A Generative and Causal Pharmacokinetic Model for Factor VIII in Hemophilia A: A Machine Learning Framework for Continuous Model Refinement.

In rare diseases, such as hemophilia A, the development of accurate population pharmacokinetic (PK) models is often hindered by the limited availability of data. Most PK models are specific to a single recombinant factor VIII (rFVIII) concentrate or measurement assay, and are generally unsuited for answering counterfactual ("what-if") queries. Ideally, data from multiple hemophilia treatment centers are combined but this is generally difficult as patient data are kept private. In this work, we utilize causal inference techniques to produce a hybrid machine learning (ML) PK model that corrects for differences between rFVIII concentrates and measurement assays. Next, we augment this model with a generative model that can simulate realistic virtual patients as well as impute missing data. This model can be shared instead of actual patient data, resolving privacy issues. The hybrid ML-PK model was trained on chromogenic assay data of lonoctocog alfa and predictive performance was then evaluated on an external data set of patients who received octocog alfa with FVIII levels measured using the one-stage assay. The model presented higher accuracy compared with three previous PK models developed on data similar to the external data set (root mean squared error = 14.6 IU/dL vs. mean of 17.7 IU/dL). Finally, we show that the generative model can be used to accurately impute missing data (< 18% error). In conclusion, the proposed approach introduces interesting new possibilities for model development. In the context of rare disease, the introduction of generative models facilitates sharing of synthetic data, enabling the iterative improvement of population PK models.

The prevalence of non-pharmacological interventions in older homecare recipients: an overview from six European countries.

PURPOSE: Non-pharmacological interventions (NPIs) play an important role in the management of older people receiving homecare. However, little is known about how often specific NPIs are being used and to what extent usage varies between countries. The aim of the current study was to investigate the prevalence of NPIs in older homecare recipients in six European countries. METHODS: This is a cross-sectional study of older homecare recipients (65+) using baseline data from the longitudinal cohort study 'Identifying best practices for care-dependent elderly by Benchmarking Costs and outcomes of community care' (IBenC). The analyzed NPIs are based on the interRAI Home Care instrument, a comprehensive geriatric assessment instrument. The prevalence of 24 NPIs was analyzed in Belgium, Germany, Finland, Iceland, Italy and the Netherlands. NPIs from seven groups were considered: psychosocial interventions, physical activity, regular care interventions, special therapies, preventive measures, special aids and environmental interventions. RESULTS: A total of 2884 homecare recipients were included. The mean age at baseline was 82.9 years and of all participants, 66.9% were female. The intervention with the highest prevalence in the study sample was 'emergency assistance available' (74%). Two other highly prevalent interventions were 'physical activity' (69%) and 'home nurse' (62%). Large differences between countries in the use of NPIs were observed and included, for example, 'going outside' (range 7-82%), 'home health aids' (range 12-93%), and 'physician visit' (range 24-94%). CONCLUSIONS: The use of NPIs varied considerably between homecare users in different European countries. It is important to better understand the barriers and facilitators of use of these potentially beneficial interventions in order to design successful uptake strategies.

Optimising the care for older persons with complex chronic conditions in home care and nursing homes: design and protocol of I-CARE4OLD, an observational study using real-world data.

INTRODUCTION: In ageing societies, the number of older adults with complex chronic conditions (CCCs) is rapidly increasing. Care for older persons with CCCs is challenging, due to interactions between multiple conditions and their treatments. In home care and nursing homes, where most older persons with CCCs receive care, professionals often lack appropriate decision support suitable and sufficient to address the medical and functional complexity of persons with CCCs. This EU-funded project aims to develop decision support systems using high-quality, internationally standardised, routine care data to support better prognostication of health trajectories and treatment impact among older persons with CCCs. METHODS AND ANALYSIS: Real-world data from older persons aged ≥60 years in home care and nursing homes, based on routinely performed comprehensive geriatric assessments using interRAI systems collected in the past 20 years, will be linked with administrative repositories on mortality and care use. These include potentially up to 51 million care recipients from eight countries: Italy, the Netherlands, Finland, Belgium, Canada, USA, Hong Kong and New Zealand. Prognostic algorithms will be developed and validated to better predict various health outcomes. In addition, the modifying impact of pharmacological and non-pharmacological interventions will be examined. A variety of analytical methods will be used, including techniques from the field of artificial intelligence such as machine learning. Based on the results, decision support tools will be developed and pilot tested among health professionals working in home care and nursing homes. ETHICS AND DISSEMINATION: The study was approved by authorised medical ethical committees in each of the participating countries, and will comply with both local and EU legislation. Study findings will be shared with relevant stakeholders, including publications in peer-reviewed journals and presentations at national and international meetings.