Clinical Specialist Advisor - Haematology

Suzanne Maynard

  • My research focuses on harnessing routinely collected healthcare data to optimise patient blood management in patients with liver disease.
  • I enjoy creating an environment where the unique viewpoint of students calls for contemporaneous reflection on knowledge and approach to cases.
  • The rebalanced haemostasis of liver disease means conventional coagulation tests cannot predict bleeding or thrombosis. But we have no alternative!
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Profile

I am a Haematology Registrar ST6 who is currently undertaking full time research as a NIHR clinical research fellow and DPhil candidate within the Blood and Transplant Research Unit (BTRU) working on Data Driven Transfusion Practice. Having completed undergraduate medical training and an intercalated BSc in clinical sciences at Birmingham University, I moved to London for my foundation years as a doctor. Subsequently I spent a year in New Zealand and completed core medical training in Edinburgh, before returning south to embark on my Haematology Specialty Training in West London. As my interest in Haemostasis and Thrombosis grew, so was I captivated by the potential of ‘big data’ and artificial intelligence. Here in Oxford I am pursuing my ambition to harness routinely collected healthcare data to achieve scaled-up, efficient research methodology to inform transfusion practice and predict outcomes such as bleeding and thrombosis in under-researched and underrepresented groups.

Teaching

I provide small group tutorials for Trinity medical students in years 4-6 based around clinical cases.

Research

My current projects focus on exploration of current accessible routinely collected health care data sets, in primary and secondary care, regarding questions such as ‘what is the burden of iron deficiency anaemia in patients with liver disease?’ and ‘what is our real time experience of peri-procedural plasma transfusion in liver disease?’ in order to inform our practice and focus intervention.

Selected Publications

Machine learning in transfusion medicine: A scoping review. Maynard S, Farrington J, Alimam S, et al. Transfusion (Paris). 2024;64(1):162-184. doi:10.1111/trf.17582

Does the Use of Viscoelastic Hemostatic Assays for Periprocedural Hemostasis Management in  Liver Disease Improve Clinical Outcomes?  Maynard S, Marrinan E, Roberts L, Stanworth S. Transfus Med Rev. 2024;38(3):150823. doi:10.1016/j.tmrv.2024.150823

Acute myeloid leukaemia presenting with Sweet syndrome.  Atkins O, Mirvis E, Maynard S, Katsomitrou V. Br J Haematol. 2022;199(5):637-637. doi:10.1111/bjh.18439

The role of ibrutinib in COVID-19 hyperinflammation. Maynard S, Ros-Soto J et al. International Journal of Infectious Diseases 105 (February 2021) 274–276

Targeting Free Light Chain (FLC) Secretion and the Unfolded Protein Response in Myeloma Cells Using Van, a Combination of Repurposed Drugs. Jiang Y, Down J, Raffles S et al. Experimental Hematology. (2018). 64. S74-S75. 10.1016/j.exphem.2018.06.256.

Epilepsy doses of valproate combined with the anti-helminthic, niclosamide, synergistically kill myeloma cells: A potent new anti-myeloma drug combination. Khanim F, Ferretti L, Raffles S et al. Experimental Hematology (2014). 42. S26. 10.1016/j.exphem.2014.07.089.

Co-Existent Osteoporosis And Multiple Myeloma: When To Investigate Further In Osteoporosis. Mumford E, Raffles S, Reynolds P. BMJ Case Report Oct 8; 2015. PMID: 26452412

Subjects
Suzanne Maynard
suzanne.maynard@trinity.ox.ac.uk

Data are just summaries of thousands of stories—tell a few of those stories to help make the data meaningful.

Dan Heath