Predictors and Prognostic Models for Early Discharge Planning of Hospitalized Acute Geriatric Patients, A Retrospective Study

Predictors and Prognostic Models for Early Discharge Planning of Hospitalized Acute Geriatric Patients, A Retrospective Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictors and Prognostic Models for Early Discharge Planning of Hospitalized Acute Geriatric Patients, A Retrospective Study Annette Hoppe, Bernhard Hoppe This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-154593/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Motivation : When treated for an acute disorder, older adults are vulnerable for functional losses and the need of care after discharge. In a specialised geriatric ward, patients get a comprehensive treatment complementary to medical care in order to maintain and improve mobility and activities of daily living (ADL) to facilitate the return to domesticity. The aim of this paper is to identify the relevant predictors for the impact of geriatric treatment on the status at discharge, which are then used in logistic models to predict a patient’s potential to reach a certain level of independence during treatment. Method : In a retrospective cohort study with 580 patients, we analysed the impact of acute geriatric early rehabilitation on the functional outcome after treatment. As a sufficient improvement of ADLs and mobility we defined as a suitable endpoint at least 60 Barthel Points (ADL) and the ability for „Timed-Up-and-Go-Test“(TUG) when discharged from acute hospital care. To identify relevant predictors in the set of the screening assessments at admission we used linear and logistic regressions as well as odds-ratios. Multivariate logistic models are used to predict the probability that at patient reaches the endpoint. Their predictive quality is tested on additional 120 test patients from a different cohort. Results : Statistical analysis shows that all patients improved during early rehabilitation significantly in ADLs and the physical function (TUG). Barthel-Score, walking distance and handgrip on admission are the strongest predictors for the outcome after geriatric treatment. Logistic models predict the outcome correctly in 70% to 80% of the cases. These models once established for a certain cohort of patients can be applied with similar accuracy to different sets of patients as well. Clinical condition, the medical treatment before admission, length of hospitalization, age or gender have no predictive quality. Discussion : We were able to show that all patients benefit significantly from early rehabilitation in an acute geriatric ward. Only a few assessments on admission related to physical function are sufficient to indicate the functional outcome after geriatric treatment. Logistic models based on these predictors are reliable with a generic predictive quality for the expected level of independence at discharge. This facilitates early discharge planning. Trial registration : The study was retrospectively registered on 2 nd July 2018 by the ethic commission of the hospital und filed under registration number (MG1/569/770/2019). Geriatrics & Gerontology Acute geriatric care geriatric assessments comprehensive treatment predictors analysis Logistic models Barthel Score Dichotomous TUG discharge planning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-154593","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":10975688,"identity":"6a25793e-1500-4e6b-851a-3380c5d363d1","order_by":0,"name":"Annette Hoppe","email":"","orcid":"","institution":"Krankenhaus Korbach","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Annette","middleName":"","lastName":"Hoppe","suffix":""},{"id":10975689,"identity":"bcd542bc-3ea2-4246-a9bb-a9b38c17fac7","order_by":1,"name":"Bernhard Hoppe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBACAyBmBmLGBgkw3yaBgQGIeEjQkka6lsOEtZiz9z57XFBzT7ZBuvnY44KK83kM7MkPGN5U4NZi2XPc3HjGsWLjBplj6cYzztwuZuB5ZsA45wweh91IY5PmYUtIbJDIMZPmbbuduP9GggEzbxseLfefAbX8g2n5dw7ISP/AzPsPny1sbEDDYVoaDoAYQFsa8PkF6DDevgTjNok0oF+OJSc28LwpODjnGG4t5uzHgA77liDbL5EMDLEau8QG9vSND97U4NYCB2xAxAzjHCBCA0QXM2E1o2AUjIJRMBIBAPAdTVZ73dK5AAAAAElFTkSuQmCC","orcid":"","institution":"Darmstadt University of Applied Sciences","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Bernhard","middleName":"","lastName":"Hoppe","suffix":""}],"badges":[],"createdAt":"2021-01-25 19:02:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-154593/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-154593/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":5826015,"identity":"fa43859f-9897-4daf-a0d4-0b663cdd3fa7","added_by":"auto","created_at":"2021-02-10 15:28:25","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37461,"visible":true,"origin":"","legend":"Barthel Scores at admission (BA) and before discharge (BD) and BS–improvement with standard deviations indicated by black lines","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-154593/v1/0cc4aadeb0fbc382f93ce7aa.jpg"},{"id":5825817,"identity":"cbe18876-4480-456f-a3cd-078a5bfb1bd1","added_by":"auto","created_at":"2021-02-10 15:25:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31570,"visible":true,"origin":"","legend":"Linear regression models for BD vs. predictor ranges in %: BA (solid line), MMSE (dashed line), HG (dash dotted) and WD (dotted) ","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-154593/v1/48dfaadca0c335e1915de138.jpg"},{"id":5825820,"identity":"697efd5d-776b-4d9a-969c-01671a6f71e9","added_by":"auto","created_at":"2021-02-10 15:25:25","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38410,"visible":true,"origin":"","legend":"Odds-ratios of predictors for reaching the endpoint calculated for 351 patients with BA \u003c 60. Lines indicate the 95% interval, filled dots are ratios with a significant difference in chances for both groups. ","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-154593/v1/a0d32dccea8b1cce5e6a0207.jpg"},{"id":5826014,"identity":"9efc0209-bd43-4b53-b8df-1a58eba15249","added_by":"auto","created_at":"2021-02-10 15:28:24","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43764,"visible":true,"origin":"","legend":"Effect sizes of significant predictors in bivariate regressions for BD, TUGd (580 patients) and reaching the endpoint (351 patients with BA \u003c 60 at admission) as outcome variables. ","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-154593/v1/60107954ef77443b243bff20.jpg"},{"id":5826017,"identity":"5fc5b90a-0562-41a8-b3c9-ccb296f77051","added_by":"auto","created_at":"2021-02-10 15:28:26","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":82858,"visible":true,"origin":"","legend":"2D-plot for P(BA, WD) ","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-154593/v1/22e88dbdf67b4a5d7edc4b95.jpg"},{"id":5825819,"identity":"d6bfd33c-232e-477b-8139-264c8d0f91c8","added_by":"auto","created_at":"2021-02-10 15:25:24","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":32930,"visible":true,"origin":"","legend":"Contour plot for P(WD, HG) with contour lines with a 0.1-spacing. Red dashed line is the P = 0.5 line, which is used for classification. The intersection of WD = 40m and HG = 10kPa determines the probability to reach the endpoint.","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-154593/v1/b6e29060f3dc3741c301742e.jpg"},{"id":5826016,"identity":"f92f4015-63dc-4e40-b151-8422e65ddd85","added_by":"auto","created_at":"2021-02-10 15:28:25","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":54300,"visible":true,"origin":"","legend":"ROC-curves for P(BA, WD, HG), P(BA, WD), and P(WD). At the top we see the curves for the basic 351-patient cohort and at the bottom the corresponding curves for the 120 test patients.","description":"","filename":"Fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-154593/v1/0efe7afd4caf2363762a5744.jpg"},{"id":13582925,"identity":"c7e19c84-f83a-429b-8651-201546959b3b","added_by":"auto","created_at":"2021-09-17 04:33:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1092911,"visible":true,"origin":"","legend":"","description":"","filename":"Predictorsandprognosticmodelswithamendments.pdf","url":"https://assets-eu.researchsquare.com/files/rs-154593/v1_covered.pdf"},{"id":5826247,"identity":"2e5a8c46-f2ad-4fd1-b562-a7a7b52a84de","added_by":"auto","created_at":"2021-02-10 15:31:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1623060,"visible":true,"origin":"","legend":"","description":"","filename":"Predictorsandprognosticmodelswithamendments.pdf","url":"https://assets-eu.researchsquare.com/files/rs-154593/v1_stamped.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePredictors and Prognostic Models for Early Discharge Planning of Hospitalized Acute Geriatric Patients, A Retrospective Study\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"\u003cp\u003eThis preprint is available for \u003ca href='/article/rs-154593/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute geriatric care, geriatric assessments, comprehensive treatment, predictors analysis, Logistic models, Barthel Score, Dichotomous TUG, discharge planning","lastPublishedDoi":"10.21203/rs.3.rs-154593/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-154593/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eMotivation\u003c/u\u003e\u003c/strong\u003e\u003cu\u003e: \u003c/u\u003eWhen treated for an acute disorder, older adults are vulnerable for functional losses and the need of care after discharge. In a specialised geriatric ward, patients get a comprehensive treatment complementary to medical care in order to maintain and improve mobility and activities of daily living (ADL) to facilitate the return to domesticity. The aim of this paper is to identify the relevant predictors for the impact of geriatric treatment on the status at discharge, which are then used in logistic models to predict a patient’s potential to reach a certain level of independence during treatment.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cu\u003eMethod\u003c/u\u003e\u003c/strong\u003e\u003cu\u003e:\u003c/u\u003e In a retrospective cohort study with 580 patients, we analysed the impact of acute geriatric early rehabilitation on the functional outcome after treatment. As a sufficient improvement of ADLs and mobility we defined as a suitable endpoint at least 60 Barthel Points (ADL) and the ability for „Timed-Up-and-Go-Test“(TUG) when discharged from acute hospital care. To identify relevant predictors in the set of the screening assessments at admission we used linear and logistic regressions as well as odds-ratios. Multivariate logistic models are used to predict the probability that at patient reaches the endpoint. Their predictive quality is tested on additional 120 test patients from a different cohort.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cu\u003eResults\u003c/u\u003e\u003c/strong\u003e\u003cu\u003e:\u003c/u\u003e Statistical analysis shows that all patients improved during early rehabilitation significantly in ADLs and the physical function (TUG).\u0026nbsp;Barthel-Score, walking distance and handgrip on admission are the strongest predictors for the outcome after geriatric treatment. Logistic models predict the outcome correctly in 70% to 80% of the cases. These models once established for a certain cohort of patients can be applied with similar accuracy to different sets of patients as well. Clinical condition, the medical treatment before admission, length of hospitalization, age or gender have no predictive quality.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cu\u003eDiscussion\u003c/u\u003e\u003c/strong\u003e\u003cu\u003e: \u003c/u\u003eWe were able to show that all patients benefit significantly from early rehabilitation in an acute geriatric ward. Only a few assessments on admission related to physical function are sufficient to indicate the functional outcome after geriatric treatment. Logistic models based on these predictors are reliable with a generic predictive quality for the expected level of independence at discharge. This facilitates early discharge planning.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cu\u003eTrial registration\u003c/u\u003e\u003c/strong\u003e: The study was retrospectively registered on 2\u003csup\u003end\u003c/sup\u003e July 2018 by the ethic commission of the hospital und filed under registration number (MG1/569/770/2019).\u0026nbsp;\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Predictors and Prognostic Models for Early Discharge Planning of Hospitalized Acute Geriatric Patients, A Retrospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-02-10 15:25:23","doi":"10.21203/rs.3.rs-154593/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3db0897d-d7c8-45af-940f-8e83818d550f","owner":[],"postedDate":"February 10th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":2339870,"name":"Geriatrics \u0026 Gerontology"}],"tags":[],"updatedAt":"2021-03-12T17:14:10+00:00","versionOfRecord":[],"versionCreatedAt":"2021-02-10 15:25:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-154593","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-154593","identity":"rs-154593","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}