Predicting discharge to institutional long-term care following acute hospitalisation: a systematic review and meta-analysis (original) (raw)

Harrison, Jennifer Kirsty ORCID: https://orcid.org/0000-0002-4752-6988, Walesby, Katherine E., Hamilton, Lorna, Armstrong, Carolyn, Starr, John M., Reynish, Emma L., MacLullich, Alasdair M.J., Quinn, Terence ORCID: https://orcid.org/0000-0003-1401-0181 and Shenkin, Susan D.(2017) Predicting discharge to institutional long-term care following acute hospitalisation: a systematic review and meta-analysis.Age and Ageing, 46(4), pp. 547-558. (doi: 10.1093/ageing/afx047) (PMID:28444124) (PMCID:PMC5860007)

Abstract

Background: moving into long-term institutional care is a significant life event for any individual. Predictors of institutional care admission from community-dwellers and people with dementia have been described, but those from the acute hospital setting have not been systematically reviewed. Our aim was to establish predictive factors for discharge to institutional care following acute hospitalisation. Methods: we registered and conducted a systematic review (PROSPERO: CRD42015023497). We searched MEDLINE; EMBASE and CINAHL Plus in September 2015. We included observational studies of patients admitted directly to long-term institutional care following acute hospitalisation where factors associated with institutionalisation were reported. Results: from 9,176 records, we included 23 studies (n = 354,985 participants). Studies were heterogeneous, with the proportions discharged to a care home 3–77% (median 15%). Eleven studies (n = 12,642), of moderate to low quality, were included in the quantitative synthesis. The need for institutional long-term care was associated with age (pooled odds ratio (OR) 1.02, 95% confidence intervals (CI): 1.00–1.04), female sex (pooled OR 1.41, 95% CI: 1.03–1.92), dementia (pooled OR 2.14, 95% CI: 1.24–3.70) and functional dependency (pooled OR 2.06, 95% CI: 1.58–2.69). Conclusions: discharge to long-term institutional care following acute hospitalisation is common, but current data do not allow prediction of who will make this transition. Potentially important predictors evaluated in community cohorts have not been examined in hospitalised cohorts. Understanding these predictors could help identify individuals at risk early in their admission, and support them in this transition or potentially intervene to reduce their risk.

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