Prognosticating Prosthetic Ambulation Ability in People With Lower Limb Amputation in Early Post-Operative Phase

Abstract

Objective: To formulate a prognostication model in the early post-operation phase of lower limb amputation to predict patient's ability to ambulate with a prosthesis post rehabilitation.

Design: Retrospective cohort study, using data collected from electronic medical records. Predictive factors and prosthetic ambulation outcomes post rehabilitation were used to develop prognostic models via machine learning techniques.

Setting: Regional hospital's ambulatory rehabilitation clinic.

Participants: Patients with major lower limb amputation (N=329).

Interventions: Not applicable.

Main outcome measures: The outcome of prosthetic ambulation ability post rehabilitation collected was categorized in 3 groups: non-ambulant with prosthesis, homebound ambulant with prosthesis (AP), and community AP.

Results: In a 2-class model of non-ambulant and AP (homebound and community), the model with highest accuracy of prediction included ethnicity, total Functional Comorbidity Index (FCI), level of amputation, being community ambulant prior to amputation, and age. The f1-score and area under receiver operator curve (AUROC) of the model is 0.78 and 0.82. In a 3-class model consisting of all 3 groups of outcomes, the model with highest accuracy of prediction required 10 factors. The additional factors from the 2-class model include presence of caregiver, history of congestive heart failure, diabetes, visual impairment, and stroke. The 3-class model has a moderate accuracy with a f1-score and AUROC of 0.60 and 0.79.

Conclusion: The 2-class prognostication model has a high accuracy which can be used early post-amputation to predict if patient would be ambulant with a prosthesis post rehabilitation. The 3-class prognostication model has moderate accuracy and is able to further differentiate the walking ability to either homebound or community ambulation with a prosthesis, which can assist in prosthetic prescription and setting realistic rehabilitation goals.

Keywords: Amputation; Machine Learning; Person with amputation; Prognosis; Rehabilitation.