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    Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review.

    241760_241760.pdf (2.275Mb)
    Access Status
    Open access
    Authors
    Zhou, Huaqiong
    Della, P.
    Roberts, P.
    Goh, L.
    Dhaliwal, S.
    Date
    2016
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Zhou, H. and Della, P. and Roberts, P. and Goh, L. and Dhaliwal, S. 2016. Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review. BMJ Open. 6 (6): pp. e011060.
    Source Title
    BMJ Open
    DOI
    10.1136/bmjopen-2016-011060
    School
    School of Nursing and Midwifery
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/LP140100563
    Remarks

    This open access article is distributed under the Creative Commons license https://creativecommons.org/licenses/by-nc/4.0/

    URI
    http://hdl.handle.net/20.500.11937/7062
    Collection
    • Curtin Research Publications
    Abstract

    Objective: To update previous systematic review of predictive models for 28-day or 30-day unplanned hospital readmissions. Design: Systematic review. Setting/data source: CINAHL, Embase, MEDLINE from 2011 to 2015. Participants: All studies of 28-day and 30-day readmission predictive model. Outcome measures Characteristics of the included studies, performance of the identified predictive models and key predictive variables included in the models. Results: Of 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.Conclusions: The 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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