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Disabil Rehabil Assist Technol. 2019 Feb 11:1-8. doi: 10.1080/17483107.2018.1555290. [Epub ahead of print]

Mobility Analysis of AmpuTees (MAAT 4): Classification tree analysis for probability of lower limb prosthesis user functional potential.

Author information

1
a Department of Clinical and Scientific Affairs , Hanger Clinic , Austin , TX , USA.
2
b Department of Biomechanics , University of Nebraska at Omaha , Omaha , NE , USA.
3
c School of Medicine , University of Utah , Salt Lake City , UT , USA.

Abstract

PURPOSE:

To develop a predictive model to inform the probability of lower limb prosthesis users' functional potential for ambulation.

MATERIALS AND METHODS:

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.

RESULTS:

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.

CONCLUSIONS:

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 Rehabilitation Classification 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.

KEYWORDS:

K-level; Mobility; ambulator; outcomes

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