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

Humans ; Aged ; Patient Readmission ; Pulmonary Disease, Chronic Obstructive ; Malnutrition/complications* ; Risk Factors ; Retrospective Studies

Objective: To analyze the factors associated with readmission within three months of surgery for gastric cancer and the impact of readmission on patients' long-term nutritional status and quality of life. Methods: This was a prospective cohort study comprising patients who underwent radical gastrectomy in the Department of Pancreatic and Gastric Surgery, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences from October 2018 to August 2019. Patients who failed to complete postoperative follow-up, whose body mass index (BMI) could not be accurately estimated, or who were unable to complete a quality-of-life questionnaire were excluded. The patients were followed up for 12 months. Time to, cause(s) of, and outcomes of readmission were followed up 1, 2 and 3 months postoperatively. BMI was followed up 1, 3, 6 and 12 months postoperatively. Results of blood tests were collected and patients' nutritional status and quality of life were assessed 12 months postoperatively. Nutritional status was evaluated by BMI, hemoglobin, albumin, and total lymphocyte count. Quality of life was evaluated using the European Organization for Research in the Treatment of Cancer (EORTC) Quality of Life scale. The higher the scores for global health and functional domains, the better the quality of life, whereas the higher the score in the symptom domain, the worse the quality of life. Results: The study cohort comprised 259 patients with gastric cancer, all of whom were followed up for 3 months and 236 of whom were followed up for 12 months. Forty-four (17.0%) patients were readmitted within 3 months. The commonest reasons for readmission were gastrointestinal dysfunction (16 cases, 36.3%), intestinal obstruction (8 cases, 18.2%), and anastomotic stenosis (8 cases, 18.2%). Logistic regression analysis showed that preoperative Patient-Generated Subjective Global Assessment score ≥ 4 points (OR=1.481, 95% CI: 1.028‒2.132), postoperative complications (OR=3.298, 95%CI:1.416‒7.684) and resection range (OR=1.582, 95% CI:1.057‒2.369) were risk factors for readmission within 3 months of surgery. Compared with patients who had not been readmitted 12 months after surgery, patients who were readmitted within 3 months of surgery tended to have greater decreases in their BMI [-2.36 (-5.13,-0.42) kg/m² vs. -1.73 (-3.33,-0.33) kg/m², Z=1.850, P=0.065), significantly lower hemoglobin and albumin concentrations [(122.1±16.6) g/L vs. (129.8±18.4) g/L, t=2.400, P=0.017]; [(40.9±5.0) g/L vs. (43.4±3.3) g/L, t=3.950, P<0.001], and significantly decreased global health scores in the quality of life assessment [83 (67, 100) vs. 100 (83, 100), Z=2.890,P=0.004]. Conclusion: Preoperative nutritional risk, total or proximal radical gastrectomy, and complications during hospitalization are risk factors for readmission within 3 months of surgery for gastric cancer. Perioperative management and postoperative follow-up should be more rigorous. Readmission within 3 months after surgery may be associated with a decline in long-term nutritional status and quality of life. Achieving improvement in long-term nutritional status and quality of life requires tracking of nutritional status, timely evaluation, and appropriate interventions in patients who need readmission.
Humans ; Nutritional Status ; Quality of Life ; Patient Readmission ; Stomach Neoplasms/complications* ; Prospective Studies ; Postoperative Complications/etiology* ; Gastrectomy/adverse effects* ; Retrospective Studies

This study aimed to explore the correlation between traditional Chinese medicine(TCM) and reduced risk of readmission in patients having rheumatoid arthritis with hypoproteinemia(RA-H). A retrospective cohort study was conducted on 2 437 rheumatoid arthritis patients in the information system database of the First Affiliated Hospital of Anhui University of Chinese Medicine from 2014 to 2021, and 476 of them were found to have hypoproteinemia. The patients were divided into TCM users and non-TCM users by propensity score matching. Exposure was defined as the use of oral Chinese patent medicine or herbal decoction for ≥1 month. Cox regression analysis was performed to explore the risk factors of clinical indicators of rheumatoid arthritis. Additionally, the use of TCM during hospitalization was analyzed, and analysis of association rules was conducted to investigate the correlation between TCM, improvement of indicators and readmission of patients. Kaplan-Meier survival curve was plotted to compare the readmission rate of TCM users and non-TCM users. It was found the readmission rate of RA-H patients was significantly higher than that of RA patients. By propensity score matching, 232 RA-H patients were divided into TCM group(116 cases) and non-TCM group(116 cases). Compared with the conditions in the non-TCM group, the readmission rate of the TCM group was lowered(P<0.01), and the readmission rate of middle-aged and elderly patients was higher than that of young patients(P<0.01). Old age was a risk factor for readmission of RA-H patients, while TCM, albumin(ALB) and total protein(TP) were the protective factors. During hospitalization, the TCMs used for RA-H patients were mainly divided into types of activating blood and resolving stasis, relaxing sinew and dredging collaterals, clearing heat and detoxifying, and invigorating spleen and resolving dampness. The improvement of rheumatoid factor(RF), immunoglobulin G(IgG), erythrocyte sedimentation rate(ESR), C-reactive protein(CRP) and ALB was closely related to TCM. On the basis of western medicine treatment, the application of TCM could reduce the readmission rate of RA-H patients, and longer use of TCM indicated lower readmission rate.
Middle Aged ; Aged ; Humans ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/therapeutic use* ; Retrospective Studies ; Patient Readmission ; Arthritis, Rheumatoid/drug therapy* ; Hypoproteinemia/drug therapy*

OBJECTIVE: To explore the incidence and risk factors of readmission of elderly patients with hip fracture after hip hemiarthroplasty. METHODS: A retrospective analysis of 237 elderly hip fracture patients who underwent hip hemiarthroplasty from February 2015 to October 2020 were performed. According to the readmission status of the patients at 3 months postoperatively, the patients were divided into readmission group (39 cases)and non-readmission group(198 cases). In readmission group, there were 7 males and 32 females with an average age of(84.59±4.34) years old, respectively, there were 34 males and 164 females with average age of (84.65±4.17) years old in non-readmission group. The general information, surgical status, hip Harris score and complications of patients in two groups were included in univariate analysis, and multivariate Logistic regression was used to analyze independent risk factors of patients' readmission. RESULTS: The proportion of complications(cerebral infarction and coronary heart disease) in readmission group was significantly higher than that of non-readmission group (P<0.05), and intraoperative blood loss in readmission group was significantly higher than that of non-readmission group(P<0.05). Harris score of hip joint was significantly lower than that of non-readmission group(P<0.05). The proportion of infection, delirium, joint dislocation, anemia and venous thrombosis in readmission group were significantly higher than that of non-readmission group (all P<0.05). Multivariate Logistic regression analysis showed that the risk factors for readmission of elderly patients with hip fracture after hip hemiarthroplasty included cerebral infarction, infection, delirium, dislocation, anemia and venous thrombosis (all P<0.05). CONCLUSION The complications of the elderly patients who were readmission after hip hemiarthroplasty for hip fractures were significantly higher than those who were non-readmission. Cerebral infarction, infection, delirium, dislocation, anemia and venous thrombosis are risk factors that lead to patient readmission. Corresponding intervention measures can be taken clinically based on these risk factors to reduce the incidence of patient readmissions.
Aged ; Aged, 80 and over ; Arthroplasty, Replacement, Hip ; Cerebral Infarction/surgery* ; Delirium ; Female ; Femoral Neck Fractures/surgery* ; Hemiarthroplasty/adverse effects* ; Hip Fractures/surgery* ; Humans ; Joint Dislocations/surgery* ; Male ; Patient Readmission ; Retrospective Studies ; Risk Factors ; Treatment Outcome

OBJECTIVES: To investigate the current status of unplanned readmission of neonates within 31 days after discharge from the neonatal intensive care unit (NICU) and risk factors for readmission. METHODS: A retrospective analysis was performed on the medical data of 1 561 infants discharged from the NICU, among whom 52 infants who were readmitted within 31 days were enrolled as the case group, and 104 infants who were not readmitted after discharge during the same period of time were enrolled as the control group. Univariate analysis and multivariate logistic regression analysis were performed to identify the risk factors for readmission. RESULTS: Among the 1 561 infants, a total of 63 readmissions occurred in 52 infants, with a readmission rate of 3.33%. hyperbilirubinemia and pneumonia were the main causes for readmission, accounting for 29% (18/63) and 24% (15/63) respectively. The multivariate logistic regression analysis showed that that gestational age <28 weeks, birth weight <1 500 g, multiple pregnancy, mechanical ventilation, and length of hospital stay <7 days were risk factors for readmission (OR=5.645, 5.750, 3.044, 3.331, and 1.718 respectively, P<0.05). CONCLUSIONS Neonates have a relatively high risk of readmission after discharge from the NICU. The medical staff should pay attention to risk factors for readmission and formulate targeted intervention measures, so as to reduce readmission and improve the quality of medical service.
Female ; Humans ; Infant ; Infant, Newborn ; Intensive Care Units, Neonatal ; Patient Discharge ; Patient Readmission ; Pregnancy ; Retrospective Studies ; Risk Factors

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

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

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 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