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

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 TM)] 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 TM 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. ä 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.

Mobility Analysis of AmpuTees (MAAT 7)

“…A recent classification analysis with amputation attributed to cancer, congenital, and trauma collapsed into a singular non-DV group when determining the functional ambulatory status among lower limb prosthesis users 10 . The present study showed no statistical differences between the amputations attributed to cancer and trauma.…”

“…Studies have generally shown reduced functional mobility in individuals with DVetiology compared with those with traumatic etiology. [8][9][10] There may not be functional differences between traumatic amputation compared with cancer or congenital etiology. However, the life journey for patients with either cancer or congenital limb deficiency differs substantially from that experienced by patients with either DV or traumatic amputation.…”

“…For example, goal setting for an individual with limb amputation/difference due to cancer or a congenital limb condition would need to be based on guidance drawn from either DV or traumatic amputation etiology. Studies have generally shown reduced functional mobility in individuals with DV etiology compared with those with traumatic etiology 8–10 . There may not be functional differences between traumatic amputation compared with cancer or congenital etiology.…”

“…It was hypothesized that older individuals would exhibit reduced mobility within each of the four primary etiologies. Second, it was hypothesized that differences 10 in mobility would be found across etiologies of trauma, cancer, congenital, and DV, with the latter experiencing the lowest mobility. Finally, it was hypothesized that the rate of mobility decline with aging would be greatest for individuals with amputation attributable to DV etiology.…”

Development of a physical mobility prediction model to guide prosthetic rehabilitation

“…1 Moreover, previous predictions have estimated outcomes at a single time point, such as 1 year after amputation, rather than the trajectory of functional mobility change over time. [7][8][9][10][11] Particularly during the initial months after the provision of a prosthesis, patient mobility is intended to improve, 12 warranting medical necessity for the prosthesis. Understanding the estimated trajectory of functional mobility change may support prosthesis justification, long-term care planning, and informing realistic expectations of both patients and providers throughout the course of recovery.…”