113
Views
0
CrossRef citations to date
0
Altmetric
Prosthetics and Orthotics

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

, , , &
Pages 1904-1910 | Received 28 Sep 2022, Accepted 22 Apr 2023, Published online: 18 May 2023
 

Abstract

Purpose

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.

Materials and Methods

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.

Results

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.

Conclusions

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.

IMPLICATIONS FOR REHABILITATION

  • 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.

Acknowledgments

The authors thank all the clinicians and patients that were involved in the collection of the outcome measures used in this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 65.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 374.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.