AI aims to improve hypertension outcomes
Published on 29 March 2023
The University of Dundee is to play a leading role in a Europe-wide project exploring whether artificial intelligence can improve outcomes for patients with high blood pressure.
Arterial hypertension (AHT) affects 40-50% of the population over the age of 40 and is the first risk factor for major health problems such as myocardial infarction, heart or kidney failure, stroke and cognitive disorders. In 2019, it was estimated that more than 10 million deaths globally could be attributed to hypertension.
Despite the existence of effective drug treatments, it remains a poorly controlled pathology. This is largely because of the difficulty of identifying the different forms of hypertension with the appropriate medication, meaning there is often a delay in finding the correct treatment for individual patients.
HT-ADVANCE is a six-year, €8 million EU HORIZON project to improve the identification of secondary or ‘endocrine’ hypertension and better treatment of primary hypertension. By combining advanced ‘multi-omics’ technologies with machine learning to identify diagnostic biomarkers in blood and urine, the project aims to identify the endocrine HT patients sooner and improve the treatment earlier. Omics refers to the characterisation and quantification of biological molecules.
Dundee is an associate partner to the consortium, having received £2.1 million of UKRI funding to develop a clinical tool based on an AI predictor created by the University team in a previous project. They will do this by integrating results from five laboratories across Europe to extract dozens of features from the data and present an automated prediction of HT-type and response to treatment to help inform diagnosis and prescription decisions.
Dr Christian Cole, from the University’s School of Medicine Health Informatics Centre (HIC), said, “Arterial hypertension is not controlled or is poorly controlled in more than half of patients. When such a vast number of people have high blood pressure and when it is linked to so many serious health conditions, the potential for real harm is significant.
“Getting the right treatment to patients sooner would dramatically improve outcomes for them, and so by taking the HIC-developed machine learning predictor and making it applicable for clinical settings using HIC’s secure infrastructure, we hope to do just that.
“Clinical trials will then test the algorithm to determine whether it is indeed effective in ensuring that patients receive the most appropriate treatment for them.”
Three complementary clinical trials will apply AI techniques that integrate the genetic, genomic and metabolomic characteristics to generate diagnostic and therapeutic response predictions for clinicians. The study will be conducted by several hypertension centres of excellence, which have already established methods and procedures to integrate datasets from multiple platforms and contribute to the prediction of different forms of hypertension.
Dundee’s Tayside Clinical Trials Unit (TCTU), a collaboration with NHS Tayside, will lead the support of the clinical trials by creating the data management systems for the three clinical trials across multiple European clinics involved in HT-ADVANCE.
HT-ADVANCE is being co-led by Maria-Christina Zennaro, Inserm research professor, and Dr Jaap Deinum, head of the ESH Hypertension Excellence Center at Radboud University Medical Centre. Professor Zennaro is a specialist in arterial hypertension at the Paris Cardiovascular Research Centre. She said, “With the launch of this European project, our objective is to use and validate multi-omics stratification biomarkers (MOMICS) for patients with hypertension to better identify those with an endocrine form, but also to predict the response to treatment in patients with primary hypertension.
“We expect that HT-ADVANCE will provide a step change in the management of HT by enabling a personalised, more efficient and cost-effective treatment strategy, and importantly, will prevent the ensuing cardio-metabolic complications.”
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+44 (0)1382 384768G.Hill@dundee.ac.uk