Generation Sequencing data.ResultsWe developed BigQ, an extension of the i2b2 framework, which integrates patient clinical phenotypes with genomic variant profiles generated by Next Generation Sequencing. A visual programming i2b2 plugin allows retrieving variants belonging to the patients in a cohort by applying filters on genomic variant annotations. We report an evaluation of the query performance of our system on more than 11 million variants, showing that the implemented solution scales linearly in terms of query time and disk space with the number of variants.ConclusionsIn this paper we describe a new i2b2 web service composed of an efficient and scalable document-based database that manages annotations of genomic variants and of a visual programming plug-in designed to dynamically perform queries on clinical and genetic data. The system therefore allows managing the fast growing volume of genomic variants and can be used to integrate heterogeneous genomic annotations.

CARDIO-i2b2: integrating arrhythmogenic disease data in i2b2.

The CARDIO-i2b2 project is an initiative to customize the i2b2 bioinformatics tool with the aim to integrate clinical and research data in order to support translational research in cardiology. In this work we describe the implementation and the customization of i2b2 to manage the data of arrhytmogenic disease patients collected at the Fondazione Salvatore Maugeri of Pavia in a joint project with the NYU Langone Medical Center (New York, USA). The i2b2 clinical research chart data warehouse is populated with the data obtained by the research database called TRIAD. The research infrastructure is extended by the development of new plug-ins for the i2b2 web client application able to properly select and export phenotypic data and to perform data analysis.

A dashboard-based system for supporting diabetes care

Objective To describe the development, as part of the European Union MOSAIC (Models and Simulation Techniques for Discovering Diabetes Influence Factors) project, of a dashboard-based system for the management of type 2 diabetes and assess its impact on clinical practice. Methods The MOSAIC dashboard system is based on predictive modeling, longitudinal data analytics, and the reuse and integration of data from hospitals and public health repositories. Data are merged into an i2b2 data warehouse, which feeds a set of advanced temporal analytic models, including temporal abstractions, care-flow mining, drug exposure pattern detection, and risk-prediction models for type 2 diabetes complications. The dashboard has 2 components, designed for (1) clinical decision support during follow-up consultations and (2) outcome assessment on populations of interest. To assess the impact of the clinical decision support component, a pre-post study was conducted considering visit duration, number of screening examinations, and lifestyle interventions. A pilot sample of 700 Italian patients was investigated. Judgments on the outcome assessment component were obtained via focus groups with clinicians and health care managers. Results The use of the decision support component in clinical activities produced a reduction in visit duration (P ≪ .01) and an increase in the number of screening exams for complications (P < .01). We also observed a relevant, although nonstatistically significant, increase in the proportion of patients receiving lifestyle interventions (from 69% to 77%). Regarding the outcome assessment component, focus groups highlighted the systems capability of identifying and understanding the characteristics of patient subgroups treated at the center. Conclusion Our study demonstrates that decision support tools based on the integration of multiple-source data and visual and predictive analytics do improve the management of a chronic disease such as type 2 diabetes by enacting a successful implementation of the learning health care system cycle.

Decision-making for tracheostomy in amyotrophic lateral sclerosis (ALS): a retrospective study

Abstract Background: ALS patients should discuss the issue of tracheostomy before the onset of terminal respiratory failure. While the process of shared decision-making is desirable, there are few data on the practical application of this real-life situation. Aim of the study: To determine how a decision-making process is actually carried out, we analysed the episodes of acute respiratory failure preceding tracheostomy. Methods: We studied the charts of a group of ALS patients after tracheostomy. An interview focusing on the existence of anticipated directives was carried out. Tracheostomies were classified as planned or unplanned according to the presence of a decision plan. Results: A total of 209 ALS patients were cared for during a three-year period. Of these patients, 34 (16%) were tracheotomised. In 38% of cases, tracheostomy was planned, 41% were unplanned, and 21% remained undiagnosed. Conclusions: A minority of ALS patients make a voluntary decision for tracheostomy before the procedure is conducted. The advising process of care still presents limits that have been thus far poorly addressed. In the future, we will need to develop guidelines for the timing and content of the shared-decision making process.

From data to the decision: A software architecture to integrate predictive modelling in clinical settings

The application of statistics and mathematics over large amounts of data is providing healthcare systems with new tools for screening and managing multiple diseases. Nonetheless, these tools have many technical and clinical limitations as they are based on datasets with concrete characteristics. This proposition paper describes a novel architecture focused on providing a validation framework for discrimination and prediction models in the screening of Type 2 diabetes. For that, the architecture has been designed to gather different data sources under a common data structure and, furthermore, to be controlled by a centralized component (Orchestrator) in charge of directing the interaction flows among data sources, models and graphical user interfaces. This innovative approach aims to overcome the data-dependency of the models by providing a validation framework for the models as they are used within clinical settings.

User Requirements for Incorporating Diabetes Modeling Techniques in Disease Management Tools

Type 2 Diabetes Mellitus (T2DM) is the most common form of diabetes. Early identification of people at risk of developing T2DM is extremely important, but the effectiveness of existing model is not clear as it is not clear the relative importance of the needs that such systems should satisfy.