Nuutinen Mikko, Leskelä Riikka-Leena, Fialova Daniela, Haavisto Ira, Finne-Soveri Harriet, Häsä Jokke, Edgren Johanna, van Hout Hein, da Cunha Leme Daniel E, Hirdes John P, Onder Graziano, Liperoti Rosa
Nordic Healthcare Group, Helsinki, Finland.
Department of Social and Clinical Pharmacy, Faculty of Pharmacy in Hradec Králové, Charles University, Hradec Králové, Czech Republic.
BMC Med. 2025 Aug 20;23(1):484. doi: 10.1186/s12916-025-04304-7.
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.
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.
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.
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.
尽管抗精神病药物疗效有限且存在相当大的安全风险，但长期护理机构（LTCF）的老年居民仍经常被开具此类药物。虽然停用这些药物可能有助于降低其相关的发病率，但停用抗精神病药物对住院风险的影响尚未得到研究。本研究旨在评估停用抗精神病药物对老年LTCF居民住院风险的影响，并确定影响该效应的相关因素。
对于这项基于登记的回顾性队列研究，分析了2014年至2018年芬兰一组老年LTCF居民的数据。数据来源是基于长期护理居民评估工具（RAI-LTC）的综合老年评估以及芬兰医疗保健护理登记册。对于初始队列，选择了5467名使用抗精神病药物且至少进行了四次评估（每次评估间隔6个月）的居民。如果在前两次评估中开具了抗精神病药物而在最后两次评估中未开具，则居民被定义为停药者；如果在所有四次评估中均开具了抗精神病药物，则居民被定义为长期使用者。应用因果机器学习（ML）方法，包括双重机器学习（DML）、双重稳健（DR）、X学习者和因果森林（CF），来评估停用抗精神病药物对住院风险的影响，并确定影响该效应的因素。随访时间为1年。使用SHAP值（SHapley加性解释）、部分依赖图（PDP）和替代模型方法进行模型解释。
研究中近43%的居民停用了抗精神病药物。停用抗精神病药物使住院概率降低了约12%（平均治疗效应，ATE）。个体治疗效应（ITE）估计值范围为-30%至+1%。在所有ITE模型中，使用约束措施、年龄和功能障碍是影响预测ITE的相关变量。
停用抗精神病药物可能会降低老年LTCF居民的住院可能性，使这些药物的大多数使用者受益。促进停用抗精神病药物可能会预防住院，并降低长期护理中的发病率和死亡率。
