BACKGROUND: Antipsychotic medications are frequently prescribed to older residents of long-term care facilities (LTCFs) despite their limited efficacy and considerable safety risks. While discontinuation of these drugs might help reduce their associated morbidity, the impact of stopping antipsychotics on the risk of hospitalization has not been studied yet. The study aimed at estimating the effect of antipsychotic discontinuation on the risk of hospitalization in older LTCF residents and at identifying relevant factors influencing such effect. METHODS: For this registry-based retrospective cohort study, data from a cohort of older LTCF residents in Finland from the years 2014 to 2018 was analyzed. Data sources were the Resident Assessment Instrument for Long-Term Care (RAI-LTC) based comprehensive geriatric assessments and the Finnish Care Register for Health Care. For the initial cohort, 5467 users of antipsychotic medications with at least four assessments, each conducted 6 months apart, were selected. Residents were defined either as discontinuing, if antipsychotics were prescribed at the first two assessments but not at the last two, or as chronic users, if antipsychotics were prescribed at all four assessments. Causal machine learning (ML) methods including double machine learning (DML), double robust (DR), X-learner, and causal forest (CF) were applied to estimate the effect of antipsychotic discontinuation on the risk of hospitalization and to identify factors influencing such effect. The follow-up time was 1 year. The methods of SHAP values (SHapley Additive exPlanations), partial dependence plots (PDP), and surrogate models were used for model interpretation. RESULTS: Nearly 43% of residents in the study discontinued antipsychotic medications. Antipsychotic discontinuation lowered the probability of hospitalization of about 12% (average treatment effect, ATE). The individual treatment effect (ITE) estimations ranged from - 30% to + 1%. The use of restraints, age, and functional impairment were relevant variables in all ITE models in influencing the predicted ITE. CONCLUSIONS: Antipsychotic discontinuation may decrease the likelihood of hospitalization among older LTCF residents, benefiting most users of these drugs. Promoting antipsychotic discontinuation may prevent hospitalizations and reduce morbidity and mortality in long-term care.
- Keywords
- Antipsychotic medications, Long-term care, Machine learning,
- MeSH
- Antipsychotic Agents * therapeutic use administration & dosage adverse effects MeSH
- Long-Term Care * statistics & numerical data MeSH
- Hospitalization * statistics & numerical data MeSH
- Humans MeSH
- Registries MeSH
- Retrospective Studies MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Machine Learning * MeSH
- Check Tag
- Humans MeSH
- Male MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Finland MeSH
- Names of Substances
- Antipsychotic Agents * MeSH
INTRODUCTION: The provision of optimal care for older adults with complex chronic conditions (CCCs) poses significant challenges due to the interplay of multiple medical, pharmacological, functional and psychosocial factors. To address these challenges, the I-CARE4OLD project, funded by the EU-Horizon 2020 programme, developed an advanced clinical decision support tool-the iCARE tool-leveraging large longitudinal data from millions of home care and nursing home recipients across eight countries. The tool uses machine learning techniques applied to data from interRAI assessments, enriched with registry data, to predict health trajectories and evaluate pharmacological and non-pharmacological interventions. This study aims to pilot the iCARE tool and assess its feasibility, usability and impact on clinical decision-making among healthcare professionals. METHODS AND ANALYSIS: A minimum of 20 participants from each of the seven countries (Italy, Belgium, the Netherlands, Poland, Finland, Czechia and the USA) participated in the study. Participants were general practitioners, geriatricians and other medical specialists, nurses, physiotherapists and other healthcare providers involved in the care of older adults with CCC. The study design involved pre-surveys and post-surveys, tool testing with hypothetical patient cases and evaluations of predictions and treatment recommendations. Two pilot modalities-decision loop and non-decision loop-were implemented to assess the effect of the iCARE tool on clinical decisions. Descriptive statistics and bivariate and multivariate analysis will be conducted. All notes and text field data will be translated into English, and a thematic analysis will be performed. The pilot testing started in September 2024, and data collection ended in January 2025. At the time this protocol was submitted for publication, data collection was complete but data analysis had not yet begun. ETHICS AND DISSEMINATION: Ethical approvals were granted in each participating country before the start of the pilot. All participants gave informed consent to participate in the study. The results of the study will be published in peer-reviewed journals and disseminated during national and international scientific and professional conferences and meetings. Stakeholders will also be informed via the project website and social media, and through targeted methods such as webinars, factsheets and (feedback) workshops. The I-CARE4OLD consortium will strive to publish as much as possible open access, including analytical scripts. Databases will not become publicly available, but the data sets used and/or analysed as part of the project can be made available on reasonable request and with the permission of the I-CARE4OLD consortium.
- Keywords
- Aged, Chronic Disease, Clinical Decision-Making, Digital Technology, GERIATRIC MEDICINE,
- MeSH
- Chronic Disease therapy MeSH
- Clinical Decision-Making * methods MeSH
- Humans MeSH
- Pilot Projects MeSH
- Prognosis MeSH
- Aged MeSH
- Machine Learning * MeSH
- Decision Support Systems, Clinical * MeSH
- Check Tag
- Humans MeSH
- Aged MeSH
- Publication type
- Journal Article MeSH
