Early Hospital Readmission After Kidney Transplantation: Patient and Center-Level Associations


Dorry Segev, dorry@jhmi.edu


Early hospital readmission (EHR) is associated with increased morbidity, costs and transition-of-care errors. We sought to quantify rates of and risk factors for EHR after kidney transplantation (KT). We studied 32 961 Medicare primary KT recipients (2000–2005) linked to Medicare claims through the United States Renal Data System. EHR was defined as at least one hospitalization within 30 days of initial discharge after KT. The association between EHR and recipient and transplant factors was explored using Poisson regression; hierarchical modeling was used to account for study center-level differences. The overall EHR rate was 31%, and 19 independent patient-level factors associated with EHR were identified: recipient factors included older age, African American race and various comorbidities; transplant factors included ECD, length of stay and lack of induction therapy. The unadjusted rate of EHR by center ranged from 18% to 47%, but conventional center-level factors (percent African American, percent age > 60, percent deceased donor and percent expanded criteria donor) were not associated with EHR. However, intermediate total volume and average length of stay were associated with increased EHR risk. Better identification of patients at risk for early hospital readmission following KT may guide discharge planning and early posttransplant outpatient monitoring.


body mass index


congestive heart failure


confidence interval


centers for medicare and medicaid services


chronic obstructive pulmonary disease


extended criteria donor


early hospital readmission


hepatitis C virus


human leukocyte antigen




inter-quartile range


kidney transplant


myocardial infarction


organ procurement and transplant network


relative risk


standard deviation


United States Renal Data System


Hospital readmission rate is a well-accepted metric of hospital quality and a strong predictor of avoidable morbidity (1). Readmission adds to patient burden and puts patients at increased risk of transition-of-care errors, such as medication regimen errors. Furthermore, hospital readmissions contribute substantially to overall healthcare costs: in 2004, unplanned hospital readmissions cost Medicare $17.4 billion ($102.6 billion total hospital payments) (2). In July 2009, the Centers for Medicare and Medicaid Services (CMS) initiated public reporting of hospital readmission rates as part of the CMS Reporting Hospital and Quality Data Annual Payment Update Program (3). In fiscal year 2013, there will be a decrease in reimbursements to hospitals with excess readmission rates (4). Therefore, practices to reduce early hospital readmission (EHR), such as transitional care and coordination of care at discharge, are gaining prominence (5–9).

Readmission after general surgery has been increasing over the last decade (10,11). In general surgery, EHR rates vary by procedure and have been reported to be as high as 22% for some procedures (12). Length of stay, comorbidities and surgical complications have been identified as factors associated with 30-day readmission in a general surgery population (13); in addition, readmission rates after general surgery vary by center volume and staff levels (12,14).

Although there has been a wealth of research on EHR in other surgical fields (15–20), the percentage of kidney transplant (KT) recipients that are readmitted to the hospital remains unknown. Understanding the burden and risk of EHR in KT recipients is important for the development of interventions to improve KT-related morbidity and reduce costs associated with transplantation. While previous studies in KT have focused on factors associated with allograft loss and mortality (21–23), none that we are aware of have explored EHR, despite its importance in cost, morbidity and medical errors. Therefore, the goals of this study were to (1) quantify the rate of EHR after KT, (2) identify recipient and transplant factors associated with EHR after KT and (3) evaluate the impact of center-level factors on EHR after KT.

Materials and Methods

Study population

The study population included 32 961 adult first-time kidney-only transplant recipients between January 1, 2000 and December 31, 2005, as reported to the Organ Procurement and Transplantation Network (OPTN) and linked to Medicare claims data by the United States Renal Data System (USRDS). EHR was defined as at least one hospital readmission (to any acute care hospital, based on Medicare claims) within 30 days after discharge from initial KT hospitalization. To allow for appropriate longitudinal follow-up, the population was limited to those recipients with Medicare as their continuous primary insurer over the 30-day period post-KT and those who did not die within 30 days of KT. We included recipients who died after EHR. Because of mechanistic differences in readmission, we excluded recipients discharged to a skilled nursing facility. Donor, recipient, and transplant factors were gleaned from the CMS 2728 Chronic Renal Disease Medical Evidence form, the OPTN transplant recipient registration form, and CMS claims. Mortality information was augmented by linkage to the Social Security Death Master File and to CMS data.

Potential factors associated with EHR

Recipient and transplant factors considered to be potential correlates of EHR are listed in Table 1. Body mass index (BMI) was categorized by the WHO classification (24). All other comorbidities were categorized as reported on 2728 or as reported to OPTN. Recipients with missing HCV serostatus were considered to be HCV negative. Use of an induction agent was categorized as interleukin-2 receptor antagonists (Zenapax or Simulect), anti-CD3 or anti-CD52 agents (OKT3, OKT4, Thymoglobulin, ALG, ATG, Alemtuzumab), or no induction. Donor type was classified into four categories (live, deceased standard criteria, donation after cardiac death, or deceased expanded criteria). Center-level factors included total KT volume, percent of KT recipients who were African American, percent of KT represented by deceased donor transplants, percent of KT recipients over the age of 60 and average length of stay. The most parsimonious and informative functional forms of the center-level factors were empirically determined to be tertiles.

Table 1.  Study population of kidney transplant recipients, stratified by early hospital readmission
 No readmission (n = 22 909)Readmitted (n = 10 052)p-Value
  1. Mean and standard deviations were reported for continuous variables. Percentages were reported for categorical variables.

  2. HCV = hepatitis C virus; HLA = human leukocyte antigen.

Recipient factors
 Female sex (%)
 Age47.1 [13.9]48.6 [13.5]<0.001
 African American race (%)30.034.4<0.001
 Body mass index (kg/m2)27.1 [5.4]27.7 [5.5]<0.001
 History of cancer (%)
 Congestive heart failure (%)11.814.3<0.001
 Cerebral vascular disease (%)
 Diabetes (%)32.039.0<0.001
 Drug abuse (%)
 History of arrhythmia (%)
 Hypertension (%)81.380.60.1
 Ischemic heart disease (%)8.210.3<0.001
 Myocardial infarction (%)
 Chronic obstructive pulmonary disease (%)1.62.2<0.001
 Peripheral vascular disease (%)
 Tobacco use (current smoker) (%)
 Dialysis vintage (years)3.3 [1.9]3.5 [1.9]<0.001
 Positive HCV status (%)4.86.0<0.001
Transplant factors
 Donor type (%)<0.001
   Live donor26.724.2 
   Deceased standard criteria donor58.556.7 
   Deceased donation after cardiac death3.13.6 
   Deceased expanded criteria donor11.715.5 
 Donor age37.8 [15.7]39.6 [16.2]<0.001
 Cold ischemia time greater than 24 h (%)
 Received induction therapy (%)66.564.3<0.001
 0 HLA mismatches (%)11.69.6<0.001
 Delayed graft function (%)18.227.4<0.001
 Length of stay for the transplant (in days)8.1 [8.7]9.8 [10.2]<0.001

Statistical analysis

Relative risk (RR) of EHR by recipient and transplant factors was estimated using modified Poisson regression, as described previously (25). Functional forms of continuous variables were based on the exploratory data analysis. KT recipient age was included in the final model as a piecewise spline with knots at age 40 and 70, which estimates the change in EHR for each decade increase in recipient age before 40, between 40 and 70 and after 70. Length of stay was also included in the final model as a spline with a knot at 5 days. The final multivariate model was selected for optimal parsimony by minimizing the Akaike Information Criterion (AIC). Sex is a well-documented important factor associated with hospital readmission (26) and thus, all significant interactions between recipient sex and comorbid conditions were included in the final model. Since EHR might depend on center practice or policies, center-level variables were included in a random intercept hierarchical model that allowed the baseline risk to vary by center while accounting for clustering within each transplant center. This model was adjusted for recipient and transplant factors and used to test the association of center-level factors and baseline rates of readmission. The ratio of the observed to expected probability of EHR for each center was plotted. All analyses were performed using STATA 12.0/MP for Linux (College Station, TX, USA).


Study population

Among 32 961 Medicare-primary KT recipients, average age was 47.5 years, 39% were female, and 31% were African American (Table 1). There were 10 052 (31%) recipients with EHR (at least one readmission within 30 days of discharge after KT). Mean time to EHR was 10.1 days (SD 7.9; median 8, IQR 3–15). The mean cost of readmissions was $10 551 (SD =$17 395; median $6 788, IQR $5 203–$9 754). The most frequent primary reason for readmission was kidney, ureter, prostate and bladder procedure (Table 2).

Table 2.  Reason for hospital readmission
Reason for readmissionPercentage
Kidney, ureter, prostate, bladder procedure36
Endocrine disorder11
Gastro-intestinal disorder7
Circulatory disorder6
Allergy or drug effects3
Renal failure2

Factors associated with EHR

Age, race, BMI, diabetes, HTN, heart disease, COPD, HCV, donor age, expanded criteria donor (ECD) status, induction, HLA mismatch and length of stay were independently associated with EHR (Table 2). For every 10-year increase in age at KT, there was a 6% increase in risk of EHR for those <40, a 2% increase in risk of EHR for those aged 40–70; and a 40% increase in risk of EHR for those aged > 70. For example, a 60-year old had a 17% increased risk of EHR compared to a 20-year old after accounting for all other factors in the model (RR 1.17 = 1.062* 1.022). African American recipients had 11% higher risk of EHR (95% CI: 1.07–1.15, p < 0.001). Associated comorbid conditions included ischemic heart disease (RR 1.08, 95% CI: 1.02–1.14, p = 0.01), COPD (RR 1.19, 95% CI: 1.07–1.32, p = 0.002) and obesity (RR 1.15, 95% CI: 1.02–1.31, p = 0.02). Compared to recipients who received a kidney from a live donor, those who received a kidney from an expanded criteria deceased donor had a 12% higher risk of EHR (95% CI: 1.05–1.19, p < 0.001). The use of an induction agent (RR 0.94, 95% CI: 0.91–0.97, p < 0.001) and 0 HLA mismatches (RR 0.91, 95% CI: 0.86–0.97, p = 0.002) were associated with a decreased incidence of EHR. There was a statistically significant interaction between sex and diabetes: women with diabetes had a 29% increase in EHR compared to women without diabetes, but men with diabetes has only an 12% increase in EHR compared to men without diabetes (p < 0.001 for interaction). Finally, recipients with a length of stay less than 5 days had a decreased risk of readmission (RR 0.83 per day, 95% CI: 0.81–0.84, p < 0.001), while those with a length of stay of 5 days or more had an increased risk of EHR (RR 1.07 per week, 95% CI: 1.05–1.09, p < 0.001).

Table 3.  Relative risk of early hospital readmission after kidney transplant (n = 33 265)
 Adjusted relative risk (95% CI)p-Value
  1. CI = confidence interval; HCV = hepatitis C virus; HLA = human leukocyte antigen.

  2. *Interaction of sex and diabetes.

Recipient factors
 Age in decades (spline)
   <401.06 (1.02, 1.10)0.003
   40–701.02 (1.00, 1.04)0.07
   >701.40 (1.15, 1.70)0.001
 African American race1.11 (1.07, 1.15)<0.001
 Body mass index (kg/m2)
   Underweight (<18.5)Reference 
   Normal (18.5–25)1.02 (0.91, 1.15)0.7
   Overweight (25–30)1.05 (0.93, 1.18)0.4
   Obese (>30)1.15 (1.02, 1.31)0.02
 Men with diabetes*1.12 (1.07, 1.17)<0.001
 Women with diabetes*1.29 (1.22, 1.36)<0.001
 Hypertension0.94 (0.90, 0.98)0.002
 Ischemic heart disease1.08 (1.02, 1.14)0.01
 Dialysis vintage (years)1.04 (1.03, 1.05)<0.001
 Chronic obstructive pulmonary disease1.19 (1.07, 1.32)0.002
 Congestive heart failure1.04 (0.99, 1.10)0.08
 Cancer1.09 (0.97, 1.22)0.1
 Tobacco use (current smoker)1.06 (0.99, 1.14)0.1
 History of arrhythmia1.11 (0.99, 1.25)0.07
 HCV status1.13 (1.06, 1.21)<0.001
Transplant factors
 Donor type
 Live donorReference 
 Deceased standard criteria donor1.02 (0.97, 1.06)0.5
 Deceased donation after cardiac death1.11 (1.01, 1.21)0.03
 Deceased expanded criteria donor1.12 (1.05, 1.19)<0.001
 Donor age (in decades)1.02 (1.01, 1.04)0.001
 Received induction therapy0.94 (0.91, 0.97)<0.001
 0 HLA mismatches0.91 (0.86, 0.97)0.002
 Length of stay for the transplant (spline)
   < 5 days (per day)0.83 (0.81, 0.84)<0.001
   ≥ 5 days (per week)1.07 (1.05, 1.09)<0.001

As a sensitivity analyses, we modeled the association of specific induction agents and found that use of interleukin-2 receptor antagonists was associated with a decreased risk of EHR (RR 0.89, 95% CI: 0.85–0.92, p < 0.002). However, anti-CD3/52 agents were not statistically significantly associated with EHR (RR 1.03, 95% CI: 0.99–1.06, p = 0.2). In other sensitivity analyses, when (1) recipients who were readmitted for kidney, ureter, prostate, or bladder procedures were excluded; (2) recipients with missing covariates were excluded and (3) EHR was defined as hospital readmission within 15 days or 60 days of KT the results were similar in magnitude and direction.

Center-level factors associated with hospital readmission

The unadjusted rate of EHR by center, accounting for center-level clustering using a multilevel model, ranged from 18% to 47% after adjusting for recipient and transplant factors, the ratio of observed to expected EHR rate varied by center from 0 to 5.2 (Figure 1). Center-level factors such as percent African American KT recipients, percent deceased donors and percent KT recipients over the age of 60, were not statistically significantly associated with EHR (Table 4). Longer average length of stay was marginally associated with increased risk of EHR. Compared to centers with the lowest volume, those with center volume in the second tertile were associated with an increased probability of EHR (RR 1.15, 95% CI: 1.02–1.29, p = 0.02), but those in the highest tertile were not.

Figure 1.

Ratio of observed to expected probability of early hospital readmission for each transplant center. The observed probability of EHR was calculated for each center and the expected was derived from the final model. We plotted the ratio of observed to expected probability of EHR.

Table 4.  Relative risk of hospital readmission by center-level factors
Center-level characteristicAdjusted relative risk (95% CI)p-Value
  1. Adjusted for patient-level factors listed in Table 2.

Total volume  
 152–2611.15 (1.02, 1.29)0.02
 265–6991.02 (0.88, 1.18)0.8
Percent African American KT recipients
 19.7–36.8%1.02 (0.90, 1.15)0.8
 37.5–100%1.03 (0.91, 1.17)0.6
Percent KT recipients aged over 60
 18.2–24.5%0.99 (0.88, 1.14)0.9
 24.6–100%1.05 (0.93, 1.19)0.4
Percent deceased donor
 69.6–80.0%1.04 (0.92, 1.18)0.5
 80.2–100%0.99 (0.88, 1.14)0.9
Percent expanded criteria donor
 9.0–14.5%0.97 (0.86, 1.10)0.6
 14.8–40.0%1.06 (0.93, 1.20)0.4
Average length of stay (days)
 7.6–9.01.14 (1.00, 1.30)0.04
 9.1–25.21.14 (1.00, 1.29)0.05


In this national study of longitudinal Medicare claims data, nearly one-third of KT recipients were readmitted within 30 days of transplantation; those who were readmitted were likely to be older, African American and have chronic conditions such as diabetes, ischemic heart disease, diabetes and COPD. Furthermore, women with diabetes had a higher relative risk of EHR after KT than men with diabetes. Transplant factors included ECD status, length of stay for the transplant and lack of induction therapy. There was wide variation in EHR rates between transplant centers, ranging from 18% to 47%, and these were not explained by conventional (volume or demographic makeup) center-level factors that could be gleaned from the registry.

Our findings in KT are consistent within the context of EHR in other diseases. For example, the higher risk of EHR in African American KT recipients is consistent with higher risks for readmission in other conditions such as MI, CHF or pneumonia (27).

Putative mechanisms for the length-of-stay associations are somewhat difficult to tease out based on this observational study and the limitations of the available data. A short length of stay was likely associated with a low risk of readmission because short length of stay, in many cases, reflects a low-risk recipient with an organ from a low-risk donor. This scenario likely dominates over the other scenario of interest, namely that of a patient discharged prematurely; in other words, without a proper counterfactual, it is difficult to answer the question of who was discharged home “early” and how they would have fared if kept another few days.

This study has a few notable limitations. In order to detect EHR, we had to limit our population to Medicare-primary patients, a criterion which could differentially affect older and younger patients and thus affect generalizability. However, given that all ESRD patients requiring dialysis therapy are eligible for Medicare, this is a common inclusion criterion in studies of ESRD patients (28–30), and one which we believe should only minimally affect the results. Furthermore, there were no measures of socioeconomic status, which may confound some of the findings.

In conclusion, nearly one-third of KT recipients were readmitted within 30 days of transplantation and there was wide variation in EHR rates between transplant centers. The results may have important implications on clinical practice by aiding to identify which KT recipients are at increased risk of readmission. Recipients at risk could be targeted for better transitions of care and coordination of care at discharge from KT. Furthermore, KT recipients at high risk for early hospital readmission may be targeted for earlier or more frequent outpatient follow-up. These strategies may decrease the total cost of transplantation in addition to decreasing posttransplant mortality and complications. However, it is unclear whether these interventions will be feasible and effective in KT populations and able to reduce total transplant associated cost.


The authors thank the Organ Procurement and Transplantation Network (OPTN) and the United States Renal Disease System (USRDS) for provision of the data. The OPTN is supported by Health Resources and Services Administration contract 234-2005-370011C. The analyses described here are the responsibility of the authors alone and do not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.

Funding sources: M.G., National Kidney Foundation of Maryland; D.S., NIH grants K23AG032885 (co-funded by the American Federation of Aging Research) and R21DK085409.


The authors of this manuscript have no conflict of interest to disclose as described by the American Journal of Transplantation.