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

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.

Incidence and Causes of Early Hospital Readmissions After Living-Donor Renal Transplant at King Abdulaziz Medical City, Riyadh

BACKGROUND

 Living-donor organ transplant has a higher long-term survival rate compared to deceased-donor organ transplant, with kidney transplantation being the optimal treatment for most kidney failure patients. However, early hospital readmission within 30 days of discharge can occur due to various factors and can negatively affect long-term outcomes. Effective communication with patients pre-and post-transplant is crucial for a better quality of life and for reducing readmissions. Chronic kidney disease and co-morbid conditions must also be addressed for better long-term outcomes. The incidence and causes of early hospital readmission vary depending on local characteristics and other factors.

METHODS

 A retrospective cohort study of outcomes in patients who underwent living-donor renal transplantation at King Abdulaziz Medical City (KAMC) between 2015 and 2022. Data were collected by chart review using the BestCare system. The data collected included patients' demographics, comorbidities, surgery-related data, and the outcome of transplantation. The categorical data were presented using percentages and frequencies, while the numerical data were presented as mean and standard deviation. The Chi-square test was used for inferential statistics to find the association between categorical variables.

RESULTS

 Regarding sociodemographic characteristics, the majority of patients were male, aged 19-50 years, and either overweight or had obesity class 1. The incidence of complications, graft failure, and mortality after renal transplant was low, with only a small percentage of patients experiencing these outcomes within one year of transplant. There is no significant association between gender, age, BMI, and the likelihood of readmission after renal transplantation. Patients with comorbidities such as hypertension, diabetes, and coronary artery disease had a higher likelihood of readmission after renal transplantation. The study provides an association between readmission after renal transplantation and various factors such as surgical complications, previous transplant, age at transplant, graft failure, and mortality. Out of the 107 readmitted patients, 2.8% had surgical complications, and 5.6% had a previous transplant, but the association was not statistically significant.

CONCLUSION

 Early hospital readmission within 30 days of discharge can be a concern for patients undergoing renal transplants. While the incidence of complications, graft failure, and mortality after renal transplant was low, patients with comorbidities such as hypertension, diabetes, and coronary artery disease had a higher likelihood of readmission after renal transplantation. Although the association between surgical complications and readmission was not statistically significant, it is important to continue monitoring this factor in future studies. Effective communication with patients pre-and post-transplant can play a crucial role in reducing readmissions and improving long-term outcomes.

Predicting hospital readmission risk in patients with COVID-19: A machine learning approach

Introduction

The Coronavirus 2019 (COVID-19) epidemic stunned the health systems with severe scarcities in hospital resources. In this critical situation, decreasing COVID-19 readmissions could potentially sustain hospital capacity. This study aimed to select the most affecting features of COVID-19 readmission and compare the capability of Machine Learning (ML) algorithms to predict COVID-19 readmission based on the selected features.

Material and methods

The data of 5791 hospitalized patients with COVID-19 were retrospectively recruited from a hospital registry system. The LASSO feature selection algorithm was used to select the most important features related to COVID-19 readmission. HistGradientBoosting classifier (HGB), Bagging classifier, Multi-Layered Perceptron (MLP), Support Vector Machine ((SVM) kernel = linear), SVM (kernel = RBF), and Extreme Gradient Boosting (XGBoost) classifiers were used for prediction. We evaluated the performance of ML algorithms with a 10-fold cross-validation method using six performance evaluation metrics.

Results

Out of the 42 features, 14 were identified as the most relevant predictors. The XGBoost classifier outperformed the other six ML models with an average accuracy of 91.7%, specificity of 91.3%, the sensitivity of 91.6%, F-measure of 91.8%, and AUC of 0.91%.

Conclusion

The experimental results prove that ML models can satisfactorily predict COVID-19 readmission. Besides considering the risk factors prioritized in this work, categorizing cases with a high risk of reinfection can make the patient triaging procedure and hospital resource utilization more effective.

Nursing Care for Kidney Transplant Patients: A Scoping Review

Objectives: Mapping nursing care in kidney transplant patients. Materials and method: A scoping review was conducted according to the recommendations of the Joanna Briggs Institute Reviewers’ Manual. Data were collected through 13 national and international databases from December 2020 to January 2021, following scientific rigor in the selection of the material. The pre-selection was made by reading the title, abstract and introductory text in advance; the materials included in this stage were read in full to define the content for the study. Results: Fifteen studies were included. Of these, 60% are articles; dissertations, manuals, protocols, guidelines and bulletins totaled 40% of the material studied. 86.6% of the material has a quantitative approach. Regarding the methodological design, 73.3% were descriptive/transversal character studies. Regarding the mapping of nursing care, it was possible to divide them into two categories: nursing care after kidney transplantation (immediate, mediated and late) and nursing care after kidney transplantation in primary health/extra-hospital care. Conclusions: It is concluded that the study allowed mapping nursing care to kidney transplant patients in the immediate, late and primary health care periods.