Early rehospitalization after kidney transplantation: assessing preventability and prognosis

Development and validation of a novel nomogram model for predicting delayed graft function in deceased donor kidney transplantation based on pre-transplant biopsies

BACKGROUND

Delayed graft function (DGF) is an important complication after kidney transplantation surgery. The present study aimed to develop and validate a nomogram for preoperative prediction of DGF on the basis of clinical and histological risk factors.

METHODS

The prediction model was constructed in a development cohort comprising 492 kidney transplant recipients from May 2018 to December 2019. Data regarding donor and recipient characteristics, pre-transplantation biopsy results, and machine perfusion parameters were collected, and univariate analysis was performed. The least absolute shrinkage and selection operator regression model was used for variable selection. The prediction model was developed by multivariate logistic regression analysis and presented as a nomogram. An external validation cohort comprising 105 transplantation cases from January 2020 to April 2020 was included in the analysis.

RESULTS

266 donors were included in the development cohort, 458 kidneys (93.1%) were preserved by hypothermic machine perfusion (HMP), 96 (19.51%) of 492 recipients developed DGF. Twenty-eight variables measured before transplantation surgery were included in the LASSO regression model. The nomogram consisted of 12 variables from donor characteristics, pre-transplantation biopsy results and machine perfusion parameters. Internal and external validation showed good discrimination and calibration of the nomogram, with Area Under Curve (AUC) 0.83 (95%CI, 0.78-0.88) and 0.87 (95%CI, 0.80-0.94). Decision curve analysis demonstrated that the nomogram was clinically useful.

CONCLUSION

A DGF predicting nomogram was developed that incorporated donor characteristics, pre-transplantation biopsy results, and machine perfusion parameters. This nomogram can be conveniently used for preoperative individualized prediction of DGF in kidney transplant recipients.

Sarcopenic obesity is associated with adverse outcomes after kidney transplantation: a retrospective cohort study

PURPOSE

Sarcopenia was found to be a poor prognostic factor in kidney transplant recipients, but the role of sarcopenia obesity remains unclear. This study aimed to explore the effect of sarcopenic obesity on kidney transplantation.

METHODS

A retrospective analysis was performed on kidney transplant recipients between 2015 and 2019. Pretransplant CT scans were utilized to assess sarcopenia and visceral obesity. Based on the presence or absence of sarcopenia and visceral obesity, the recipients were classified into four distinct groups.

RESULTS

The recipients were categorized into four groups based on their characteristics: the nonsarcopenic nonobesity group (n = 493, 49.85%), the nonsarcopenic obesity group (n = 248, 25.08%), the sarcopenic nonobesity group (n = 188, 19.01%), and the sarcopenic obesity group (n = 60, 6.07%). Multivariate analysis, identified sarcopenic obesity was as an independent risk factor for mortality following kidney transplantation (adjusted hazard ratio, 5.861; 95% confidence interval [CI]: 1.627-21.108; P = 0.007). Additionally, sarcopenic obesity was associated with an increased risk of delayed graft function (adjusted odds ratio [aOR], 3.342; 95% CI 1.421-7.745; P = 0.005), perioperative incision infection (aOR, 9.654; 95% CI 1.572-60.648; P = 0.011), perioperative pulmonary infection (aOR, 2.557; 95% CI 1.208-5.215; P = 0.011), and readmission within 3 months (aOR, 2.100; 95% CI 1.051-4.017; P = 0.029). While sarcopenic obesity was found to be associated with impaired graft renal function, it did not show a significant correlation with death-censored graft survival or quality of life.

CONCLUSION

The presence of sarcopenic obesity prior to kidney transplantation represents an independent risk factor for mortality, and it is also linked to a range of unfavorable outcomes.

Weekend Discharge Is Not Associated With Increased Readmission After Hyperthermic Intraperitoneal Chemotherapy

INTRODUCTION

We explored the association between weekend discharge and 30- and 90-d readmission rates in patients undergoing hyperthermic intraperitoneal chemotherapy (CRS/HIPEC) for peritoneal carcinomatosis.

METHODS

The US HIPEC Collaborative database, comprised of a longitudinal cohort of patients undergoing CRS/HIPEC for peritoneal carcinomatosis at twelve academic institutions between 2000 and 2017, was queried for date of discharge information. Patients were retrospectively divided into weekday and weekend/holiday discharge groups. Patients <18 y old, lacking day of discharge information, or who experienced intraoperative/in-hospital mortality were excluded. Comparisons were made between patients discharged on a weekday versus those discharged on a weekend or major holiday.

RESULTS

1415 patients met inclusion criteria for the study: 1108 (78%) patients with a weekday discharge and 308 (22%) with a weekend/holiday discharge. Median age at time of surgery was 55 y (Interquartile Range: 46-63); 59% (n = 841) patients were female, 25% (n = 328) of patients had high volume disease (defined as a peritoneal cancer index >20 intraoperatively), and 92% (n = 1210) of patients had a complete cytoreduction (defined as a completeness of cytoreduction score of 0 or 1). Overall, 15% (n = 218) of patients were readmitted within 30 d and 19% (n = 265) within 90 d. In a linear mixed effects model, weekend discharge was not associated with higher 30- or 90-d readmissions (P = 0.291, P = 0.743).

CONCLUSIONS

Weekend discharges are safe following CRS/HIPEC. Length of stay initiatives should focus on discharging the patient when medically ready, rather than avoiding weekend discharge out of an abundance of caution.

Diabetes mellitus and blood glucose variability increases the 30-day readmission rate after kidney transplantation

INTRODUCTION

Inpatient hyperglycemia is an established independent risk factor among several patient cohorts for hospital readmission. This has not been studied after kidney transplantation. Nearly one-third of patients who have undergone a kidney transplant reportedly experience 30-day readmission.

METHODS

Data on first-time solitary kidney transplantations were retrieved between September 2015 and December 2018. Information was linked to the electronic health records to determine diagnosis of diabetes mellitus and extract glucometric and insulin therapy data. Univariate logistic regression analysis and the XGBoost algorithm were used to predict 30-day readmission. We report the average performance of the models on the testing set on bootstrapped partitions of the data to ensure statistical significance.

RESULTS

The cohort included 1036 patients who received kidney transplantation; 224 (22%) experienced 30-day readmission. The machine learning algorithm was able to predict 30-day readmission with an average area under the receiver operator curve (AUC) of 78% with (76.1%, 79.9%) 95% confidence interval (CI). We observed statistically significant differences in the presence of pretransplant diabetes, inpatient-hyperglycemia, inpatient-hypoglycemia, minimum and maximum glucose values among those with higher 30-day readmission rates. The XGBoost model identified the index admission length of stay, presence of hyper- and hypoglycemia, the recipient and donor body mass index (BMI) values, presence of delayed graft function, and African American race as the most predictive risk factors of 30-day readmission. Additionally, significant variations in the therapeutic management of blood glucose by providers were observed.

CONCLUSIONS

Suboptimal glucose metrics during hospitalization after kidney transplantation are associated with an increased risk for 30-day hospital readmission. Optimizing hospital blood glucose management, a modifiable factor, after kidney transplantation may reduce the risk of 30-day readmission.

Outcomes of early hospital readmission after kidney transplantation: Perspectives from a Canadian transplant centre

BACKGROUND

Early hospital readmissions (EHRs) after kidney transplantation range in incidence from 18%-47% and are important and substantial healthcare quality indicators. EHR can adversely impact clinical outcomes such as graft function and patient mortality as well as healthcare costs. EHRs have been extensively studied in American healthcare systems, but these associations have not been explored within a Canadian setting. Due to significant differences in the delivery of healthcare and patient outcomes, results from American studies cannot be readily applicable to Canadian populations. A better understanding of EHR can facilitate improved discharge planning and long-term outpatient management post kidney transplant.

AIM

To explore the burden of EHR on kidney transplant recipients (KTRs) and the Canadian healthcare system in a large transplant centre.

METHODS

This single centre cohort study included 1564 KTRs recruited from January 1, 2009 to December 31, 2017, with a 1-year follow-up. We defined EHR as hospitalizations within 30 d or 90 d of transplant discharge, excluding elective procedures. Multivariable Cox and linear regression models were used to examine EHR, late hospital readmissions (defined as hospitalizations within 31-365 d for 30-d EHR and within 91-365 d for 90-d EHR), and outcomes including graft function and patient mortality.

RESULTS

In this study, 307 (22.4%) and 394 (29.6%) KTRs had 30-d and 90-d EHRs, respectively. Factors such as having previous cases of rejection, being transplanted in more recent years, having a longer duration of dialysis pretransplant, and having an expanded criteria donor were associated with EHR post-transplant. The cumulative probability of death censored graft failure, as well as total graft failure, was higher among the 90-d EHR group as compared to patients with no EHR. While multivariable models found no significant association between EHR and patient mortality, patients with EHR were at an increased risk of late hospital readmissions, poorer kidney function throughout the 1st year post-transplant, and higher hospital-based care costs within the 1st year of follow-up.

CONCLUSION

EHRs are associated with suboptimal outcomes after kidney transplant and increased financial burden on the healthcare system. The results warrant the need for effective strategies to reduce post-transplant EHR.

The impact of universal induction therapy on early hospital readmission of kidney transplant recipients

BACKGROUND

Early hospital readmission (EHR) is associated with worse outcomes. The use of anti-thymocyte globulin (rATG) induction therapy is associated with increased efficacy in preventing acute rejection, although safety concerns still exist.

METHODS

This retrospective single-center study compared the incidence, causes of EHR, and one-year clinical outcomes of patients receiving a kidney transplant between August 18, 2011 and December 31, 2012 (old era), in which only high-risk patients received 5 mg/kg rATG, with those transplanted between August 18, 2014 and December 31, 2015 (new era), in which all patients received a single 3 mg/kg dose of rATG.

RESULTS

There were 788 patients from the Old Era and 800 from the New Era. The EHR incidence in the old era patients was 26.4% and in the new era patients, 22.5% (p = 0.071). The main cause of EHR in both eras was infection (67% vs. 68%). The incidence of acute rejection episodes was lower (22.7% vs 3.5%, p < 0.001) and the one-year patient survival was higher (95.6% vs. 98.1%, vs. p = 0.004) in new era patients.

CONCLUSION

The universal use of 3 mg/kg rATG single-dose induction therapy in the new era was associated with a trend towards reduced EHR and a reduction in the incidence of acute rejection and mortality.

Early readmissions post kidney transplantation: lessons learned

INTRODUCTION

Kidney transplantation (KT) is the gold standard treatment for end-stage renal disease (ESRD) patients. Hospital readmissions post-transplant is a common complication and can be considered an indication of avoidable morbidity and hospital quality, and there is a significant correlation between EHR and adverse patient outcomes. This study aimed to assess the readmission rate following kidney transplants, the underlying causes, and possible ways to prevent it.

MATERIAL AND METHODS

We retrospectively reviewed the medical records of recipients from January 2016 to December 2021 in a single center. The primary objective of this study is to find the readmission rate for kidney transplants and the variables that contribute to readmission. Post-transplant complications that were resulted in the readmission categorized into surgical complications, graft-related complications, infections, DVT, and other medical complications.

RESULTS

Four hundred seventy-four renal allograft recipients met our inclusion criteria and were included in the study. 248 (52.3%) of the allograft recipients had at least one readmission during the first 90 days after the transplantation. 89 (18.8%) allograft recipients had more than one readmission episode in the first 90 days post-transplant. The perinephric fluid collection was the most common surgical complication (52.4%), and UTI was the most common infection (50%), causing readmission in the first 90 days post-transplant. The readmission odd ratio was significantly higher in patients above 60 years old and in kidneys with KDPI ≥ 85, and in recipients with DGF.

CONCLUSION

Early hospital readmission (EHR) following a kidney transplant is a common complication. Identifying the causes not only helps the transplant centers to take further steps to prevent some incidents and help to improve the patients' morbidities and mortalities, but also it can reduce the unnecessary costs of readmissions.

Predicting Kidney Transplant Recipient Cohorts' 30-Day Rehospitalization Using Clinical Notes and Electronic Health Care Record Data

Introduction

Rehospitalization after kidney transplant is costly to patients and health care systems and is associated with poor outcomes. Few prediction model studies have examined whether inclusion of clinical notes data from the electronic medical record (EMR) enhances prediction of rehospitalization.

Methods

In a retrospective, observational study of first-time, adult kidney transplant recipients at a large, urban hospital in southeastern United States (2005-2015), we examined 30-day rehospitalization (30DR) using structured EMR and unstructured (i.e., clinical notes) data. We used natural language processing (NLP) methods on 8 types of clinical notes and included terms in predictive models using unsupervised machine learning approaches. Both the area under the receiver operating curve and precision-recall curve (ROC and PRC, respectively) were used to determine and compare model accuracy, and 5-fold cross-validation tested model performance.

Results

Among 2060 kidney transplant recipients, 30.7% were readmitted within 30 days. Predictive models using clinical notes did not meaningfully improve performance over previous models using structured data alone (ROC 0.6821; 95% confidence interval [CI]: 0.6644, 0.6998). Predictive models built using solely clinical notes performed worse than models using both clinical notes and structured data. The data that contributed to the top performing models were not identical but both included structured data and progress notes (ROC 0.6902; 95% CI: 0.6699, 0.7105).

Conclusions

Including new features from clinical notes in risk prediction models did not substantially increase predictive accuracy for 30DR for kidney transplant recipients. Future research should consider pooling data from multiple institutions to increase sample size and avoid overfitting models.

Delayed graft function: current status and future directions

PURPOSE OF REVIEW

Delayed graft function is a common early posttransplant event predictive of adverse outcomes including hospital readmission, impaired long-term graft function, and decreased graft and patient survival. The purpose of this review is to summarize recent literature describing delayed graft function in hopes of better understanding and managing this condition.

RECENT FINDINGS

Recent research efforts have been garnered towards risk factor modification, prevention, and earlier detection of delayed graft function. In this review, we aim to summarize current innovative approaches and future directions.

SUMMARY

Delayed graft function portends worse graft and patient outcomes. Continued research to prevent, and detect early perturbations in allograft function, and more optimally manage this disease will hopefully improve graft function, along with graft/patient survival.

O impacto da terapia de indução universal na readmissão hospitalar precoce de receptores de transplante renal

Resumo Histórico: A Readmissão Hospitalar Precoce (RHP) está associada a piores desfechos. O uso de terapia de indução com globulina antitimócito (rATG, por sua sigla em inglês) está associado ao aumento da eficácia na prevenção de rejeição aguda, embora ainda existam preocupações quanto à segurança. Métodos: Este estudo retrospectivo de centro único comparou a incidência, as causas da RHP e os desfechos clínicos de um ano de pacientes que receberam transplante renal entre 18 de Agosto de 2011 e 31 de Dezembro de 2012 (Antiga Era), em que apenas pacientes de alto risco receberam 5 mg/kg de rATG, com aqueles transplantados entre 18 de Agosto de 2014 e 31 de Dezembro de 2015 (Nova Era), em que todos os pacientes receberam uma única dose de 3 mg/kg de rATG. Resultados: Houve 788 pacientes da Antiga Era e 800 da Nova Era. A incidência de RHP nos pacientes da antiga era foi de 26,4% e nos pacientes da nova era, 22,5% (p = 0,071). A principal causa de RHP em ambas as eras foi infecção (67% vs. 68%). A incidência de episódios de rejeição aguda foi menor (22,7% vs. 3,5%; p < 0,001) e a sobrevida do paciente em um ano foi maior (95,6% vs. 98,1%; vs. p = 0,004) em pacientes da nova era. Conclusão: O uso universal de terapia de indução de 3 mg/kg de rATG em dose única na nova era foi associado a uma tendência à redução da RHP e a uma redução na incidência de rejeição aguda e mortalidade.

Re-Hospitalization in First Six Months After Live Related Renal Transplantation: Risk Factors, Burden, Causes and Outcomes

Objective

The aim of our study was to evaluate the incidence, causes, risk factors, outcomes, and cost of hospital readmission after live related renal transplantation (LRRT).

Methods

We conducted a cross-sectional study and followed patients’ re-admissions for six months whose LRRT was done in our center between September 2019 and June 2020.

Results

We recruited 53 patients, 40 (75.5%) were male. The mean age was 36.9 ± 11.9 years. Donor gender was similar, and their mean age was 31.6 ± 9.2 years. The mean length of hospital stay after LRRT was 14 ± 2.2 days. A total of 81.1% were readmitted after LRRT within the first six months, with a total of 113 readmissions. The median time of readmission after LRRT was 66 days. The median readmission hospital stay was four days. The causes of readmission were surgical in 11 (9.7%), medical in 89 (78.8%), and combined medical and surgical in 13 (11.5%). Infection was the most common medical cause, followed by rejection. Statistically significant difference between readmission and non-readmission groups was found in estimated glomerular filtration rate (eGFR) at six month 61.3 ± 25.9 vs. 84.3 ± 36.1 mL/min/1.73 m² respectively (p = 0.02). The median cost of readmission was PKR 40629, equivalent to USD 261.

Conclusion

Over three-fourths of the patients were readmitted after LRRT within the first six months. The most common causes were infection and rejection. Readmissions after LRRT are associated with lower graft function at six months and a significant cost burden on the health system.

Risk Factors and Outcomes of Early Hospital Readmission in Canadian Kidney Transplant Recipients: A Population-Based Multi-Center Cohort Study

Background:

Early hospital readmissions (EHRs) occur commonly in kidney transplant recipients. Conflicting evidence exists regarding risk factors and outcomes of EHRs.

Objective:

To determine risk factors and outcomes associated with EHRs (ie, hospitalization within 30 days of discharge from transplant hospitalization) in kidney transplant recipients.

Design:

Population-based cohort study using linked, administrative health care databases.

Setting:

Ontario, Canada.

Patients:

We included 5437 kidney transplant recipients from 2002 to 2015.

Measurements:

Risk factors and outcomes associated with EHRs. We assessed donor, recipient, and transplant risk factors. We also assessed the following outcomes: total graft failure, death-censored graft failure, death with a functioning graft, mortality, and late hospital readmission.

Methods:

We used multivariable logistic regression to examine the association of each risk factor and the odds of EHR. To examine the relationship between EHR status (yes vs no [reference]) and the outcomes associated with EHR (eg, total graft failure), we used a multivariable Cox proportional hazards model.

Results:

In all, 1128 kidney transplant recipients (20.7%) experienced an EHR. We found the following risk factors were associated with an increased risk of EHR: older recipient age, lower income quintile, several comorbidities, longer hospitalization for initial kidney transplant, and older donor age. After adjusting for clinical characteristics, compared to recipients without an EHR, recipients with an EHR had an increased risk of total graft failure (adjusted hazard ratio [aHR]: 1.46, 95% CI: 1.29, 1.65), death-censored graft failure (aHR: 1.62, 95% CI: 1.36, 1.94), death with graft function (aHR: 1.34, 95% CI: 1.13, 1.59), mortality (aHR: 1.41, 95% CI: 1.22, 1.63), and late hospital readmission in the first 0.5 years of follow-up (eg, 0 to <0.25 years: aHR: 2.11, 95% CI: 1.85, 2.40).

Limitations:

We were not able to identify which readmissions could have been preventable and there is a potential for residual confounding.

Conclusions:

Results can be used to identify kidney transplant recipients at risk of EHR and emphasize the need for interventions to reduce the risk of EHRs.

Trial registration:

This is not applicable as this is a population-based cohort study and not a clinical trial.

Surgical site complications in kidney transplant recipients: incidence, risk factors and outcomes in the modern era

BACKGROUND

Surgical site complications (SSCs) are an important source of morbidity after kidney transplantation. We assessed the incidence, risk factors, outcomes and economic impact of SSCs in a large, diverse population of kidney transplant recipients.

METHODS

We conducted a single-centre, observational cohort study of adult (age ≥ 18 yr) patients who underwent kidney transplantation between Jan. 1, 2005, and Dec. 31, 2015, with a minimum of 1 year of follow-up. Cases of SSC, including infections and wound dehiscence, were determined from patient records. Inpatient and outpatient hospital costs were determined 6 and 12 months after transplantation. We used the Kaplan-Meier product-limit method to determine the cumulative probability of SSCs and other outcomes. We evaluated risk factors and clinical outcomes using Cox proportional hazard ratios. Linear regression models were used to study the effect of SSCs on graft function.

RESULTS

The incidence rate of SSCs within 30 days after transplantation was 4.19 per 100 person-months. The cumulative probability of developing an SSC within 30 days after transplantation was 4.13% (95% confidence interval [CI] 3.23%-5.28%). Increased recipient body mass index (BMI) (hazard ratio [HR] 1.07, 95% CI 1.02-1.11), longer cold ischemic time (HR 1.05, 95% CI 1.01-1.09) and transplantation in 2010-2012 versus 2005-2009 (HR 2.20, 95% CI 1.19-4.04) were risk factors for SSC development. In multivariable stepwise Cox proportional hazard models, SSC was a significant risk factor for death-censored graft failure (HR 3.08, 95% CI 1.60-5.90) and total graft failure (HR 2.09, 95% CI 1.32-3.32). Cumulative median hospital costs were $2238.46 greater for patients with an SSC than for those without.

CONCLUSION

Increased BMI, longer cold ischemic time and the 2010-2012 transplantation period predisposed to SSCs. The development of SSCs was associated with a higher risk of graft failure. Strategies to minimize SSCs may improve outcomes after kidney transplantation and reduce costs.

Ambient air pollution and posttransplant outcomes among kidney transplant recipients

Fine particulate matter (PM2.5 ), a common form of air pollution which can induce systemic inflammatory response, is a risk factor for adverse health outcomes. Kidney transplant (KT) recipients are likely vulnerable to PM2.5 due to comorbidity and chronic immunosuppression. We sought to quantify the association between PM2.5 and post-KT outcomes. For adult KT recipients (1/1/2010-12/31/2016) in the Scientific Registry of Transplant Recipients, we estimated annual zip-code level PM2.5 concentrations at the time of KT using NASA's SEDAC Global PM2.5 Grids. We determined the associations between PM2.5 and delayed graft function (DGF) and 1-year acute rejection using logistic regression and death-censored graft failure (DCGF) and mortality using Cox proportional hazard models. All models were adjusted for sociodemographics, recipient, transplant, and ZIP code level confounders. Among 87 233 KT recipients, PM2.5 was associated with increased odds of DGF (OR = 1.59; 95% CI: 1.48-1.71) and 1-year acute rejection (OR = 1.31; 95% CI: 1.17-1.46) and increased risk of all-cause mortality (HR = 1.15; 95% CI: 1.07-1.23) but not DCGF (HR = 1.05; 95% CI: 0.97-1.51). In conclusion, PM2.5 was associated with higher odds of DGF and 1-year acute rejection and elevated risk of mortality among KT recipients. Our study highlights the importance of considering environmental exposure as risk factors for post-KT outcomes.

Donor type and 3-month hospital readmission following kidney transplantation: results from the Netherlands organ transplant registry

BACKGROUND

Hospital readmission after transplantation is common in kidney transplant recipients (KTRs). In this study, we aim to compare the risk of 3-month hospital readmission after kidney transplantation with different donor types in the overall population and in both young (< 65 years) and elderly (≥65 years) KTRs.

METHODS

We included all first-time adult KTRs from 2016 to 2018 in the Netherlands Organ Transplant Registry. Multivariable logistic regression models were used to estimate the effect while adjusting for baseline confounders.

RESULTS

Among 1917 KTRs, 615 (32.1%) had at least one hospital readmission. Living donor kidney transplantation (LDKT) recipients had an adjusted OR of 0.76 (95%CI, 0.61 to 0.96; p = 0.02) for hospital readmission compared to deceased donor kidney transplantation (DDKT) recipients. In the young and elderly, the adjusted ORs were 0.69 (95%CI, 0.52 to 0.90, p = 0.01) and 0.93 (95%CI, 0.62 to 1.39, p = 0.73) and did not differ significantly from each other (p-value for interaction = 0.38). In DDKT, the risk of hospital readmission is similar between recipients with donation after cardiac death (DCD) or brain death (DBD) and the risk was similar between the young and elderly.

CONCLUSION

A lower risk of post-transplant 3-month hospital readmission was found in recipients after LDKT compared to DDKT, and this benefit of LDKT might be less dominant in elderly patients. In DDKT, having either DCD or DBD donors is not associated with post-transplant 3-month hospital readmission, regardless of age. Tailored patient management is needed for recipients with DDKT and elderly KTRs.

What Are the Burden, Causes, and Costs of Early Hospital Readmissions After Kidney Transplantation?

Introduction:

Kidney transplant recipients are at risk for complications resulting in early hospital readmission. This study sought to determine the incidences, risk factors, causes, and financial costs of early readmissions.

Design:

This single-centre cohort study included 1461 kidney recipients from 1 Jul 2004 to 31 Dec 2012, with at least 1-year follow-up. Early readmission was defined as hospitalization within 30 or 90-days postdischarge from transplant admission. Associations between various parameters and 30 and 90-days posttransplant were determined using multivariable Cox proportional hazards models. The hospital-associated costs of were assessed.

Results:

The rates of early readmission were 19.4% at 30 days and 26.8% at 90 days posttransplant. Mean cost per 30-day readmission was 11 606 CAD. Infectious complications were the most common reasons and resulted in the greatest cost burden. Factors associated with 30 and 90-days in multivariable models were recipient history of chronic lung disease (hazard ratio or HR 1.78 [95%CI: 1.14, 2.76] and HR 1.68 [1.14, 2.48], respectively), median time on dialysis (HR 1.07 [95% CI: 1.01, 1.13]and HR 1.06 [95% CI: 1.01, 1.11], respectively), being transplanted preemptively (HR 1.75 [95% CI: 1.07, 2.88] and HR 1.66 [95% CI: 1.07, 2.57], respectively), and having a transplant hospitalization lasting of and more than 11 days (HR 1.52 [95% CI: 1.01, 2.27] and HR 1.65 [95% CI: 1.16, 2.34], respectively).

Discussion:

Early hospital readmission after transplantation was common and costly. Strategies to reduce the burden of early hospital readmissions are needed for all patients.

Factors Influencing Emergency Department Utilization and Hospital Re-Admissions in a Predominantly Obese, Racially Diverse Urban Renal Transplant Population

INTRODUCTION

Early emergency department and hospital re-admissions are common in renal transplant recipients, but data are lacking in unique populations. Study Aim: The purpose of this study was to identify patient risk factors for multiple acute care utilization events within the first year of renal transplantation.

DESIGN

This was a single-center, retrospective cohort study of adult renal transplant recipients between 9/2013-9/2016. Patients were compared across number of emergency department visits and by hospital re-admissions. Diagnoses were categorized. Univariate and multivariate logistic regression was used to assess risk for multiple acute care utilization events within the first 12 months post-transplant.

RESULTS

A total of 216 patients were analyzed and were on average 50.5 (SD 13.9) years old, redominantly Black (49.77%) with an average body mass index of 33.33 (9.8) and were recipients of deceased donor renal transplants (61.11%). A total of 105 (48.6%) patients visited the emergency epartment and 119 (55.1%) patients had a hospital readmission. Patients having a body mass index >35 kg/m2 did not differ across emergency department visit or hospitalization groups. Delayed graft function (OR 2.86, 95% CI 1.07-7.65) and previous renal transplant (OR 2.77, 95% CI 1.04-7.39) were significantly associated with multiple acute care utilizations.

DISCUSSION

Acute care utilization following renal transplantation was similar to previously reported experiences. Obesity did not impact use of acute care resources or patient outcomes. Strategies addressing potential preventable emergency visits and hospital re-dmissions should be promoted.

Antithymocyte induction dosing and incidence of opportunistic viral infections using steroid-free maintenance immunosuppression

BACKGROUND

Currently, there is limited literature evaluating rATG induction dosing and incidence of opportunistic viral infections when using steroid-free maintenance immunosuppression.

METHODS

This single-center, retrospective, study compared high rATG (>4.5 mg/kg) versus low (<4.5 mg/kg) induction dosing and the overall incidence of early opportunistic viral infection at 180 days in the setting of maintenance immunosuppression consisting of tacrolimus, mycophenolate, rapid steroid withdrawal, and a tiered antiviral prevention strategy based on donor-recipient Cytomegalovirus (CMV) serostatus.

RESULTS

A total of 209 patients were included; 76 patients received low-dose and 133 patients received high-dose rATG. Incidence of overall opportunistic viral infection occurred more frequently in patients who received high compared to low dose (29.8% vs 25% p = .030). Incidence of CMV infection was also significantly increased in the high-dose group (31.6% vs 18.4% p = .039). In a multivariable model, rATG dose, as a continuous variable, remained a significant independent predictor of infection along with CMV risk (OR 1.46, 95% CI 1.02-2.09) controlling for age and CMV risk. There were no differences in graft-related outcomes at 180 days.

CONCLUSION

Higher cumulative rATG induction dose was associated with increased incidence of opportunistic viral infections, in the setting of a steroid-free maintenance immunosuppression in the early post-transplant period.

Re-hospitalization after pediatric kidney transplant: A single-center study

BACKGROUND

Little data exist on re-hospitalization rates in pediatric kidney recipients (KTx) particularly with the evolution of transplant immunosuppression.

METHODS

In a single-center, retrospective study of pediatric KTx between 2006 and 2016, we assessed re-hospitalization after KTx admission, stratified by whether the re-admit was early (<30 days post-KTx discharge) or late (>30 days), and compared two different immunosuppression eras (one with and one without steroids).

RESULTS

Of 197 KTx, 156 (79%) patients were re-hospitalized in 1st year, 85 (56%) within 30 days of discharge (total 490 1st year re-hospitalizations). Younger age was associated with early and late re-hospitalizations. African American race was associated with early re-hospitalizations. Of the 123 and 74 discharged on steroid-avoidance (maintenance immunosuppression included MMF in 95%; FK in 50%; CSA in 50%) and steroid-inclusive (AZA in 66%; MMF in 34%; FK in 30%; CSA in 70%), re-hospitalization rates, timing post-transplant, length, and number were not significantly different (P .38; .1; .56; .11). Admission diagnoses analysis demonstrated that steroid-avoidance recipients had anemia/leucopenia/thrombocytopenia, significantly more often, as one of their admission diagnoses (16% vs 4%; P < .001) and had a rejection diagnosis significantly less often (6% vs 18%; P < .001). Infection diagnoses were not statistically different between groups. Re-hospitalization, early or late, did not predict worse graft/ patient survival but predicted further hospitalizations.

CONCLUSIONS

Re-hospitalization is common after pediatric transplant discharge and predicts further hospitalization regardless of discharge on or off steroids.

Quality assessment of weekend discharge: a systematic review and meta-analysis

PURPOSE

Hospital bed utility and length of stay affect the healthcare budget and quality of patient care. Prior studies already show admission and operation on weekends have higher mortality rates compared with weekdays, which has been identified as the 'weekend effect.' However, discharges on weekends are also linked with quality of care, and have been evaluated in the recent decade with different dimensions. This meta-analysis aims to discuss weekend discharges associated with 30-day readmission, 30-day mortality, 30-day emergency department visits and 14-day follow-up visits compared with weekday discharges.

DATA SOURCES

PubMed, EMBASE, Cochrane Library and ClinicalTrials.gov were searched from January 2000 to November 2019.

STUDY SELECTION

Preferred reporting items for systematic reviews and meta-analyses guidelines were followed. Only studies published in English were reviewed. The random-effects model was applied to assess the effects of heterogeneity among the selected studies.

DATA EXTRACTION

Year of publication, country, sample size, number of weekday/weekend discharges, 30-day readmission, 30-day mortality, 30-day ED visits and 14-day appointment follow-up rate.

RESULTS OF DATA SYNTHESIS

There are 20 studies from seven countries, including 13 articles from America, in the present meta-analysis. There was no significant difference in odds ratio (OR) in 30-day readmission, 30-day mortality, 30-day ED visit, and 14-day follow-up between weekday and weekend. However, the OR for 30-day readmission was significantly higher among patients in the USA, including studies with high heterogeneity.

CONCLUSION

In the USA, the 30-day readmission rate was higher in patients who had been discharged on the weekend compared with the weekday. However, interpretation should be cautious because of data limitation and high heterogeneity. Further intervention should be conducted to eliminate any healthcare inequality within the healthcare system and to improve the quality of patient care.

Unplanned hospital readmissions after kidney transplantation among patients in Hefei, China: Incidence, causes and risk factors

Objectives

Unplanned readmissions severely affect a patient's physical and mental well-being after kidney transplantation (KT), which is also independently associated with morbidity. A retrospective study was conducted to identify the incidence, causes and risk factors for unplanned readmission after KT among Chinese patients.

Methods

Patients who underwent KT were admitted to the organ transplant center of the Affiliated Hospital of University of Science and Technology of China (2017-2018). Medical records for these patients were obtained through the hospital information system (HIS).

Results

In 518 patients, the incidence of unplanned readmissions within 30 days (n = 9) was 1.74%, and 90 days (n = 64) was 12.35%. The one-year unplanned readmission rate was 22.59% (n = 122). Overall, 122 patients were readmitted because of infection, renal events, metabolic disturbances, surgical complications, etc. Hemodialysis (OR = 10.462, 95% CI: 1.355-80.748), peritoneal dialysis (OR = 8.746, 95% CI: 1.074-71.238) and length of stay (OR = 1.023, 95% CI: 1.006-1.040) were independent risk factors for unplanned readmissions.

Conclusion

Unplanned readmission rates increased with time after KT. Certain risk factors related to unplanned readmissions should be deeply excavated. Targeted interventions for controllable factors to alleviate the rate of unplanned readmissions should be identified.

Early and recurrent hospitalization after kidney transplantation: Analysis of a contemporary canadian cohort of kidney transplant recipients

Hospital readmission is a common occurrence following kidney transplantation, but less is known about the predictors of early and recurrent hospitalization. We analyzed a cohort of adult kidney transplant recipients in Nova Scotia, Canada, from January 2010 to December 2015. Readmission rates for 30 days, 6 months, and 1 year were calculated as a proportion of total transplants. Factors independently associated with early readmission were investigated using multivariable Cox hazards models with multivariable Anderson-Gill Cox models being used for factors independently associated with recurrent readmission. Of the 213 patients included, 41 (19.2%), 78 (36.6%), and 88 (41.3%) were readmitted to hospital within 30 days, 6 months, and 1 year, respectively. On multivariable analyses, a history of congestive heart failure (HR 1.741, 95% CI 1.039-2.918), peptic ulcer disease (HR 2.290, 95% CI 1.054-4.973), and liver disease (HR 2.492, 95% CI 1.162-5.344) was associated with higher risk of first rehospitalization. Recurrent hospital admission was associated with initial hospital duration ≥ 8 days (HR 2.140, 95% CI 1.265-3.618), congestive heart failure (HR 1.366, 95% CI 1.044-1.787), and liver disease (HR 1.785, 95% CI 1.257-2.534). Increasing duration of initial hospitalization, congestive heart failure, and liver disease are important to consider when evaluating a patient's risk for recurrent readmission following kidney transplant.

The effects of a transitional care program on discharge readiness, transitional care quality, health services utilization and satisfaction among Chinese kidney transplant recipients: A randomized controlled trial

BACKGROUND

Kidney transplantation is the major treatment for end-stage renal disease (ESRD). However, kidney transplant recipients (KTRs) face severe challenges during the transition period from hospital discharge to home, increasing the risk of early hospital readmission (EHR) and affecting patient safety. Nevertheless, knowledge of effective transitional care for KTRs is limited in China.

OBJECTIVE

To evaluate the effectiveness of an innovative transitional care program in improving discharge readiness, transitional care quality, health services utilization and patient satisfaction among KTRs in China.

DESIGN

A prospective randomized controlled trial.

SETTINGS AND PARTICIPANTS

Patients admitted to undergo kidney transplantation were recruited in a general tertiary hospital in Chengdu, China.

METHODS

A total of 220 eligible patients were recruited and randomly assigned to the intervention and control groups. Participants in the intervention group received a transitional care intervention developed by the research team, including a risk assessment for early readmission, health education from admission to predischarge, individualized discharge planning, and a telephone follow-up once per week for one month and WeChat follow-up postdischarge. The control group received routine care of comparable length and follow-up contact. A trained research assistant collected all patients' baseline data on admission (T0), evaluated the discharge readiness (by the Readiness for Hospital Discharge Scale) on the day of discharge (T1), collected data on transitional care quality (by the Care Transition Measure-15) and patients' satisfaction with transitional care services (by a self-developed patient satisfaction scale) on the 30th day postdischarge (T2), and collected data on hospital readmission, unscheduled outpatient department visits, and emergency room visits on the 30th and 90th days (by a self-developed health services utilization record table) (T3) postdischarge. Intervention effects were analyzed using independent samples t-tests, Wilcoxon-Mann-Whitney U tests, Chi-square tests or Fisher's exact test.

RESULTS

Compared with the control group, the intervention group showed significantly better discharge readiness (personal status, P<0.001; knowledge, P = 0.010; coping ability, P<0.001; expected support, P = 0.007; total score, P<0.001), better transitional care quality (importance of preferences, P<0.001; management preparation, P<0.001; critical understanding, P = 0.003; written and understandable care plan, P = 0.012; total score, P<0.001), lower readmission rate at T2 (P = 0.033) and at T3 (P = 0.013), lower emergency room visit rate at T3 (P = 0.014), and better satisfaction with transitional care services (P<0.001).

CONCLUSIONS

This study provides evidence that an innovative transitional care program is effective in promoting KTRs' discharge readiness, transitional care quality, reducing hospital readmission and emergency room visits, and improving their satisfaction with transitional care services.

TRIAL REGISTRATION

Clinical Trials ChiCTR1800014971.

Determinants of Poor Glycemic Control in Patients with Kidney Transplants: A Single-Center Retrospective Cohort Study in Canada

Background:

Kidney transplant immunosuppressive medications are known to impair glucose metabolism, causing worsened glycemic control in patients with pre-transplant diabetes mellitus (PrTDM) and new onset of diabetes after transplant (NODAT).

Objectives:

To determine the incidence, risk factors, and outcomes of both PrTDM and NODAT patients.

Design:

This is a single-center retrospective observational cohort study.

Setting:

The Ottawa Hospital, Ontario, Canada.

Participant:

A total of 132 adult (>18 years) kidney transplant patients from 2013 to 2015 were retrospectively followed 3 years post-transplant.

Measurements:

Patient characteristics, transplant information, pre- and post-transplant HbA1C and random glucose, follow-up appointments, complications, and readmissions.

Methods:

We looked at the prevalence of poor glycemic control (HbA1c >8.5%) in the PrTDM group before and after transplant and compared the prevalence, follow-up appointments, and rate of complications and readmission rates in both the PrTDM and NODAT groups. We determined the risk factors of developing poor glycemic control in PrTDM patients and NODAT. Student t-test was used to compare means, chi-squared test was used to compare percentages, and univariate analysis to determine risk factors was performed by logistical regression.

Results:

A total of 42 patients (31.8%) had PrTDM and 12 patients (13.3%) developed NODAT. Poor glycemic control (HbA1c >8.5%) was more prevalent in the PrTDM (76.4%) patients compared to those with NODAT (16.7%; P < .01). PrTDM patients were more likely to receive follow-up with an endocrinologist (P < .01) and diabetes nurse (P < .01) compared to those with NODAT. There were no differences in the complication and readmission rates for PrTDM and NODAT patients. Receiving a transplant from a deceased donor was associated with having poor glycemic control, odds ratio (OR) = 3.34, confidence interval (CI = 1.08, 10.4), P = .04. Both patient age, OR = 1.07, CI (1.02, 1.3), P < .01, and peritoneal dialysis prior to transplant, OR = 4.57, CI (1.28, 16.3), P = .02, were associated with NODAT.

Limitations:

Our study was limited by our small sample size. We also could not account for any diabetes screening performed outside of our center or follow-up appointments with family physicians or community endocrinologists.

Conclusion:

Poor glycemic control is common in the kidney transplant population. Glycemic targets for patients with PrTDM are not being met in our center and our study highlights the gap in the literature focusing on the prevalence and outcomes of poor glycemic control in these patients. Closer follow-up and attention may be needed for those who are at risk for worse glycemic control, which include older patients, those who received a deceased donor kidney, and/or prior peritoneal dialysis.

Early Hospital Readmission (EHR) in kidney transplantation: a review article

Early hospital readmission (EHR), defined as all readmissions within 30 days of initial hospital discharge, is a health care quality measure. It is influenced by the demographic characteristics of the population at risk, the multidisciplinary approach for hospital discharge, the access, coverage, and comprehensiveness of the health care system, and reimbursement policies. EHR is associated with higher morbidity, mortality, and increased health care costs. Monitoring EHR enables the identification of hospital and outpatient healthcare weaknesses and the implementation of corrective interventions. Among kidney transplant recipients in the USA, EHR ranges between 18 and 47%, and is associated with one-year increased mortality and graft loss. One study in Brazil showed an incidence of 19.8% of EHR. The main causes of readmission were infections and surgical and metabolic complications. Strategies to reduce early hospital readmission are therefore essential and should consider the local factors, including socio-economic conditions, epidemiology and endemic diseases, and mobility.

"Early" and "Late" Hospital readmissions in the first year after kidney transplant at a single center

BACKGROUND

Hospital readmission (HR) after surgery is considered a quality metric.

METHODS

Data on 2371 first-time adult kidney transplant (KT) recipients were collected to analyze the "early" (≤30 days) and "late" (31-365 days) HR patterns after KT at a single center over a 12-year time span (2002-2013).

RESULTS

30-day, 90-day, and 1-year HR were 31%, 41%, and 53%, respectively. Risk factors for HR included age >50, female sex, black race, BMI >30, transplant LOS >5 days, and pre-transplant time on dialysis >765 days. Indications for early (n = 749) and late (n = 508) HR were similar. Early HR (OR: 3.80, P = .007) and black race (OR: 2.38, P = .009) were associated with higher odds of 1-year graft failure while frequency (1-2, 3-4, 5+) of HR (ORs: 4.68, 8.36, 9.44, P < .001) and age > 50 (OR: 2.11, P = .007) were associated with higher odds of 1-year mortality. Transplant LOS > 5 days increased both odds of 1-year graft failure (OR: 3.51, P = .001) and mortality (OR: 2.05, P = .006). One-year graft and recipient survival were 96.7% and 94.8%, respectively.

CONCLUSIONS

Hospital readmission was associated with reduced graft and patient survival; however, despite a relatively high and consistent HR rate after KT, overall 1-year graft and patient survival was high.

Use of natural language processing to improve predictive models for imaging utilization in children presenting to the emergency department

OBJECTIVE

To examine the association between the medical imaging utilization and information related to patients' socioeconomic, demographic and clinical factors during the patients' ED visits; and to develop predictive models using these associated factors including natural language elements to predict the medical imaging utilization at pediatric ED.

METHODS

Pediatric patients' data from the 2012-2016 United States National Hospital Ambulatory Medical Care Survey was included to build the models to predict the use of imaging in children presenting to the ED. Multivariable logistic regression models were built with structured variables such as temperature, heart rate, age, and unstructured variables such as reason for visit, free text nursing notes and combined data available at triage. NLP techniques were used to extract information from the unstructured data.

RESULTS

Of the 27,665 pediatric ED visits included in the study, 8394 (30.3%) received medical imaging in the ED, including 6922 (25.0%) who had an X-ray and 1367 (4.9%) who had a computed tomography (CT) scan. In the predictive model including only structured variables, the c-statistic was 0.71 (95% CI: 0.70-0.71) for any imaging use, 0.69 (95% CI: 0.68-0.70) for X-ray, and 0.77 (95% CI: 0.76-0.78) for CT. Models including only unstructured information had c-statistics of 0.81 (95% CI: 0.81-0.82) for any imaging use, 0.82 (95% CI: 0.82-0.83) for X-ray, and 0.85 (95% CI: 0.83-0.86) for CT scans. When both structured variables and free text variables were included, the c-statistics reached 0.82 (95% CI: 0.82-0.83) for any imaging use, 0.83 (95% CI: 0.83-0.84) for X-ray, and 0.87 (95% CI: 0.86-0.88) for CT.

CONCLUSIONS

Both CT and X-rays are commonly used in the pediatric ED with one third of the visits receiving at least one. Patients' socioeconomic, demographic and clinical factors presented at ED triage period were associated with the medical imaging utilization. Predictive models combining structured and unstructured variables available at triage performed better than models using structured or unstructured variables alone, suggesting the potential for use of NLP in determining resource utilization.

Socioeconomic Status and Kidney Transplant Outcomes in a Universal Healthcare System: A Population-based Cohort Study

BACKGROUND

Conflicting evidence exists regarding the relationship between socioeconomic status (SES) and outcomes after kidney transplantation.

METHODS

We conducted a population-based cohort study in a publicly funded healthcare system using linked administrative healthcare databases from Ontario, Canada to assess the relationship between SES and total graft failure (ie, return to chronic dialysis, preemptive retransplantation, or death) in individuals who received their first kidney transplant between 2004 and 2014. Secondary outcomes included death-censored graft failure, death with a functioning graft, all-cause mortality, and all-cause hospitalization (post hoc outcome).

RESULTS

Four thousand four hundred-fourteen kidney transplant recipients were included (median age, 53 years; 36.5% female), and the median (25th, 75th percentile) follow-up was 4.3 (2.1-7.1) years. In an unadjusted Cox proportional hazards model, each CAD $10000 increase in neighborhood median income was associated with an 8% decline in the rate of total graft failure (hazard ratio [HR], 0.92; 95% confidence interval [CI], 0.87-0.97). After adjusting for recipient, donor, and transplant characteristics, SES was not significantly associated with total or death-censored graft failure. However, each CAD $10000 increase in neighborhood median income remained associated with a decline in the rate of death with a functioning graft (adjusted (a)HR, 0.91; 95% CI, 0.83-0.98), all-cause mortality (aHR, 0.92; 95% CI, 0.86-0.99), and all-cause hospitalization (aHR, 0.95; 95% CI, 0.92-0.98).

CONCLUSIONS

In conclusion, in a universal healthcare system, SES may not adversely influence graft health, but SES gradients may negatively impact other kidney transplant outcomes and could be used to identify patients at increased risk of death or hospitalization.

Towards a patient journey perspective on causes of unplanned readmissions using a classification framework: results of a systematic review with narrative synthesis

Background

Several literature reviews have been published focusing on the prevalence and/or preventability of hospital readmissions. To our knowledge, none focused on the different causes which have been used to evaluate the preventability of readmissions. Insight into the range of causes is crucial to understand the complex nature of readmissions.

With this review we aim to: 1) evaluate the range of causes of unplanned readmissions in a patient journey, and 2) present a cause classification framework that can support future readmission studies.

Methods

A literature search was conducted in PUBMED and EMBASE using “readmission” and “avoidability” or “preventability” as key terms. Studies that specified causes of unplanned readmissions were included. The causes were classified into eight preliminary root causes: Technical, Organization (integrated care), Organization (hospital department level), Human (care provider), Human (informal caregiver), Patient (self-management), Patient (disease), and Other. The root causes were based on expert opinions and the root cause analysis tool of PRISMA (Prevention and Recovery Information System for Monitoring and Analysis). The range of different causes were analyzed using Microsoft Excel.

Results

Forty-five studies that reported 381 causes of readmissions were included. All studies reported causes related to organization of care at the hospital department level. These causes were often reported as preventable. Twenty-two studies included causes related to patient’s self-management and 19 studies reported causes related to patient’s disease. Studies differed in which causes were seen as preventable or unpreventable. None reported causes related to technical failures and causes due to integrated care issues were reported in 18 studies.

Conclusions

This review showed that causes for readmissions were mainly evaluated from a hospital perspective. However, causes beyond the scope of the hospital can also play a major role in unplanned readmissions. Opinions regarding preventability seem to depend on contextual factors of the readmission. This study presents a cause classification framework that could help future readmission studies to gain insight into a broad range of causes for readmissions in a patient journey.

In conclusion, we aimed to: 1) evaluate the range of causes for unplanned readmissions, and 2) present a cause classification framework for causes related to readmissions.

Electronic supplementary material

The online version of this article (10.1186/s12874-019-0822-9) contains supplementary material, which is available to authorized users.

Association Between Weight Loss Before Deceased Donor Kidney Transplantation and Posttransplantation Outcomes

RATIONALE & OBJECTIVE

There is debate on whether weight loss, a hallmark of frailty, signals higher risk for adverse outcomes among recipients of deceased donor kidney transplantation (DDKT).

STUDY DESIGN

Retrospective cohort study.

SETTING & PARTICIPANTS

Using national Organ Procurement and Transplantation Network data, we included all DDKT recipients in the United States between December 4, 2004, and December 3, 2014, who were adults (aged ≥ 18 years) when listed for DDKT.

EXPOSURES

Relative pre-DDKT weight change as a continuous predictor and categorized as <5% weight change from listing to DDKT, ≥5% to <10% weight loss, ≥10% weight loss, ≥5% to <10% weight gain, and ≥10% weight gain.

OUTCOMES

We examined 3 post-DDKT outcomes: (1) transplant hospitalization length of stay (LOS) in days, (2) all-cause graft failure, and (3) mortality.

ANALYTIC APPROACH

Unadjusted fractional polynomial methods, multivariable log-gamma models, and multivariable Cox proportional hazards models.

RESULTS

Among 94,465 recipients of DDKT, median pre-DDKT weight change was 0 (interquartile range, -3.5 to +3.9) kg. There were nonlinear unadjusted associations between relative pre-DDKT weight loss and longer transplant hospitalization LOS, higher all-cause graft loss, and higher mortality. Compared with recipients with <5% pre-DDKT weight change (n = 49,366; 52%), recipients who lost ≥10% of their listing weight (n = 10,614; 11%) had 0.66 (95% CI, 0.23-1.09) days longer average transplant hospitalization LOS (P = 0.003), 1.11-fold higher graft loss (adjusted HR [aHR], 1.11; 95% CI, 1.06-1.17; P < 0.001), and 1.18-fold higher mortality (aHR, 1.18; 95% CI, 1.11-1.25; P < 0.001) independent of recipient, donor, and transplant factors. Pre-DDKT dialysis exposure, listing body mass index category, and waiting time modified the association of pre-DDKT weight change with hospital LOS (interaction P < 0.10), but not with all-cause graft loss and mortality.

LIMITATIONS

Unmeasured confounders and inability to identify volitional weight change. Also, the higher significance level set to increase the power of detecting interactions with the fixed sample size may have resulted in increased risk for type 1 error.

CONCLUSIONS

DDKT recipients with ≥10% pre-DDKT weight loss are at increased risk for adverse outcomes and may benefit from augmented support post-DDKT.

Blessing and a curse of outpatient management of delayed graft function

Delayed graft function (DGF) is a common complication occurring most often after deceased donor kidney transplant with several donor characteristics as well as immunologic factors that lead to its development post-transplant. These patients require dialysis and close kidney function monitoring until sufficient allograft function is achieved. This has resulted in limited options for DGF management, either prolonged hospitalization until graft function improves to the point where dialysis is no longer needed or discharge back to their home dialysis unit with periodic follow up in the transplant clinic. DGF is associated with a higher risk for acute rejection, premature graft failure, and 30-d readmission; therefore, these patients need close monitoring, immunosuppression management, and prompt allograft biopsy if prolonged DGF is observed. This may not occur if these patients are discharged back to their home dialysis unit. To address this issue, the University of Wisconsin-Madison created a clinic in 2011 specialized in outpatient DGF management. This clinic was able to successfully reduce hospital length of stay without an increase in 30-d readmission, graft loss, and patient death.

Assessing Predictors of Early and Late Hospital Readmission After Kidney Transplantation

Supplemental Digital Content is available in the text.

Background.

A better understanding of the risk factors of posttransplant hospital readmission is needed to develop accurate predictive models.

Methods.

We included 40 461 kidney transplant recipients from United States renal data system (USRDS) between 2005 and 2014. We used Prentice, Williams and Peterson Total time model to compare the importance of various risk factors in predicting posttransplant readmission based on the number of the readmissions (first vs subsequent) and a random forest model to compare risk factors based on the timing of readmission (early vs late).

Results.

Twelve thousand nine hundred eighty-five (31.8%) and 25 444 (62.9%) were readmitted within 30 days and 1 year postdischarge, respectively. Fifteen thousand eight hundred (39.0%) had multiple readmissions. Predictive accuracies of our models ranged from 0.61 to 0.63. Transplant factors remained the main predictors for early and late readmission but decreased with time. Although recipients’ demographics and socioeconomic factors only accounted for 2.5% and 11% of the prediction at 30 days, respectively, their contribution to the prediction of later readmission increased to 7% and 14%, respectively. Donor characteristics remained poor predictors at all times. The association between recipient characteristics and posttransplant readmission was consistent between the first and subsequent readmissions. Donor and transplant characteristics presented a stronger association with the first readmission compared with subsequent readmissions.

Conclusions.

These results may inform the development of future predictive models of hospital readmission that could be used to identify kidney transplant recipients at high risk for posttransplant hospitalization and design interventions to prevent readmission.

How do studies assess the preventability of readmissions? A systematic review with narrative synthesis

Background

A large number of articles examined the preventability rate of readmissions, but comparison and interpretability of these preventability rates is complicated due to the large heterogeneity of methods that were used.

To compare (the implications of) the different methods used to assess the preventability of readmissions by means of medical record review.

Methods

A literature search was conducted in PUBMED and EMBASE using “readmission” and “avoidability” or “preventability” as key terms. A consensus-based narrative data synthesis was performed to compare and discuss the different methods.

Results

Abstracts of 2504 unique citations were screened resulting in 48 full text articles which were included in the final analysis. Synthesis led to the identification of a set of important variables on which the studies differed considerably (type of readmissions, sources of information, definition of preventability, cause classification and reviewer process). In 69% of the studies the cause classification and preventability assessment were integrated; meaning specific causes were predefined as preventable or not preventable. The reviewers were most often medical specialist (67%), and 27% of the studies added interview as a source of information.

Conclusion

A consensus-based standardised approach to assess preventability of readmission is warranted to reduce the unwanted bias in preventability rates. Patient-related and integrated care related factors are potentially underreported in readmission studies.

Electronic supplementary material

The online version of this article (10.1186/s12874-019-0766-0) contains supplementary material, which is available to authorized users.

Advanced diagnostic imaging utilization during emergency department visits in the United States: A predictive modeling study for emergency department triage

Background

Emergency department (ED) crowding is associated with negative health outcomes, patient dissatisfaction, and longer length of stay (LOS). The addition of advanced diagnostic imaging (ADI), namely CT, ultrasound (U/S), and MRI to ED encounter work up is a predictor of longer length of stay. Earlier and improved prediction of patients’ need for advanced imaging may improve overall ED efficiency. The aim of the study was to detect the association between ADI utilization and the structured and unstructured information immediately available during ED triage, and to develop and validate models to predict utilization of ADI during an ED encounter.

Methods

We used the United States National Hospital Ambulatory Medical Care Survey data from 2009 to 2014 to examine which sociodemographic and clinical factors immediately available at ED triage were associated with the utilization of CT, U/S, MRI, and multiple ADI during a patient’s ED stay. We used natural language processing (NLP) topic modeling to incorporate free-text reason for visit data available at time of ED triage in addition to other structured patient data to predict the use of ADI using multivariable logistic regression models.

Results

Among the 139,150 adult ED visits from a national probability sample of hospitals across the U.S, 21.9% resulted in ADI use, including 16.8% who had a CT, 3.6% who had an ultrasound, 0.4% who had an MRI, and 1.2% of the population who had multiple types of ADI. The c-statistic of the predictive models was greater than or equal to 0.78 for all imaging outcomes, and the addition of text-based reason for visit information improved the accuracy of all predictive models.

Conclusions

Patient information immediately available during ED triage can accurately predict the eventual use of advanced diagnostic imaging during an ED visit. Such models have the potential to be incorporated into the ED triage workflow in order to more rapidly identify patients who may require advanced imaging during their ED stay and assist with medical decision-making.

Early hospital readmissions after ABO- or HLA- incompatible living donor kidney transplantation

Early hospital readmission (EHR) after kidney transplantation (KT) is associated with adverse outcomes and significant healthcare costs. Despite survival benefits, ABO- and HLA-incompatible (ABOi and HLAi) KTs require desensitization and potent immunosuppression that increase risk of EHR. However, little data exist regarding EHR after incompatible KT. We defined EHR as admission for any reason within 30 days of discharge from the index hospitalization. Patients who underwent living donor KT from 2010–2017 were classified into one of three groups (control, ABOi KT, or HLAi KT). Our study included 732 patients, 96 (13.1%) of who experienced EHR. HLAi KT patients had a significantly higher incidence of EHR than other groups (26.6%; P < 0.001). In addition, HLAi KT (HR, 2.26; 95% CI, 1.35–3.77; P = 0.002) and advanced age (≥60 years) (HR, 1.93; 95% CI, 1.20–3.12; P = 0.007) were independent risk factors for EHR. Patients with EHR showed 1.5 times and 3 times greater risk of late hospital readmission and death-censored graft loss, respectively, and consistently exhibited inferior renal function compared to those without EHR, regardless of immunologic incompatibilities. We recommend that KT recipients experiencing EHR or its risk factors be managed with extreme care due to their increased susceptibility to adverse outcomes.

Causes, Preventability, and Cost of Unplanned Rehospitalizations Within 30 Days of Discharge After Lung Transplantation

BACKGROUND

Unplanned rehospitalizations (UR) within 30 days of discharge are common after lung transplantation. It is unknown whether UR represents preventable gaps in care or necessary interventions for complex patients. The objective of this study was to assess the incidence, causes, risk factors, and preventability of UR after initial discharge after lung transplantation.

METHODS

This was a single-center prospective cohort study. Subjects completed a modified short physical performance battery to assess frailty at listing and at initial hospital discharge after transplantation and the State-Trait Anxiety Inventory at discharge. For each UR, a study staff member and the patient's admitting or attending clinician used an ordinal scale (0, not; 1, possibly; 2, definitely preventable) to rate readmission preventability. A total sum score of 2 or higher defined a preventable UR.

RESULTS

Of the 90 enrolled patients, 30 (33.3%) had an UR. The single most common reasons were infection (7 [23.3%]) and atrial tachyarrhythmia (5 [16.7%]). Among the 30 URs, 9 (30.0%) were deemed preventable. Unplanned rehospitalization that happened before day 30 were more likely to be considered preventable than those between days 30 and 90 (30.0% versus 6.2%, P = 0.04). Discharge frailty, defined as short physical performance battery less than 6, was the only variable associated with UR on multivariable analysis (odds ratio, 3.4; 95% confidence interval, 1.1-11.8; P = 0.04).

CONCLUSIONS

Although clinicians do not rate the majority of UR after lung transplant as preventable, discharge frailty is associated with UR. Further research should identify whether modification of discharge frailty can reduce UR.

A Retrospective Cohort Study on Rehospitalization following Expanded Criteria Donor Kidney Transplantation

Background

Expanded criteria donor (ECD) kidneys are commonly used but are associated with increased graft failure. Graft failure is in turn related to rehospitalization within thirty days post transplant. Our goal was to determine whether ECD kidneys independently lead to rehospitalization within 30 days, 1 year, and 2 years after transplant.

Methods

All adult first-time recipients of deceased donor kidneys transplanted from 2003-2012 at our center were reviewed. Models included demographics, medical comorbidities, center for disease control high-risk kidney, ECD kidney, ischemia times, cause of renal failure, immunosuppressive regimen, positive psychiatric screening, alcoholism, surgeon, year the transplant was performed, years on dialysis before transplant, and the number of inpatient hospitalizations within 6 months prior to transplant. We conducted Andersen-Gill modeling and propensity score matching followed by logistic regression. We also used multivariable linear regression to predict average length of stay during rehospitalization.

Results

More ECD patients had a rehospitalization at 1 year (70.3% versus 59%, log-rank test p=0.014). Thirty-day and 2-year time marks were not significant. Andersen-Gill models predicting successive hospitalizations yielded HR of 1.42 (p=0.002) and 1.32 (p=0.015) for ECD patients at 1 and 2 years of after transplantation, respectively. Propensity score matching and logistic regression showed a significant relative risk of 1.630 at one year (p=0.033) and 1.313 at two years (p=0.268). There was no significant association between ECD and subsequent lengths of hospital stay.

Conclusion

Receiving an ECD kidney is independently associated with multiple readmissions within 2 years of transplant but unrelated to length of stay.

Association of Dialysis Duration With Outcomes After Kidney Transplantation in the Setting of Long Cold Ischemia Time

Background

There is no mechanism that matches hard-to-place kidneys with the most appropriate candidate. Thus, unwanted kidney offers are typically to recipients with long renal replacement time (vintage) which is a strong risk factor for mortality and graft failure, and in combination with prolonged cold ischemia time (CIT), may promote interactive effects on outcomes.

Methods

Consecutive adult isolated kidney transplants between October 2015 and December 2017 were stratified by vintage younger than 1 year and CIT longer than 30 hours.

Results

Long (n = 169) relative to short (n = 93) vintage recipients were significantly more likely to be younger (32.2 years vs 56.9 years, P = 0.02), black race (40.8% vs 18.3%, P = 0.02), have higher estimated posttransplant survival (52.6 vs 42.0, P = 0.04), and have a comorbid condition (45.6% vs 30.1%, P = 0.02); they were less likely to receive a donation after circulatory death kidney (27.8% vs 39.8%, P = 0.05). Long vintage was significantly associated with length of stay longer than 4 days (45.5% vs 30.1%, P = 0.02), and 30-day readmission (37.3% vs 22.6%, P = 0.02) but not additional operations (17.8% vs 15.1%, P = 0.58), short-term patient mortality (3.0% vs 2.2%, P = 0.70), or overall graft survival (P = 0.23). On multivariate logistic regression, long vintage remained an independent risk factor for 30-day readmission (adjusted odds ratio, 1.92; 95% confidence interval, 1.06-3.47); however, there was no interaction of vintage and CIT for this outcome (P = 0.84).

Conclusions

Readmission is significantly associated with pretransplant dialysis duration; however, CIT is not a modifying factor for this outcome.

Prior Hospitalization Burden and the Relatedness of 30-Day Readmissions in Patients Receiving Hemodialysis

BACKGROUND

Thirty-day readmissions are common in patients receiving hemodialysis and costly to Medicare. Because patients on hemodialysis have a high background hospitalization rate, 30-day readmissions might be less likely related to the index hospitalization than in patients with other conditions.

METHODS

In adults with Medicare receiving hemodialysis in the United States, we used multinomial logistic regression to evaluate whether prior hospitalization burden was associated with increased 30-day readmissions unrelated to index hospitalizations with a discharge date from January 1, 2013 to December 31, 2014. We categorized a hospitalization, 30-day readmission pair as "related" if the principal diagnoses came from the same organ system.

RESULTS

The adjusted probability of unrelated 30-day readmission after any index hospitalization was 19.1% (95% confidence interval [95% CI] 18.9% to 19.3%), 22.6% (95% CI, 22.4% to 22.8%), and 31.2% (95% CI, 30.8% to 31.5%) in patients with 0-1, 2-4, and ≥5 hospitalizations, respectively. Cardiovascular index hospitalizations had the highest adjusted probability of related 30-day readmission: 10.4% (95% CI, 10.2% to 10.7%), 13.6% (95% CI, 13.4% to 13.9%), and 20.8% (95% CI, 20.2% to 21.4%), respectively. Renal index hospitalizations had the lowest adjusted probability of related 30-day readmission: 2.0% (95% CI, 1.8% to 2.3%), 3.9% (95% CI, 3.4% to 4.4%), and 5.1% (95% CI, 4.3% to 5.9%), respectively.

CONCLUSIONS

High prior hospitalization burden increases the likelihood that patients receiving hemodialysis experience a 30-day readmission unrelated to the index hospitalization. Health care payers such as Medicare should consider incorporating clinical relatedness into 30-day readmission quality measures.

Early Hospital Readmission in Older and Younger Kidney Transplant Recipients

BACKGROUND

Up to 31% of kidney transplant (KT) recipients experience early hospital readmission (EHR). We hypothesized that EHR among older KT recipients is higher than younger recipients due to increased comorbidities and higher prevalence of frailty.

METHODS

We identified 22,458 older (age ≥65) and 86,372 younger (18 to < 65) first-time KT recipients (December 1, 1999 - December 31, 2014) using United States Renal Data System data. We estimated the association between patient-level characteristics and EHR (30 days post-KT discharge) with modified Poisson regression among older and younger KT recipients, separately. We estimated the association between graft loss and mortality and EHR using Cox proportional hazards.

RESULTS

EHR was more common in older KT recipients (30.1 vs. 27.6%; p < 0.001). Risk factors for EHR that differed by recipient age included female sex, African American race, diabetes, smoking, dialysis vintage, donor age, and length of stay. Risk of graft loss associated with EHR was greater among older KT recipients (adjusted hazard ratio [aHR] 1.64, 95% CI 1.51-1.77, p < 0.001) than younger KT recipients (aHR 1.43, 95% CI 1.38-1.48, p < 0.001; interaction p < 0.01). However, the risk of mortality associated with EHR was greater among younger recipients (aHR 1.52, 95% CI 1.47-1.57, p < 0.001) than that in older -recipients (aHR 1.40, 95% CI 1.34-1.47, p < 0.001; interaction p < 0.01).

CONCLUSIONS

Older KT recipients are more likely to experience EHR and are at a higher risk of graft loss after EHR than younger recipients. Targeted interventions to prevent EHR and subsequent graft loss in this population should be identified.

Is the Clinical Outcome Good or Bad in Patients Hospitalized Within 1 Year After Kidney Transplantation?

BACKGROUND

Although the hospitalization rate at early period of kidney transplantation (KT) is still high, the association between the hospitalization within 1 year after KT and graft survival is unclear. We investigated the incidence and causes of hospitalization and clinical outcome of the patients hospitalized within 1 year after KT.

METHODS

We retrospectively analyzed 174 KT recipients (KTRs) hospitalized within 1 year after KT between 2013 and 2015.

RESULTS

Among them, 84 (48%) KTRs were admitted within 1 year after KT, and the number of hospitalizations was 116. The mean time from KT to first hospitalization was 4.2 months. Seventy-eight percent of the patients were hospitalized for medical causes and 22% for surgical causes. The most common cause was cytomegalovirus infection (CMV) (23.3%), followed by acute rejection (11.2%) and urinary tract infection (10.3%). Recipients and donors in the hospitalized group were significantly older than the nonhospitalized group. The proportions of deceased donor KT, acute rejection, more than 50% panel-reactive antibody, and positive donor-specific antibody were significantly higher in the hospitalized group than in the nonhospitalized group. Graft and patient survivals were lower in the hospitalized group than in the nonhospitalized group. Deceased donor KT and acute rejection were independent risk factors for hospitalization.

CONCLUSION

The incidence of KTRs hospitalized within 1 year after KT was high. Most causes of hospitalization were CMV infection, acute rejection, and urinary tract infection. Therefore, the immunosuppression status of these patients should be closely monitored to reduce the hospitalization rate.

What is the supporting role of health care providers from the perspective of patients with renal allograft rejection? A qualitative study

Coping with the new conditions for patients with renal allograft rejection is a complex phenomenon influenced by a number of factors. It appears that the supportive role of health care providers in many aspects is 1 of the most important factors in patient satisfaction, increased quality of life, and application of proper coping mechanisms. This study explored the perceptions of patients with renal allograft rejection about the supportive role of health care providers. This study was conducted with a qualitative research approach and content analysis. Participants were 19 patients with renal allograft rejection in teaching hospitals in southeastern Iran who were selected by purposive sampling. Data were collected through semistructured interviews and analyzed by qualitative content analysis. During content analysis, 2 categories emerged: empathy and emotional support and educational counseling. These themes reflect the nature of patient perceptions of the supportive role of health care providers. The findings suggest that the supportive role of health care providers plays an important role in the promoting a feeling of comfort, reliability, and security in patients with renal allograft rejection and improves their coping mechanisms. These findings give health care providers greater motivation to provide comprehensive care from knowledge of their supportive role.

Quality Metrics in Solid Organ Transplantation: A Systematic Review

BACKGROUND

The best approach for determining whether a transplant program is delivering high-quality care is unknown. This review aims to identify and characterize quality metrics in solid organ transplantation.

METHODS

Medline, Embase, and Cochrane Central Register of Controlled Trials were searched from inception until February 1, 2017. Relevant full text reports and conference abstracts that examined quality metrics in organ transplantation were included. Two reviewers independently extracted study characteristics and quality metrics from 52 full text reports and 24 abstracts. PROSPERO registration: CRD42016035353.

RESULTS

Three hundred seventeen quality metrics were identified and condensed into 114 unique indicators with sufficient detail to be measured in practice; however, many lacked details on development and selection, were poorly defined, or had inconsistent definitions. The process for selecting quality indicators was described in only 5 publications and patient involvement was noted in only 1. Twenty-four reports used the indicators in clinical care, including 12 quality improvement studies. Only 14 quality metrics were assessed against patient and graft survivals.

CONCLUSIONS

More than 300 quality metrics have been reported in transplantation but many lacked details on development and selection, were poorly defined, or had inconsistent definitions. Measures have focused on safety and effectiveness with very few addressing other quality domains, such as equity and patient-centeredness. Future research will need to focus on transparent and objective metric development with proper testing, evaluation, and implementation in practice. Patients will need to be involved to ensure that transplantation quality metrics measure what is important to them.

Emergency department use among kidney transplant recipients in the United States

Patients with end-stage renal disease use the emergency department (ED) at a 6-fold higher rate than do other US adults. No national studies have described ED use rates among kidney transplant (KTx) recipients, and the factors associated with higher ED use. We examined a cohort of 132 725 adult KTx recipients in the United States Renal Data System (2005-2013). Data on ED visits, hospitalization, and outpatient nephrology visits were obtained from Medicare claims databases. Nearly half (46.1%) of KTx recipients had at least one ED visit (1.61 ED visits/patient-year [PY]), and 39.7% of ED visits resulted in hospitalization in the first year posttransplantation. ED visit rate was high in the first 30 days (5.26 visits/PY) but declined substantially thereafter (1.81 visits/PY in months 1-3; 1.13 visits/PY in months 3-12 posttransplantation). ED visit rates were higher in the first 30 days versus rates for dialysis patients but less than half the rate thereafter. Female sex, public insurance, medical comorbidities, longer pretransplantation dialysis vintage, and delayed graft function were associated with higher ED use in the first year post-KTx. Policies and strategies addressing potentially preventable ED visits should be promoted to help improve patient care and increase efficient use of ED resources.

The association of discharge decisions after deceased donor kidney transplantation with the risk of early readmission: Results from the deceased donor study

BACKGROUND

Kidney transplant (KT) recipients experience high rates of early (≤30 days) hospital readmission (EHR) after KT, and existing studies provide limited data on modifiable discharge factors that may mitigate EHR risk.

METHODS

We performed a retrospective cohort study of 468 adult deceased donor KT recipients transplanted between 4/2010 and 11/2013 at 5 United States transplant centers. We fit multivariable mixed effects models to assess the association of two potentially modifiable discharge factors with the probability of EHR after KT: (i) weekend discharge and (ii) days to first scheduled follow-up.

RESULTS

Among 468 KT recipients, 38% (n = 178) experienced EHR after KT. In fully adjusted analyses, compared to weekday discharges, KT recipients discharged on the weekend had a 29% lower risk of EHR (adjusted odds ratio [aOR] 0.71, 95% confidence interval [CI] 0.41-0.94). Compared to follow-up within 2 days of discharge, KT recipients with follow-up within 3 to 6 days had a 28% higher probability of EHR (aOR 1.28, 95% CI 1.13-1.45).

CONCLUSIONS

These findings suggest that clinical decisions related to the timing of discharge and follow-up modify EHR risk after KT, independent of traditional risk factors.