Periodic Reporting for period 1 - I-CARE4OLD (Individualized CARE for Older Persons with Complex Chronic Conditions at home and in nursing homes) Reporting period: 2021-06-01 to 2022-11-30 Summary of the context and overall objectives of the project Optimal care for older patients with complex chronic conditions (CCC) is challenging. In patients receiving home care or living in nursing home the care process is further complicated by the presence of multiple impairments in non-clinical dimensions (i.e. function, cognition, social function). Tools to support care approach in this population are needed. The main aim of the I-CARE4OLD project is to develop decision support for better prognostication of (i) health trajectories and (ii) treatment impact in older care dependent patients with complex chronic conditions (CCC) receiving home care or living in nursing home, using real-world high-quality data. We specified our aim into five objectives: 1. Create enriched real-life high-quality datasets on older care recipients with CCC. 2. To identify homogeneous groups of patients sharing common disease patterns. 3. Develop and validate predictive models for specific health outcomes. 4. Identification of pharmacological and non-pharmacological interventions modifying health trajectories. 5. Develop and pilot test individualized prognostications. Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far Ethical approvals (only Belgium pending), evaluation of ethical risks and legal compliance have been performed. A Data Management Plan is in place (WP2). In WP3, six homogeneous groups disease patterns were identified. These six groups were further validated and characterized across different countries and care settings. These analyses will be further validated and capture all I-CARE4OLD samples. In WP4 beneficiaries developed and refined multiple risk prediction algorithms (3 models for Home Care and 2 models for Nursing Homes). Next, application of machine learning strategies aims to create even more powerful algorithms, resulting in the specification of multiple distinct sub-groups that share common future health trajectories. WP5 reached consensus on prioritizing eight specific pharmacological treatments through a three -step selection process (literature review, e-survey among practitioners across Europe and final consensus within I-CARE4OLD consortium through a Delphi methodology). The first algorithms to estimate the impact of the selected medications on study outcomes have been developed. Propensity score matching methodology was applied in the Finnish linked data to tease out bias by indication data from real life observational cohorts. In WP6 75 unique non-pharmacological Interventions (NPIs) were identified and specified in the available data repositories, their distribution across countries was explored and stratified for care settings in existing data sets, and an analytic plan developed. A first analytic model was developed to estimate the impact of physical therapy applying propensity score adjustment in order to control for bias by indication in data on home care recipients from 6 European countries. In WP7 initial platform development has started that will integrate of the analytic models from the previous WPs. Initial stakeholder engagement has taken place and will continue in spring 2023. WP8 issued a communication & dissemination plan including a local stakeholder survey, built the website, created a project newsletter, maintains presence on social media channels, and developed a conference package for further promotion of the project. Available results of the project were presented in national and international meetings. The major efforts this year in WP9 were directed toward establishing the analytic datasets that will be used to examine the effect of COVID-19 on persons in home care, nursing home, and complex continuing care hospital settings. In addition, we demonstrated the use of multistate transition models to analyse trajectories of health changes while controlling for censoring due to death, hospitalization, or other types of discharges. Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far) The main challenge is to improve health and healthy ageing. In many parts of the world the aged population with complex chronic conditions (CCC) grows rapidly. High quality individualised decision support for prognostications and estimating impact of treatment of persons with CCC is expected to improve patient outcomes, decrease adverse effects, and allocate sparse resources in a more rational and equal way. We specifically push the state of the art regarding the methodology to estimate treatment effects in observations cohort data by finding innovative ways to control for bias by indication. In I-CARE4OLD we address this challenge by focussing on better evidence informed decision making for persons with CCC concentrated in home care and nursing home settings. We anticipate that our project will have direct and indirect impact on Quality of life (by optimising appropriate (non)pharmacological treatments), Quality of care (Access to high quality decision support facilitates professionals and patients to make better decisions) and cost of care (High quality prognostic information may support better informed decisions that may reduce acute admissions and postpone nursing home admissions). logo-i-care4old-cropped.png
