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      Factors affecting hospital admission, hospital length of stay and new discharge destination post proximal humeral fracture: a retrospective audit

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          Abstract

          Background

          Outcomes following proximal humeral fracture (PHF) may be impacted by a range of clinical, fracture and premorbid factors. The aim of this study was to examine factors impacting hospital admission; length of stay (LOS) and new discharge destination for patients presenting to hospital with PHF.

          Methods

          Retrospective audit conducted at a tertiary health service. Data was collected from adult patients presenting to hospital with a PHF over a 54-month period. Fractures that were pathological or sustained during admission were excluded. Univariable and multivariable logistic and negative binomial regression were used to explore factors associated with hospital admission, LOS and new discharge destination.

          Results

          Data were analyzed from 701 participants (age 70 years (IQR 60, 81); 72.8% female); 276 (39.4%) participants required a hospital admission. New discharge destination was required for 109 (15.5%) participants, of whom 49 (45%) changed from home alone to home with family/friend(s). Greater comorbidities, as indicated by the Charlson Comorbidity Index score, were associated with hospital admission, longer LOS and new discharge destination. Premorbid living situations of home with family/friend(s) or from an external care facility were associated with a decreased likelihood of hospital admission, shorter LOS and reduced risk of a new discharge destination. Surgical treatment was associated with shorter LOS. Older age and dementia diagnosis were associated with a new discharge destination.

          Conclusions

          Many factors potentially impact on the likelihood or risk of hospitalization, LOS and new discharge destination post PHF. Patients with greater comorbidities are more likely to have negative outcomes, while patients who had premorbid living situations of home with family/friend(s) or from an external care facility are more likely to have positive outcomes. Early identification of factors that may impact patient outcomes may assist timely decision making in hospital settings. Further research should focus on developing tools to predict hospital outcomes in the PHF population.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12877-024-04928-z.

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          Most cited references38

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          A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

          The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.
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            Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries.

            With advances in the effectiveness of treatment and disease management, the contribution of chronic comorbid diseases (comorbidities) found within the Charlson comorbidity index to mortality is likely to have changed since development of the index in 1984. The authors reevaluated the Charlson index and reassigned weights to each condition by identifying and following patients to observe mortality within 1 year after hospital discharge. They applied the updated index and weights to hospital discharge data from 6 countries and tested for their ability to predict in-hospital mortality. Compared with the original Charlson weights, weights generated from the Calgary, Alberta, Canada, data (2004) were 0 for 5 comorbidities, decreased for 3 comorbidities, increased for 4 comorbidities, and did not change for 5 comorbidities. The C statistics for discriminating in-hospital mortality between the new score generated from the 12 comorbidities and the Charlson score were 0.825 (new) and 0.808 (old), respectively, in Australian data (2008), 0.828 and 0.825 in Canadian data (2008), 0.878 and 0.882 in French data (2004), 0.727 and 0.723 in Japanese data (2008), 0.831 and 0.836 in New Zealand data (2008), and 0.869 and 0.876 in Swiss data (2008). The updated index of 12 comorbidities showed good-to-excellent discrimination in predicting in-hospital mortality in data from 6 countries and may be more appropriate for use with more recent administrative data. © The Author 2011. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved.
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              New 5-Factor Modified Frailty Index Using American College of Surgeons NSQIP Data

              The modified frailty index (mFI-11) is a NSQIP-based 11-factor index that has been proven to adequately reflect frailty and predict mortality and morbidity. These 11 factors, made of 16 variables, map to the original 70-item Canada Study of Health and Aging Frailty Index. In past years, certain NSQIP variables have been removed from the database; as of 2015, only 5 of the original 11 factors remained. The predictive power and usefulness of these 5 factors in an index (mFI-5) have not been proven in past literature. The goal of our study was to compare the mFI-5 to the mFI-11 in terms of value and predictive ability for mortality, postoperative infection, and unplanned 30-day readmission.
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                Author and article information

                Contributors
                csaid@unimelb.edu.au
                Journal
                BMC Geriatr
                BMC Geriatr
                BMC Geriatrics
                BioMed Central (London )
                1471-2318
                12 April 2024
                12 April 2024
                2024
                : 24
                : 334
                Affiliations
                [1 ]Department of Physiotherapy, Western Health, ( https://ror.org/02p4mwa83) St Albans, VIC Australia
                [2 ]The University of Melbourne, ( https://ror.org/01ej9dk98) Parkville, VIC Australia
                [3 ]Australian Institute for Musculoskeletal Science, ( https://ror.org/02f2nvw78) St Alban, VIC Australia
                [4 ]Department of Medicine, Western Health, ( https://ror.org/02p4mwa83) St Albans, VIC Australia
                Article
                4928
                10.1186/s12877-024-04928-z
                11015557
                38609852
                246680ec-7657-48db-8a25-4bb499ef388b
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 7 August 2023
                : 28 March 2024
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Geriatric medicine
                hospitalization,length of stay,discharge,proximal humeral fracture
                Geriatric medicine
                hospitalization, length of stay, discharge, proximal humeral fracture

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