目的：基于国际功能、残疾和健康分类（international classification of functioning， disability and health，ICF）构建适用于脑卒中非急性期患者功能评估的项目反应理论（item response theory，IRT）模型，并验证其信度和效度。通过图模型（graphical model，GM）探索功能条目的获益度以及条目间关系。

方法：采用横断面研究设计，纳入2023年7月—2023年12月在上海市第一康复医院就诊的非急性期卒中患者。相关评估包括简易精神状态检查量表（Mini-mental State Examination，MMSE）、功能独立性评定（Functional Independence Measure，FIM），卒中社交量表（Stroke Social Network Scale，SSNS）和ICF康复组合（ICF Rehabilitation Set，ICF-RS）。基于摩肯假设的非参数IRT（nonparametric IRT，npIRT）对ICF-RS的条目进行摩肯量表分析（Mokken Scale Analysis，MSA），筛选出符合参数IRT模型（parametric IRT model，pIRM）的条目，绘制人-项图比较患者个人能力和功能条目难度。基于贝叶斯学习的伊辛网络计算条目的获益度和功能条目网络参数，生成最大生成树可视化功能条目的分布和可干预路径。

结果：遵循最大变异原则，共纳入100例非急性期卒中患者（其中，男性70例、女性30例），析出含22个ICF条目的Rasch量表，潜在类别信度系数（latent class reliability coefficient，LCRC）为0.959。二分值化Rasch模型得分与FIM得分显著强相关性（P＜0.001，r＝0.89）。难度最大的条目包括“b455运动耐受功能”“d660帮助别人”“d640做家务”，难度最小的条目为“d550进食”。结合IRT和GM，“d420移动自身”是在患者能力范围内，仍然存在障碍的功能条目，并且该条目的改善对其他功能障碍也有“桥梁”作用。伊辛模型提示改善患者“d510盥洗自身”能力对患者整体功能获益度最大。

结论：基于pIRM可析出信效度较好的Rasch量表用于非急性期卒中患者功能评估，而IRT与伊辛网络分析的应用可为患者定制个性化康复目标。

Objective: To construct an item response theory (IRT) model for functional assessment of non-acute stroke patients based on the international classification of functioning, disability and health (ICF) for functional assessment, verification of its reliability and validity, and exploration of the overall benefit index and interrelationships of functional items through graphical models (GMs).

Methods: A cross-sectional study was conducted, enrolling non-acute stroke patients admitted by the First Rehabilitation Hospital of Shanghai from July 2023 to December 2023. Assessments included the Mini-Mental State Examination (MMSE), Functional Independence Measure (FIM), Stroke Social Network Scale (SSNS), and ICF Rehabilitation Set (ICF-RS). Based on the Mokken assumption, nonparametric IRT (npIRT) was applied to the ICF-RS items through Mokken Scale Analysis (MSA), by which items suitable for a parametric IRT model (pIRM) were identified, and person-item maps were generated to compare patient's abilities with the difficulty levels of functional items. Based on Bayesian learning, the benefit values of the items and the network parameters of the functional items were estimated using the Ising model, and a maximum spanning tree was generated to visualize the distribution of functional items and the potential intervention pathways.

Results: Adhering to the maximum heterogeneity sampling, a total of 100 non-acute stroke patients (males = 70, females = 30) were included, yielding a Rasch scale containing 22 ICF items with a latent class reliability coefficient (LCRC) of 0.959. The dichotomized Rasch model scores significantly correlated strongly with FIM scores (P < 0.001, r= 0.89). The most difficult items included "b455 Exercise tolerance", "d660 Assisting others" and "d640 Doing housework". The easiest item was "d550 Eating". By integrating IRT and GM, the item "d420 Moving oneself" was identified as a functional item that, although within the patient's ability range, still presents impairments, and its improvement may serve as a "bridge" for alleviating other functional impairments. The Ising model indicated that enhancing the ability of patients in "d510 Washing oneself" would yield the greatest overall functional benefit.

Conclusion: Based on pIRM, a Rasch model with good reliability and validity can be extracted for functional assessment of patients with non-acute phase of stroke, and the application of the IRT model and Ising network analysis can customize personalized rehabilitation goals for patients.