Predicting Future Mobility Limitation in Older Adults: A Machine Learning Analysis of Health ABC Study Data.
Predicting Future Mobility Limitation in Older Adults: A Machine Learning Analysis of Health ABC Study Data.
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预测老年人未来的行动限制：Health ABC 研究数据的机器学习分析。
DOI：
10.1093/gerona/glab269
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Houston,DeniseK
中科院分区：
文献类型：
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作者：
Speiser,JaimeL;Callahan,KathrynE;Ip,EdwardH;Miller,MichaelE;Tooze,JanetA;Kritchevsky,StephenB;Houston,DeniseK
背景：老年人活动受限是常见的，并与健康状况不佳和独立性丧失有关。由于耗时的临床评估和统计模型随时间变化的动态结果的局限性，识别高危个体仍然具有挑战性。因此，我们的目标是开发机器学习模型，利用重复测量数据预测老年人未来的行动能力限制。方法：我们使用健康、衰老和身体组成研究的9年随访年度评估来模拟活动限制，定义为自我报告行走1 / 4英里或爬10步有任何困难。我们考虑了46个预测因素，包括人口统计、生活方式、慢性病和身体功能。通过分裂样本方法，我们开发了混合模型（广义线性和二元混合模型森林），使用(a)所有46个预测因子，(b)变量选择算法，以及(c)前5个最重要的预测因子。所有模型都包含年龄。在2个内部验证数据集中，使用受试者工作曲线下的面积来评估疗效。结果各模型的受试者工作曲线下面积在0.80 ~ 0.84之间。活动受限最重要的预测指标是从椅子上站起来的难易程度、步态速度、自我报告的健康状况、体重指数和抑郁症。结论使用重复测量的机器学习模型在识别老年人活动受限风险方面具有良好的性能。未来的研究应评估预测模型作为临床工具的效用和效率，以识别可能受益于旨在预防或延迟行动能力限制的干预措施的高危老年人。
BackgroundMobility limitation in older adults is common and associated with poor health outcomes and loss of independence. Identification of at-risk individuals remains challenging because of time-consuming clinical assessments and limitations of statistical models for dynamic outcomes over time. Therefore, we aimed to develop machine learning models for predicting future mobility limitation in older adults using repeated measures data.MethodsWe used annual assessments over 9 years of follow-up from the Health, Aging, and Body Composition study to model mobility limitation, defined as self-report of any difficulty walking a quarter mile or climbing 10 steps. We considered 46 predictors, including demographics, lifestyle, chronic conditions, and physical function. With a split sample approach, we developed mixed models (generalized linear and Binary Mixed Model forest) using (a) all 46 predictors, (b) a variable selection algorithm, and (c) the top 5 most important predictors. Age was included in all models. Performance was evaluated using area under the receiver operating curve in 2 internal validation data sets.ResultsArea under the receiver operating curve ranged from 0.80 to 0.84 for the models. The most important predictors of mobility limitation were ease of getting up from a chair, gait speed, self-reported health status, body mass index, and depression.ConclusionsMachine learning models using repeated measures had good performance for identifying older adults at risk of developing mobility limitation. Future studies should evaluate the utility and efficiency of the prediction models as a tool in clinical settings for identifying at-risk older adults who may benefit from interventions aimed to prevent or delay mobility limitation.