Abstract
Our objective was to determine the relationship between receipt of a prescription for a prosthetic limb and three-year mortality post-surgery among veterans with lower extremity amputation. We conducted a retrospective observational study that included 4,578 veterans hospitalized for lower extremity amputation and discharged in Fiscal Years 2003 and 2004. The outcome was time to all-cause mortality from the amputation surgical date up to the 3-year anniversary of the surgical date. There were 1,300 (28.4%) veterans with lower extremity amputations who received a prescription for a prosthetic limb within a year after the surgical amputation. About 46% (n=2086) died within three-years of the surgical anniversary. Among those who received a prescription for a prosthetic limb, only 25.2% died within 3 years of the surgical anniversary. After adjustment, veterans who received a prescription for a prosthetic limb were less likely to die after the surgery than veterans without a prescription with a hazard ratio of 0.68 (95% CI, 0.60-0.77). Findings demonstrated that veterans with lower extremity amputations who received a prescription for a prosthetic limb within a year after the surgical amputation were less likely to die within three years of the surgical amputation after controlling for patient-, treatment-, and facility-level characteristics.
Keywords: prosthetic limb, mortality, veterans, lower extremity amputation, outcomes, time dependent covariate, methods
INTRODUCTION
Lower extremity amputation is a life altering event, affecting the patient’s quality of life and health, as well as their functional, economic, and psychosocial status.(1) It has been shown that there is a decrease in social participation, employment, and overall general functioning within the community following an amputation.(2) Comorbid conditions,(3) such as depression, may set in, making it difficult for people to return to their normal activities post-amputation. In addition, families may be affected as they may view the amputation as a burden forcing unwanted changes in their own lives such as modifying their work schedules or adjusting their living environment if the patient cannot return home safely without the changes.(4) Conversely, family members may become very overprotective and assist the person with the amputation such that the person may not achieve optimal functional recovery.
Rehabilitation can assist the person with an amputation regain as much function as possible. One important component of rehabilitation is the prescription and receipt of a prosthetic limb for those who are able to use one.(5) A prosthetic limb could help improve mobility, potentially leading to better quality of life. Improved mobility can allow the prosthetic wearer to be more independent in performing Activities of Daily Living (ADLs), potentially requiring less assistance from another person. Patients who have had rehabilitation for prosthetic training have shown to have higher levels of physical and cognitive functioning.(6) Even gains in low levels of physical independence achieved through rehabilitation showed improved one-year survival post-surgery among veterans with lower extremity amputation.(7)
We previously demonstrated that younger veterans, those with trans-tibial compared to trans-femoral lower extremity amputations, and those initially evaluated with higher physical and cognitive abilities were more likely to receive a prescription for a prosthetic limb. Factors negatively influencing receipt of a prosthetic limb included admission to the hospital from extended care compared to home, numerous amputation etiologies, comorbidities, and undergoing certain procedures while hospitalized.(8) We also showed that older age, higher amputation level, more comorbidities, evidence of inpatient procedures for pulmonary and renal problems, central nervous system (CNS) procedures, treatment on an intensive care unit (ICU), or medical bed section (i.e., cardiology, pulmonary) compared to a surgical bed section (i.e., neurosurgery, thoracic surgery), and regional differences were associated with one-year mortality post-surgery among veterans with lower extremity amputation.(9, 10)
One study conducted at a non-university teaching center found that the hazards of death for prosthetic users among patients who had lower extremity amputations was 80% less than for non-prosthetic wearers after adjusting for age, diagnosis, pre-functional status, and presence of coronary artery disease.(11) However, we do not know the relationship between the receipt of a prescription for a prosthetic limb and mortality post-surgery among veterans with lower extremity amputation. We hypothesized that after controlling for various patient-level, treatment-level, and facility-level characteristics, receiving a prescription for a prosthetic limb would reduce the hazards of death.
METHODS
This study was approved by the institutional review boards at the University of Pennsylvania, Philadelphia, Pennsylvania, the Samuel S. Stratton Veterans Affairs Medical Center (VAMC), Albany, New York, and the North Florida/South Georgia Veterans Health System (NF/SG VHS), Gainesville, Florida as well as by the Department of Veterans Affairs (VA) Research and Development Committees in Albany and Gainesville.
Data Description
Data used for this study were obtained from eight Veterans Health Administration (VHA) administrative databases: Patient Treatment File (PTF) inpatient database (main, bed section, surgical, procedure), two Outpatient Care Files, VA Beneficiary Identification Records Locator Subsystem (BIRLS) Death File, and the National Prosthetics Patient Database (NPPD). These databases were described and applied in previous articles,(8, 9) and have been shown to be accurate and reliable.(12-20)
In order to acquire additional information on those veterans who obtained a prescription for a prosthetic limb in the private sector after being discharged from the VAMC where the surgical amputation occurred, the Durable Medical Equipment (DME) file from the Chronic Condition Data Warehouse of the Centers for Medicare & Medicaid Services (CMS) was used to extract receipt of a prescription for a prosthetic limb. DME is a Non-Institutional claims database, which includes International Classification of Diseases-Ninth Revision-Clinical Modification (ICD-9-CM) diagnosis codes, Healthcare Common Procedure Coding System (HCPCS) codes, and dates of service submitted by DME suppliers and providers.(21)
Study Cohort Selection
There were 4,697 veterans admitted for lower extremity amputation from 125 VAMCs with acute discharge dates between October 1, 2002, and September 30, 2004 (Fiscal Years 2003 and 2004). Cases were limited to trans-tibial, trans-femoral, and hip disarticulation (surgical ICD-9-CM procedure codes: 84.10, 84.13-84.19, and 84.91).(22) Our exposure timeframe was limited to one-year post-surgery because typically patients only receive a prescription for a prosthetic limb within this timeframe (see below). Prescriptions received after the one-year post-surgery anniversary may be related to another amputation or may affect longer-term survival thus, 116 subjects were removed because their prescriptions were obtained outside of the one-year post-surgery criteria. One subject was removed for missing living location before hospitalization, and two subjects were excluded for their unspecified amputation level. Thus, 4,578 subjects remained in the study for analyses.
Exposure Measure
Receipt of a prescription for a prosthetic limb was the time varying independent exposure, which was measured from the amputation surgical date to its one-year anniversary date and was obtained from the NPPD and DME files.
Outcome
The outcome was time to all-cause mortality measured from the amputation surgical date up to 3-years. The BIRLS database was used to identify the date of death.(23)
Covariates Description
Patient-level characteristics
The sociodemographics included age, categorized as ≤65, 66-80, and ≥81 years old, gender, marital status (married versus unmarried), living location before hospitalization (extended care versus non-VA hospital or home), and patient residential setting (rural versus urban). Amputation level was categorized as either trans-tibial or trans-femoral amputation. Hip disarticulations were combined with trans-femoral amputation because of low prevalence (n=33) and to create one “above knee” category as in our previous studies.(7-10, 24-35) The new category is referred to as trans-femoral/hip disarticulation.
The contributing amputation etiologies, previously categorized by our group,(9) were identified by using ICD-9-CM diagnosis codes from PTF main and bed section files from admission up to the surgical date and from the outpatient care files from 3 months before the hospitalization admission date
The Elixhauser comorbidity index was our primary measure of comorbidities, identified through ICD-9-CM codes from the outpatient care files 3 months before the hospitalization, and from the inpatient main and bed section files up to the surgical date.(36) Diabetes mellitus and peripheral vascular disease were categorized as amputation etiologies rather than comorbidities.
Three additional clinical covariates were controlled for because the literature(8, 37-47) showed that they may contribute to the receipt of a prescription for a prosthetic limb. These clinical covariates included dementia, stroke, and vision impairment based on ICD-9-CM codes found in Table 1.
Table 1.
ICD-9-CM codes for Dementia, Stroke, and Visual Impairment
| Condition | ICD-9-CM codes |
|---|---|
| Dementia | 2900, 2901, 29010, 29011, 29012, 29013, 2902, 29020, 29021, 2903, 2904, 29040, 29041, 29042, 29043, 2908, 2909, 2941, 29410, 29411, 2948, 2942, 29420, 29421, 2949, 3310, 3311, 33111, 33119, 3312, 33182, 797 |
| Stroke | 430, 431, 432, 4320, 4321, 4329, 433, 43300, 43301, 43310, 43311, 43330, 43331 43380, 43381, 43390, 43391, 434, 43400, 43401, 43410, 43411, 43490, 43491, 436, 438, 4380, 4381, 43810, 43811, 43812, 43819, 4382, 43821, 43822, 43830, 43831, 43832, 43840, 43841, 43842, 4385, 43850, 43851, 43852, 43853, 4386, 4387, 4388, 43882, 43883, 43884, 43885, 43889, 4389 |
| Visual Impairment |
36041, 36042, 36841, 36845, 36846, 36847, 369, 3690, 36900, 36901, 36902, 36903, 36904, 36905, 36906, 36907, 36908, 3691, 36910, 36911, 36912, 36913, 36914, 36915, 36916, 36917, 36918, 3692, 36920, 36921, 36922, 36923, 36924, 36925, 3693, 3694, 37775, 9509 |
Treatment- level characteristics
Hospital events and diagnostic tests were captured from the Inpatient Procedure File from the hospital admission up to the surgical date, which included data regarding active pulmonary pathology, acute central nervous system, ongoing active cardiac pathology, ongoing wound problems, serious nutritional compromise, severe renal disease, and substance abuse/mental health issues.(8) Admission on a medical or surgical ICU anytime between the hospital admission to the surgical date, and the length to stay from admission to surgery were included to approximate patient complexity.
Facility-level characteristics
Hospital geographic setting (rural versus urban), VAMC geographic regions (Southeast versus Northeast, Midwest, South Central, or Mountain Pacific), and hospital size estimated by total number of beds (8-126 versus 127-244, 245-362, or 363-480) were included.
Other
A year variable was added to control for any differences that may have occurred in clinical practice over the two years. Year 1 (Fiscal Year 2003) included veterans discharged from October 1, 2002 to September 30, 2003, and year 2 (Fiscal Year 2004) included veterans discharged from October 1, 2003 to September 30, 2004.
Statistical Analyses
Baseline patient-, treatment-, and facility-level characteristics were compared between veterans who received and did not receive a prescription of prosthetic limb, and between those who died and did not die within 3 years of the surgery to provide a frequency distribution of the variables. Frequency and proportions were presented for the categorical variables, and means and standard deviations (SDs) for the continuous variables. Chi-square tests and Student t-tests were used to evaluate the differences between the exposure and outcome of interest to describe the cohort.
A Cox regression model was used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs). Patient-level, treatment-level, facility-level characteristics, and year were the dependent variables. The receipt of a prescription for a prosthetic limb was added to the model as a time dependent covariate. For veterans who died within the three-year anniversary period after the surgery, the time to event variable was defined as the time between the PTF surgery date and date of death. For veterans who did not die within the three-year anniversary period, time to event was defined as the time between the PTF surgery date and end of study. Variables included in the final model were selected based on clinical relevance and were only used to control for potential confounding. We used ID statement in PROC PHREG in SAS 9.2 (SAS Institute Inc., Cary, North Carolina) and VAMC is specified as the ID variable so the correlation among veterans from the same VAMC was accounted for. We applied criteria established by Belsley and coworkers(48) and used an approach illustrated by Mason(49) to test collinearity between the variables. A condition index larger than 20 is a clear indication of harmful collinearity. All statistical significance was defined by p<0.05, two-sided.
RESULTS
Among the total sample of 4,578 veterans, 1,300 (28.4%) veterans with lower extremity amputations received a prescription for a prosthetic limb within a year after the surgical amputation. Two thousand eight-six veterans (45.6%) died within three-years of the surgical anniversary. Among those who received a prescription for a prosthetic limb, only 25.2% died within 3 years of the surgical anniversary. The overall average age of this cohort was 66.9 (SD=11.2) years and 99.1% were male.
Table 2 shows the unadjusted patient characteristics comparing patients who received and did not receive a prescription for a prosthetic limb within a year of the surgical amputation. Table 3 shows the unadjusted associations of patient characteristics between patients who did and did not die within 3-years of the surgery. Both tables provide a frequency distribution of the variables and only display variables that were significant in the Cox regression model.
Table 2.
Distribution of characteristics by receipt of a prescription for a prosthetic limb
| Variable | Total N (%) | Received prescription |
Did not receive prescription |
p-value |
|---|---|---|---|---|
| Patient-level | ||||
| Age in years, mean (SD) | 66.9 (11.2) | 63.6 (10.5) | 68.3 (11.2) | <0.001 |
| Amputation level | ||||
| Trans-tibial | 2762 (60.3) | 1045 (37.8) | 1717 (62.2) | <0.001 |
| Trans-femoral/hip disarticulation | 1816 (39.7) | 255 (14.0) | 1561 (86.0) | |
| Etiologies | ||||
| Systemic sepsis, yes | 516 (11.3) | 104 (20.2) | 412 (79.8) | <0.001 |
| Systemic sepsis, no | 4062 (88.7) | 1196 (29.4) | 2866 (70.6) | |
| Comorbidities | ||||
| Arrhythmias, yes | 803 (17.5) | 177 (22.0) | 626 (78.0) | <0.001 |
| Arrhythmias, no | 3775 (82.5) | 1123 (29.7) | 2652 (70.3) | |
| Chronic pulmonary disease, yes | 923 (20.2) | 226 (24.5) | 697 (75.5) | 0.003 |
| Chronic pulmonary disease, no | 3655 (79.8) | 1074 (29.4) | 2581 (70.6) | |
| Congestive heart failure, yes | 1094 (23.9) | 242 (22.1) | 852 (77.9) | <0.001 |
| Congestive heart failure, no | 3484 (76.1) | 1058 (30.4) | 2426 (69.6) | |
| Dementia, yes | 340 (7.4) | 29 (8.5) | 311 (91.5) | <0.001 |
| Dementia, no | 4238 (92.6) | 1271 (30.0) | 2967 (70.0) | |
| Fluid and electrolyte disorders, yes | 1020 (22.3) | 230 (22.5) | 790 (77.5) | <0.001 |
| Fluid and electrolyte disorders, no | 3558 (77.7) | 1070 (30.1) | 2488 (69.9) | |
| Liver disease, yes | 164 (3.6) | 56 (34.1) | 108 (65.9) | 0.096 |
| Liver disease, no | 4414 (96.4) | 1244 (28.2) | 3170 (71.8) | |
| Metastatic cancer, yes | 61 (1.3) | 11 (18.0) | 50 (82.0) | 0.071 |
| Metastatic cancer, no | 4517 (98.7) | 1289 (28.5) | 3228 (71.5) | |
| Pulmonary circulation disease, yes | 39 (0.9) | 10 (25.6) | 29 (74.4) | 0.702 |
| Pulmonary circulation disease, no | 4539 (99.1) | 1290 (28.4) | 3249 (71.6) | |
| Renal failure, yes | 863 (18.9) | 229 (26.5) | 634 (73.5) | 0.178 |
| Renal failure, no | 3715 (81.1) | 1071 (28.8) | 2644 (71.2) | |
| Stroke, yes | 373 (8.1) | 67 (18.0) | 306 (82.0) | <0.001 |
| Stroke, no | 4205 (91.9) | 1233 (29.3) | 2972 (70.7) | |
| Treatment-level | ||||
| Acute procedure before surgery | ||||
| Severe renal disease, yes | 218 (4.8) | 42 (19.3) | 176 (80.7) | 0.002 |
| Severe renal disease, no | 4360 (95.2) | 1258 (28.9) | 3102 (71.1) | |
| ICU admission, yes | 1256 (27.4) | 277 (22.1) | 979 (77.9) | <0.001 |
| ICU admission, no | 3322 (72.6) | 1023 (30.8) | 2299 (69.2) | |
| Other | ||||
| Year 1 | 2319 (50.7) | 657 (28.3) | 1662 (71.7) | 0.921 |
| Year 2 | 2259 (49.3) | 643 (28.5) | 1616 (71.5) |
Note: This table shows a frequency distribution of each variable by receipt of a prescription for a prosthetic limb. The following variables were removed from the table since they were not significant in the final model: gender, marital status, living location before hospitalization, patient residential setting, device infection, diabetes type I, diabetes type II, local significant infection, problems with peripheral circulation, skin breakdown, trauma, alcohol abuse, chronic blood loss anemia, coagulopathy, depression, drug abuse, other neurological disorders, paralysis, psychoses, visual impairment, weight loss, active pulmonary pathology, acute central nervous system, ongoing active cardiac pathology, serious nutritional compromise, length of stay from admission to surgery, hospital geographic setting, geographic region, and number of beds.
Table 3.
Distribution of characteristics comparing those who died to those who remained alive for 3-years post-surgery
| Variable | Total N (%) | Died N=2086 |
Alive N=2492 |
p-value |
|---|---|---|---|---|
| Receipt of a prescription for a prosthetic limb | 1300 (28.4) | 328 (25.2) | 972 (74.8) | <0.001 |
| No receipt of a prescription for a prosthetic limb | 3278 (71.6) | 1758 (53.6) | 1520 (46.4) | |
| Patient-level | ||||
| Age in years, mean (SD) | 66.9 (11.2) | 69.7 (10.8) | 64.6 (11.0) | <0.001 |
| Amputation level | ||||
| Trans-tibial | 2762 (60.3) | 1097 (39.7) | 1665 (60.3) | <0.001 |
| Trans-femoral/hip disarticulation | 1816 (39.7) | 989 (54.5) | 827 (45.5) | |
| Contributing etiologies | ||||
| Systemic sepsis, yes | 516 (11.3) | 299 (57.9) | 217 (42.1) | <.0001 |
| Systemic sepsis, no | 4062 (88.7) | 1787 (44.0) | 2275 (56.0) | |
| Comorbidities | ||||
| Arrhythmias, yes | 803 (17.5) | 467 (58.2) | 336 (41.8) | <0.001 |
| Arrhythmias, no | 3775 (82.5) | 1619 (42.9) | 2156 (57.1) | |
| Chronic pulmonary disease, yes | 223 (4.9) | 125 (56.1) | 98 (43.9) | <0.001 |
| Chronic pulmonary disease, no | 3655 (79.8) | 1576 (43.1) | 2079 (56.9) | |
| Congestive heart failure, yes | 223 (4.9) | 125 (56.1) | 98 (43.9) | 0.001 |
| Congestive heart failure, no | 4355 (95.1) | 1961 (45.0) | 2394 (55.0) | |
| Dementia, yes | 340 (7.4) | 227 (66.8) | 113 (33.2) | <0.001 |
| Dementia, no | 4238 (92.6) | 1859 (43.9) | 2379 (56.1) | |
| Fluid and electrolyte disorders, yes | 1020 (22.3) | 578 (56.7) | 442 (43.3) | <0.001 |
| Fluid and electrolyte disorders, no | 4471 (97.7) | 2053 (45.9) | 2418 (54.1) | |
| Liver disease, yes | 164 (3.6) | 80 (48.8) | 84 (51.2) | 0.400 |
| Liver disease, no | 4414 (96.4) | 2006 (45.4) | 2408 (54.6) | |
| Metastatic cancer, yes | 61 (1.3) | 50 (82.0) | 11 (18.0) | <0.001 |
| Metastatic cancer, no | 4517 (98.7) | 2036 (45.1) | 2481 (54.9) | |
| Pulmonary circulation disease, yes | 39 (0.9) | 26 (66.7) | 13 (33.3) | 0.008 |
| Pulmonary circulation disease, no | 4539 (99.1) | 2060 (45.4) | 2479 (54.6) | |
| Renal failure, yes | 863 (18.9) | 530 (61.4) | 333 (38.6) | <0.001 |
| Renal failure, no | 3715 (81.1) | 1556 (41.9) | 2159 (58.1) | |
| Stroke, yes | 373 (8.1) | 215 (57.6) | 158 (42.4) | <0.001 |
| Stroke, no | 4205 (91.9) | 1871 (44.5) | 2334 (55.5) | |
| Treatment-level | ||||
| Acute procedure before surgery | ||||
| Severe renal disease, yes | 218 (4.8) | 153 (70.2) | 65 (29.8) | <0.001 |
| Severe renal disease, no | 4360 (95.2) | 1933 (44.3) | 2427 (55.7) | |
| ICU admission, yes | 1256 (27.4) | 688 (54.8) | 568 (45.2) | <0.001 |
| ICU admission, no | 3322 (72.6) | 1398 (42.1) | 1924 (57.9) | |
| Other | ||||
| Year 1 | 2319 (50.7) | 1095 (47.2) | 1224 (52.8) | 0.023 |
| Year 2 | 2259 (49.3) | 991 (43.9) | 1268 (56.1) |
Note: This table shows a frequency distribution of each variable by three-year mortality post-surgery. The following variables were removed from the table since they were not significant in the final model: gender, marital status, living location before hospitalization, patient residential setting, device infection, diabetes type I, diabetes type II, local significant infection, problems with peripheral circulation, skin breakdown, trauma, alcohol abuse, chronic blood loss anemia, coagulopathy, depression, drug abuse, other neurological disorders, paralysis, psychoses, visual impairment, weight loss, active pulmonary pathology, acute central nervous system, ongoing active cardiac pathology, serious nutritional compromise, length of stay from admission to surgery, hospital geographic setting, geographic region, and number of beds.
There were no concerns regarding collinearity among the variables in the final model since the largest condition index was 1.91. After adjusting for patient-, treatment-, and facility-level variables and year, veterans who received a prescription for a prosthetic limb were less likely to die after the surgery than veterans without a prescription (HR=0.68, 95% CI, 0.60-0.77). The hazard ratios of other patient-level and treatment-level variables significant in the Cox regression model are presented in Table 4 and are only in the model for adjustment purposes.
Table 4.
Clinically-based hypothesis-driven final model results
| Variable | Hazard Ratio |
95% Confidence Interval | p-value |
|---|---|---|---|
| Receipt of a prescription for a prosthetic limb | 0.68 | 0.60-0.77 | <0.001 |
| Patient-level | |||
| Age | 1.03 | 1.02-1.03 | <0.001 |
| Amputation level (ref: trans-femoral/hip disarticulation) |
1.33 | 1.21-1.46 | <0.001 |
| Contributing etiologies (ref: no) | |||
| Systemic Sepsis | 1.50 | 1.32-1.71 | <0.001 |
| Comorbidities (ref: no) | |||
| Arrhythmias | 1.13 | 1.01-1.26 | 0.030 |
| Chronic pulmonary disease | 1.23 | 1.11-1.36 | 0.001 |
| Congestive heart failure | 1.33 | 1.20-1.47 | <0.001 |
| Dementia | 1.32 | 1.14-1.54 | 0.003 |
| Fluid and electrolyte disorders | 1.21 | 1.10-1.34 | 0.002 |
| Liver disease | 1.57 | 1.24-1.98 | 0.002 |
| Metastatic cancer | 2.86 | 2.15-3.82 | <0.001 |
| Pulmonary circulation disease | 1.54 | 1.04-2.29 | 0.031 |
| Renal failure | 1.57 | 1.39-1.76 | <0.001 |
| Stroke | 1.31 | 1.13-1.51 | 0.004 |
| Valvular disease | 1.34 | 1.12-1.60 | 0.001 |
| Treatment-level | |||
| Acute procedure before surgery | |||
| Severe renal disease | 1.67 | 1.38-2.01 | <0.001 |
| Intensive Care Unit admission | 1.19 | 1.08-1.31 | 0.004 |
| Other | |||
| Year (ref: year 2) | 1.20 | 1.09-1.31 | 0.001 |
Note: The following variables were included in the final model but removed from the table because they were insignificant: gender, marital status, living location before hospitalization, patient residential setting, device infection, diabetes type I, diabetes type II, local significant infection, problems with peripheral circulation, skin breakdown, trauma, alcohol abuse, chronic blood loss anemia, coagulopathy, depression, drug abuse, other neurological disorders, paralysis, psychoses, visual impairment, weight loss, active pulmonary pathology, acute central nervous system, ongoing active cardiac pathology, serious nutritional compromise, length of stay from admission to surgery, hospital geographic setting, geographic region, and number of beds.
DISCUSSION
Findings supported our hypothesis that veterans with lower extremity amputations who received a prescription for a prosthetic limb within a year after the surgical amputation were less likely to die within three years of the surgical amputation after controlling for patient-, treatment-, and facility-level characteristics and year. Moreover, our time varying variable of receipt for prescription for a prosthetic limb demonstrated that the time to receipt of the prescription for a limb was associated with lower hazards of mortality. More specifically, the earlier the prescription for a prosthetic limb was received, the lower the hazards of mortality were. This makes clinical sense as patients who do not have any post-surgery or late complications may be more likely to be fitted early with a prosthesis.(50) Those with wound infections or slow healing surgical sites, severe cardiovascular disease, local or systemic infections, or other complicating factors such as non-healing wounds on their sound limb may see significant delays in the prosthetic fitting process. These patients may be more medically compromised in general than those who tolerate the surgery and post-surgery recovery without any complications, and thus have a higher mortality rate in general. The early prosthetic prescription may simply be a marker for good health and healthier people generally live longer.
However, it is critical that patients who would benefit from a prescription for a prosthetic limb obtain one as soon as they are medically stable. Post-discharge plans to assure patients return to see a prosthetic team should be a priority as intensive rehabilitation follow-up after hospital discharge could increase the prevalence of prosthetic limb users. Tracking patients via an amputation clinic or other rehabilitation clinic until they are ready for a prosthetic prescription might make a difference in timing of the prescription for those delayed by complicating factors and ultimately may result in greater survival.
Early fitting for a prosthesis may also provide a psychological boost to patients’ motivation, thus further promoting their overall health status.(51) The perception that they are “ready” for a prosthesis and the therapy needed to be a successful prosthetic limb user can bolster a patient’s confidence and sense of wellbeing. There needs to be emphasis on the role of social and psychological factors when evaluating and tailoring interventions to enable the optimal use of the prosthetic limb so that patients experience positive outcomes, such as increased social participation and community reintegration, higher quality of life, and reduce burden on the family and community.(41)
Considering psychosocial factors(50) along with physical when prescribing prosthetic limbs and targeting rehabilitation to the specific needs of the prosthetic wearer rather than focusing solely on improving function will be beneficial to the prosthetic wearer. The way the person with amputation creates meaning out of their experience influences their attitude towards use and acceptance of the prosthetic limb. The user has to believe that his or her life is worth living and needs to be able to integrate the new device into their lives.(52) When other factors are considered, quality of life will be improved and prolong survival in general. More often than not, the psychological wellbeing of the individual is more important than the fact the individual had an amputation.(53)
Provision of a prosthetic limb is typically considered for community-level ambulation, but this may be an unreachable goal for many patients with lower extremity amputations.(4) Safe transfers and short distance household ambulation may be the highest functional goal achieved by many and for others, provision of a prosthetic limb for aesthetics may be the only goal but it should not be discounted.
Prosthetic fitting rates have ranged from 50%-90% among patients with trans-tibial or trans-femoral amputations(54) while rates of actual prosthetic use have ranged from 49%-95%.(44, 55, 56) This raises a question regarding the appropriateness and effectiveness of the prosthetic intervention. Patients who receive a prosthesis may not be using the device for its full potential(57) or appropriate rehabilitation goals were not clearly developed in collaboration with the patient. One study showed that veterans who had lower extremity amputations who were in possession of a prosthetic limb primarily used a wheelchair for mobility.(57) Another study demonstrated that 16% of non-users never wore their prosthetic limb and 33% had thrown their prosthesis out during the first year.(58) Non-users may reject a prosthetic limb because of poor fit(58) or possibly because they were not adequately trained and do not feel safe using it. It will be important to address the use and nonuse of prosthetic limbs in the future especially because of the increasing costs of prosthetic limbs and the increasing prevalence of lower extremity amputations.
There were several limitations in this study. Findings may not generalize to the entire population since this was a study which included veterans. Moreover, the majority of veterans are males so it is unknown if findings can be generalized to females. Race was not included because of the large amount of missing information in the VA administrative databases. Also, although functional outcomes of those with hip disarticulations differ from those with trans-femoral amputations, we combined the two types of amputation levels because of low prevalence among those with hip disarticulations. Furthermore, it may be that more prosthetic prescriptions are written for healthier patients or that people who are most seriously ill or disabled and likely to die are much less likely to be written a prescription. Although we have attempted to control for patient characteristics, it is difficult to fully adjust for confounding by indication in clinical research especially using retrospective data. There may be other factors influencing the receipt of a prosthetic prescription that are also related to mortality that we were not able to identify, obtain, or differentiate in the data. Consequently, the association between receipt of a prosthetic limb and lower mortality should not be interpreted as causal. While we cannot make strict causal inferences, it is noteworthy that the statistical significance of the receipt of a prescription for a prosthetic limb remains even after accounting for many potential confounding clinical conditions.
CONCLUSIONS
Even after controlling for other factors known to be associated with mortality such as age, amputation level, and numerous comorbidities,(7, 9, 10, 22, 59-65) early receipt of a prescription for a prosthetic limb was a strong factor associated with lower mortality. Given that some patients may not be suitable, combined with the small cost of a prosthesis, this leads to a dilemma worth exploring because of the mortality benefit demonstrated in this study among those who received a prescription for a prosthetic limb. Clinicians need to address the users’ specific psychological, social, and physical needs when prescribing prosthetic limbs. Follow-up physical examinations should also resolve issues surrounding fit and delivery systems. Patient-centered care, including patient’s choice, opinion, and acceptance of prosthesis, can lead to higher prosthetic use and longer survival.
ACKNOWLEDGEMENT
We would like to thank Margaret G. Stineman, MD Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, for her conceptual design of this study.
No authors reported disclosures beyond the funding source. The opinions and conclusions of the authors are not necessarily those of the sponsoring agencies.
The research for this manuscript was supported by the National Institutes of Health (R01-HD042588-05A2). It is also supported by resources and the use of facilities at the University of Pennsylvania in Philadelphia, Pennsylvania, the Samuel S. Stratton Department of Veterans Affairs Medical Center in Albany, New York, and the North Florida/South Georgia Veterans Health System (NF/SG VHS) in Gainesville, Florida. Support for VA/CMS data is provided by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Health Services Research and Development, VA Information Resource Center (Project Numbers SDR 02-237 and 98-004).
Abbreviations
- (ADLs)
Activities of Daily Living
- (CNS)
Central nervous system
- (ICU)
Intensive care unit
- (VAMC)
Veterans Affairs Medical Center
- (NF/SG VHS)
North Florida/South Georgia Veterans Health System
- (VA)
Department of Veterans Affairs
- (VHA)
Veterans Health Administration
- (PTF)
Patient Treatment File
- (BIRLS)
Beneficiary Identification Records Locator Subsystem
- (NPPD)
National Prosthetics Patient Database
- (DME)
Durable Medical Equipment
- (CMS)
Centers for Medicare & Medicaid Services
- (ICD-9-CM)
International Classification of Diseases-Ninth Revision-Clinical Modification
- (HCPCS)
Healthcare Common Procedure Coding System
- (SDs)
Standard deviations
- (HRs)
Hazard ratios
- (CIs)
Confidence intervals
Footnotes
Author contributions:
Study design: Kurichi, Xie, Kwong
Analysis and statistical interpretation of the data: Kwong, Xie, Kurichi, Vogel
Drafting of the manuscript: Kurichi
Critical revision of the manuscript for important intellectual content: Bates, Kwong, Xie, Cowper, Vogel
Obtained funding: Bates, Kurichi
Administrative technical or material support: Kurichi
There are no personal conflicts of interest of any of the authors.
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