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• infectious disease and the immune system
• epidemiology/population health research

• Medicine
• Public Health

• Geriatric Medicine
• Health Informatics
• Immunology
• Nephrology

The Trinity Health Kidney Centre is an academic health science centre comprising the academic department of Nephrology in Trinity College Dublin and the clinical nephrology units in St James?s and Tallaght Hospitals. Research is driven through several strands:

1. Renal Inflammation Group, a translational medicine programme focused on investigation of pathogenesis and discovery of biomarkers of disease in glomerulonephritis. The principal research interest is in ANCA vasculitis, an autoimmune condition that causes multi-organ failure as a consequence of overwhelming necrotising inflammation affecting small blood vessels. To assist in answering these questions, we have set up infrastructure that allows collection of longitudinal clinical data in the form of a national registry and biobank (www.medicine.tcd.ie/thkc/research/RKD-Registry-Biobank.php), which is linked to a basic science laboratory.
2. Chronic Disease Informatics group, a trans-disciplinary unit that combines Nephrology, computer science and statistics to address research questions related to autoimmune and kidney disease using novel data science methodologies. We are linked closely with TILDA (http://tilda.tcd.ie/).
3. Familial Kidney Disease Genomics, a collaboration between THKC, RCSI, Harvard University and Beaumont Hospital aimed at uncovering the genetic causes of rare inherited kidney disease.

The THKC Chronic Disease Informatics Group are seeking clinician innovators with an interest in computer science and/or statistics to drive forward a novel project aimed at uncovering the environmental causes of relapse or autoimmune disease.

We propose a new way of managing chronic diseases bringing together the fields of medicine and data science. We hypothesise that the interaction between individuals with the relapsing and remitting autoimmune kidney disease ANCA vasculitis, and their environment, can be detected and defined by observing the whole system in action and integrating a wide array of data sources.

The ultimate goal of this approach is to define the signature of vasculitis relapse and use this to aid in planning and delivery of optimum immunosuppressive therapy at the level of the patient. To achieve this, we will use advanced data science methodologies and Bayesian statistical techniques to develop a data architecture that curates and combines from four sources: Fixed patient-level factors (HLA-DP phenotype, granular clinical dataset obtained at diagnosis), External medical influences (maintenance immunosuppression, antibiotic prescriptions, Hospital Inpatient Enquiry records), External environmental influences, linked to patient location through time (meteorological data streams, community pathogen patterns: readily available as online data streams) and Direct patient-derived data sources (location, patient-reported quality of life and accelerometer defined activity).

We shall describe for the first time the relapse prodrome and define in great detail the environmental influences linking to this event.