Risk model-based al location of prophylact ic implantable cardioverter defibril lators Scenarios from patient and societal perspectives

Implantable cardioverter defibrillators (ICDs) are widely used for the prevention of sudden cardiac death (SCD) in patients with reduced left ventricular function. Within the current guidelines only a minority of recipients benefit from ICD implantation. At the same time, all recipients face risks ICD-related complications and inappropriate shocks including.
This thesis investigates how patient selection for prophylactic ICD implantation can be improved. It leverages data from the Dutch Outcome in ICD Therapy (DO-IT) Registry, a nationwide cohort study evaluating outcomes in patients receiving ICDs for primary prevention. Using these data, risk prediction models were developed and validated to identify patient subgroups most likely to benefit. These models integrate modern treatment outcomes and routine clinical data, enabling clinicians to stratify patients and identify those for whom ICD implantation could be postponed or avoided.
Additionally, the study highlights substantial variability in cost-effectiveness across patient groups. Scenario analyses demonstrate that targeted allocation of ICDs, based on the proposed risk models, can reduce complications, inappropriate shocks, and healthcare expenditures, while improving both clinical outcomes and societal value.
This thesis emphasizes the importance of shared decision-making and proposes refined risk stratification strategies to optimize ICD allocation. It underscores the need to incorporate both clinical and economic considerations into medical decision-making, aiming to enhance patient outcomes while ensuring the efficient use of healthcare resources.