A total of 1128 of 5437 kidney transplant recipients experienced an EHR. The 30-day cumulative incidence of EHR was 20.8% (95% confidence interval [CI], 19.7-21.9%). The median length of stay during the EHR was 4 days (25th, 75th percentile: 2, 8 days). Of recipients with an EHR, 11 (0.98%) died during their admission, 27 (2.4%) were admitted to the intensive care unit, and 1028 (91.1%) were readmitted to the transplant hospital. The period of highest risk for EHRs was the 2 to 4 days after discharge from the kidney transplant admission, with 25% (n = 278) of all EHRs occurring during this time (Figure 2).
Trends in EHR
There was no trend in EHR across eras, with a 30-day cumulative incidence of 23.0% (95% CI, 20.4-25.9%), 21.4% (95% CI, 19.2-23.8%), 18.4% (95% CI, 16.4-20.5%), and 21.1% (95% CI, 19.3-22.9%) (P for trend = 0.197) for the eras 2002 to 2004, 2005 to 2007, 2008 to 2010, and 2011 to 2014, respectively (Table 2). Similarly, no trend was found when examining the 30-day cumulative incidence of EHR across years (P for trend = 0.946) (Figure S1, SDC, http://links.lww.com/TP/B510). After adjusting for covariates in the Cox proportional hazards model, we found no association between era of transplant and EHR (Table 2). For example, when compared with recipients who received a transplant from 2002 to 2004, recipients who received a kidney in 2011 to 2014 had an adjusted hazard ratio of 1.0 (95% CI, 0.8-1.2). When using restricted cubic splines to examine year as a continuous variable, a significant nonlinear relationship was observed in both the unadjusted and adjusted analyses (P < 0.05).
Recipients 60 years or older had a higher cumulative incidence of EHR compared with recipients < 60 years in both men and women. For example, women 60 years or older had an EHR cumulative incidence of 23.2% (95% CI, 19.9-27.0%) versus 18.9% (95% CI, 17.0-21.0%) in women younger than 60 years. The cumulative incidence of EHR in men and women were similar (data not shown).
When examining EHR by sex and age, we found no significant trends across eras (Figure 3). Similarly, no significant trend was observed by donor type (living vs deceased) (Figure 3). When examining EHR by length of stay during the kidney transplant admission, we found that, generally, a longer initial hospital stay resulted in a higher incidence of EHR, and this was consistent across eras (Figure 3). For example, the cumulative incidence of EHR in recipients with a kidney transplant admission of 5 days or less was 13.6% (95% CI, 9.8-18.7%), whereas recipients with an admission of 10 days or longer had a cumulative incidence of 27.1% (95% CI, 25.2-29.2%). There were no statistically significant trends in EHR by length of kidney transplant admission across eras.
Most Common Diagnoses for EHR
Using the first 3 digits of the most responsible International Classification of Diseases (10th Revision) code, the 5 most common diagnoses for EHR included failure and rejection of transplanted organs and tissues (18.7%); complications of procedures, not elsewhere classified (13.6%); acute renal failure (5.7%); other disorders of urinary system (4.3%); and postprocedural disorders of genitourinary system, not elsewhere classified (2.6%). The 5 most common diagnoses for EHR across eras are presented in Table 3. Of the diagnoses that were common, failure and rejection of transplanted organs and tissues significantly decreased across eras (P for trend < 0.001), complications of procedures, not elsewhere classified. significantly increased (P for trend = 0.003) and there was no significant change in acute renal failure (P for trend = 0.586).
Variation in EHRs Across Transplant Centers
When examining variation in EHR across the 6 transplant centers, we found the 30-day cumulative incidence varied from 15.5% to 27.1% (P < 0.001). After adjusting for center- and patient-level factors, we found the odds of EHR at transplant center C was significantly higher, whereas the odds of EHR at transplant center D was significantly lower (Table 4).
In this study, we found that 1 in 5 kidney transplant recipients were readmitted to the hospital within 30 days of hospital discharge for kidney transplantation. Despite an increase in recipient age and comorbidities across eras, we found EHR had not increased in the last decade. This study highlights the high burden of EHR in kidney transplant recipients, the need to understand which EHRs might be preventable, and the importance of evaluating interventions to decrease these admissions.
The incidence of EHR in our study was considerably lower than that found in a large population-based US study (21% vs 31%, n = 32 961).8 There are several potential explanations for this finding. First, there were several differences between studies in recipient and donor characteristics that reduced the risk of EHR in our study (eg, living donor kidney transplants 41% vs 25% and black recipient race 7% vs 31% in the current vs US study, respectively).8 However, recipients in our study also had risk factors that may have increased their risk of EHR (eg, older mean recipient and donor age).8 Second, there may be differences between the 2 studies in the EHR definition. We excluded elective procedures from our EHR definition; it is unclear whether these procedures were excluded in the US study. Of note, when we examined EHR, including elective procedures, the incidence changed minimally (less than 2%). Last, differences in healthcare systems and variation in practice patterns could account for some differences in the EHR incidence.9,10 Previous studies in the kidney transplant population, as well as in other populations, have found differences in outcomes between US and Canadian patients.9,21,22 However, it is important to note that wide variability in EHR has also been found across studies within the United States (11% to 32%)3,12,23 and between US transplant centers (18 to 47%).8 Similarly, we found significant variation across our transplant centers (16% to 27%), although to a lesser degree.
The incidence of EHR found in our study was higher when compared with the EHR in major surgeries (21% vs 13%), despite a significantly younger age in our study (52 years vs 78 years).24 Similarly, a Canadian study found that the cumulative incidence of unplanned EHR for surgical (7%) and medical patients (13%) was much lower compared to our study.25 Potential explanations for the higher risk of EHR in kidney transplant recipients versus other patient populations include the high burden of recipient comorbidities (eg, diabetes mellitus, hypertension), increased risk of infectious and noninfectious complications posttransplant, and the functional status of ESRD patients before kidney transplant.
We did not find an increase in EHR over time despite an increase in recipient and donor age and an increase in several comorbidities across eras. There are several possible reasons for this finding. Posttransplant care may have improved (eg, improvement in immunosuppression protocols and posttransplant rehabilitation programs). Although the selection of kidney transplant and deceased donor candidates has become less restrictive, advances in the medical management of dialysis patients may have improved recipients’ general fitness to undergo transplant surgery. Moreover, the significantly shorter length of stay for kidney transplantation across eras may have decreased the number of readmissions for hospital-acquired infections. Of note, we observed a significant nonlinear relationship between year of transplant and EHR. However, we are unclear about the reason for this observation and hypothesize this observation is likely due to chance.
Although no increase in EHR was observed across eras, the high incidence of EHR in kidney transplant recipients is concerning because previous studies have found that EHR is associated with graft failure, mortality, and increased economic costs.4-8 Moreover, EHRs are considered an indicator of healthcare quality.26 Additionally, although no increase in EHR was observed over time, we should not be satisfied since kidney transplant recipient outcomes have shown improvement in various domains over time (eg, reduction in graft failure, posttransplant diabetes).27 Therefore, these results highlight the need for a better understanding of EHR risk factors, which EHRs may be preventable, and the need to develop tools to accurately identify recipients who may benefit from interventions to reduce EHR risk.
Studies examining risk factors for EHR in kidney transplant recipients have found that many risk factors are not modifiable (eg, older recipient age, black race).3 Furthermore, only 1 study in the kidney transplant population has evaluated the predictive ability of a model for EHR (c-statistic = 0.73); however, this model has not been externally validated.11 Notably, there have been no randomized controlled trials to examine the impact of interventions to reduce EHR in kidney transplant recipients. In the general population, numerous randomized controlled trials have been conducted.28 A meta-analysis examining the efficacy of interventions to reduce EHR in nontransplant patients found several interventions to be effective in reducing EHR risk, with those aiming to increase self-care being the most efficacious.28 Recently published nonrandomized studies in kidney transplant recipients suggest that readmissions in kidney transplant recipients could be reduced through additional outpatient care, improving posttransplant medication knowledge, and reducing postdischarge anxiety.12,29,30 Cumulative incidence estimates from our study can be used to guide sample size calculations when designing future randomized controlled trials to reduce the impact of EHR in kidney transplant recipients.
Similar to previously conducted studies in kidney transplant recipients, we found that a longer initial hospital stay resulted in a higher number of EHR.5,6,8 Although not all studies in kidney transplant recipients have observed these results,11 most studies in kidney transplant and other patient populations have shown that a longer initial hospitalization is associated with an increased risk of EHR.31,32 Longer hospitalizations may be a marker of baseline health status, which may have implications for EHR risk and long-term outcome.
When examining the most common diagnoses of EHR across eras, we found that the proportion of recipients with failure and rejection of transplanted organs and tissues as the reason for readmission significantly decreased across eras. This suggests that improvements in posttransplant care (eg, counseling on medication adherence, quality of immunosuppression protocols) may have reduced early graft failure events. However, although declines in graft failure as a cause of EHR were observed, there were increases in other causes resulting in no overall decrease in EHR across eras. Caution should be taken when interpreting these results. To define diagnoses for EHR, we only included the most responsible diagnosis; recipients could still have failure and rejection as a secondary diagnosis on readmission. Therefore, to more accurately understand trends in EHR diagnoses a medical chart abstraction would need to be performed.
This is the first study to examine trends in EHR events in kidney transplant recipients over time. We clearly defined EHR and ensured that elective readmissions were excluded from the definition.3 We were able to include all Ontario kidney transplant recipients (total of 6 transplant centers), due to universal healthcare coverage, thus minimizing selection bias. Despite these strengths, several limitations of our study merit discussion. First, we were not able to adjust for all relevant confounders (eg, smoking, body mass index, rejection risk factors, prescription drugs) due to limitations of our administrative healthcare databases. Second, we were not able to determine EHRs that were preventable; this would require medical chart abstraction. Previous studies conducted in the U.S. have found that preventable admissions after kidney transplantation range from 10 to 21%.3 Third, we could only hypothesize reasons for finding no increase in EHR over time. Finally, approximately 65% of recipients were of white race, potentially limiting generalizability to predominantly non-white kidney transplant populations. Further, our results may not be generalizable to other Canadian provinces and countries.
In conclusion, the burden of EHR in kidney transplant recipients is high but an increase in the incidence of EHR has not been observed over time. These results serve as a call to action to better understand factors that predict EHR, to understand the long-term outcomes in patients with EHR, and to develop/test interventions to prevent EHR in this unique patient population.
This research was made possible by infrastructure support from the Lilibeth Caberto Kidney Clinical Research Unit. This study was supported by the ICES Western site. ICES is funded by an annual grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC). Core funding for ICES Western is provided by the Academic Medical Organization of Southwestern Ontario (AMOSO), the Schulich School of Medicine and Dentistry (SSMD), Western University, and the Lawson Health Research Institute (LHRI). The research was conducted by members of the ICES Kidney, Dialysis and Transplantation team, at the ICES Western facility, who are supported by a grant from the Canadian Institutes of Health Research (CIHR). The opinions, results and conclusions are those of the authors and are independent from the funding sources. No endorsement by ICES, AMOSO, SSMD, LHRI, CIHR, or the MOHLTC is intended or should be inferred. Parts of this material are based on data and information compiled and provided by CIHI. However, the analyses, conclusions, opinions and statements expressed herein are those of the author, and not necessarily those of CIHI. The authors thank Eric McArthur for his help with data analysis and interpretation.
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