These Virtual Workshop presentations aim to characterie measurable markers of success that work in real-world medically assisted reproduction (MAR) practice. They will also describe how clinicians can communicate effectively about key performance indicators (KPIs) to patients (who are often well-informed and data-literate but also need to know the individual relevance of population-level statistics). In addition, the presentations consider the evolving use of big data and AI in MAR, and will and evaluate the quality of real-world evidence relating to MAR treatment, focusing on recent studies that utilise large-scale registry datasets.
Globally, infertility rates are increasing, and birth rates are declining, because of concomitant socio-economic, physiological and environmental factors. When women present for infertility care in their mid- or late 30s there are clinical challenges for MAR providers, given that treatment protocols (and expectations) are typically established in populations of normoresponsive women aged ≤35 years. How do clinicians set realistic expectations and plan the optimal treatment course for women of advanced maternal age (AMA) who do not fit the profile of normoresponsive women? Understanding both global and local trends in fertility populations is important; treatment improvements centre on the relevance of statistical KPIs, but acknowledge that individual experiences of MAR will be personal and unique. The primary learning points from this webinar are for health professionals to understand how MAR populations are changing (globally and locally), and how and why approaches to treatment need to adapt to these changes. Such understanding affects treatment and procedural choices in MAR: breaking the data down into what matters most to the patient is extremely relevant to good clinical practice.
KPIs benchmark clinical practice at local, national and regional levels, especially in the era of ‘big data’ and information sharing. To be widely adopted in clinical settings, KPI must be measurable, reproducible and consistent, and look beyond live birth rates or even cumulative live birth rates:. However, MAR and AI or big data technologies are developing so rapidly that relevant clinical benchmarks, expectations, definitions and processes can be unclear in everyday practice. Indeed, information sharing in healthcare is behind the curve, compared with other sectors. Although some reasons for this are valid (relating to concerns about data protection, information security and sensitivity), data to be obtained for registries, for example, need to be sufficiently granular to drive powerful insights without compromising patient integrity or routine clinical practice (where not all data are obtained, cleaned and stored consistently).KPI should be defined for each treatment step and MAR milestone, to reduce bias as much as possible.
Certainly, traditional reliance on randomized study data is evolving andreal-world outcomes are often highly regarded in MAR.Exploring the value of real-world data is key for future clinical decision making.
After participating in this workshop and related activities, participants will be able to:
The program is intended for clinicians, embryologists, andrologists, scientists, and managers working in ART who wish to update their knowledge of advanced techniques and scientific innovations and understand best practices using evidence-based models of care.
This programme will be submitted for CME accreditation from the European Accreditation Council for Continuing Medical Education (EACCME®) and for Continuing Professional Development (CPD) credits.
MAR prediction tools: KPIs, biological factors, RWE, and clinical decisions
Chairs: Carlo Alviggi, Italy and Jérôme Chamboust, France
13.00 Med.E.A. Medical Education Academy welcome
13.05 Workshop introduction, objectives
Carlo Alviggi, Italy
13.10 L1 Quantifying a successful outcome in MAR
Carlo Alviggi, Italy
13.35 Q&A: Faculty and Participants
13.55 L2 Benchmarking the biology
Danilo Cimadomo, Italy
14.20 Q&A: Faculty and Participants
14.40 L3 AI, data science, machine learning and big data in MAR outcome measurement
Jérôme Chamboust, France
15.05 Q&A: Faculty and Participants
15.15 L4 What does big data mean for clinical practice?
Biljana Popovic-Todorovic, Serbia
15.40 Q&A: Faculty and Participants
Jérôme Chambost, France
Carlo Alviggi earned his MD degree in 1994, from the Faculty of Medicine of the University of Naples “Federico II” in Naples, Italy; he specialized in obstetrics and gynaecology in 1998, and gained a PhD in 2001, from the same university. During his career, he collaborated with: The Imperial College of London, in London, UK; the Laboratory of Immunology of the Italian National Research Council in Naples; and the Autoimmunity and Tolerance Laboratory of the University of California in Los Angeles, CA, USA. This network resulted in various publications, some of which concern new hypotheses on the pathogenesis of pelvic endometriosis. He is now an associate professor in reproductive medicine at the Fertility Unit of the University of Naples “Federico II”. Professor Alviggi’s current research interests are the role of luteinizing hormone (LH) in folliculogenesis; the use of LH-containing drugs in patients undergoing ovarian controlled stimulation for IVF; the pathogenesis of pelvic endometriosis; oncofertility; reproductive endicrinology and the genetics of human reproduction. He has participated in several national and international (phase 2 and 3) multicentric, prospective randomized trials. Professor Alviggi has published extensively and has been invited to lecture at over 200 international meetings dealing with reproductive medicine and gynaecological endocrinology; he is also starter and co-founder of the Poseidon group, an international collaborative network dedicated to low prognosis patients in Assisted Reproductive Technology and vice-President of the “Società Italiana di Fertilità e Sterilità – Medicina della Riproduzione”.
Jerome Chambost received a Master of Engineering from Centrale Paris, France and a Master of Engineering Science and management from the University of Queensland, Australia with a strong focus on data science.
He worked for three years as a senior consultant and data scientist for Roland Bergerspecializing in big data and artificial intelligence projects for the healthcare and Life sciences industries.
He joined APRICITY in 2019 as head of AI to leverage artificial intelligence technologies and improve care, provide transparency and maximise chances of success for fertility treatments with a focus on stimulation and embryology.
He became Chief Data Officer of Apricity in 2021, in charge of accelerating the data transformation of the company and is since 2022 Chief Technology Officer of Apricity, in charge of the artificial intelligence and the technical team, with the goal to design personalize treatments algorithms an implement them in Apricity suite of services for the patients and medical staff.
APRICITY is a virtual fertility clinic. It aims, first, at disrupting the fertility treatment experience and then, at improving chances for women and couples who have difficulties to conceive leveraging data and artificial intelligence. The Apricity project is developed with leading global IVF experts and has been launched in the UK and in Spain to start with.
Jerome Chambost is also board member of Labelia Labs (formerly Substra Foundation) since 2020. Labelia Labs is a non-profit organization with the mission of developing the positive impacts of Data Science by equipping and promoting collaborative, responsible and trustworthy approaches.
Dr Cimadomo is Innovation in Embryology Director and Science and Research manager Italy of IVIRMA Global Research Alliance. He has authored >120 papers. He is Basic Science officer and Coordinator Elect of the ESHRE SIG Implantation and Early Pregnancy, Associate Editor of Human Reproduction and Section Editor in Clinical Embryology of Reproductive BioMedicine Online. He participated to the ESHRE working groups updating the GPRs for embryo biopsy, and for RIF management. He coordinates the Master “Biology and Biotechnology of Reproduction: from Research to Clinics” at the University of Pavia.
Martin Savage is Emeritus Professor of Paediatric Endocrinology at William Harvey Research Institute, Barts and the London School of Medicine & Dentistry, Queen Mary, University of London. He was head of the Paediatric Endocrine Unit at Barts and the London School of Medicine from 1982 to 2007. He has interests in growth disorders, specifically those with abnormalities in the GH-IGF-1 axis and in phenotype-genotype relationships of GH-IGF-1 axis defects, notably GH resistance. He published the first human case of an IGF-1 gene defect in the New England Journal of Medicine in 1996. His other interests are Cushing’s syndrome and growth in chronic inflammatory diseases. He was General Secretary of the European Society for Paediatric Endocrinology (ESPE) from 1997 to 2004. He has lectured in 61 countries and has published 472 original articles, reviews, textbook chapters and books. In 2007, he was awarded the ESPE Andrea Prader Prize for contributions to paediatric endocrinology and in 2018 he received a Visionary Award from the American Human Growth Foundation. In 2022, he received a Research Excellence Award from the Dr Sulaiman Al Habib Medical Journal in Riyadh, and the British Society of Paediatric Endocrinology & Diabetes James M. Tanner Lifetime Achievement Award. He continues to lecture nationally and internationally.