Author + information
- Received January 30, 2017
- Accepted February 12, 2017
- Published online April 10, 2017.
- Shikhar Agarwal, MD, MPHa,∗ (, )
- James M. Pitcavage, MSPHb,
- Karan Sud, MDc and
- Badal Thakkar, MD, MPHd
- aDepartment of Cardiology, Section of Interventional Cardiology, Geisinger Medical Center, Danville, Pennsylvania
- bInstitute for Advanced Application, Geisinger Health System, Danville, Pennsylvania
- cDepartment of Internal Medicine, Mount Sinai St. Luke’s Hospital, New York, New York
- dDepartment of Internal Medicine, Rutgers New Jersey Medical School, Newark, New Jersey
- ↵∗Address for correspondence:
Dr. Shikhar Agarwal, Department of Cardiology, Section of Interventional Cardiology, Geisinger Medical Center, 100 North Academy Avenue, Danville, Pennsylvania 17822.
Background Readmissions constitute a major health care burden among critical limb ischemia (CLI) patients.
Objectives This study aimed to determine the incidence of readmission and factors affecting readmission in CLI patients.
Methods All adult hospitalizations with a diagnosis code for CLI were included from State Inpatient Databases from Florida (2009 to 2013), New York (2010 to 2013), and California (2009 to 2011). Data were merged with the directory available from the American Hospital Association to obtain detailed information on hospital-related characteristics. Geographic and routing analysis was performed to evaluate the effect of travel time to the hospital on readmission rate.
Results Overall, 695,782 admissions from 212,241 patients were analyzed. Of these, 284,189 were admissions with a principal diagnosis of CLI (primary CLI admissions). All-cause readmission rates at 30 days and 6 months were 27.1% and 56.6%, respectively. The majority of these were unplanned readmissions. Unplanned readmission rates at 30 days and 6 months were 23.6% and 47.7%, respectively. The major predictors of 6-month unplanned readmissions included age, female sex, black/Hispanic race, prior amputation, Charlson comorbidity index, and need for home health care or rehabilitation facility upon discharge. Patients covered by private insurance were least likely to have a readmission compared with Medicaid/no insurance and Medicare populations. Travel time to the hospital was inversely associated with 6-month unplanned readmission rates. There was a significant interaction between travel time and major amputation as well as travel time and revascularization strategy; however, the inverse association between travel time and unplanned readmission rate was evident in all subgroups. Furthermore, length of stay during index hospitalization was directly associated with the likelihood of 6-month unplanned readmission (odds ratio for log-transformed length of stay: 2.39 [99% confidence interval: 2.31 to 2.47]).
Conclusions Readmission among patients with CLI is high, the majority of them being unplanned readmissions. Several demographic, clinical, and socioeconomic factors play important roles in predicting readmissions.
Hospital readmissions have been identified as a major source of health care burden and have been associated with increased risk of adverse outcomes (1,2). Readmission has now become an important performance metric for hospitals and will soon become a target of reimbursement-based improvement incentives by Medicare and Medicaid (3). This is of particular interest to the care of vascular patients, as the rate of readmissions following vascular procedures has traditionally been high (4,5).
Critical limb ischemia (CLI) represents an advanced stage in the spectrum of peripheral arterial disease (PAD) and is associated with considerable morbidity and mortality (6–8). There have been considerable changes in the landscape of CLI over the last decade (9). As we have reported earlier, there has been a significant reduction in the rate of surgical revascularization procedures and major amputations among these patients, with a simultaneous increase in the number of endovascular revascularization procedures (9). Despite this, the annual rate of CLI admissions has been constant in the last decade (9). The current data on readmissions in CLI patients are heterogeneous (2,5,10–13). The rate of readmission at 30 days has ranged from 11.9% to 23.9% (2,5,10–13). To the best of our knowledge, the data on readmission rates beyond 30 days are relatively scarce. In addition, most of the readmission-related data have been published only in the context of surgical revascularization, endovascular revascularization, or amputation procedures. It may be pertinent to note that CLI represents a spectrum of a disease process, wherein patients experience a multitude of other comorbidities like coronary artery disease, cerebrovascular disease, diabetes, congestive heart failure, and so on, and have been shown to have a high rate of subsequent ischemic events (14). In addition, ∼45% of admissions among patients with CLI have been related to non-CLI causes (9). Furthermore, the proportion of admissions among CLI that require therapeutic procedures during the hospital stay is low (∼30%) (9).
To that end, we aimed to evaluate the incidence of 30-day and 6-month readmission rates among patients admitted primarily for CLI using a large administrative in-hospital database spanning multiple states in the United States. We also evaluated independent predictors of 30-day and 6-month unplanned readmissions. In addition, we evaluated the effect of hospital characteristics, length of stay (LOS), as well as travel time to the index hospital upon in-hospital mortality and unplanned readmissions.
Data were obtained from the State Inpatient Databases (SID), comprising of all in-hospital admissions in a specific state. We used data from the states of Florida (2009 to 2013), New York (2010 to 2013), and California (2009 to 2011), as these representative states provided data on repeat admissions. The SID is sponsored by the Agency for Healthcare Research and Quality as a part of Healthcare Cost and Utilization Project (HCUP). The SID for the states of Florida, New York, and California include a visit linkage variable (VisitLink) that can be used in tandem with the timing variable (DaysToEvent) to study multiple hospital visits for the same patient across hospitals and time while adhering to strict privacy regulations.
The SID provides the list of diagnoses and procedures for each hospitalization record. These have been coded using the standard International Classification of Diseases-9th Edition-Clinical Modification (ICD-9-CM) codes. All adult hospitalizations (>18 years of age) with a diagnosis code corresponding to CLI were included in our study. The list of diagnosis codes used to identify patients with CLI and PAD is shown in Online Table 1. The first diagnosis in the database is referred to as the “principal diagnosis” and is considered the primary reason for admission to the hospital. A patient was said to have a “primary CLI admission” if the principal diagnosis for admission corresponded to CLI or the principal diagnosis for admission corresponded to PAD along with secondary diagnoses of ulcers, osteomyelitis, and so on, or the patient underwent a revascularization procedure or major amputation procedure during the hospitalization. The ICD-9-CM codes for surgical and endovascular procedures performed during the hospitalization are shown in Online Table 2. Sequential revascularization was defined as both endovascular and surgical revascularizations performed during a single hospital admission. We used the Charlson comorbidity index to quantitate the burden of comorbidities of each admitted patient based on 17 categories of diagnoses (15). In addition, the SID provides 29 Elixhauser comorbidities on each hospital admission, based on standard ICD-9 codes (16). These were used to derive the prevalence of hypertension, diabetes, obesity, and chronic kidney disease in our population. To avoid double-counting admissions in the study, we excluded admissions that resulted in a transfer of a patient to another acute care hospital.
Our study aimed to evaluate the incidence of readmission at 30 days and 6 months among patients with primary CLI admissions. All admissions labeled as “emergent,” “urgent,” or “unscheduled” in SID were coded as unplanned readmissions. The incidence of readmission has been expressed as a proportion of all admissions that represented readmissions within a particular time interval. In-hospital mortality was a secondary endpoint of the study. In addition, we aimed to evaluate the factors that predicted in-hospital mortality, as well as 30-day and 6-month unplanned readmissions among primary CLI admissions. All available demographic, clinical, as well as hospital-related characteristics were considered.
To evaluate the effect of hospital type upon study outcomes, we procured the hospital-specific data from the American Hospital Association for the corresponding years (17). Four specific variables were studied: location (rural vs. urban), teaching affiliation (nonteaching, minor, major), turnover status quartile, and occupancy status quartile. The turnover status was calculated based on the ratio of annual number of admissions to total number of hospital beds. The occupancy status quartile was calculated based on the ratio of average daily census to total number of hospital beds.
The incidence of all-cause readmissions and unplanned readmissions was calculated at 30 days and 6 months for all eligible primary CLI admissions. Multivariable multilevel hierarchical logistic regression analysis with exchangeable matrix (clustered by unique patients) was utilized to determine independent predictors of 30-day and 6-month unplanned readmissions among patients with primary CLI admissions who subsequently survived to hospital discharge. The primary hospital of presentation and the hospital state served as the levels of strata in the hierarchical modeling to account for clustering of outcomes in particular hospitals as well as particular states. Besides this, covariates included age, sex, race, history of prior amputation, revascularization during hospitalization, major amputation during hospitalization (ankle and above), Charlson comorbidity index, hospital characteristics (location, teaching affiliation, occupancy status, turnover status), disposition, and LOS. For regression modeling, LOS was logarithm transformed to eliminate the rightward skew. In addition, we evaluated the possibility of statistical interactions between the covariates in a systematic fashion. To avoid the possibility of type I error due to multiple testing, we report 99% confidence intervals (CIs) for all of our regression analyses. All statistical analyses were performed using the statistical software Stata version 13.1 (Stata Corp., College Station, Texas).
We hypothesized that the travel time between the patient’s primary residence and the hospital would affect the incidence of readmission in CLI population due to limitations on mobility that the underlying disease imposes on patients. Residential zip codes for each patient were available for patients living in New York and Florida. We used geographic and routing analysis available from the CDXzipstream (CDX Technologies, Randolph, New Jersey) to calculate the travel time between the patient’s residential zip code and the hospital location (available as zip + 4). The effect of travel time upon outcomes was determined by incorporating this as a covariate in the previously mentioned multivariable hierarchical logistic regression model that was utilized to determine independent predictors of in-hospital mortality and readmission. For regression modeling, travel time was incorporated as discrete categories (≤20, 20 to 40, 40 to 60, and >60 min). We evaluated interactions of travel time with primary payer, revascularization method, amputation status, as well as disposition.
A total of 212,241 unique patients with 695,782 hospital admissions were included. Of these, 284,189 hospital admissions were primary CLI admissions. Table 1 demonstrates the baseline characteristics of all hospital admissions and primary CLI admissions included in the study. Table 1 also demonstrates the important hospital characteristics of all hospitals that were included in our study.
Table 2 demonstrates important aspects of hospitalizations among the study population. In-hospital mortality among primary CLI admissions was 2.3%. The rate of endovascular revascularization alone, surgical revascularization alone, sequential revascularization, and major amputation was 16.6%, 10.7%, 2.9%, and 11.8%, respectively. The need for rehabilitation facility and home health care was observed in 36.9% and 28.1% of primary CLI admissions. Only 33.6% of all primary CLI admissions were discharged home from the hospital.
All-cause readmissions and unplanned readmissions
All-cause readmission rates within 30 days and 6 months for all primary CLI admissions were 27.1% and 56.6%, respectively (Table 3). The majority of these readmissions were unplanned readmissions. Unplanned readmission rates within 30 days and 6 months for primary CLI admissions were 23.6% and 47.7%, respectively. The Central Illustration demonstrates the progressive increase in the proportion of all admissions that represented readmissions. Although there was a small but statistically significant difference in the unplanned readmission rates in New York, compared with California and Florida, the trend of progressive increase over time remained the same in all states (Figure 1). Table 4 demonstrates the outcomes among planned readmissions compared with unplanned readmissions. In-hospital mortality was higher among unplanned readmissions as compared with planned readmissions. However, the incidence of revascularization procedures and major amputations was higher during planned readmissions as compared with unplanned readmissions.
Reasons for unplanned readmission
Figure 2 demonstrates the common reasons for unplanned readmission within first 30 days (Figure 2A) and during 30 days to 6 months (Figure 2B) following initial admission. Besides miscellaneous causes for readmission, primary CLI-related causes, post-procedure complications, septicemia, and diabetes-related nonvascular causes were most common causes for unplanned readmissions. Online Figures 1 to 4 demonstrate the common reasons for readmission within first 30 days and during 30 days to 6 months following initial admission for endovascular revascularization, surgical revascularization, sequential revascularization, and major amputation, respectively. Post-procedure complications constituted 15.7% and 16.8% of 30-day and 30-day to 6-month repeat admissions in the surgical revascularization cohort, which were significantly higher than those undergoing endovascular revascularization (30-day: 9.1%; 30-day to 6-month: 9.4%). Among those undergoing major amputation (ankle and above), readmission secondary to septicemia (30-day: 15.8%; 30-day to 6 months: 13.0%), respiratory disorders (30-day: 6.0%; 30-day to 6-month: 6.1%), and acute kidney injury (30-day: 2.4%; 30-day to 6-month: 2.3%) were significantly higher compared with the entire cohort of primary CLI patients.
Predictors of in-hospital mortality and unplanned readmission
Table 5 demonstrates the predictors of in-hospital mortality in the study cohort. There was a significant interaction noted between the age and the type of insurance. There was an increase in in-hospital mortality with increase in age. However, among patients age >80 years, privately insured patients had significantly higher in-hospital mortality compared with those insured by Medicare or those with Medicaid/no insurance. Besides this, female sex, revascularization during hospitalization, major amputation during hospitalization, and greater number of comorbidities were associated with significantly higher in-hospital mortality.
The predictors of 6-month unplanned readmission are shown in Table 6. There was a significant interaction noted between the age and the type of insurance. Compared with privately insured patients, those with Medicare and Medicaid/no insurance had significantly higher 30-day and 6-month unplanned readmissions among all age-strata (Table 6). Besides this, female sex, black or Hispanic race, prior amputation, and higher Charlson comorbidity index were associated with higher incidence of unplanned readmission. Notably, revascularization (surgical, endovascular, or sequential) was associated with lower 6-month unplanned readmission as compared with no revascularization (Table 6). In addition, discharge to home health care or discharge to rehabilitation facility was associated with a small but significant increase in 6-month unplanned readmissions, compared with those that were discharged home. Furthermore, LOS during index admission was significantly associated with the incidence of unplanned readmission within 30 days and 6 months. As shown in Figure 3, there is a progressive increase in the incidence of unplanned readmission at 30 days and 6 months with increasing LOS during prior admission. Online Table 3 demonstrates the predictors of 30-day unplanned readmission among primary CLI admissions.
Impact of hospital characteristics
Online Table 4 demonstrates the unadjusted in-hospital mortality and unplanned readmission rates stratified by hospital type and characteristics. Compared with rural hospitals, the unadjusted (Online Table 4) as well as adjusted (Table 5) in-hospital mortality was significantly higher among urban hospitals. There was a progressive decrease in in-hospital mortality with increase in annual hospital turnover. Besides this, there was a progressive increase in in-hospital mortality with increase in the occupancy level of the hospital (based on the average daily census of hospital) (Table 5).
The 30-day and 6-month unplanned readmission rates for urban hospitals were significantly higher than the rural hospitals (p < 0.001 for both comparisons) (Table 6, Online Tables 3 and 4). Similarly, the 30-day and 6-month unplanned readmission rates for hospitals with major teaching affiliation were significantly higher than the nonteaching hospitals (p < 0.001 for both comparisons). On adjusted analysis, there was a progressive increase in the 6-month unplanned readmission rate across the hospital turnover quartiles (Table 6). The mean LOS for highest turnover hospitals was 8.8 days (95% CI: 8.7 to 8.9 days), which was significantly smaller than that for the lowest turnover hospitals (mean LOS: 11.9 days [95% CI: 11.7 to 12.2 days]; p < 0.001). On adjusted analysis, there was no significant effect of occupancy status on 30-day unplanned readmission rates; however, the hospitals in the highest occupancy quartile had significantly higher 6-month unplanned readmission rates compared with the lowest quartile hospitals.
Subgroup analysis: Effect of travel time
Data from patients residing in New York and Florida were used for this analysis. We used geographic and routing analysis to calculate the travel time between the patients’ residential zip codes. The 6-month unplanned readmission rate for patients residing ≤20, 20 to 40, 40 to 60, and >60 min from the hospital were 50.2%, 46.9%, 43.2%, and 38.1%, respectively (p trend <0.001). Multivariable hierarchical logistic regression analysis was performed in this subgroup, adding travel time as a covariate. There was a significant interaction between travel time and revascularization as well as travel time and amputation status. Figure 4 demonstrates the effect of travel time on 6-month unplanned readmission rates, stratified by the major interaction variables. Despite significant interaction, the inverse association between travel time and unplanned readmission rate was evident in all of these subgroups (Figure 4). Similar to the 6-month readmission, the inverse association between travel time and 30-day unplanned readmission rates was also clearly evident in all the previously mentioned subgroups (data not shown).
The current study has evaluated the incidence and predictors of all-cause and unplanned readmission at 30 days and 6 months among patients admitted with CLI, using a large administrative database spanning multiple states. We have several important findings. First, readmission rate at 30 days and 6 months was high in CLI patients, the majority being unplanned readmissions. Second, female sex, black/Hispanic race, prior amputation, and higher number of comorbidities were associated with higher rate of unplanned readmissions. Third, revascularization-related admissions were associated with less frequent unplanned readmissions compared with admissions where no revascularization was performed. Fourth, unplanned readmissions were associated with higher mortality compared with planned readmissions. The incidence of revascularization procedures and major amputations were higher during planned readmissions compared with unplanned readmissions. Fifth, post-procedure complications accounted for a greater proportion of readmissions among surgical revascularization patients as compared with those undergoing endovascular revascularization. Among patients undergoing major amputations, septicemia was a frequent cause for readmission and accounted for as high as 16% of repeat admissions at 30 days. Sixth, high turnover hospitals had lower in-hospital mortality but a higher rate of 30-day and 6-month unplanned readmissions compared with low turnover hospitals. Seventh, higher LOS during index admission was associated with higher incidence of readmission at 30 days and 6 months. Last, greater travel time between a patient’s residence and the hospital was associated with a lower incidence of unplanned readmission during follow-up.
These study results have important implications for the U.S. health care system, which is facing a dramatic transformation with the implementation of the Affordable Care Act (ACA). Besides provision of health care insurance and ensuring high-quality care for all U.S. citizens, ACA primarily aims to address the rising health care costs. Unplanned hospital readmissions contribute to a staggering amount of annual health care expenditure (18–20). It is therefore not surprising that unplanned hospital readmission rates are being used as a quality-of-care metric for hospitals with adverse financial implications for excessive rates of readmission.
CLI is among the most difficult conditions to manage due to significant overall patient complexity. CLI represents a disease spectrum that is not only limited to peripheral arteries but includes systemic deterioration including brain, heart, and kidneys, along with adverse alterations in inflammation and homeostasis. As pointed out earlier, less than one-third of admissions among CLI patients result in a revascularization procedure (9). In addition, we and others have demonstrated that procedure-related complications are infrequent causes of repeat admission among these patients (2). It has been demonstrated that CLI patients with prior amputations were at a significantly higher risk of subsequent ischemic events including coronary artery disease, cerebral ischemia, and other atherosclerotic ischemic events as compared with those without CLI and prior amputations (14). This suggests that CLI is a multiorgan systemic disorder that may not be solely addressed by a revascularization procedure, but requires a multifaceted and multidimensional approach to care. Despite improvements in this specialty, CLI care remains heterogeneous with a lack of adherence to current guidelines with respect to the management of these patients (21–23).
We have demonstrated that a greater number of comorbidities were associated with more readmissions at 30 days and 6 months. Using a combined cohort of medical and surgical patients, van Walraven et al. (24) have demonstrated a direct effect of rising Charlson comorbidity index on the rate of readmission. In the context of vascular surgical procedures, an increased number of comorbidities has been associated with a greater likelihood of readmission at 30 days (25). Another predictive factor from our analysis for readmission was LOS, with a greater LOS during index admission predictive of a higher incidence of readmission in the future. This is consistent with findings reported earlier (25). In addition, we have demonstrated earlier that LOS is an important predictor of long-term mortality among patients that present with acute myocardial infarction (26). LOS is associated with age and other comorbidities along with several immeasurable variables like patient frailty, social support, self-confidence, and education status, as well as patient’s ability to deal with a new problem along with physician’s interpretation of the patient’s outpatient needs. As several of these variables are highly subjective, LOS might serve as an important determinant of this “immeasurable comorbidity.” With a rising emphasis on shortening LOS, health care providers are often hard-pressed to discharge patients early; however, providers and policy makers must realize that patients who require longer hospital stays are the ones that possess maximum risk of subsequent readmission. Although 30-day readmissions have been traditionally used as a quality metric on which the U.S. Centers of Medicare and Medicaid Services bases reimbursement for percutaneous coronary intervention, adoption of a similar policy for CLI requires some deliberation and caution. This is because most readmissions in CLI are not for procedural complications, and most of these are for noncardiovascular or chronic wound-care–related or infectious issues. Our study, along with several others, reinstates the need for rational clinical pathways where the focus should be on treating underlying medical comorbidities, improving access to medical care, and intensifying outpatient management of wound care and pain control (2,27).
Besides this, there were several other important factors that predicted readmission in our study. Female sex was associated with higher readmission at 6 months compared with male sex. It has been suggested that women with PAD are more likely to experience faster functional decline as compared with men (28). In addition, sex-based disparities in treatment also exist, which may contribute to different sex-based outcomes (29). Female sex has also been shown to be associated with wound complications in lower extremity bypass surgery, which is a common reason for readmission (30,31). In our analysis, black and Hispanic race was associated with more frequent readmission at 30 days and 6 months, compared with white race. This may be suggestive of racial disparities prevalent in the current health care environment along with more severe disease present among blacks compared with white patients (25,29).
There was a significant role of hospital type in in-hospital mortality and readmission rates. The differences in in-hospital mortality and readmission rates between the rural and urban hospitals as well as major teaching and nonteaching hospitals could be explained by the referral bias and the differences in the acuity of illness of the patients presenting at these hospitals. Interestingly, we also demonstrated differences in outcomes based on turnover status and occupancy status. High-turnover hospitals had significantly low in-hospital mortality but a higher unplanned readmission rate compared with low-turnover hospitals. In addition, high-occupancy hospitals had significantly higher in-hospital mortality compared with low-occupancy hospitals. Whether this is secondary to inherent differences in the nature of patients presenting to these hospitals or because of differences in care rendered at these hospitals is not completely clear from our analysis. Despite this, these findings are certainly hypothesis generating for future studies.
Our study demonstrated a significant effect of travel time on unplanned readmission rates among patients with CLI. To the best of our knowledge, this finding has never been reported in the context of CLI patients. Identification of a causal relationship between travel time and health outcomes is not straightforward because patient preferences as well as patient characteristics may be differentially related to “willingness to travel” and health outcomes. Patients may travel further as they may have a stronger will to live or a better understanding of differences between hospital types, and hence choose to be treated at a “center of excellence” and experience better outcomes. In contrast, patients traveling further to seek better hospitals may just be sicker and thus experience adverse clinical outcomes. This phenomenon has been described in other contexts like coronary artery bypass grafting and other surgical admissions; however, its application to CLI is novel (32,33).
Our study has several important limitations that are inherent to large administrative databases. First, there may be errors in coding of diseases or procedures. Second, this is a retrospective observational study, which may be subject to traditional biases of observational studies such as selection bias. Due to funding constraints, the current study includes data from only 3 states, which might have resulted in selection bias. Although the readmission rates were marginally higher in New York compared with Florida and California, the overall trends in readmissions were similar in all 3 states. Besides this, all adjusted analyses were performed by accounting for the state as 1 of the adjusted stratum. Third, SID does not provide clinical details about anatomic characteristics that are important in deciding the mode of revascularization in CLI patients or the extent of amputation. It was also not possible to determine the type and invasiveness of the surgical or endovascular therapy using the SID. It is possible that simple lesions were preferentially treated with endovascular therapy, whereas more complex lesions were treated using surgical therapy, leading to obvious differences in outcomes. Alternatively, it may be likely that the findings underestimate the effect of endovascular therapy, because sicker patients with higher comorbidities and poor targets were more likely to undergo endovascular procedures. Although the comparison of outcomes was adjusted for Charlson comorbidity index, it is possible that differences might arise due to residual confounding. Fourth, readmission data in our study is state-specific and does not capture readmissions of patients in different states. Fifth, SID fails to capture out-of-hospital mortality. Therefore, our analysis cannot account for the competing risk of mortality post-discharge and readmissions. Therefore, we have not presented comparison of readmission rates between endovascular and surgical revascularization as post-discharge mortality is likely to be different between these 2 treatment strategies. Last, the travel time was estimated based on residential zip codes and hospital zip codes. Even though these are likely to provide an overall generalization of travel time in each scenario, they are not completely accurate and do not account for traffic conditions in specific regions. Despite these limitations, this study presents a unique analysis of readmissions for CLI patients and identifies issues related to both care of patients with CLI as well as providing further information regarding the relationship between LOS, readmissions, and patient distance from facility.
The readmission rate at 30 days and 6 months was high in CLI patients, the majority representing unplanned readmissions. Among CLI patients, female sex, black or Hispanic race, prior amputation, and a higher number of comorbidities were associated with higher rate of unplanned readmissions. Unplanned readmissions were associated with higher subsequent in-hospital mortality compared with planned readmissions. However, the incidence of revascularization procedures and major amputations was higher during planned readmissions compared with unplanned readmissions. Higher LOS during index admission was associated with a higher incidence of unplanned readmission at 30 days and 6 months. Lastly, greater travel time between a patient’s residence and the hospital was associated with a lower incidence of unplanned readmission. These findings have implications for how providers manage patient discharge as well as for policy makers, as payment reforms are implemented based on LOS or readmissions.
COMPETENCY IN SYSTEMS-BASED PRACTICE: The 30-day and 6-month hospital readmission rates are high in patients with CLI, and most are unplanned.
TRANSLATIONAL OUTLOOK: Additional work is needed to develop systems of care for patients with CLI that define optimum management earlier in the course of therapy that improve both limb-related outcomes and survival while reducing the need for hospital readmission during long-term follow-up.
For supplemental tables and figures, please see the online version of this article.
The authors have reported that they have no relationships relevant to the contents of this paper to disclose. Mehdi Shishehbor, DO, served as Guest Editor for this paper.
- Abbreviations and Acronyms
- confidence interval
- critical limb ischemia
- International Classification of Diseases-9th Edition-Clinical Modification
- length of stay
- peripheral artery disease
- State Inpatient Database
- Received January 30, 2017.
- Accepted February 12, 2017.
- 2017 American College of Cardiology Foundation
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