The results of this analysis underscore the effect of patient SES factors including minority race or ethnicity, low income, non-private insurance and hospital factors related to processes of care, such as LER case volume, on the risk of major amputation for CLI in a large nationally representative study. Indicators of low SES were clustered among non-White persons and those with low income. This population of vulnerable patients are not only at higher risk of undergoing major amputation but are also more likely to receive care at institutions with lower LER operative volume. These findings were also seen in a sub-analysis of patients who received a diagnostic angiogram, and thus presumably were considered as candidates for limb salvage. We also found evidence to support relationships between the number of LER procedures performed annually at each institution and the likelihood of patients being evaluated for limb salvage and undergoing an LER procedure.
The patient cohort was unique in that elderly patients over age 78 represented a quarter of the patients included in the analysis. Incidence of PAD increases with age, as does peri-operative morbidity following open surgical LER procedures. As expected, older persons remained at higher risk for limb loss after adjusting for other patient and hospital level factors. A retrospective analysis of 344 patients undergoing LER procedures found that octogenarians might benefit more than younger patients following endovascular interventions, possibly because of the high morbidity following open procedures.14
This analysis demonstrated the increased risk of major amputation among minority patients in a multiethnic population while adjusting for income, insurance status, hospital-level factors, and LER volume. The estimated effect of non-White race persisted but was diminished in the multivariate analysis that included income level and insurance status, as well as hospital-level factors and LER volume. This can be attributed to the clustering of race, income, and insurance status. With small data sets, the effect of these indicators of SES may cancel out one another due to collinearity. One of the strengths of the NIS is the large number of inpatient discharges and weighted sample design that allow sufficient power to determine the separate effects of each of the SES factors of interest.
There are several potential explanations for the increased frequency of major amputation in minority populations. A higher prevalence of distal occlusive disease possibly due to DM or genetic variations, unsuitable autogenous conduits, and unreconstructable disease may account for the greater frequency of major amputation in Black and Hispanic patients.8–9, 15–16
Although race or ethnicity may be a proxy for genetic polymorphisms that contribute to atherosclerotic disease, current knowledge of how some polymorphisms affect disease progression does not explain the heterogeneity in the severity of PAD between racial groups.17–21
Access to specialty care also varies by demographic group and may explain these disparities. Low income and minority patients are more likely to receive care at hospitals with fewer resources and limited vascular surgery and angiography capacity.7
This may contribute to the higher risk of major amputation in these populations. In a study of patients with coronary artery disease or congestive heart failure receiving primary care at community practices affiliated with academic medical centers, women, Black, and Hispanic patients had reduced access to specialist cardiology consultations and these differences contributed to a gap in clinical performance measures.22
Income and insurance status are important determinants of access to care. Patients who have low income, have Medicaid as their primary insurance, or are uninsured are more likely to seek care in emergency departments and at community health centers with limited resources.22–24
In our analysis, persons with low income, Medicaid, and Medicare were more commonly admitted to facilities that performed low numbers of LER procedures and these patients had higher odds of undergoing major amputation than LER. Patients without private insurance were also more commonly identified as members of minority groups and lower income. These results are corroborated by two studies reporting 44–91% increased risk of major amputation for patients with Medicare or Medicaid and those without insurance.3–4
Insurance status may be a proxy for quality of care for those with chronic diseases that are related to atherosclerosis such as DM. The presence of DM with complications was a significant predictor of major amputation in this analysis. Using administrative discharge abstracts and ICD-9-CM codes to indicate severity of a chronic disease, such as DM, has limitations. However, in the absence of laboratory data such as hemoglobin A1c measurements, these results are consistent with the notion that CLI severity is related to glycemic control and that better DM management improves chances of limb salvage.25–26
One of the strongest predictors in favor of LER for patients in this cohort was the presence of a procedure code for a diagnostic angiogram. Undergoing a pre-operative angiogram is negatively associated with major amputation.2
Diagnostic angiogram may be an indicator of the level of aggressiveness with which a patient is evaluated for limb salvage. We found that Black patients and those with lower income were less likely to be evaluated with angiography during the index admission. Insurance type may be related to reimbursement for angiography. In a sub-analysis of patients who did have diagnostic angiograms, we found that disparities between patients with higher and lower income and between White and minority patients persisted. In addition to the availability of angiography facilities, operative volume is an imperfect but quantifiable measure of the vascular surgery capacity of a hospital. Hospital LER volume had a significant relationship to risk of major amputation. Compared to patients at the highest volume centers those at lower volume hospitals had up to 15.2 times higher odds of undergoing major amputation. These patients were also more commonly non-White and had lower income. Patients at low volume facilities were also less likely to undergo diagnostic angiogram during the discharge recorded in NIS. Our data is similar to other reports that patients with CLI who present to higher volume hospitals are more likely to undergo a limb salvage procedure than major amputation.4
In evaluating the volume-outcome relationship for carotid endarterectomy and coronary artery bypass grafting, several authors have reported that that Black and Hispanic patients are more likely to be treated at the lowest-volume hospitals and by surgeons who perform fewer of these procedures.27–28
Higher volume hospitals may have more fellowship-trained vascular specialists, established protocols for peri-operative care of patients with CLI, and greater access to angiography facilities. Adjusted mortality is significantly lower for patients undergoing CEA, AAA, and LER in higher volume hospitals compared to those receiving care in the lowest volume centers although the differences in mortality following LER procedures may be a low as 2%.29
While these findings are informative and demonstrate the need for further research, the healthcare system characteristics that are the driving forces behind this finding are not easily studied.29–32
Surgeon training has also been found to be an important factor related to mortality and amputation rates. Vascular surgery training is associated with a 1.2% decrease in risk adjusted mortality rates and a 2.3% decrease in amputation rates.33–34
Regional variability in amputation rates may be partially explained by the availability of vascular surgeons as fewer vascular surgeons choose to live and work in medically under-served areas.35
This study has several limitations. The first is the reliability of race and ethnicity in administrative datasets. Data on race is often collected in broad categories. Hispanic, Asian/Pacific Islander, and Native American persons may be under-represented or designated as “other” which dilutes any inferences made regarding non-White persons.36
One study matched race and ethnicity data in Minnesota Medicaid enrollee files to self-report information from a telephone/mail survey and found the administrative data correctly classified 94% of cases.37
A similar study corroborating race/ethnicity data recorded in the NIS has not been published although we assume a similarly high rate of reliability in this administrative dataset.
A second limitation is missing categorical data for race. Twenty-four percent of our sample had missing values for race. Because race is recorded in administrative data by self-report which may be influenced by any number of factors related to SES, co-morbidities, and geography, these data cannot be assumed to be missing at random or missing completely at random and as such, using multiple imputation methods to adjust for missing data have their own limitations. There are several methods in the literature to address this dilemma.12
The first is to perform a complete case analysis and to exclude any discharges with missing values for a primary predictor such as race. This method would have decreased our sample size and statistical power. A second method is to create a seventh race/ethnicity category for patients with missing race data, designated as “missing race”, and to retain them in the dataset. Lastly, re-weighted estimating equations can be used to adjust the survey sampling weights by the inverse probability that race would be “observed” for a patient. The data presented in this manuscript employed re-weighted estimating equations. A complete case analysis and an analysis conducted with a “missing race” categorical variable produced results that were similar in magnitude and direction but with potentially biased standard errors compared to the analysis using the re-weighted estimating equations.
The lack of detailed information regarding angiography presents another limitation. NIS is a cross-sectional dataset and does not reflect evaluation for limb salvage preceding the discharge recorded in NIS or evaluations occurring at institutions outside of the facility where the index discharge occurred. The ICD-9-CM diagnosis codes do not capture in specific detail the anatomic level of disease, presence of outflow vessels, or availability of suitable autogenous conduit. We attempted to address this issue by excluding patients who had ICD-9-CM diagnosis codes for atherosclerosis of a bypass graft or procedure codes for revision of a lower extremity bypass graft. NIS hospital level data does not indicate the availability of angiography facilities or staff trained in endovascular techniques at the hospital where the patient underwent the procedure. To account for the availability of angiography facilities and trained staff we conducted a sub-analysis of patients who underwent a diagnostic angiogram and found that the associations between patient-level factors and hospital-level factors persisted.
Barring these limitations, our work provides additional insight into the associations between SES, co-morbidities, and hospital-level factors as they relate to LER vs. major amputation for CLI patients. The observed clustering of factors is indicative of complex social issues affecting disadvantaged patients in the healthcare system beyond the CLI condition studied here. Patient access to primary and specialist care, perception of the disease process, health literacy, and cultural values may also influence when a person with CLI seeks treatment and how treatment options are chosen. A hospital’s access to angiography facilities, the quality of peri-operative care for patients with multiple co-morbidities, and the vascular provider’s level of training also impact the aggressiveness of patient evaluation and which procedures can be safely performed.
Studying the separate contributions of these factors to disparities using administrative data allows the benefit of a large national representative sample but requires the use of imperfect proxies to describe a patient’s socioeconomic environment, access to care, and the vascular care capacity of the facilities where they are treated.38
Race, income, and insurance status are useful indicators of SES and access to care. We have shown that after controlling for co-morbidities and hospital-level factors, patients who identify as Black or Native American, have low income, and those who have Medicare or Medicaid are at higher risk for major amputation than White patients and those who have higher income or private insurance. These findings suggest there are gaps in access to care despite controlling for hospital-level factors and procedural volume. Further analysis of datasets that contain information on referral patterns and utilization of outpatient healthcare could guide potential interventions which target patients at high risk for PAD and major amputation and lead the way for implementing screening protocols focused on risk factor modification and appropriate early vascular surgery referral pathways.
The inverse relationship between LER procedure volume and risk of major amputation for CLI highlights potential solutions for disparities related to hospital-level factors. Increasing state and local funding to facilities that provide care to patients at high risk for major amputation may improve professional resources. Given the highly positive impact of pre-operative angiography on the likelihood of undergoing an LER procedure, studying the factors influencing the clinical decision to evaluate revascularization options may illustrate reasons for the less frequent use of angiography in certain patient populations and help to more widely implement standard diagnostic protocols.