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ObjectiveTo update previous systematic review of predictive models for 28-day or 30-day unplanned hospital readmissions.DesignSystematic review.Setting/data sourceCINAHL, Embase, MEDLINE from 2011 to 2015.ParticipantsAll studies of 28-day and 30-day readmission predictive model.Outcome measuresCharacteristics of the included studies, performance of the identified predictive models and key predictive variables included in the models.ResultsOf 7310 records, a total of 60 studies with 73 unique predictive models met the inclusion criteria. The utilisation outcome of the models included all-cause readmissions, cardiovascular disease including pneumonia, medical conditions, surgical conditions and mental health condition-related readmissions. Overall, a wide-range C-statistic was reported in 56/60 studies (0.21–0.88). 11 of 13 predictive models for medical condition-related readmissions were found to have consistent moderate discrimination ability (C-statistic ≥0.7). Only two models were designed for the potentially preventable/avoidable readmissions and had C-statistic >0.8. The variables ‘comorbidities’, ‘length of stay’ and ‘previous admissions’ were frequently cited across 73 models. The variables ‘laboratory tests’ and ‘medication’ had more weight in the models for cardiovascular disease and medical condition-related readmissions.ConclusionsThe predictive models which focused on general medical condition-related unplanned hospital readmissions reported moderate discriminative ability. Two models for potentially preventable/avoidable readmissions showed high discriminative ability. This updated systematic review, however, found inconsistent performance across the included unique 73 risk predictive models. It is critical to define clearly the utilisation outcomes and the type of accessible data source before the selection of the predictive model. Rigorous validation of the predictive models with moderate-to-high discriminative ability is essential, especially for the two models for the potentially preventable/avoidable readmissions. Given the limited available evidence, the development of a predictive model specifically for paediatric 28-day all-cause, unplanned hospital readmissions is a high priority.

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Aims and objectivesThis paper aims to provide an updated comprehensive review of the research‐based evidence related to the transitions of care process for adolescents and young adults with chronic illness/disabilities since 2010.BackgroundTransitioning adolescent and young adults with chronic disease and/or disabilities to adult care services is a complex process, which requires coordination and continuity of health care. The quality of the transition process not only impacts on special health care needs of the patients, but also their psychosocial development. Inconsistent evidence was found regarding the process of transitioning adolescent and young adults.DesignAn integrative review was conducted using a five‐stage process: problem identification, literature search, data evaluation, data analysis and presentation.MethodsA search was carried out using the EBSCOhost, Embase, MEDLINE, PsycINFO, and AustHealth, from 2010 to 31 October 2014. The key search terms were (adolescent or young adult) AND (chronic disease or long‐term illness/conditions or disability) AND (transition to adult care or continuity of patient care or transfer or transition).ResultsA total of 5719 records were initially identified. After applying the inclusion criteria a final 61 studies were included. Six main categories derived from the data synthesis process are Timing of transition; Perceptions of the transition; Preparation for the transition; Patients’ outcomes post‐transition; Barriers to the transition; and Facilitating factors to the transition. A further 15 subcategories also surfaced.ConclusionsIn the last five years, there has been improvement in health outcomes of adolescent and young adults post‐transition by applying a structured multidisciplinary transition programme, especially for patients with cystic fibrosis and diabetes. However, overall patients’ outcomes after being transited to adult health care services, if recorded, have remained poor both physically and psychosocially. An accurate tracking mechanism needs to be established by stakeholders as a formal channel to monitor patients’ outcomes post‐ transition.

Aim: To identify risk factors associated with 30-day all-cause unplanned hospital readmission at a tertiary children's hospital in Western Australia. Methods: An administrative paediatric inpatient dataset was analysed retrospectively. Patients of all ages discharged between 1 January 2010 and 31 December 2014 were included. Demographic and clinical information at the index admission was examined using multivariate logistic regression analysis. Results: A total of 3330 patients (4.55%) experienced at least one unplanned readmission after discharge. Readmission was more likely to occur in patients who were either older than 16 years (odds ratio (OR) = 1.46; 95% confidence interval (CI) 1.07-1.98), utilising private insurance as an inpatient (OR = 1.16; 95% CI 1.00-1.34), with greater socio-economic advantage (OR = 1.20; 95% CI 1.02-1.41), admitted on Friday (OR = 1.21; 95% CI 1.05-1.39), discharged on Friday/Saturday/Sunday (OR = 1.26, 95% CI 1.10-1.44; OR = 1.34, 95% CI 1.15-1.57; OR = 1.24, 95% CI 1.05-1.47, respectively), with four or more diagnoses at the index admission (OR = 2.41; 95% CI 2.08-2.80) or hospitalised for 15 days or longer (OR = 2.39; 95% CI 1.88-2.98). Area under receiver operating characteristic curve of the predictive model is 0.645. Conclusions: A moderate discriminative ability predictive model for 30-day all-cause same hospital readmission was developed. A structured discharge plan is suggested to be commenced from admission to ensure continuity of care for patients identified as being at higher risk of readmission. A recommendation is made that a designated staff member be assigned to co-ordinate the plan, including assessment of patients' and primary carers' readiness for discharge. Further research is required to establish comprehensive paediatric readmission rates by accessing linkage data to capture different hospital readmissions.