Multimorbidity is a common condition in older age, and can substantially influence individuals’ health and quality of life, making management more difficult. A single-disease approach with fragmented care is still prevalent in current healthcare systems despite multimorbidity representing a heterogeneous spectrum of disease combination(s). On this background, the novel approach underpinning the AFFIRMO project is to focus on clusters of multimorbidity where atrial fibrillation (AF) represents one of the chronic conditions. Improving the management of AF in the context of multimorbidity may benefit individuals on a larger scale, with a holistic approach to optimize clinical management of older AF patients taking into account the multifaceted aspects of individuals’ health, including multimorbidity, polypharmacy, personal preferences, and
social context.

First, the project aims to identify different clusters of multimorbidity in older patients with AF. Second, AFFIRMO aims to assess the needs of patients, caregivers, and health professionals for the comprehensive management of multimorbidity including AF, and to examine ways of optimizing care and self-management. Third, AFFIRMO will develop, implement and test the effectiveness of a patient-centered approach on older multimorbid AF patients in the clinical practice. We aim to adapt, implement and promote a care pathway, in older patients with multimorbidity. A specific objective will be to develop an interoperable care framework that can facilitate the application of this personalized care pathway, that bridges the continuum between primary and secondary care, with the active involvement of patients with shared decision-making. A further aim will be to model the impact of multimorbidity including AF on healthcare costs and the health economic benefits by the proposed integrated care pathway. Finally, subgroup analyses would assess differences on outcomes of in relation to gender and
social inequalities.

WP3 (prof. Mirko Petrovic, GE35)-To quantify the polypharmacy burden and assess the quality of drug prescribing in different clusters of patients with multimorbidity and AF and to describe how different patterns of inappropriate prescribing are distributed within the population of older individuals with AF, who have worse prognosis as identified in WP2, and their impact on health-related outcomes. The ultimate goal – carried out in synergy with WP2 – is to identify individual characteristics that will be used to a) produce a novel stratification tool (WP2 and W3), and b) fine tune the selection of the RCT target population and personalize the delivered intervention (WP7). The same high-quality data collected from existing European population-based studies, administrative datasets and clinical registers as in WP2 will be used to determine the occurrence of polypharmacy and to describe the most common prescribing patterns, potential adverse drug reactions as well as drug-drug interactions. Special attention will be paid to identification of the most common avoidable adverse drug effects and drug-drug interactions associated with oral anticoagulation within the different clusters of multimorbidity. WP8 (prof. Delphine De Smedt, GE39)- WP8 has four main objectives: to develop a microsimulation model based on the previously validated IMPACT NCD approach; to provide the model source code and documentation under an open source license; to use the modelling tool to determine the effectiveness, cost -effectiveness and equity impact of health costs related to the management of AF in different disease clusters, and to provide real cost data on economic burden of FA and of different clusters of comorbidities. The UGent will be involved in the different tasks and will take the lead in providing the health economic inputs to the model (task 8.2) by identifying relevant data sources (literature data and analyses of administrative databases from participating countries, if available) to provide cost data of AF disease alone and in different comorbidity clusters (from WP2), and of different health consequences of the proposed interventions; by preparing utilities inputs to incorporate in the model; by test the health economic outputs for consistency and validity, taking into account also the inputs received during clustering events (T9.7); and by contributing to health economics analysis conducted with the model.