Creating Adjusted Scores Targeting mobiLity Empowerment (CASTLE 1): determination of normative mobility scores after lower limb amputation for each year of adulthood.

TRAUMA surgery; LEG surgery; ARTIFICIAL limbs; REFERENCE values; CROSS-sectional method; SECONDARY analysis; FUNCTIONAL assessment; MEDICAL care; VALUE-based healthcare; QUESTIONNAIRES; AMPUTEES; AGE; RETROSPECTIVE studies; MICROPROCESSORS; LEG amputation; DIABETIC foot; HEALTH outcome assessment; CONFIDENCE intervals; COMPARATIVE studies; EVALUATION

As United States healthcare transitions from traditional fee-for-service models to value-based care, there is increased need to demonstrate quality care through clinical outcomes. Therefore, the purpose of this study was to create equations to calculate an expected mobility score for lower limb prosthesis users specific to their age, etiology, and amputation level to provide benchmarks to qualify good outcomes. A retrospective cross-sectional analysis of outcomes collected during clinical care was performed. Individuals were grouped based on amputation level (unilateral above-knee (AKA) or below-knee (BKA)) and etiology (trauma or diabetes/dysvascular (DV)). The mean mobility score (PLUS-M® T-score) for each year of age was calculated. AKAs were further stratified into having a microprocessor knee (MPK) or non-microprocessor (nMPK) for secondary analysis. As expected, average prosthetic mobility declined with age. Overall, BKAs had higher PLUS-M T-scores compared to AKAs and trauma etiologies had higher scores compared to DV. For AKAs, those with a MPK had higher T-scores compared to those with a nMPK. Results from this study provide average mobility for adult patients across every year of life. This can be leveraged to create a mobility adjustment factor to qualify good outcomes in lower limb prosthetic care. Normative values of mobility are needed to qualify good outcomes in prosthetic care as healthcare shifts towards value-based care. Understanding where an individual is relative to others with similar characteristics (e.g., age, etiology, gender, amputation level, and device type) can provide clinicians with better benchmarks for individual goal-setting. The ability to generate predicted mobility scores specific to each individual can create a mobility adjustment factor to better qualify good outcomes. [ABSTRACT FROM AUTHOR]

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