Why do we collect patient outcomes? For everyday clinical practice, collecting outcomes allows clinicians to track patient progress, make changes as needed to the patient’s care plan, and afford patients the ability to understand how they are doing.
The value of outcomes is increased when they are benchmarked against normative scores. These normative scores allow clinicians to understand how their patients have improved from the prior visit and that their improvement has them on track with comparable patient cases, based on various characteristics. Normative values not only allow us to track patient progress, but also contextualize patient progress in a meaningful way. It’s the difference between simply saying, “You’re doing well—you’ve improved your mobility by five points compared to earlier this year!” and, “You’re doing well—you are now in the top quarter percentile for mobility compared to your peers with similar age, amputation level, and cause of amputation!”
The Hanger Institute recently published the results of two studies aiming to provide context and benchmarks to clinical outcomes measuring mobility. GGEM (Gender, Geography, and EMployment) and CASTLE (Creating Adjusted Scores Targeting mobiLity Empowerment) both summarize large sets of patient data for individuals with lower-limb amputations.1-2
Why Study Mobility?
Improving mobility has been shown to improve a patient’s quality of life and decrease the risk of experiencing injurious falls.3-6 Decreased mobility can result in increased healthcare costs or utilization and result in a loss of employment.7-9 Therefore, there is a strong emphasis on restoring functional mobility in individuals with lower-limb amputations to improve their outcomes physically, mentally, and financially.
Effect of Gender, Geography, and Employment
In the Hanger Institute’s recent GGEM study, data from 7,524 patients was analyzed to understand trends in mobility relative to gender, geography, and employment.1 Results found employed individuals were 3.6 times more likely to report increased mobility compared to those unemployed. Men were 1.86 times more likely to report increased mobility compared to women. Lastly, those in the Northeast United States were 1.30 times more likely to report increased mobility compared to those in the Southern United States.
The main results of this study highlight deficits in mobility to inform future clinical care. Additional research is needed to investigate the underlying causes of these disparities to properly address a solution. For example, there are known differences in state Medicaid prosthesis coverage policies for states in the Northeast versus the Southern region. In addition, there are broader healthcare inequities that may also trickle down into patients’ prognosis for success in prosthetic rehabilitation. Beyond the health inequities that GGEM is starting to demonstrate, there are opportunities related to employment and workplace accommodations for those re-entering the workforce after amputation that may also show benefits in improving mobility.
Understanding Trends, Benchmarks, and Goal Setting
In the CASTLE study, mobility scores from 29,522 patients were analyzed to generate age-mobility plots showing the change in expected mobility across age, based on amputation level and cause of amputation.2 This study was an expansion of the Institute’s previously published MAAT 7 study.10 Overall, investigators found that mobility declined with age, men reported higher mobility than women (similar to the results from GGEM), individuals with transtibial amputations had higher mobility than those with transfemoral amputations, and individuals with traumatic amputations had higher mobility than those with amputations due to diabetes/dysvascular disease.
Equations were generated from the plots to enable clinicians to calculate expected mobility using a patient’s age. This provides patients and clinicians with a goal for patients’ mobility potential (as defined by data from patients’ peers), expectations as patients age, and normative values to compare performance throughout their care.
Influence of Prosthesis Technology
The CASTLE 1 manuscript also preliminarily explored the impact of prosthesis technology on mobility across age. Comparisons were made between microprocessor knees and nonmicroprocessor knees for individuals with amputations due to either trauma or diabetes/dysvascular disease. Results from this analysis demonstrated increased mobility across all ages for both causes of amputations when patients were fitted with a microprocessor knee. Further, for those with amputations due to diabetes/dysvascular disease, microprocessor knees may change the expected trajectory of mobility—especially in advanced age. Further research studies are underway to understand the causation of this relationship and how microprocessor knee technology can benefit older patients.
The Future of Outcomes
Aside from goal setting, we can also leverage normative datasets to compare patient performance while accounting for intrinsic and extrinsic factors. For example, two patients, Patient A and Patient B, have identical PLUS-M t-scores of 54. Based on how t-scores are calculated, a t-score of 50 is the population average. Therefore, we expect both patients to be slightly above average in their mobility compared to the entire population of lower-limb prosthesis users. However, if we consider their unique patient characteristics (age, amputation level, and cause of amputation), we can adjust their t-scores and better understand how they compare to their peers with similar demographics.
For example, Patient A is a 55-year-old individual with a transfemoral amputation due to diabetes. From CASTLE 1, we can calculate that the average t-score for this subgroup of patients is 41.3, quite a bit below the population average of 50. Subsequently, it becomes clear Patient A is slightly outperforming the average lower-limb prosthesis user, but greatly surpassing peers of similar demographics. His or her t-score of 54 is much higher than the t-score of his or her peers at 41.3. On the other hand, Patient B is similar age of 55 years old but has a transtibial amputation due to trauma. The average t-score for this subgroup of patients is 54.3; therefore, Patient B is close to the average of his or her peers. In these cases, the normative scores help clinicians and patients easily understand whether they are above or below average compared to their peers.
What’s Next?
As additional data is aggregated, we can further stratify patients into groups, such as those with specific comorbid health conditions, those with amputation etiologies other than trauma or dysvascular disease, and those with amputation levels other than transfemoral or transtibial (i.e., partial foot and hip disarticulation). We can also explore the relationship between mobility and well-being against other social determinants of health (i.e., community distress, education level, poverty level, and race/ethnicity). Large patient datasets allow us to better understand the prosthetic limb–user community and provide targeted care based on a patient’s specific needs.
In addition, the current research provides clinicians with expectations surrounding mobility, but it is missing a systematic process to improve mobility. One of the Hanger Institute’s next steps is to understand the components that make up mobility to provide clinicians and patients with a road map of targeted rehabilitation goals. For example, if a patient has a t-score of 42, what are the specific exercises to work on in physical therapy or targeted prosthetic device options to improve his or her t-score and overall mobility?
These future studies and research questions would not be possible without access to large patient datasets. The future of O&P research hinges on the ability to analyze patient trends and subsequently implement clinical practice guidelines and programs to attain the highest possible outcome for each patient.
We want to thank all the clinicians and patients who have participated and continue to participate in the collection of outcome measures. These are the leaders in our field, and the change agents that are advancing O&P care beyond just device delivery.
Bretta L. Fylstra, PhD, is a research scientist with the Hanger Institute for Clinical Research and Education, Texas. She can be contacted at [email protected].
References
- England, D. L., T. A. Miller, P. M. Stevens, J. H. Campbell, and S. R. Wurdeman. 2023. GGEM: Gender, geography, and employment differences based on mobility levels among lower- limb prosthesis users living in the United States. Prosthetics & Orthotics International 47:265-71.
- Fylstra, B. L., D. L. England, P. M. Stevens, J. H. Campbell, and S. R. Wurdeman. 2023. Creating adjusted scores targeting mobiLity empowerment (CASTLE 1): Determination of normative mobility scores after lower-limb amputation for each year of adulthood. Disability and Rehabilitation 1-7. https://doi.org:10.1080/09638288.2023.2208376
- Wurdeman, S. R., P. M. Stevens, and J. H. Campbell. 2018. Mobility analysis of AmpuTees (MAAT I): Quality of life and satisfaction are strongly related to mobility for patients with a lower-limb prosthesis. Prosthetics and Orthotics International 42:498-503.
- Norvell, D. C., A. P. Turner, R. M. Williams, K. N. Hakimi, and J. M. Czerniecki, 2011.Defining successful mobility after lower extremity amputation for complications of peripheral vascular disease and diabetes. Journal of Vascular Surgery 54:412-9.
- Pell, J. P., P. T. Donnan, F. G. Fowkes, and C. V. Ruckley. 1993. Quality of life following lower limb amputation for peripheral arterial disease. European Journal of Vascular Surgery 7:448-51.
- Miller, T. A., R. Paul, M. Forthofer, and S. R. Wurdeman. 2023. Stability and falls evaluations in AMPutees (SAFE-AMP 2): Reduced functional mobility is associated with a history of injurious falls in lower limb prosthesis users. Annals of Physical and Rehabilitation Medicine 66(4):101679.
- Miller, T. A., R. Paul, M. Forthofer, and S. R. Wurdeman. 2020. Impact of time to receipt of prosthesis on total healthcare costs 12 months postamputation. American Journal of Physical Medicine and Rehabilitation 99(11):1026-31.
- Miller, T. A., R. Paul, M. Forthofer, and S. R. Wurdeman. 2021. The role of earlier receipt of a lower limb prosthesis on emergency department utilization. PM&R 13:819-26.
- Whyte, A. S., and L. J. Carroll. 2002. A preliminary examination of the relationship between employment, pain and disability in an amputee population. Disability and Rehabilitation 24:462-70.
- England, D. L., T. A. Miller, P. M. Stevens, J. H. Campbell, and S. R. Wurdeman. 2022. Mobility analysis of AmpuTees (MAAT 7): Normative mobility values for lower limb prosthesis users of varying age, etiology, and amputation level. American Journal of Physical Medicine and Rehabilitation 101:850-8.
