Burden and causes of hospital admissions and readmissions in patients undergoing hemodialysis and peritoneal dialysis: a nationwide study

Characteristics of patients and facility of peritoneal dialysis in Korea: Results from the Peritoneal Dialysis Outcomes and Practice Patterns Study (PDOPPS) Korea

BACKGROUND

Varying peritoneal dialysis (PD)-related clinical outcomes have been reported in different countries. As a participant of the Peritoneal Dialysis Outcomes and Practice Patterns Study (PDOPPS), this study investigated the characteristics of Korean PD patients, PD facilities and the incidence rates of clinical outcomes including mortality and PD-related outcomes.

METHODS

From July 2019 to December 2021, a total of 766 Korean PD patients were included for analysis. Poisson regression analysis was used to explore the incidence rates of various clinical events including mortality, modality transfer, exit site or catheter tunnel infection and peritonitis.

RESULTS

Among the 766 patients (median age 55.5 years, males 59.5%), 276 were incident and 490 were prevalent PD patients. The incidence rates of events were as follows: all-cause mortality (0.048), modality transfer (0.051), exit site or catheter tunnel infection (0.054) and peritonitis (0.136) events per person year. The most common causative organism for exit site or tunnel infection was staphylococcus species (47%) and that for peritonitis was streptococcus (28%) followed by staphylococcus (27%) species.

CONCLUSIONS

Up to now, PDOPPS Korea has recruited 766 Korean PD patients and started documentation of major PD-related outcomes which occurred during the follow-up period. The overall incidence rates of clinical outcomes in Korean PD patients were relatively favourable. There was no statistically significant difference in the incidence rates of clinical outcomes according to both facility and patient factors.

Analyzing Monthly Blood Test Data to Forecast 30-Day Hospital Readmissions among Maintenance Hemodialysis Patients

Background: The increase in the global population of hemodialysis patients is linked to aging demographics and the prevalence of conditions such as arterial hypertension and diabetes mellitus. While previous research in hemodialysis has mainly focused on mortality predictions, there is a gap in studies targeting short-term hospitalization predictions using detailed, monthly blood test data. Methods: This study employs advanced data preprocessing and machine learning techniques to predict hospitalizations within a 30-day period among hemodialysis patients. Initial steps include employing K-Nearest Neighbor (KNN) imputation to address missing data and using the Synthesized Minority Oversampling Technique (SMOTE) to ensure data balance. The study then applies a Support Vector Machine (SVM) algorithm for the predictive analysis, with an additional enhancement through ensemble learning techniques, in order to improve prediction accuracy. Results: The application of SVM in predicting hospitalizations within a 30-day period among hemodialysis patients resulted in an impressive accuracy rate of 93%. This accuracy rate further improved to 96% upon incorporating ensemble learning methods, demonstrating the efficacy of the chosen machine learning approach in this context. Conclusions: This study highlights the potential of utilizing machine learning to predict hospital readmissions within a 30-day period among hemodialysis patients based on monthly blood test data. It represents a significant leap towards precision medicine and personalized healthcare for this patient group, suggesting a paradigm shift in patient care through the proactive identification of hospitalization risks.

Hospitalizations of Chronic Dialysis Patients: A National Study in China

Background

Patients receiving chronic dialysis are usually with multiple comorbidities and at high risk for hospitalization, which lead to tremendous health care resource utilization. This study aims to explore the characteristics of hospitalizations among chronic dialysis patients in China.

Methods

Hospital admissions from January 2013 to December 2015 were extracted from a national inpatient database in China. Chronic dialysis, including hemodialysis and peritoneal dialysis, was identified according to inpatient discharge records and International Classification of Diseases-10 (ICD-10) codes. The primary kidney disease, causes of admissions, modalities of dialysis, and comorbidities were analyzed. Multivariable logistic regression model was used to assess the association of patient characteristics with multiple hospitalizations per year.

Results

Altogether, 266,636 hospitalizations from 124,721 chronic dialysis patients were included in the study. The mean age was 54.46 ± 15.63 years and 78.29% of them were receiving hemodialysis. The leading cause of hospitalizations was dialysis access-related, including dialysis access creation (25.06%) and complications of access (21.09%). The following causes were nonaccess surgery (1.89%), cardiovascular disease (1.66%), and infectious diseases (1.43%). One-fourth of the patients were hospitalized more than once per year. Multivariate logistic regression models indicated that the primary kidney disease of diabetic kidney disease (odds ratio [OR]: 1.16, 95% confidence interval [CI]: 1.11-1.22) or hypertensive nephropathy (OR: 1.33, 95% CI: 1.27-1.40), coronary heart disease (OR: 1.09, 95% CI: 1.05-1.14), cancer (OR: 1.21, 95% CI: 1.13-1.30), or modality of peritoneal dialysis (OR: 2.67, 95% CI: 2.59-2.75) was risk factors for multiple hospitalizations.

Conclusion

Our study described characteristics and revealed the burden of hospitalizations of chronic dialysis patients in China. These findings highlight the importance of effective and efficient management strategies to reduce the high burden of hospitalization in dialysis population.

Break-in Period ≤24 Hours as an Option for Urgent-start Peritoneal Dialysis in Patients With Diabetes

BACKGROUND

The optimal break-in period (BI) of urgent-start peritoneal dialysis (USPD) initiation for patients with end-stage renal disease (ESRD) and diabetes is unclear. We aimed to explore the safety and applicability of a BI ≤24 h in patients with ESRD and diabetes.

METHODS

We used a retrospective cohort design wherein we recruited patients with ESRD and diabetes who underwent USPD at five institutions in China between January 2013 and August 2020. The enrolled patients were grouped according to BI. The primary outcomes were mechanical and infectious complication occurrences, whereas the secondary outcome was technique survival.

RESULTS

We enrolled 310 patients with diabetes, of whom 155 and 155 patients were in the BI ≤24 h and BI >24 h groups, respectively. The two groups showed a comparable incidence of infectious and mechanical complications within 6 months after catheter insertion (p>0.05). Logistic regression analysis revealed that a BI ≤24 h was not an independent risk factor for mechanical or infectious complications. Kaplan-Meier estimates showed no statistically significant between-group differences in technique survival rates (p>0.05). Cox multivariate regression analysis revealed that a BI ≤24 h was not an independent risk factor for technique failure.

CONCLUSION

USPD initiation with a BI ≤24 h may be safe and feasible for patients with ESRD and diabetes.