Readiness for hospital discharge and its association with post-discharge outcomes among oesophageal cancer patients after oesophagectomy: A prospective observational study.

AIM: To examine the level and influencing factors of discharge readiness among patients with oesophageal cancer following oesophagectomy and to explore its association with post-discharge outcomes (post-discharge coping difficulty and unplanned readmission). BACKGROUND: Oesophageal cancer is common and usually treated via oesophagectomy in China. The assessment of patient's discharge readiness gradually attracts attention as patients tend to be discharged more quickly. DESIGN: Prospective observational study. The STROBE statement was followed. METHODS: In total, 154 participants with oesophageal cancer after oesophagectomy were recruited in a tertiary cancer centre in Southern China from July 2019 to January 2020. The participants completed a demographic and disease-related questionnaire, the Quality of Discharge Teaching Scale and Readiness for Hospital Discharge Scale before discharge. Post-discharge outcomes were investigated on the 21st day (post-discharge coping difficulty) and 30th day (unplanned readmission) after discharge separately. Multiple linear regressions were used for statistical analysis. RESULTS: The mean scores of discharge readiness and quality of discharge teaching were (154.02 ± 31.58) and (138.20 ± 24.20) respectively. The quality of discharge teaching, self-care ability, dysphagia and primary caregiver mainly influenced patient's discharge readiness and explained 63.0% of the variance. The low discharge readiness could predict more risk of post-discharge coping difficulty (r = -0.729, p < 0.01) and unplanned readmission (t = -2.721, p < 0.01). CONCLUSIONS: Discharge readiness among patients with oesophageal cancer following oesophagectomy is influenced by various factors, especially the quality of discharge teaching. A high discharge readiness corresponds to good post-discharge outcomes. IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE: Healthcare professionals should improve the discharge readiness by constructing high-quality discharge teaching, cultivating patients' self-care ability, mobilizing family participation and alleviating dysphagia to decrease adverse post-discharge outcomes among patients with oesophageal cancer. PATIENTS OR PUBLIC CONTRIBUTION: Patients with oesophageal cancer after oesophagectomy who met the inclusion criteria were recruited.

Development and validation of a risk score to predict unplanned hospital readmissions in ICU survivors: A data linkage study.

BACKGROUND: Intensive Care Unit (ICU) follow-up clinics are growing in popularity internationally; however, there is limited evidence as to which patients would benefit most from a referral to this service. OBJECTIVES: The objective of this study was to develop and validate a model to predict which ICU survivors are most likely to experience an unplanned hospital readmission or death in the year after hospital discharge and derive a risk score capable of identifying high-risk patients who may benefit from referral to follow-up services. METHODS: A multicentre, retrospective observational cohort study using linked administrative data from eight ICUs was conducted in the state of New South Wales, Australia. A logistic regression model was developed for the composite outcome of death or unplanned readmission in the 12 months after discharge from the index hospitalisation. RESULTS: 12,862 ICU survivors were included in the study, of which 5940 (46.2%) patients experienced unplanned readmission or death. Strong predictors of readmission or death included the presence of a pre-existing mental health disorder (odds ratio [OR]: 1.52, 95% confidence interval [CI]: 1.40-1.65), severity of critical illness (OR: 1.57, 95% CI: 1.39-1.76), and two or more physical comorbidities (OR: 2.39, 95% CI: 2.14-2.68). The prediction model demonstrated reasonable discrimination (area under the receiver operating characteristic curve: 0.68, 95% CI: 0.67-0.69) and overall performance (scaled Brier score: 0.10). The risk score was capable of stratifying patients into three distinct risk groups-high (64.05% readmitted or died), medium (45.77% readmitted or died), and low (29.30% readmitted or died). CONCLUSIONS: Unplanned readmission or death is common amongst survivors of critical illness. The risk score presented here allows patients to be stratified by risk level, enabling targeted referral to preventative follow-up services.

Prevalence and risk factors associated within 48-hour unplanned paediatric intensive care unit readmissions: An integrative review.

BACKGROUND: Unplanned paediatric intensive care unit (PICU) readmission is associated with increased morbidity/mortality, hospital length of stay, and health service cost and is recognised as a key performance indicator of quality-of-care delivery. However, research evidence on unplanned PICU readmission risk factors is limited, and results were inconsistent across studies. AIM: The aim of this experiment was to collate and synthesise unplanned within-48-h PICU readmission prevalence and associated risk factors. METHODS: An integrative review was conducted, guided by a five-stage framework. Seven electronic databases were searched (2013-30th June 2023). Studies published in English with full-text accessibility and detailed methodologies were included. The quality of included studies was critically appraised using the Joanna Briggs Institute checklists. Prevalence and risk factors were extracted, synthesised, and presented narratively. RESULTS: Ten studies met eligibility criteria and reported a varied readmission rate from 0.008% to 6.49%. Fifteen types of significant risk factors were extracted. Twelve consistently cited risk factors were age, weight, complex chronic conditions, admission source, unplanned admission, PICU length of stay, positive pressure ventilation, discharge disposition, oxygen requirements, respiratory rate, heart rate, and Glasgow Coma Score at discharge. Of the 12, five predictors were classified as modifiable factors, including discharge disposition, oxygen requirement, abnormal respiratory rate, abnormal heart rate, and decreased Glasgow Coma Score at discharge. CONCLUSION: This review acknowledges the complexity of confounding factors impacting unplanned PICU readmission and the lack of standardisation examining potential risk factors. The five modifiable factors are suggestive of clinical instability and premature PICU discharge. Patients with modifiable risk factors should have their readiness for discharge re-evaluated. Scaffolding support to manage patients at risk of readmission includes senior bedside nursing allocation, use of PICU outreach services, and 1:2 nurse-to-patient ratios in the ward setting, which are warranted to ensure patient safety.

Development of a prediction model for the risk of 30-day unplanned readmission in older patients with heart failure: A multicenter retrospective study.

BACKGROUND AND AIM: Heart failure (HF) imposes significant global health costs due to its high incidence, readmission, and mortality rate. Accurate assessment of readmission risk and precise interventions have become important measures to improve health for patients with HF. Therefore, this study aimed to develop a machine learning (ML) model to predict 30-day unplanned readmissions in older patients with HF. METHODS AND RESULTS: This study collected data on hospitalized older patients with HF from the medical data platform of Chongqing Medical University from January 1, 2012, to December 31, 2021. A total of 5 candidate algorithms were selected from 15 ML algorithms with excellent performance, which was evaluated by area under the operating characteristic curve (AUC) and accuracy. Then, the 5 candidate algorithms were hyperparameter tuned by 5-fold cross-validation grid search, and performance was evaluated by AUC, accuracy, sensitivity, specificity, and recall. Finally, an optimal ML model was constructed, and the predictive results were explained using the SHapley Additive exPlanations (SHAP) framework. A total of 14,843 older patients with HF were consecutively enrolled. CatBoost model was selected as the best prediction model, and AUC was 0.732, with 0.712 accuracy, 0.619 sensitivity, and 0.722 specificity. NT.proBNP, length of stay (LOS), triglycerides, blood phosphorus, blood potassium, and lactate dehydrogenase had the greatest effect on 30-day unplanned readmission in older patients with HF, according to SHAP results. CONCLUSIONS: The study developed a CatBoost model to predict the risk of unplanned 30-day special-cause readmission in older patients with HF, which showed more significant performance compared with the traditional logistic regression model.

All-cause unplanned readmissions in the United States: insights from the Nationwide Readmission Database.

BACKGROUND: There are few studies looking into adult, all-cause and age-group-specific unplanned readmissions. The predictors of such unplanned readmissions for all inpatient encounters remain obscure. AIMS: To describe the incidence and factors associated with unplanned readmissions in all inpatient encounters in the United States. METHODS: The US Nationwide Readmission Database (NRD) is a representative sample of hospitalisations in the United States (from approximately 28 states) accounting for approximately 60% of the US population. All inpatient encounters during January-November 2017 in the NRD were evaluated for the rates, predictors and costs of unplanned 30 days readmissions for age groups 18-44 years, 45-64 years, 65-75 years and ≥75 years. Elective readmissions and those patients who died on their index hospitalisations were excluded. Weighted analysis was performed to obtain nationally representative data. RESULTS: We identified 28 942 224 inpatient encounters with a total of 3 051 189 (10.5%) unplanned readmissions within 30 days. The age groups 18-44 years, 45-64 years, 65-74 years and ≥75 years had 7.0%, 12.0%, 11.7% and 12.3% readmissions respectively. Female gender, private insurance and elective admissions were negative predictors for readmissions. For the group aged 18-44 years, schizophrenia and diabetes mellitus complications were the most frequent primary diagnosis for readmissions, while in all older age groups septicaemia and heart failure were the most frequent primary diagnosis for readmissions. CONCLUSIONS: Thirty-day unplanned readmissions are common in patients over age 45 years, leading to significant morbidity. Effective strategies for reducing unplanned readmission may help to improve quality of care, outcomes and higher value care.

One-year unplanned readmission after total hip arthroplasty in patients with osteonecrosis of the femoral head: rate, causes, and risk factors.

BACKGROUND: The primary objectives of this study were to focus on one - year unplanned readmissions after THA in ONFH patients and to investigate rates, causes, and independent risk factors. METHODS: Between October 2014 and April 2019, eligible patients undergoing THA were enrolled and divided into unplanned readmission within one year and no readmission in this study. All unplanned readmissions within 1 year of discharge were reviewed for causes and the rate of unplanned readmissions was calculated. Demographic information, ONFH characteristics, and treatment-related variables of both groups were compared and analysed. RESULTS: Finally, 41 out of 876 patients experienced unplanned readmission. The readmission rate was 1.83% in 30 days 2.63% in 90 days, and 4.68% in 1 year. Prosthesis dislocation was always the most common cause at all time points studied within a year. The final logistic regression model revealed that higher risks of unplanned readmission were associated with age > 60 years (P = 0.001), urban residence (P = 0.001), ARCO stage IV (P = 0.025), and smoking (P = 0.033). CONCLUSIONS: We recommend the introduction of a strict smoking cessation program prior to surgery and the development of comprehensive management strategies, especially for the elderly and end-stage ONFH patients, and pay more attention to preventing prosthesis dislocation in the early days after surgery.

Association between obesity and risk of delayed discharge and unplanned readmission for day surgery: A systematic review and meta-analysis.

AIMS: This work aims to investigate the association between obesity and risk of delayed discharge and unplanned readmission in day surgery patients. BACKGROUND: Day surgeries are well received and developing rapidly. Associations between obesity and delayed discharge and unplanned readmission, which are clinically relevant outcomes in day surgeries, are complex. DESIGN: A systematic review and meta-analysis was conducted. DATA SOURCES: The PubMed, Web of Science, EMBASE, Cochrane Library, CNKI, VIP, and Wan Fang databases were comprehensively searched from inception until January 2021. REVIEW METHODS: Two independent reviewers assessed the studies and extracted data. Pooled estimates were obtained using a random-effects model. RESULTS: Eleven articles published between 2007 and 2020 were finally included. Obesity appeared not to increase the risk of delayed discharge. However, morbid obesity seemed to be associated with a higher risk of delayed discharge. The meta-analysis revealed no relationship between higher body mass index (BMI) and unplanned readmission for day surgery patients. CONCLUSIONS: Obesity appeared not to increase the risk of delayed discharge except in patients with morbid obesity. Additionally, a higher BMI was not associated with increased risk of unplanned readmission after day surgery. Future studies are required to address this issue further in different types of surgery and areas.

Frequency, Risk Factors, and Outcomes of Unplanned Readmission to the Neurological Intensive Care Unit after Spontaneous Intracerebral Hemorrhage.

BACKGROUND: Unplanned readmission to the neurological intensive care unit (ICU) is an underinvestigated topic in patients admitted after spontaneous intracerebral hemorrhage (ICH). The purpose of this study is to investigate the frequency, clinical risk factors, and outcome of bounce back to the neurological ICU in a cohort of patients admitted after ICH. METHODS: This is a retrospective observational study inspecting bounce back to the neurological ICU in patients admitted with spontaneous ICH over an 8-year period. For each patient, demographics, medical history, clinical presentation, length of ICU stay, unplanned readmission to neurological ICU, cause of readmission, and mortality were reviewed. Bounce back to the neurological ICU was defined as an unplanned readmission to the neurological ICU from a general floor service during the same hospitalization. A multivariable analysis was used to define independent variables associated with bounce back to the neurological ICU as well as association between bounce back to the neurological ICU and mortality. The significance level was set at p < 0.05. RESULTS: A total of 221 patients were included. Among those, 20 (9%) had a bounce back to the neurological ICU. Respiratory complications (n = 11) was the most common reason for bounce back to the neurological ICU, followed by neurological (n = 5) and cardiological (n = 4) complications. In a multivariable logistic regression, location of hemorrhage in the basal ganglia (odds ratio [OR]: 3.0, 95% confidence interval [CI]: 1.0-8.9, p = 0.03) and dysphagia at the time of transfer (OR: 3.9, 95% CI: 1.0-15.4, p = 0.04) were significantly associated with bounce back to the neurological ICU. After we controlled for ICH score, readmission to the ICU was also independently associated with higher mortality (OR: 14.1, 95% CI: 2.8-71.7, p < 0.01). CONCLUSIONS: Bounce back to the neurological ICU is not an infrequent complication in patients with spontaneous ICH and is associated with higher hospital length of stay and mortality. We identified relevant and potentially modifiable risk factors associated with bounce back to the neurological ICU. Future prospective studies are necessary to develop patient-centered strategies that may improve transition from the neurological ICU to the general floor.

Current status of unplanned readmission of neonates within 31 days after discharge from the neonatal intensive care unit and risk factors for readmission. / 新生儿重症监护室患儿出院31 då† éžè®¡åˆ’å†å ¥é™¢çŽ°çŠ¶åŠå½±å“å› ç´ åˆ†æž.

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.

Pediatric asthma severity scores distinguish suitable inpatient level of care for children admitted for status asthmaticus.

Objective: To determine if the Pediatric Asthma Severity Score (PASS) can distinguish "late-rescues" (transfer to the pediatric intensive care unit [PICU] within 24-hours of general pediatric floor admission), "PICU readmissions" (readmission within 24-h after transfer to a lower inpatient level of care), and unplanned 30-day hospital readmission in children admitted with status asthmaticus.Methods: We performed a single center, retrospective cohort study in 328 children admitted for asthma exacerbation aged 5-18 years from May 2015 to October 2017. We sought to determine if PASS values preceding admission from the emergency department or transfer to the general pediatric unit will be greater in children with late rescues and PICU readmissions and if a cutoff PASS values exist to discriminate these events prior to intrafacility transfer.Results: Nine (5%) late-rescues and 5 (3%) PICU readmissions accounted for 14/328 (4%) composite outcomes. PASS values were greater in children with these events (8 [IQR:5-8] vs. 5 [IQR:3-6], p < .01). Logistic regression of PASS on composite outcome yielded an odds ratio of 1.4 (1.1-1.8, p < .01) and ROC curve of PASS on a composite outcome yielded an AUC of 0.74 (0.61-0.87) with a threshold of ≥ 9. Nine (3%) children experienced unplanned 30-day hospital readmissions but PASS preceding hospital discharge was neither discriminative nor associated with hospital readmission.Conclusions: PASS values ≥ 9 identify children at increased risk for late-rescue and PICU readmission. Applied with traditionally criteria for selection of inpatient level of care, PASS may assist providers in reducing acute inpatient disposition errors.

Factors associated with a 30-day unplanned readmission after elective spine surgery: a retrospective cohort study.

PURPOSE: The aim of this study was to identify factors that are independently associated with the 30-day unplanned readmission rate of patients who underwent elective spine surgery. METHODS: This study was a retrospective cohort study conducted in a single tertiary academic hospital. The study analyzed the electronic health records of adult patients aged 18 years or older who underwent inpatient elective spine surgery under general anesthesia between January 2010 and March 2018. The primary endpoint was an unplanned readmission within 30 days. The study used uni- and multivariable logistic regression analyses. RESULT: A total of 7,025 patients were included in the analysis. Among the patients included in the analysis, 215 patients (3.1%) had unplanned readmission within 30 days after being discharged following elective spine surgery. In the complete-case analysis in the multivariable model, the factors associated with a 30-day unplanned readmission were found to be preoperative ASA physical status of ≥ 3 (vs 1) (OR: 2.21, 95% CI: 1.27, 3.84; P = 0.005), cancer (OR: 4.60, 95% CI: 2.72, 7.77; P < 0.001), and pRBC transfusion (OR: 1.81, 95% CI: 1.20, 2.71; P = 0.004). CONCLUSION: The present study showed that preoperative ASA physical status of ≥ 3, diagnosis of cancer, and transfusion of pRBC were associated with an increased 30-day unplanned readmission rate after elective spine surgery.

Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms.

BACKGROUND: Early unplanned hospital readmissions are associated with increased harm to patients, increased medical costs, and negative hospital reputation. With the identification of at-risk patients, a crucial step toward improving care, appropriate interventions can be adopted to prevent readmission. This study aimed to build machine learning models to predict 14-day unplanned readmissions. METHODS: We conducted a retrospective cohort study on 37,091 consecutive hospitalized adult patients with 55,933 discharges between September 1, 2018, and August 31, 2019, in an 1193-bed university hospital. Patients who were aged < 20 years, were admitted for cancer-related treatment, participated in clinical trial, were discharged against medical advice, died during admission, or lived abroad were excluded. Predictors for analysis included 7 categories of variables extracted from hospital's medical record dataset. In total, four machine learning algorithms, namely logistic regression, random forest, extreme gradient boosting, and categorical boosting, were used to build classifiers for prediction. The performance of prediction models for 14-day unplanned readmission risk was evaluated using precision, recall, F1-score, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC). RESULTS: In total, 24,722 patients were included for the analysis. The mean age of the cohort was 57.34 ± 18.13 years. The 14-day unplanned readmission rate was 1.22%. Among the 4 machine learning algorithms selected, Catboost had the best average performance in fivefold cross-validation (precision: 0.9377, recall: 0.5333, F1-score: 0.6780, AUROC: 0.9903, and AUPRC: 0.7515). After incorporating 21 most influential features in the Catboost model, its performance improved (precision: 0.9470, recall: 0.5600, F1-score: 0.7010, AUROC: 0.9909, and AUPRC: 0.7711). CONCLUSIONS: Our models reliably predicted 14-day unplanned readmissions and were explainable. They can be used to identify patients with a high risk of unplanned readmission based on influential features, particularly features related to diagnoses. The operation of the models with physiological indicators also corresponded to clinical experience and literature. Identifying patients at high risk with these models can enable early discharge planning and transitional care to prevent readmissions. Further studies should include additional features that may enable further sensitivity in identifying patients at a risk of early unplanned readmissions.

Unplanned readmission for older persons: A concept analysis.

AIM: The purpose of this concept analysis is to define and analyse the concept of unplanned readmission to hospital for older persons. DESIGN: Review the literature and analyse the concept of unplanned readmission. METHOD: Guided by Walker and Avant's eight-stage method of concept analysis, four databases (Ovid MEDLINE, Scopus, CINAHL, and Embase) were searched between 1946 and 2020 for empirical studies focused on older persons with multiple chronic conditions, experiences or perspectives and unplanned readmission. A total of 34 articles (10 quantitative, 17 qualitative, three mixed methods), one concept analysis and three historical articles were included. RESULTS: An unplanned readmission is an experience, process and event. The proposed definition of unplanned readmission is an older person's need for acute care treatment for an urgent or emergent health crisis that has occurred after a previous hospitalization(s). Unplanned readmission is characterized by the attributes of older persons' previous hospitalization(s), the urgent or emergent nature of the older persons' health and the older persons' need for acute care hospital services to resolve their health crisis. CONCLUSION: Unplanned readmission is a complex concept that is different from planned and emergency visits/admissions and readiness for discharge. These findings provide a link for understanding unplanned readmission as a consequence of discharge readiness. Analysing this concept supports the need for older persons to seek unplanned readmission for acute care treatment of urgent and emergent health crisis, reduces the blame that older persons may feel from questions related to preventability, and stresses the need to include older persons' experiences in the development and expansion of nursing theory, interventions and current understandings of unplanned readmission.

Early unplanned readmission of neurosurgical patients after treatment of intracranial lesions: a comparison between surgical and non-surgical intervention group.

BACKGROUND: Recent health care policy making has highlighted the necessity for understanding factors that influence readmission. To elucidate the rate, reason, and predictors of readmissions in neurosurgical patients, we analyzed unscheduled readmissions to our neurosurgical department after treatment for cranial or cerebral lesions. METHODS: From 2015 to 2017, all adult patients who had been discharged from our Department of Neurosurgery and were readmitted within 30 days were included into the study cohort. The patients were divided into a surgical and a non-surgical group. The main outcome measure was unplanned inpatient admission within 30 days of discharge. RESULTS: During the observation period, 183 (7.4%) of 2486 patients had to be readmitted unexpectedly within 30 days after discharge. The main readmission causes were surgical site infection (34.4 %) and seizure (16.4%) in the surgical group, compared to natural progression of the original diagnosis (38.2%) in the non-surgical group. Most important predictors for an unplanned readmission were younger age, presence of malignoma (OR: 2.44), and presence of cardiovascular side diagnoses in the surgical group. In the non-surgical group, predictors were length of stay (OR: 1.07) and the need for intensive care (OR: 5.79). CONCLUSIONS: We demonstrated that reasons for readmission vary between operated and non-operated patients and are preventable in large numbers. In addition, we identified treatment-related partly modifiable factors as predictors of unplanned readmission in the non-surgical group, while unmodifiable patient-related factors predominated in the surgical group. Further patient-related risk adjustment models are needed to establish an individualized preventive strategy in order to reduce unplanned readmissions.

Predictors of unplanned readmission to acute care from inpatient brain injury rehabilitation.

AIMS AND OBJECTIVES: To identify the predictors of unplanned readmission to acute care (RTAC) from inpatient brain injury rehabilitation and to develop a risk prediction model. BACKGROUND: RTAC from inpatient rehabilitation is not uncommon. Individual rehabilitation patient populations require their own body of evidence regarding predictors of RTAC. DESIGN: Retrospective cohort study. METHODS: Adult patients with new onset acquired brain injury admitted to a stand-alone rehabilitation facility between 1 January 2012-31 December 2018 were included in the study. The main measures were RTAC, sensitivity, specificity, the C-statistic and Youden's index. This paper is reported using the Strengthening the Reporting of Observational Studies in Epidemiology guidelines. RESULTS: Of 383 patients admitted for rehabilitation, 83 (22%) experienced a RTAC; 69 (18%) patients had at least one unplanned RTAC episode. Patients requiring unplanned RTAC were more likely to have lower Glasgow Coma Scale (GCS) and Functional Independence Measure (FIM) scores on rehabilitation admission, a higher burden of care on rehabilitation discharge and be discharged to a nonhome residence. Rehabilitation admission GCS and motor FIM were identified as the independent RTAC predictors in multivariate regression modelling. The combined C-statistic was 0.86. A GCS cut-off score of ≤14 and motor FIM cut-off score of ≤40 were identified as optimal, yielding a combined Youden's index of 0.56 (sensitivity = 0.72; specificity = 0.83). CONCLUSION: Patients requiring an unplanned RTAC had a lower functional status on rehabilitation admission. A prediction model for unplanned RTAC has been developed using validated and readily available clinical measures. RELEVANCE TO CLINICAL PRACTICE: The developed RTAC risk prediction model is the first step in preventing unplanned RTAC from inpatient brain injury rehabilitation. Future research should focus on discrete interventions for preventing unplanned RTAC from inpatient brain injury rehabilitation.

The 30-day unplanned readmission rate and hospital volume: a national population-based study in South Korea.

OBJECTIVE: To examine the association between hospital volume and the unplanned 30-day readmission rate as a quality measure. DESIGN: A retrospective cross-sectional study. SETTING: The Korea healthcare system is operated by a single payer under the National Health Insurance Service. PARTICIPANTS: Using national health claims data of the Health Insurance Review and Assessment in South Korea, we examined 1 296 275 adult discharges (≥18 years old) from 90 hospitals (≥500 beds) in the 2013 calendar year. MAIN OUTCOME MEASURES: We analysed the 30-day, unplanned, observed-to-expected standardized readmission rate for hospitals and for five specialty cohorts: medicine, surgery/gynaecology, cardiovascular, cardiorespiratory, and neurology. We assessed the association between hospital volume by tertiles and the 30-day standardized readmission rates with and without adjustment for hospital characteristics. RESULTS: The rate for the lowest-volume hospitals was 6.10 compared with 6.20 for the highest-volume hospitals. We observed the standardized readmission rates did not differ significantly between the lowest- and highest-volume groups, except for the neurology cohort, which remained significant after adjusting for hospital characteristics. CONCLUSIONS: The standardized readmission rates were not associated with hospital volume, except for the neurology cohort, in which the standardized readmission rate was significantly higher in the highest-volume hospitals than in lowest- and intermediate-volume hospitals, which was not consistent with the typical association of greater hospital volume with better outcomes. This association was independent of hospital characteristics. Therefore, the rate of readmissions should be used with caution when gauging the quality of hospital care according to hospital volume.

Spine surgeon specialty differences in single-level percutaneous kyphoplasty.

BACKGROUND: Percutaneous kyphoplasty (PKP) is a procedure performed by a spine surgeon who undergoes either orthopedic or neurosurgical training. The relationship between short-term adverse outcomes and spine specialty is presently unknown. To compare short-term adverse outcomes of single-level PKP when performed by neurosurgeons and orthopedic surgeons in order to develop more concretely preventive strategies for patients under consideration for single-level PKP. METHODS: We evaluated patients who underwent single-level PKP from 2012 to 2014 through the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP). We used univariate analysis and multivariate logistic regression to assess the association between spine surgeon specialty and short-term adverse events, including postoperative complication and unplanned readmission, and to identify different independent risk predictors between two specialties. RESULTS: Of 2248 patients who underwent single-level PKP procedure, 1229 patients (54.7%) had their operations completed by a neurosurgeon. There were no significant differences in the development of the majority of postoperative complications and the occurrence of unplanned readmission between the neurosurgical cohort (NC) and the orthopedic cohort (OC). A difference in the postoperative blood transfusion rate (0.7% NS vs. 1.7% OC, P = 0.039) was noted and may due to the differences in comorbidities between patients. Multivariate regression analysis revealed different independent predictors of postoperative adverse events for the two spine specialties. CONCLUSIONS: By comparing a large range of demographic feature, preoperative comorbidities, and intraoperative factors, we find that short-term adverse events in single-level PKP patients does not affect by spine surgeon specialty, except that the OC had higher postoperative blood transfusion rate. In addition, the different perioperative predictors of postoperative complications and unplanned readmissions were identified between the two specialties. These findings can lead to better evidence-based patient counseling and provide valuable information for medical evaluation and potentially devise methods to reduce patients' risk.

Broken expectations of early motherhood: Mothers' experiences of early discharge after birth and readmission of their infants.

BACKGROUND AND AIM: Today, mothers are discharged early after birth, and national monitoring shows an increase in readmission of infants. Readmission of the infant might diminish the possibility of bonding and weaken maternal confidence in taking care of the infant. The aim was to explore how new mothers experience the time from birth to being discharged after readmission with their infants. DESIGN: A phenomenological and hermeneutic study. Data were collected through telephone interviews. The study followed the COREQ requirements and was conducted in the Region of Southern Denmark in a University Hospital setting. Convenience sampling was applied, and eight mothers were included from November 2015-February 2016. Seven were interviewed. RESULTS: The data analysis revealed the following six themes: "Early discharge," "Being at home," "Readmission-shock or relief," "Problems with breastfeeding in early motherhood," "Empowering or disempowering guidance" and "Back home with broken expectations." These six themes were all covered by the overall theme: "Broken expectations of a tranquil beginning of early motherhood." CONCLUSIONS: Our study points out that mothers wish for a tranquil beginning with their infants at home. Some already experienced problems at home, while others first were confronted at the check-up at the outpatient clinic. Yet the common denominator was that the mothers experienced broken expectations regarding early motherhood when facing readmission. Readmission may influence the initial process either positively or negatively, depending on how the mothers experience their challenges and how the healthcare professionals support them. This highlights the importance of the way in which healthcare professionals support new mothers when they are readmitted. The study emphasises the importance of maternal feelings of security and confidence in their maternal role, as they are closely connected to the process of becoming a mother.

Risk factors associated with 31-day unplanned readmission in 50,912 discharged patients after stroke in China.

BACKGROUND: Unplanned readmission within 31 days of discharge after stroke is a useful indicator for monitoring quality of hospital care. We evaluated the risk factors associated with 31-day unplanned readmission of stroke patients in China. METHODS: We identified 50,912 patients from 375 hospitals in 29 provinces, municipalities or autonomous districts across China who experienced an unplanned readmission after stroke between 2015 and 2016, and extracted data from the inpatients' cover sheet data from the Medical Record Monitoring Database. Patients were grouped into readmission within 31 days or beyond for analysis. Chi-squared test was used to analyze demographic information, health system and clinical process-related factors according to the data type. Multilevel logistic modeling was used to examine the effects of patient (level 1) and hospital (level 2) characteristics on an unplanned readmission ≤31 days. RESULTS: Among 50,912 patients, 14,664 (28.8%) were readmitted within 31 days after discharge. The commonest cause of readmissions were recurrent stroke (34.8%), hypertension (22.94%), cardio/cerebrovascular disease (13.26%) and diabetes/diabetic complications (7.34%). Higher risks of unplanned readmissions were associated with diabetes (OR = 1.089, P = 0.001), use of clinical pathways (OR = 1.174, P < 0.001), and being discharged without doctor's advice (OR = 1.485, P < 0.001). Lower risks were associated with basic medical insurances (OR ranging from 0.225 to 0.716, P < 0.001) and commercial medical insurance (OR = 0.636, P = 0.021), compared to self-paying for medical services. And patients aged 50 years old and above (OR ranging from 0.650 to 0.985, P < 0.05), with haemorrhagic stroke (OR = 0.467, P < 0.001), with length of stay more than 7 days in hospital (OR ranging from 0.082 to 0.566, P < 0.001), also had lower risks. CONCLUSIONS: Age, type of stroke, medical insurance status, type of discharge, use of clinical pathways, length of hospital stay and comorbidities were the most influential factors for readmission within 31 days.

The impact of telephone follow up on adverse events for Aboriginal people with chronic disease in new South Wales, Australia: a retrospective cohort study.

BACKGROUND: Chronic diseases are more prevalent and occur at a much younger age in Aboriginal people in Australia compared with non-Aboriginal people. Aboriginal people also have higher rates of unplanned hospital readmissions and emergency department presentations. There is a paucity of research on the effectiveness of follow up programs after discharge from hospital in Aboriginal populations. This study aimed to assess the impact of a telephone follow up program, 48 Hour Follow Up, on rates of unplanned hospital readmissions, unplanned emergency department presentations and mortality within 28 days of discharge among Aboriginal people with chronic disease. METHODS: A retrospective cohort of eligible Aboriginal people with chronic diseases was obtained through linkage of routinely-collected health datasets for the period May 2009 to December 2014. The primary outcome was unplanned hospital readmissions within 28 days of separation from any acute New South Wales public hospital. Secondary outcomes were mortality, unplanned emergency department presentations, and at least one adverse event (unplanned hospital readmission, unplanned emergency department presentation or mortality) within 28 days of separation. Logistic regression models were used to assess outcomes among Aboriginal patients who received 48 Hour Follow Up compared with eligible Aboriginal patients who did not receive 48 Hour Follow Up. RESULTS: The final study cohort included 18,659 patients with 49,721 separations, of which 8469 separations (17.0, 95% confidence interval (CI): 16.7-17.4) were recorded as having received 48 Hour Follow Up. After adjusting for potential confounders, there were no significant differences in rates of unplanned readmission or mortality within 28 days between people who received or did not receive 48 Hour Follow Up. Conversely, the odds of an unplanned emergency department presentation (Odds ratio (OR) = 0.92; 95% CI: 0.85, 0.99; P = 0.0312) and at least one adverse event (OR = 0.91; 95% CI: 0.85,0.98; P = 0.0136) within 28 days were significantly lower for separations where the patient received 48 Hour Follow Up compared with those that did not receive follow up. CONCLUSIONS: Receipt of 48 Hour Follow Up was associated with both a reduction in emergency department presentations and at least one  adverse event within 28 days of discharge, suggesting there may be merit in providing post-discharge telephone follow up to Aboriginal people with chronic disease.