Wurdeman Shane R, Stevens Phillip M, Campbell James H
Department of Clinical and Scientific Affairs, Hanger Clinic, Austin, TX, USA.
Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE, USA.
Disabil Rehabil Assist Technol. 2020 Feb;15(2):211-218. doi: 10.1080/17483107.2018.1555290. Epub 2019 Feb 11.
To develop a predictive model to inform the probability of lower limb prosthesis users' functional potential for ambulation. A retrospective analysis of a database of outcomes for 2770 lower limb prosthesis users was used to inform a classification and regression tree analysis. Gender, age, height, weight, body mass index adjusted for amputation, amputation level, cause of amputation, comorbid health status and functional mobility score [Prosthetic Limb Users Survey of Mobility (PLUS-M™)] were entered as potential predictive variables. Patient K-Level was used to assign dependent variable status as unlimited community ambulator (i.e., K3 or K4) or limited community/household ambulator (i.e., K1 or K2). The classification tree was initially trained from 20% of the sample and subsequently tested with the remaining sample. A classification tree was successfully developed, able to accurately classify 87.4% of individuals within the model's training group (standard error 1.4%), and 81.6% within the model's testing group (standard error 0.82%). Age, PLUS-M™ T-score, cause of amputation and body weight were retained within the tree logic. The resultant classification tree has the ability to provide members of the clinical care team with predictive probabilities of a patient's functional potential to help assist care decisions.Implications for RehabilitationClassification and regression tree analysis is a simple analytical tool that can be used to provide simple predictive models for patients with a lower limb prosthesis.The resultant classification tree had an 81.6% (standard error 0.82%) accuracy predicting functional potential as an unlimited community ambulator (i.e., K3 or K4) or limited community/ household ambulator (i.e., K1 or K2) in an unknown group of 2770 lower limb prosthesis users.The resultant classification tree can assist with the rehabilitation team's care planning providing probabilities of functional potential for the lower limb prosthesis user.
开发一种预测模型，以了解下肢假肢使用者的步行功能潜力概率。对2770名下肢假肢使用者的结果数据库进行回顾性分析，以进行分类和回归树分析。将性别、年龄、身高、体重、截肢调整后的体重指数、截肢水平、截肢原因、合并健康状况和功能移动评分[假肢使用者移动性调查（PLUS-M™）]作为潜在预测变量输入。患者K级用于将因变量状态指定为无限制社区步行者（即K3或K4）或有限社区/家庭步行者（即K1或K2）。分类树最初从20%的样本中进行训练，随后用其余样本进行测试。成功开发了一个分类树，能够在模型训练组中准确分类87.4%的个体（标准误差1.4%），在模型测试组中准确分类81.6%的个体（标准误差0.82%）。年龄、PLUS-M™ T评分、截肢原因和体重保留在树逻辑中。所得的分类树有能力为临床护理团队成员提供患者功能潜力的预测概率，以帮助辅助护理决策。康复意义分类和回归树分析是一种简单的分析工具，可用于为下肢假肢患者提供简单的预测模型。所得的分类树在预测2770名下肢假肢使用者未知群体作为无限制社区步行者（即K3或K4）或有限社区/家庭步行者（即K1或K2）的功能潜力方面具有81.6%（标准误差0.82%）的准确率。所得的分类树可以协助康复团队进行护理规划，为下肢假肢使用者提供功能潜力概率。
