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      Factors influencing early and late readmissions in Australian hospitalised patients and investigating role of admission nutrition status as a predictor of hospital readmissions: a cohort study

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          Abstract

          Objectives

          Limited studies have identified predictors of early and late hospital readmissions in Australian healthcare settings. Some of these predictors may be modifiable through targeted interventions. A recent study has identified malnutrition as a predictor of readmissions in older patients but this has not been verified in a larger population. This study investigated what predictors are associated with early and late readmissions and determined whether nutrition status during index hospitalisation can be used as a modifiable predictor of unplanned hospital readmissions.

          Design

          A retrospective cohort study.

          Setting

          Two tertiary-level hospitals in Australia.

          Participants

          All medical admissions ≥18 years over a period of 1 year.

          Outcomes

          Primary objective was to determine predictors of early (0–7 days) and late (8–180 days) readmissions. Secondary objective was to determine whether nutrition status as determined by malnutrition universal screening tool (MUST) can be used to predict readmissions.

          Results

          There were 11 750 (44.8%) readmissions within 6 months, with 2897 (11%) early and 8853 (33.8%) late readmissions. MUST was completed in 16.2% patients and prevalence of malnutrition during index admission was 31%. Malnourished patients had a higher risk of both early (OR 1.39, 95% CI 1.12 to 1.73) and late readmissions (OR 1.23, 95% CI 1.06 to 128). Weekend discharges were less likely to be associated with both early (OR 0.81, 95% CI 0.74 to 0.91) and late readmissions (OR 0.91, 95% CI 0.84 to 0.97). Indigenous Australians had a higher risk of early readmissions while those living alone had a higher risk of late readmissions. Patients ≥80 years had a lower risk of early readmissions while admission to intensive care unit was associated with a lower risk of late readmissions.

          Conclusions

          Malnutrition is a strong predictor of unplanned readmissions while weekend discharges are less likely to be associated with readmissions. Targeted nutrition intervention may prevent unplanned hospital readmissions.

          Trial registration

          ANZCTRN 12617001362381; Results.

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          Most cited references25

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          Malnutrition and its impact on cost of hospitalization, length of stay, readmission and 3-year mortality.

          The confounding effect of disease on the outcomes of malnutrition using diagnosis-related groups (DRG) has never been studied in a multidisciplinary setting. This study aims to determine the prevalence of malnutrition in a tertiary hospital in Singapore and its impact on hospitalization outcomes and costs, controlling for DRG. This prospective cohort study included a matched case control study. Subjective Global Assessment was used to assess the nutritional status on admission of 818 adults. Hospitalization outcomes over 3 years were adjusted for gender, age, ethnicity, and matched for DRG. Malnourished patients (29%) had longer hospital stays (6.9±7.3 days vs. 4.6±5.6 days, p<0.001) and were more likely to be readmitted within 15 days (adjusted relative risk=1.9, 95% CI 1.1-3.2, p=0.025). Within a DRG, the mean difference between actual cost of hospitalization and the average cost for malnourished patients was greater than well-nourished patients (p=0.014). Mortality was higher in malnourished patients at 1 year (34% vs. 4.1 %), 2 years (42.6% vs. 6.7%) and 3 years (48.5% vs. 9.9%); p<0.001 for all. Overall, malnutrition was a significant predictor of mortality (adjusted hazard ratio=4.4, 95% CI 3.3-6.0, p<0.001). Malnutrition was evident in up to one third of the inpatients and led to poor hospitalization outcomes and survival as well as increased costs of care, even after matching for DRG. Strategies to prevent and treat malnutrition in the hospital and post-discharge are needed. Copyright © 2011 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.
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            Causes and patterns of readmissions in patients with common comorbidities: retrospective cohort study

            Objective To evaluate the primary diagnoses and patterns of 30 day readmissions and potentially avoidable readmissions in medical patients with each of the most common comorbidities. Design Retrospective cohort study. Setting Academic tertiary medical centre in Boston, 2009-10. Participants 10 731 consecutive adult discharges from a medical department. Main outcome measures Primary readmission diagnoses of readmissions within 30 days of discharge and potentially avoidable 30 day readmissions to the index hospital or two other hospitals in its network. Results Among 10 731 discharges, 2398 (22.3%) were followed by a 30 day readmission, of which 858 (8.0%) were identified as potentially avoidable. Overall, infection, neoplasm, heart failure, gastrointestinal disorder, and liver disorder were the most frequent primary diagnoses of potentially avoidable readmissions. Almost all of the top five diagnoses of potentially avoidable readmissions for each comorbidity were possible direct or indirect complications of that comorbidity. In patients with a comorbidity of heart failure, diabetes, ischemic heart disease, atrial fibrillation, or chronic kidney disease, the most common diagnosis of potentially avoidable readmission was acute heart failure. Patients with neoplasm, heart failure, and chronic kidney disease had a higher risk of potentially avoidable readmissions than did those without those comorbidities. Conclusions The five most common primary diagnoses of potentially avoidable readmissions were usually possible complications of an underlying comorbidity. Post-discharge care should focus attention not just on the primary index admission diagnosis but also on the comorbidities patients have.
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              A systematic review and meta-analysis of the impact of oral nutritional supplements on hospital readmissions.

              The adverse effects of disease-related malnutrition (DRM) on health care use and costs are well substantiated. However, the impact of managing DRM with nutritional support on health care use, including hospital (re)admissions requires exploration. This systematic review (9 RCT, N=1190) examined the effects of oral nutritional supplements (ONS) on hospital (re)admissions. Meta-analysis of 6 RCT (N=852) with data on the proportion of patients (re)admitted to hospital showed significant reductions with ONS vs. routine care (OR 0.59, 95% CI 0.43-0.80, P=0.001), including 5 RCT (N=826) that recorded readmissions (OR 0.59, 95% CI 0.43-0.80, P=0.001). The significant reduction in (re)admissions was found in meta-analyses of ONS trials in various settings and in patients with DRM or of varied nutritional status. A larger meta-analysis (8 RCT, N=999) that combined other (re)admissions data using standardised differences also showed a significant reduction with ONS (effect size -0.23, 95% CI -0.36 to -0.10, P=0.001). Most of these trials (75%) were in older people aged ≥65 years (6 RCT, N=834, effect size -0.18, 95% CI -0.31 to -0.04, P=0.011). This systematic review shows that ONS significantly reduce hospital (re)admissions, particularly in older patient groups, with economic implications for health care.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2018
                27 June 2018
                : 8
                : 6
                : e022246
                Affiliations
                [1 ] departmentDepartment of General Medicine , Flinders Medical Centre , Bedford Park, South Australia, Australia
                [2 ] departmentCollege of Medicine and Public Health , Flinders University , Adelaide, South Australia, Australia
                [3 ] departmentDepartment of Nutrition and Dietetics , Flinders University Faculty of Medicine Nursing and Health Sciences , Adelaide, South Australia, Australia
                [4 ] departmentHealth Economics Unit , Flinders University , Adelaide, South Australia, Australia
                [5 ] departmentFaculty of Health Sciences , Flinders University , Adelaide, South Australia, Australia
                [6 ] departmentDepartment of Clinical Epidemiology , Flinders Medical Centre , Bedford Park, South Australia, Australia
                [7 ] departmentDiscipline of Medicine , University of Adelaide , Adelaide, South Australia, Australia
                Author notes
                [Correspondence to ] Dr Yogesh Sharma; Yogesh.Sharma@ 123456sa.gov.au
                Author information
                http://orcid.org/0000-0002-7716-2599
                Article
                bmjopen-2018-022246
                10.1136/bmjopen-2018-022246
                6020977
                29950478
                7c33d1dc-a2c9-479a-b7a1-97943dd7446b
                © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

                History
                : 08 February 2018
                : 22 May 2018
                : 01 June 2018
                Funding
                Funded by: Flinders Centre for clinical change and health care research (FCCCHCR);
                Categories
                Epidemiology
                Research
                1506
                1692
                Custom metadata
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                Medicine
                quality in health care,internal medicine,epidemiology
                Medicine
                quality in health care, internal medicine, epidemiology

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