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