Dehkordi, Arash

Arash Dehkordi is an Honorary Fellow (Research) in the Faculty of Health at the University of Dundee, based at Ninewells Hospital & Medical School. He works with the cardiovascular group in Molecular and Clinical Medicine alongside Dr Anna-Maria Choy and Professor Chim C. Lang, on the molecular and clinical characterisation of hypertrophic cardiomyopathy.

His current work draws on the Tayside Inherited Cardiac Conditions Clinic cohort and its longitudinal registry. Three questions run through it: how genotype shapes phenotypic expression and clinical outcome; how symptom burden and echocardiographic parameters change over years of follow-up; and how often, and in whom, phenotype conversion occurs among relatives of probands. This work has been presented at the European Society of Cardiology Congress, the British Cardiovascular Society Congress, the Scottish Cardiac Society Congress — where it received the Society’s abstract prize in 2025 — and the SHARP & Scottish Lipid Forum annual meeting.

He came to medicine from mathematics and computer science. He read pure mathematics at the University of Bath, graduating with a focus on category theory, group theory and graph theory and their application to mathematical biology, before completing his MD as the top student of his cohort. That route shapes how he works: he brings survival and mixed-effects modelling, Bayesian hierarchical methods, causal inference and machine learning to bear on clinical cardiology data, and places a high value on pre-specified, reproducible analysis. He is also an experienced systematic reviewer and meta-analyst, working to PRISMA standards on protocol registration, structured search, dual-independent screening and risk-of-bias assessment.

Alongside his Dundee appointment he holds an honorary research fellowship in the Department of Surgery and Cancer at Imperial College London, working with Professor Nagy Habib on RNA therapeutics and advanced delivery platforms across liver disease, CNS disorders and rare genetic conditions.

He first came to Dundee in 2024 as an elective student and INSPIRE SSV scholar, and has taught medical statistics to junior doctors here since. He peer reviews for journals indexed in Web of Science, and is a member of the British Cardiovascular Society, the European Society of Cardiology, the Scottish Heart & Arterial Disease Risk Prevention group and the American Society of Gene & Cell Therapy.

Hypertrophic cardiomyopathy and inherited cardiac conditions: genotype–phenotype correlation, phenotype conversion in family screening, symptom burden, and longitudinal disease progression. Heart failure and the cardio-hepatic axis. Risk stratification from echocardiographic and imaging phenotypes. Methodologically, survival and mixed-effects modelling, Bayesian hierarchical inference, causal inference, and machine learning applied to disease-progression and phenotype prediction; systematic review and meta-analysis; and mathematical modelling of biological systems. He maintains a secondary interest in RNA therapeutics and delivery systems, and in precision medicine approaches that integrate genomic, imaging and clinical data.

Keywords — Hypertrophic cardiomyopathy · Inherited cardiac conditions · Genotype–phenotype correlation · Phenotype conversion · Family screening · Heart failure · Echocardiography · Cardiovascular risk stratification · Biostatistics · Survival analysis · Bayesian modelling · Machine learning in medicine · Systematic review and meta-analysis · RNA therapeutics

He teaches medical statistics to junior doctors as part of cardiovascular and translational medicine research training at Dundee. Structured tutorials in SPSS and R cover data handling, descriptive analysis, logistic regression and survival modelling, with an emphasis on reproducibility and clinical interpretation. He previously taught core and clinical medicine to undergraduate medical students where he studied his MD, and pure mathematics at A-level and undergraduate level in the UK. He has supervised an undergraduate thesis project.