Table of Contents  
ORIGINAL ARTICLE
Year : 2021  |  Volume : 8  |  Issue : 6  |  Page : 120-129

Preoperative factors influencing functional rehabilitation after major lower limb amputation and validation of a preoperative scoring tool


Department of Vascular Surgery, University Hospitals of Derby and Burton NHS Foundation Trust, Derby DE22 3NE, UK

Date of Submission13-Nov-2020
Date of Acceptance14-Dec-2020
Date of Web Publication20-Jan-2022

Correspondence Address:
Sivaram Premnath
Department of Vascular Surgery, University Hospitals of Derby and Burton NHS Foundation Trust, Derby DE22 3NE
UK
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijves.ijves_159_20

Rights and Permissions
  Abstract 


Introduction: The ability to walk with prosthesis is a major determinant of functional independence after major lower limb (MLL) amputation. The Blatchford Leicester Allman-Russell Tool (BLARt) score is a potentially valuable tool that can predict the functional outcome after MLL amputation. The study aimed to identify preoperative factors that influence functional rehabilitation after MLL amputation and validation of BLARt score. Methods: This retrospective study analyzed 71 patients referred for rehabilitation postamputation. The level of functional outcome at 6 and 12 months were recorded using Special Interest Group in Amputee Medicine grading. Preoperative factors and BLARt score were analyzed for association with functional outcome. Results: BLARt score was found to have a significant correlation with functional outcome in 6 and 12 months. BLARt showed a fair to good predictive ability with an area under the receiver operating characteristic curve of 0.713 (SE 0.61, 95% confidence intervals 0.593–0.832) and 0.705 (SE 0.061, 95% confidence intervals 0.585–0.825) for the nonfunctional outcome at 6 and 12 months, respectively. Preoperative mobility was the only significant risk factor that was associated with functional mobility (P = 0.02). Conclusion: With the validation analysis showing a fair to good predictive ability, BLARt score does serve its proposed role in risk stratification.

Keywords: Blatchford Leicester Allman-Russell Tool score, lower limb amputation, rehabilitation


How to cite this article:
Premnath S, Cox M, Hostalery A, Kuhan G, Rowlands T, Quarmby J, Singh S. Preoperative factors influencing functional rehabilitation after major lower limb amputation and validation of a preoperative scoring tool. Indian J Vasc Endovasc Surg 2021;8, Suppl S2:120-9

How to cite this URL:
Premnath S, Cox M, Hostalery A, Kuhan G, Rowlands T, Quarmby J, Singh S. Preoperative factors influencing functional rehabilitation after major lower limb amputation and validation of a preoperative scoring tool. Indian J Vasc Endovasc Surg [serial online] 2021 [cited 2022 May 25];8, Suppl S2:120-9. Available from: https://www.indjvascsurg.org/text.asp?2021/8/6/120/336015




  Introduction Top


Major lower limb (MLL) amputation is one of the most common causes of physical disability worldwide with a global incidence between 1.2 and 4.4 per 10,000 population.[1] An estimated 5500 people in the United Kingdom undergo lower-limb amputation each year, of which nearly three quarters are due to peripheral arterial disease and 7% of are due to trauma.[2] MLL amputations not only affects mobility but also impacts body image perception and participation in domestic and social activities. The quality of life after lower limb amputation is significantly associated with effective rehabilitation.[3] The ability to walk with a prosthetic limb is a primary goal of rehabilitation and is of paramount importance as far as psychological and social aspects are concerned. Being unable to walk after a lower limb amputation can lead to physical deterioration and comorbidities and be detrimental to overall health.[4] Of the total leg amputations occurring in the UK each year, 65% are referred to an amputee rehabilitation center for consideration for a prosthesis.[2]

Prediction of rehabilitation outcome, mainly walking with a prosthesis, is of great interest to physicians and therapists as well as from a patient perspective. Accurate and informed preoperative information aids in better decision-making and rehabilitation planning before surgery.[4] Meanwhile, incorrect estimation of walking potential may lead to the provision of prostheses to patients who will not be able to utilize them at a high cost to the patient and health services.[5] Age, level of amputation, comorbidities, patient motivation, and cause of amputation have been identified as significant factors that affect the ability to ambulate with a prosthesis[5],[6],[7] successfully. Bowrey et al. developed Blatchford Leicester Allman-Russell tool (BLARt) score as a preoperative assessment tool that could be used to predict the likelihood of walking with a prosthesis after surgery[8] [Table 1]. Eight preoperative variables were considered to be potentially affecting the success of rehabilitation and mobility outcome. These variables were identified from the regression analysis of 338 patients and literature review.[8] The variables identified were age, sex, body mass index (BMI), level of amputation, indication for amputation, mobility before amputation, cognitive function, and special risks which incorporated co-existing comorbidities and contralateral limb involvement. A simplified scoring system was developed and was validated in a dataset of 199 patients.[8] No further validation studies on BLARt scoring tool has been published in the literature.
Table 1: Blatchford Leicester Allman-Russell Tool scoring system

Click here to view


This study aimed to identify the preoperative factors influencing functional rehabilitation after MLL amputation and validation of the preoperative scoring tool (BLARt score) that predicts the probability of walking with a prosthetic limb.


  Methods Top


This was a retrospective study of 71 patients who underwent MLL amputation and were referred for rehabilitation to the Amputee Rehabilitation Centre of a tertiary care center from March 2018 to March 2019. Data on age, gender, BMI, cognitive level, comorbidities, contralateral limb problems, preamputation mobility, cause, and level of amputation were collected from the medical records.

Data on preamputation mobility, comorbidities, contralateral limb problems, and cognitive capacity were collected as per the BLARt assessment tool definition[8] for these variables. Severe respiratory disease was defined as a history of chronic obstructive pulmonary disease, home oxygen therapy or shortness of breath at rest. Recent myocardial infarction (MI)/Angina was defined as MI within the past 6 months or ongoing angina. The scoring for contralateral limb problems varied depending on the degree of disability. For example, underlying claudication (can weight bear), leg ulcers or knee replacement was scored 2; Score 3 was given for toe/partial foot amputation (difficulty weight bearing due to neuropathy or balance issues) and Score 4 for amputation or severe disease to a limb (not able to weight bear or stand). Bilateral limb amputations were scored as Score 4 – bilateral below knee amputations, Score 5 – one above and one below knee amputation, Score 6 – bilateral above-knee amputations. Cognitive impairment was defined as the inability of patients to retain information shortly after it had been discussed and was categorized as Score 5 – Confused (unable to understand and retain information), Score 3 – Limited Carry Over (able to understand but not retain information), Score 0 – Alert/Aware (able to understand and retain information).

Each variable was scored, and the final BLARt score [Table 1] was calculated for all patients. The levels of functional outcome at 6 and 12 months were recorded using Special Interest Group in Amputee Medicine (SIGAM) grading (A – nonlimb user, B – Therapeutic, wears only for transfers, C – limited/restricted, walks up to 50 m, D – Impaired, walks 50 m or more with walking aid, E – Independent, walks 50 m or more without walking aid, F – Normal/Near normal walking).[9] The outcome variables of rehabilitation were categorized as functional mobility in patients with a SIGAM grade C or above and nonfunctional mobility in patients with a SIGAM grade of A or B. Patients who died while on rehabilitation were also classified under the nonfunctional mobility group.

The data were entered into a Microsoft Excel template, and Statistical Package for Social Sciences 16.0 (SPSS Inc., Chicago, IL, USA) software for Windows was used for statistical analysis. Descriptive data on age and gender distribution, BMI, cognitive level, the prevalence of comorbidities and contralateral limb problems, preamputation mobility, cause and level of amputation, BLARt score and functional outcome at 6 and 12 months were analyzed. Univariate analysis was performed on the preoperative variables for their association with the functional outcome. The sensitivity, specificity, positive, and negative predictive values of the BLARt score in predicting the functional outcome were evaluated at values of 13.15 and 17. Odds ratio with 95% confidence interval of having a functional outcome at 6 and 12 months for each cut off value of BLARt score was calculated. The level of significance (P value) was evaluated using Pearson's Chi-squared test. A P < 0.050 was considered statistically significant. Predictive ability of the test was assessed by plotting the receiver operating characteristic (ROC) curve for BLARt score with the functional rehabilitation outcome in 6- and 12-month postamputation.


  Results Top


The demographic and clinical characteristics of enrolled patients are depicted in [Table 2]. A total of 71 consecutive cases were analyzed. The mean age was 65 (range 24–91) years, and 45 were males (63.4%). The mean BMI of the study population was 25.23 (standard deviation of 7.7), and 12 patients (16.9%) had a BMI >30. The peripheral arterial disease was the major cause of amputation (80.3%). Other indications included trauma (14%) and malignancy (4.2%). The level of amputation was above/through knee in 60.6% and below the knee in 36.6%.
Table 2: Patient characteristics (n=71)

Click here to view


Diabetes mellitus was present in 39.4% while recent MI (in the past 6 months) or ongoing angina was present in 15.5%. Stroke was present in 11.3%, and renal failure requiring dialysis was present in 7.0%. Severe respiratory disease was present in 5.6% of the population. Contralateral limb problems were present in 28.2%. Functional mobility (defined as SIGAM score C/D/E/F) was achieved by 38% of the MLL amputees within 6 months of rehabilitation, which increased to 46.5% at 12 months.

Patient characteristics were compared among the patients who underwent amputation for a vascular cause with amputation for other reasons [Table 3]. The mean age of patients who underwent amputation for peripheral arterial disease was higher (68.63 years standard deviation [SD] 11.725) than others (51.14 years SD 16.961). Nearly a quarter (24.6%) of the patients in the vascular cause group were of age >80 years. The proportion of males was more in the vascular group (68.4%) compared to the nonvascular group (42.9%). Approximately half (50.8%) of the patients with a vascular cause for amputation had a BMI above the average (>25). The level of amputation showed a similar distribution in both patients amputated for vascular and nonvascular causes. Nearly one-third (31.6%) of the patients who underwent amputation for vascular causes had contralateral limb problems. The proportion of comorbidities such as diabetes (45.6%), recent MI/angina (17.5%), neurological deficits (10.5%), renal problems (8.8%), and respiratory disease (7.0%) was also high in the vascular cause group. The functional outcome 6 and 12 months were better in patients operated for a nonvascular cause.
Table 3: Patient characteristics (cause of amputation-vascular/nonvascular)

Click here to view


Patient characteristics were also separately analyzed for subsets created based on the level of amputation [Table 4]. The mean age of patients who underwent below knee amputation was 62.42 while that of patients who underwent above knee/through knee amputation was 66.93. The age distribution showed a similar trend in both groups. More than a quarter (27.9%) of the patients in above knee/through knee group were aged >80 years. Nearly three quarters (73.1%) of the patients who underwent below knee amputation were male, and 58% of patients who underwent above knee/through knee amputation were male. Nearly one-third of the patients in both amputation level groups had BMI in the normal range (18.5–24.9). 7.7% and 18.6% in below knee amputation group and above/through knee amputation respectively had a BMI >30. The cause of amputation was related to arterial disease in more than three fourth of the patients in both the groups. The study of comorbidities did not show any clinically significant variation. Nearly 40% of the patients in both groups had diabetes. The functional mobility in 6 and 12 months was better in patients who underwent below knee amputation when compared to the above knee/through knee amputation group.
Table 4: Patient characteristics (level of amputation)

Click here to view


Chi-squared analysis for the association of study variables with the functional outcome at 6 months is shown in [Table 5]. Preamputation mobility was a significant risk factor for a good functional outcome (P = 0.002). More than half of the patients who were able to walk >3 miles before the surgery reached the good functional mobility level in 6 months while only 6% of the wheelchair-bound group managed to do the same showed good functional outcome. Only 26.9% of females achieved the same (P = 0.143) compared to 44.4% of men. In patients aged >80, only a quarter (26.7%) of the patients achieved a good functional outcome in the first 6 months (P = 0.65). Analysis of BMI showed that only one out of 10 patients (9.1%) with BMI >30 managed to achieve a good outcome; meanwhile, nearly half of the patients with normal BMI made a good outcome. More than half (54.4%) of the patients who underwent amputation for dysvascularity and 70% of patients with contralateral limb problems could not achieve a good outcome, but these factors were not found to be statistically significant. Even though not statistically significant (P = 0.344), patients with below knee amputation were found to have a better outcome when compared to above/through the knee and hip disarticulation. Patients with comorbidities such as diabetes, recent MI/angina, stroke, renal failure, and severe respiratory disease were all factors linked with the poor functional outcome, but this was not significant statistically.
Table 5: Univariate analysis of study variables for association with the functional outcome at 6 months

Click here to view


Chi-squared analysis for the association of the factors with the level of functional rehabilitation at 12 months as the outcome endpoint is shown in [Table 6]. Similar to the 6 months outcome results preamputation mobility status was the only significant variable that was associated with good functional mobility. Male gender, younger age, normal BMI, and below-knee amputation favoring better mobility outcome while poor preamputation mobility, presence of contralateral limb problems, dysvascularity, and comorbidities pointing to poor functional outcome. However, these factors did not reach statistical significance.
Table 6: Univariate analysis of study variables for association with the functional outcome at 12 months

Click here to view


The calculated BLARt scores at the various cut off levels were analyzed for its association with functional mobility outcome in 6 [Table 7] and 12 months [Table 8]. Even though the variables that were incorporated in BLARt score failed to reveal a significant association, the final BLARt score was found to have a statistically significant correlation with functional outcome in 6 and 12 months of rehabilitation.
Table 7: Validation analysis of Blatchford Allman Russell tool score in predicting functional mobility at 6 months

Click here to view
Table 8: Validation analysis of Blatchford Allman Russell Tool score in predicting functional mobility at 12 months

Click here to view


A BLARt <13 was significantly related to good functional outcome at 6 months (sensitivity 70.4%, specificity 59.1%, P value 0.016) and 12 months (sensitivity 66.7%, specificity 60.5%, P value 0.022). An increasing value of BLARt score showed a significant correlation with poor functional outcome. 94% and 82.4% of patients with a BLARt score of ≥17 did not achieve good functional outcome at 6 and 12 months, respectively (P = 0.002 and 0.006). Meanwhile, more than half of the patients at 6 months and nearly 60% of patients at 12 months with a BLARt score of <13 achieved a good functional outcome. The BLARt assessment was sensitive in identifying the level of walking function, but the level of specificity was found to be inadequate at higher BLARt score cut off values. Validation analysis showed a fair to good predictive ability with an area under the ROC curve of 0.713 (SE 0.61, 95% confidence intervals 0.593–0.832) and 0.705 (SE 0.061, 95% confidence intervals 0.585–0.825) for nonfunctional outcome at 6 [Figure 1] and 12 months [Figure 2], respectively.
Figure 1: ROC Curve of BLARt score against functional outcome at 6 months. The area under- the-curve is 0.713 (SE 0.061, 95% confidence intervals 0.593 – 0.832)

Click here to view
Figure 2: ROC Curve of BLARt score against functional outcome at 12 months. The area under- the-curve is 0.705 (SE 0.061, 95% confidence intervals 0.585 – 0.825)

Click here to view



  Discussion Top


MLL amputation represents a detrimental situation that not only affects patients' social function but also causes significant psychological morbidity.[10] The failure to achieve a good functional rehabilitation leads to an increase in dependence that implies a significant financial burden both for families and the health-care system.[11] Our study examined and compared the association between some critical preoperative factors and BLARt score with successful prosthetic rehabilitation.

The mean age of our study population was 65 years, with 21.1% of patients >80 years. The age distribution was similar to recent studies on lower limb amputations in the UK.[8] In most studies, older age at the time of amputation had an adverse effect on walking potential.[12],[13],[14] It may be attributed to the frailty and difficulty in acquiring new skills.[13] It has been proposed that the apparent association of age with walking ability may be confounded by comorbidities in the elderly.[12] While studies by Leung et al. and Traballesi et al did not show any significant association between age and functional outcome.[15],[16] Our study did not reveal a statistically significant association with age and functional outcome.

Female gender was associated with (but not statistically significant, P = 0.303) poor mobility outcome in our study. Most previous studies found no association between gender and walking ability after lower limb amputation.[13],[14],[17] An 8-year follow-up study on transtibial amputation patients by Hermodsson et al. revealed a better outcome in male patients.[18] The analysis of BMI showed that less proportion of patients with BMI >30 managed to achieve a good outcome, but the association was not significant. High BMI may have an impact on effective fitting and functioning of the prosthesis, and a low BMI can be associated with lack of strength. However, similar to our study, the literature review shows that BMI is not a significant predictor of walking potential, although low weight and strength may adversely affect the outcome.[19] The effect of obesity on the functional outcome in vascular amputations was studied by Kalbaugh et al. in 2006.[19]

Preoperative walking status was found to be significantly associated with good functional outcome at 6 (P = 0.002) and 12 months (P = 0.002) in our study. Patients who were wheelchair-bound before surgery are less likely to walk independently after amputation, which may be contributed to the contractures from wheelchair use, reduced core muscle strength and problems with balance.[20] The majority of studies in literature reported better walking ability after below knee amputations when compared with above/through knee amputations.[13],[15],[21] Our study demonstrated better outcome in below knee amputation patients against above/through the knee and hip disarticulation patients in 12 months (P = 0.085). Previous studies on the indication for amputation and its predictive value in the success of rehabilitation have reported a poorer outcome in those done for dysvascularity when compared to nonvascular causes.[21],[22] Meanwhile, our study did not demonstrate a significant independent association between the cause of amputation and functional outcome. Similar studies by Munin et al., Johnson et al. and Melchiorre et al. did not reveal an association with the indication for amputation and outcome of rehabilitation.[12],[13],[23]

The effect of comorbid conditions on walking has been investigated by several previous studies. This study did not reveal any statistically significant relationship between the analyzed comorbidity variables (diabetes, recent MI/angina, stroke, renal failure, and severe respiratory disease) and final functional outcome. A similar finding of no significant relationship of functional outcome with comorbidities was reported in several studies.[6],[13],[14],[17],[24],[25],[26]

Bowrey et al. selected eight preoperative variables that were considered to be potentially affecting the success of rehabilitation and mobility outcome based on regression analysis of his creation data and literature review.[8] The variables identified were age, sex, BMI, level of amputation, indication for amputation, mobility before amputation, cognitive function, and special risks which incorporated co-existing comorbidities and contralateral limb involvement. BLARt score was devised on the above variables.

The previous BLARt validation study by the parent team showed a good predictive ability of BLARt score for the nonfunctional outcome at 12 months. The area under the ROC curve was 0.914 (SE 0.2, 95% confidence intervals 0.87–0.95).[8] Unlike the parent validation study, our validation analysis showed a fair to good predictive ability with an area under the ROC curve of 0.713 (SE 0.61, 95% confidence intervals 0.593–0.832) and 0.705 (SE 0.061, 95% confidence intervals 0.585–0.825) for the nonfunctional outcome at 6 and 12 months, respectively. Using a threshold BLARt score of <13 gave a sensitivity of 70.4% and specificity of 59.1% in identifying good functional outcome in 6 months and a sensitivity of 66.7% and specificity of 60.5% for functional outcome in 12 months. The level of specificity was found to be inadequate at higher BLARt score cut off levels in predicting the functional outcome in 6 and 12 months [Table 5] and [Table 6].

A review of the literature revealed other factors which were studied for its association with good functional outcome. Representation of these variables into BLARt score can probably improve its predictive value. The interval between surgery and rehabilitation or definitive prosthetic fitting has been studied and found to be significantly associated with outcome, with a longer duration having poorer functional outcome.[16],[27] This can be attributed to the postoperative complications delaying rehabilitation. Fewer postoperative stump-related complications including wound infection, stump pain, and phantom limb have been reported to be significantly associated with a better walking ability on rehabilitation.[14],[27],[28],[29] Pohjolainen and Alaranta described the association of longer stump length with the better walking ability.[27]

Meanwhile, Munin et al. and Traballesi et al. studied the effects of contractures on the contralateral limb on postoperative walking ability.[6],[13] The ability to stand on one leg was also found to be associated with a good outcome.[30],[31] Previous two studies by Chin et al. demonstrated a significant relationship for physical fitness with walking ability following unilateral amputation. Both studies used %VO2 max during 1-leg cycling before rehabilitation as an index of physical fitness and concluded that a %VO2 max of at least 50% could be considered as a guide value for the physical fitness needed for good ambulation.[31],[32] Furthermore, the type of prosthesis and rehabilitation techniques can also have a role in walking ability. BLARt score does not take into account of these postoperative variables affecting the outcome.

Apart from these physical factors, various soft variables such as social support, patient motivation, mood disturbances, and other psychological factors do play a pivotal role in determining the outcome. Even though the grading and interpretation of these variables may not be easy, but taking into consideration may help in improving the predictive value of the overall score. Williams et al. describe the effect of social support in a 2-year study on rehabilitation.[33] Patient motivation determined subjectively from the patient discussion, and records were found to be significantly related to final walking outcome.[34] Mood disturbances including anxiety and depression have been studied as a predictor of poor walking ability. Schoppen et al. identified a significant relationship between the Beck Depression Inventory measured 2 weeks of postamputation and rehabilitation in 1 year.[30]

There were some limitations to the current study. The sample population consisted of 71 patients. Ideally, a larger data set will be needed for external validation. In the current study, BLARt scores were calculated retrospectively. Prospective validation might improve the accuracy of the dataset. The current study only included the variables used in the BLART score. There might be essential variables not included in the BLARt tool that should be included in the data set. Despite these limitations, this is the first study to try to validate existing risk model on functional mobility.

Analysis of multicenter data with an artificial neural network can also help in formulating better risk prediction models. The outcome after lower limb amputation is multidimensional, hence apart from mere walking ability, other indicators such as the postoperative mortality, degree of mobility, walking speed, as well as subjective outcomes like quality of life, changes in body image perception, psychological effects, and satisfaction as perceived by patients also need to be considered.

In summary, even though the results of this study did not demonstrate a statistically significant independent relationship with many of the preoperative variables that form the BLARt scoring, the final BLARt score was found to significantly associate with the ability to walk in 6 and 12 months of rehabilitation. With the validation analysis showing a fair to good predictive ability, BLARt score does serve its proposed role as a simple tool for risk stratification that can aid the informed consent process. However, specificity can be improved in future risk models. Postoperative factors and other significant variables can be incorporated into future models. Risk model can also be extended to predict other outcome variables including 30-day mortality and health-related quality of life. Nonlinear risk modeling techniques can be used to improve the predictive ability of risk models. A larger, adequately powered multicenter study is needed to develop new models and validate them.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Ephraim PL, Dillingham TR, Sector M, Pezzin LE, Mackenzie EJ. Epidemiology of limb loss and congenital limb deficiency: A review of the literature. Arch Phys Med Rehabil 2003;84:747-61.  Back to cited text no. 1
    
2.
Information Service Division NHS Scotland. Edinburgh. The Amputee Statistical Database for the United Kingdom 2006/2007. http://www.limbless-statistics.org. [Last accessed on 2019 Apr 08].  Back to cited text no. 2
    
3.
Samuelsson KA, Töytäri O, Salminen AL, Brandt A. Effects of lower limb prosthesis on activity, participation, and quality of life: A systematic review. Prosthet Orthot Int 2012;36:145-58.  Back to cited text no. 3
    
4.
The Vascular Society for Great Britain and Ireland. A Best Practice Clinical Care Pathway for Major Amputation Surgery. 2012. http://www.vascularsociety.org.uk. [Last accessed on 2019 Apr 08].  Back to cited text no. 4
    
5.
Sansam K, Neumann V, O'Connor R, Bhakta B. Predicting walking ability following lower limb amputation: A systematic review of the literature. J Rehabil Med 2009;41:593-603.  Back to cited text no. 5
    
6.
Traballesi M, Brunelli S, Pratesi L, Pulcini M, Angioni C, Paolucci S. Prognostic factors in rehabilitation of above knee amputees for vascular diseases. Disabil Rehabil 1998;20:380-4.  Back to cited text no. 6
    
7.
Pinzur MS, Gottschalk F, Smith D, Shanfield S, de Andrade R, Osterman H, et al. Functional outcome of below-knee amputation in peripheral vascular insufficiency: A multicenter review. Clin Orthop Relat Res 1993;286:247-9.  Back to cited text no. 7
    
8.
Bowrey S, Naylor H, Russell P, Thompson J. Development of a scoring tool (BLARt score) to predict functional outcome in lower limb amputees. Disabil Rehabil 2019;41:2324-32.  Back to cited text no. 8
    
9.
Ryall NH, Eyres SB, Neumann VC, Bhakta BB, Tennant A. The SIGAM mobility grades: A new population-specific measure for lower limb amputees. Disabil Rehabil 2003;25:833-44.  Back to cited text no. 9
    
10.
Chan KM, Tan ES. Use of lower limb prosthesis among elderly amputees. Ann Acad Med Singap 1990;19:811-6.  Back to cited text no. 10
    
11.
O'Toole DM, Goldberg RT, Ryan B. Functional changes in vascular amputee patients: Evaluation by Barthel Index, PULSES profile and ESCROW scale. Arch Phys Med Rehabil 1985;66:508-11.  Back to cited text no. 11
    
12.
Johnson VJ, Kondziela S, Gottschalk F. Pre and post-amputation mobility of trans-tibial amputees: Correlation to medical problems, age and mortality. Prosthet Orthot Int 1995;19:159-64.  Back to cited text no. 12
    
13.
Munin MC, Espejo-De Guzman MC, Boninger ML, Fitzgerald SG, Penrod LE, Singh J. Predictive factors for successful early prosthetic ambulation among lower-limb amputees. J Rehabil Res Dev 2001;38:379-84.  Back to cited text no. 13
    
14.
Helm P, Engel T, Holm A, Kristiansen VB, Rosendahl S. Function after lower limb amputation. Acta Orthop Scand 1986;57:154-7.  Back to cited text no. 14
    
15.
Leung EC, Rush PJ, Devlin M. Predicting prosthetic rehabilitation outcome in lower limb amputee patients with the functional independence measure. Arch Phys Med Rehabil 1996;77:605-8.  Back to cited text no. 15
    
16.
Traballesi M, Paolucci S, Lubich S, Pratesi L, Brunelli S. Nontraumatic above-knee amputation in elderly patients. Results of rehabilitation and prognostic factors. Eura Medicophys 1995;31:21-6.  Back to cited text no. 16
    
17.
Moore TJ, Barron J, Hutchinson F 3rd, Golden C, Ellis C, Humphries D. Prosthetic usage following major lower extremity amputation. Clin Orthop Relat Res 1989;238:219-24.  Back to cited text no. 17
    
18.
Hermodsson Y, Ekdahl C, Persson BM. Outcome after trans-tibial amputation for vascular disease. A follow-up after eight years. Scand J Caring Sci 1998;12:73-80.  Back to cited text no. 18
    
19.
Kalbaugh CA, Taylor SM, Kalbaugh BA, Halliday M, Daniel G, Cass AL, et al. Does obesity predict functional outcome in the dysvascular amputee? Am Surg 2006;72:707-12.  Back to cited text no. 19
    
20.
Taylor SM, Kalbaugh CA, Blackhurst DW, Hamontree SE, Cull DL, Messich HS, et al. Pre-operative clinical factors predict post-operative functional outcomes after major lower limb amputation: An analysis of 553 consecutive patients. J Vasc Surg 2005;42:227-35.  Back to cited text no. 20
    
21.
Geertzen JH, Bosmans JC, van der Schans CP, Dijkstra PU. Claimed walking distance of lower limb amputees. Disabil Rehabil 2005;27:101-4.  Back to cited text no. 21
    
22.
Ng EK, Berbrayer D, Hunter GA. Transtibial amputation: Pre-operative vascular assessment and functional outcome. J Prosthet Orthot 1996;8:123-9.  Back to cited text no. 22
    
23.
Melchiorre PJ, Findley T, Boda W. Functional outcome and comorbidity indexes in the rehabilitation of the traumatic versus the vascular unilateral lower limb amputee. Am J Phys Med Rehabil 1996;75:9-14.  Back to cited text no. 23
    
24.
Hubbard WA. Rehabilitation outcomes for elderly lower limb amputees. Aust J Physiother 1989;35:219-24.  Back to cited text no. 24
    
25.
OConnell PG, Gnatz S. Hemiplegia and amputation: Rehabilitation in the dual disability. Arch Phys Med Rehabil 1989;70:451-4.  Back to cited text no. 25
    
26.
Turney BW, Kent SJ, Walker RT, Loftus IM. Amputations: No longer the end of the road. J R Coll Surg Edinb 2001;46:271-3.  Back to cited text no. 26
    
27.
Pohjolainen T, Alaranta H. Predictive factors of functional ability after lower-limb amputation. Ann Chir Gynaecol 1991;80:36-9.  Back to cited text no. 27
    
28.
Datta D, Nair PN, Payne J. Outcome of prosthetic management of bilateral lower-limb amputees. Disabil Rehabil 1992;14:98-102.  Back to cited text no. 28
    
29.
Blume P, Salonga C, Garbalosa J, Pierre-Paul D, Key J, Gahtan V, et al. Predictors for the healing of transmetatarsal amputations: Retrospective study of 91 amputations. Vascular 2007;15:126-33.  Back to cited text no. 29
    
30.
Schoppen T, Boonstra A, Groothoff JW, de Vries J, Göeken LN, Eisma WH. Physical, mental, and social predictors of functional outcome in unilateral lower-limb amputees. Arch Phys Med Rehabil 2003;84:803-11.  Back to cited text no. 30
    
31.
Chin T, Sawamura S, Fujita H, Ojima I, Oyabu H, Nagakura Y, et al. %VO2 max as an indicator of prosthetic rehabilitation outcome after dysvascular amputation. Prosthet Orthot Int 2002;26:44-9.  Back to cited text no. 31
    
32.
Chin T, Sawamura S, Shiba R. Effect of physical fitness on prosthetic ambulation in elderly amputees. Am J Phys Med Rehabil 2006;85:992-6.  Back to cited text no. 32
    
33.
Williams RM, Ehde DM, Smith DG, Czerniecki JM, Hoffman AJ, Robinson LR. A two-year longitudinal study of social support following amputation. Disabil Rehabil 2004;26:862-74.  Back to cited text no. 33
    
34.
Zijp EM, Rasenberg EM, van den Bosch JS. Rehabilitation of nursing home patients with a leg amputation. A retrospective study. Tijdschr Gerontol Geriatr 1992;23:54-9.  Back to cited text no. 34
    


    Figures

  [Figure 1], [Figure 2]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
   Abstract
  Introduction
  Methods
  Results
  Discussion
   References
   Article Figures
   Article Tables

 Article Access Statistics
    Viewed827    
    Printed20    
    Emailed0    
    PDF Downloaded70    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]