New tool to better share clinico-genomic data

A research team led by Universidad Politécnica de Madrid (UPM) has developed a new tool that enables clinico-genomic data to be easily shared among different institutions. Published in the journal Computers in Biology and Medicine, their work addresses two main challenges in multicentric interoperability: harmonising heterogeneities from different data sources, and integrating omic and clinical data to enhance prevention, diagnosis and treatments of diverse diseases.

The introduction of omics data (from genomics, transcriptomics, proteomics, etc) involved in clinical treatment has led to a broad range of approaches to represent clinical information. Within this context, patient stratification across health institutions by omic profiling presents a complex scenario to carry out multicentre clinical trials. In the past, the patients needed to carry out a clinical study were found in the same hospital or healthcare centre; however, nowadays clinical studies often involve multiple clinical institutions from different regions or countries.

As a result, the data exchange among different centres is complex not only due to legal aspects but also technical issues. The information required for clinical studies is stored in each hospital and even in each department within a hospital in heterogeneous information systems that present different format and are encoded in diverse medical terminologies and languages.

The Biomedical Informatics Group (GIB) from UPM has spent the last few years working on the integration of clinico-genomic data from heterogeneous sources. In order to facilitate homogeneous access to clinical and genetic information, the researchers have developed a Semantic Interoperability Layer (SIL) — a software layer based on “different well-established biomedical standards and following International Health (IHE) recommendations”, they said.

As explained by GIB member Rául Alonso, the SIL tool is composed of four main elements. “First, a Common Data Model, which is able to link in a standard way with the hospital information systems; second, a Core Dataset to codify the information and data from different hospitals; third, a Terminology binding between the common data model and the core dataset; and four, a set of services to access and manage the stored information.”

The approach taken by the SIL means that data is not only translated into the common vocabulary, it is also standardised. For example, when there is a diagnosis of ‘neoplasia of the respiratory tract’ in a hospital, such data is stored in a way comparable to other more specific or similar diagnosis from other hospitals, such as ‘primary adenocarcinoma of the lower lobe of the right lung’ or ‘malignant tumour of the bronchi’. Additionally, the SIL is able to store genetic testing data in the same storage than clinical data in a way that the information can be homogenously stored and consulted.

“The SIL has shown suitability for integrating biomedical knowledge and technologies to match the latest clinical advances in healthcare and the use of genomic information,” the study authors wrote. “This genomic data integration in the SIL has been tested with a diagnostic classifier tool that takes advantage of harmonized multi-center clinico-genomic data for training statistical predictive models.”

According to the researchers, the SIL has already been proved with real data from hospitals in Spain, Germany, Belgium, Holland and the UK. They wrote, “The SIL has been adopted in national and international research initiatives, such as the EURECA-EU research project and the CIMED collaborative Spanish project, where the proposed solution has been applied and evaluated by clinical experts focused on clinico-genomic studies.”

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