Geert-Jan Geersing

Over the past 10-15 years, I combined clinical work as a general practitioner with research in the field of cardiovascular disease management in primary care. I have a strong background in prediction analytics, notably in the field of cardiovascular conditions associated with hypercoagulability and anticoagulation, like venous thrombo-embolism (VTE) and atrial fibrillation (AF). As such, I worked on prediction models for early detection of pulmonary embolism (i.e., the most severe condition in the VTE spectrum), using individual patient data meta-analytical techniques. Additionally, my group worked on prognostic prediction models for stroke risk in AF, as well as bleeding risk in patients receiving anticoagulant drugs.

More recently, my work focusses on chronic care models for the growing group of frail elderly cardiovascular patients with multimorbidity, for example our recent ESC 2023 hotline session presentation on the FRAIL-AF randomized controlled trial and the succesful Horizon EU application RAPHAEL (as co-lead) on palliative care in chronic heart failure patients.

Throughout my carreer as a clinical researcher, I used my patient encounters and clinical duties to guide my research interest. As an example, I currently see two important developments in clinical primary care that should be tackled with good science.

First, in our ageing population, many patients in primary care suffer from multiple conditions, use multiple medications and overall increasingly live with the frailty syndrome, a clinical syndrome characterised by advanced biological ageing (e.g., leading to sarcopenia, immunosenescence, etc.) and higher vulnerability to stressors. This is even more so in the field of cardiovascular disease as (i) the underlying pathofysiological processes of cardiovascular disease (e.g., atherosclerosis, endothelial dysfynction, cardiac remodelling) are by definition slowly, progressive processes, and (ii) survival rates for acute cardiac events increased dramatically over the last decades, consequently leading to a growing group of chronic cardiovascular patients in need for secondary prevention.

Second – again notably in the field of cardiovascular medicine – we observe an increase in technological possibilites, in terms of diagnostic tests, monitoring devices and minimally invasive therapeutic options. From a clinical perspective this puts physicians and their patients in a challenging dilemma ‘what to choose for what patient to optimize his/her healthcare experience’. From a research perspective, this technology push combined with the increasing availability of linked routine healthcare data leads to an enormous wealth in multi-modal data that can be used to refine predictive decision support tools, e.g. using techniques from generative AI. It thereby has the promise of helping physicians and their patients making optimal medical decisions.

Both developments - cardio-geriatry and the availability of multi-modal healthcare data - ask for personalized approaches. On the patient side – due to multimorbidity and frailty – differences between patients increase, leading to heterogeneity in diagnostic and prognostic test perfomance as well as heterogeneity in expected treatment effect. On the technology side, as explained above, medical decision-making requires information on variables that relate to optimizing healthcare delivery; variables that increasingly will originate from linked data sources, from prospective cohorts, trials and routine healthcare data, i.e., big data AI analytics.

As a clinical researcher in the field of cardiovascular disease management in primary care, it the aim of our research group to provide the required evidence needed in this new ecosystem of personalized primary care cardiovascular medicine. The methods I will use for this will come from the field of predictive analytics, preferably combining advanced regression analysis techniques with methods from AI (e.g., natural language processing techniques for free-text available in routine healthcare data). Additionally, when it concerns evaluation of interventions or novel care pathways, I will use pragmatic trial designs, including cluster and stepped-wedge randomized controlled trials. 

It is my aim to bring these novel care pathways back to daily primary care and evaluate its impacts in a learning healthcare system.