Background and Objectives: Patients on dialysis are twice as likely to have early readmissions. This study aimed to identify risk factors for 30-day unplanned readmission among patients on maintenance dialysis in a tertiary hospital. Methods: We conducted a retrospective, unmatched, case-control study. Data were taken from patients on maintenance hemodialysis admitted in the University of the Philippines–Philippine General Hospital (UP-PGH) between January 2018 and December 2020. Patients with 30-day readmission were included as cases and patients with >30-day readmissions were taken as controls. Multivariable regression with 30-day readmission as the outcome was used to identify significant predictors of early readmission. Results: The prevalence of 30-day unplanned readmission among patients on dialysis is 36.96%, 95%CI [31.67, 42.48]. In total, 119 cases and 203 controls were analyzed. Two factors were significantly associated with early readmission: the presence of chronic glomerulonephritis [OR 2.35, 95% CI 1.36 to 4.07, p-value=0.002] and number of comorbidities [OR 1.34, 95% CI 1.12 to 1.61, p-value=0.002]. The most common reasons for early readmission are infection, anemia, and uremia/underdialysis. Conclusion Patients with chronic glomerulonephritis and multiple comorbidities have significantly increased odds of early readmission. Careful discharge planning and close follow up of these patients may reduce early readmissions.
Patient Readmission ; Dialysis ; Risk Factors

No abstract available.
Patient Readmission* ; Vertebral Artery Dissection* ; Vertebral Artery*

Asians ; China/epidemiology* ; Humans ; Patient Discharge ; Patient Readmission

Humans ; Patient Discharge ; Patient Readmission ; Risk Factors ; Technology

The hospital readmission rate has been widely used as an indicator of the quality of hospital care in many countries. However, the transferrability of this indicator that has been developed in a different health care system can be questioned. We reviewed what should be considered when using the risk-standardized readmission rate (RSRR) as a generic quality indicator in the Korean setting. We addressed the relationship between RSRR and the quality of hospital care, methodological aspects of RSRR, and use of RSRR for external purposes. These issues can influence the validity of the readmission rate as a generic quality indicator. Therefore RSRR should be used with care and further studies are needed to enhance the validity of the readmission rate indicator.
Delivery of Health Care ; Patient Readmission ; Quality Indicators, Health Care

INTRODUCTION: Analysis of risk factors can pave the way for reducing unscheduled hospital readmissions and improve resource utilisation. METHODS: This was a concurrent nested, mixed method study. Factors associated with patients readmitted within 30 days between 2011 and 2015 at the National University Hospital, Singapore (N=104,496) were examined. Fifty patients were sampled in 2016 to inform an embedded qualitative study. Narrative interviews explored the periods of readmissions and related experiences, contrasted against those of non-readmitted patients. RESULTS: Neoplastic disease (odds ratio [OR] 1.91, 95% confidence interval [CI] 1.70-2.15), number of discharged medications (5 to 10 medications OR 1.21, 95% CI 1.14-1.29; ≥11 medications OR 1.80, 95% CI 1.66-1.95) and length of stay >7 days (OR 1.46, 95% CI 1.36-1.58) were most significantly associated with readmissions. Other factors including number of surgical operations, subvention class, number of emergency department visits in the previous year, hospital bill size, gender, age, Charlson comorbidity index and ethnicity were also independently associated with hospital readmissions. Although readmitted and non-readmitted patients shared some common experiences, they reported different psychological reactions to their illnesses and viewed hospital care differently. Negative emotions, feeling of being left out by the healthcare team and perception of ineffective or inappropriate treatment were expressed by readmitted patients. CONCLUSION Patient, hospital and system-related factors were associated with readmissions, which may allow early identification of at-risk patients. Qualitative analysis suggested several areas of improvement in care including greater empowerment and involvement of patients in care and decision making.
Hospitals ; Humans ; Length of Stay ; Patient Readmission ; Retrospective Studies ; Risk Factors

OBJECTIVE: To investigate the length of hospital stay (LOS) after stroke using the database of the Korean Health Insurance Review & Assessment Service. METHODS: We matched the data of patients admitted for ischemic stroke onset within 7 days in the Departments of Neurology of 12 hospitals to the data from the database of the Korean Health Insurance Review & Assessment Service. We recruited 3,839 patients who were hospitalized between January 2011 and December 2011, had a previous modified Rankin Scale of 0, and no acute hospital readmission after discharge. The patients were divided according to the initial National Institute of Health Stroke Scale score (mild, ≤5; moderate, >5 and ≤13; severe, >13); we compared the number of hospitals that admitted patients and LOS after stroke according to severity, age, and sex. RESULTS: The mean LOS was 115.6±219.0 days (median, 19.4 days) and the mean number of hospitals was 3.3±2.1 (median, 2.0). LOS was longer in patients with severe stroke (mild, 65.1±146.7 days; moderate, 223.1±286.0 days; and severe, 313.2±336.8 days). The number of admitting hospitals was greater for severe stroke (mild, 2.9±1.7; moderate, 4.3±2.6; and severe, 4.5±2.4). LOS was longer in women and shorter in patients less than 65 years of age. CONCLUSION: LOS after stroke differed according to the stroke severity, sex, and age. These results will be useful in determining the appropriate LOS after stroke in the Korean medical system.
Female ; Humans ; Insurance, Health ; Length of Stay* ; Neurology ; Patient Acuity ; Patient Readmission ; Stroke*

Many findings suggest that chronic obstructive pulmonary disease (COPD) imposes an enormous burden on patients, health-care professionals and society. COPD contributes to morbidity and mortality and to a significant use of health-care resources. In spite of a higher prevalence of COPD in Korea, the result of COPD treatment is not effective. The purpose of this article was to review recent advances in the study of COPD in Korea with the aim of improving effective management. This review highlights articles pertaining to the following topics; prevalence, assessment of COPD, risk factors for hospitalization, co-morbid diseases, phenotypes, and treatment issues.
Comorbidity ; Hospitalization ; Humans ; Korea ; Patient Readmission ; Phenotype ; Prevalence ; Pulmonary Disease, Chronic Obstructive ; Risk Factors

OBJECTIVE: To investigate the current status of readmission of neonates with hyperbilirubinemia and risk factors for readmission. METHODS: From January 2017 to December 2019, a total of 85 infants who were readmitted due to hyperbilirubinemia were enrolled as the study group. A total of 170 neonates with hyperbilirubinemia but without readmission during the same period of time were randomly selected as the control group. The medical data were compared between the two groups. Multivariate logistic regression was used to assess the risk factors for readmission due to hyperbilirubinemia. RESULTS: The readmission rate was 2.30%, and the interval between readmission and initial admission was 5 days. Compared with the control group, the study group had significantly higher levels of total bilirubin and indirect bilirubin at discharge (P<0.05) and a significantly longer duration of phototherapy during the first hospitalization (P<0.05). The univariate analysis showed that compared with the control group, the study group had significantly lower birth weight, gestational age, and age on initial admission (P<0.05) and a significantly higher proportion of infants with glucose-6-phosphate dehydrogenase (G-6-PD) deficiency or hemolytic disease (P<0.05). The multivariate analysis showed that low gestational age (OR=1.792, P<0.05), young age on initial admission (OR=1.415, P<0.05), and G-6-PD deficiency (OR=2.829, P<0.05) were independent risk factors for readmission of neonates with hyperbilirubinemia. CONCLUSIONS The infants with hyperbilirubinemia who have lower gestational age, younger age on initial admission, and G-6-PD deficiency have a higher risk of readmission due to hyperbilirubinemia. It is thus important to strengthen the management during hospitalization and after discharge for these infants to prevent the occurrence of readmission.
Bilirubin ; Glucosephosphate Dehydrogenase Deficiency ; Humans ; Hyperbilirubinemia, Neonatal ; Infant, Newborn ; Patient Readmission ; Risk Factors

OBJECTIVE: To develop machine learning models for predicting intensive care unit (ICU) readmission using ensemble learning algorithms. METHODS: A publicly accessible American ICU database, medical information mart for intensive care (MIMIC)-Ⅲ as the data source was used, and the patients were selected by the inclusion and exclusion criteria. A set of variables that had the predictive ability of outcome including demographics, vital signs, laboratory tests, and comorbidities of patients were extracted from the dataset. We built the ICU readmission prediction models based on ensemble learning methods including random forest, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT), and compared the prediction performance of the machine learning models with a conventional Logistic regression model. Five-fold cross validation was used to train and validate the prediction models. Average sensitivity, positive prediction value, negative prediction value, false positive rate, false negative rate, area under the receiver operating characteristic curve (AUROC) and Brier score were used as performance measures. After constructing the prediction models, top 10 predictive variables based on importance ranking were identified by the model with the best discrimination. RESULTS: Among these ICU readmission prediction models, GBDT (AUROC=0.858) had better performance than random forest (AUROC=0.827), and was slightly superior to AdaBoost (AUROC=0.851) in terms of AUROC. Compared with Logistic regression (AUROC=0.810), the discrimination of the three ensemble learning models was much better. The feature importance provided by GBDT showed that the top ranking variables included vital signs and laboratory tests. The patients with ICU readmission had higher mean arterial pressure, systolic blood pressure, diastolic blood pressure, and heart rate than the patients without ICU readmission. Meanwhile, the patients readmitted to ICU experienced lower urine output and higher serum creatinine. Overall, the patients having repeated admissions during their hospitalization showed worse heart function and renal function compared with the patients without ICU readmission. CONCLUSION The ensemble learning based ICU readmission prediction models had better performance than Logistic regression model. Such ensemble learning models have the potential to aid ICU physicians in identifying those patients with high risk of ICU readmission and thus help improve overall clinical outcomes.
Critical Illness ; Humans ; Intensive Care Units ; Machine Learning ; Patient Readmission ; ROC Curve