IFRA: 一种基于机器学习的仪器化跌倒风险评估量表，源自仪器化定时上升和行走测试的脑卒中患者
IFRA: a machine learning-based Instrumented Fall Risk Assessment Scale derived from Instrumented Timed Up and Go test in stroke patients
AI总结：
本研究提出了一种基于机器学习的IFRA量表，通过分析ITUG测试数据，有效识别脑卒中患者跌倒风险，优于传统量表。
摘要：
背景/目的：跌倒是脑卒中幸存者的主要健康问题，需要有效的风险评估工具。本研究提出了一种名为仪器化跌倒风险评估（IFRA）的量表，这是一种从仪器化定时上升和行走测试（ITUG）测试数据中衍生出的新型筛查工具，旨在捕捉传统量表常遗漏的移动指标。方法：我们采用了两步机器学习方法来开发IFRA量表：首先，从ITUG数据中识别预测性移动特征，然后创建一个分层策略将患者分为低、中、高跌倒风险类别。本研究包括142名参与者，他们被分为训练集（包括合成案例）、验证集和测试集（包括22名非跌倒者和10名跌倒者）。IFRA的表现与传统临床量表（如标准TUG和Mini-BESTest）通过Fisher精确检验进行比较。结果：机器学习分析确定了特定特征作为关键预测因子，即行走和坐起-行走转换期间的垂直和前后加速度以及角速度。IFRA在跌倒状态上表现出统计学显著关联（Fisher精确检验p=0.004），并且是唯一一个将超过一半的实际跌倒者分配到高风险类别中的量表，在此数据集中优于比较的临床量表。结论：这项概念验证研究展示了IFRA作为脑卒中患者跌倒风险分层的自动化、补充方法的潜力。尽管IFRA在识别高风险个体方面显示出有希望的区分能力，但这些初步发现需要在更大的队列中验证，才能在临床应用中实施。
英文摘要：
Background/Objectives: Falls represent a major health concern for stroke survivors, necessitating effective risk assessment tools. This study proposes the Instrumented Fall Risk Assessment (IFRA) scale, a novel screening tool derived from Instrumented Timed Up and Go (ITUG) test data, designed to capture mobility measures often missed by traditional scales. Methods: We employed a two-step machine learning approach to develop the IFRA scale: first, identifying predictive mobility features from ITUG data and, second, creating a stratification strategy to classify patients into low-, medium-, or high-fall-risk categories. This study included 142 participants, who were divided into training (including synthetic cases), validation, and testing sets (comprising 22 non-fallers and 10 fallers). IFRA's performance was compared against traditional clinical scales (e.g., standard TUG and Mini-BESTest) using Fisher's Exact test. Results: Machine learning analysis identified specific features as key predictors, namely vertical and medio-lateral acceleration, and angular velocity during walking and sit-to-walk transitions. IFRA demonstrated a statistically significant association with fall status (Fisher's Exact test p = 0.004) and was the only scale to assign more than half of the actual fallers to the high-risk category, outperforming the comparative clinical scales in this dataset. Conclusions: This proof-of-concept study demonstrates IFRA's potential as an automated, complementary approach for fall risk stratification in post-stroke patients. While IFRA shows promising discriminative capability, particularly for identifying high-risk individuals, these preliminary findings require validation in larger cohorts before clinical implementation.
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