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Clinically useful prediction of hospital admissions in an older population
Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Local Health Care Services in Central Östergötland, Department of Acute Internal Medicine and Geriatrics.ORCID iD: 0000-0002-6452-3930
Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences.
Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Pain and Rehabilitation Center.
Linköping University, Department of Health, Medicine and Caring Sciences, Division of Society and Health. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Operations management Region Östergötland, Research and Development Unit.
2020 (English)In: BMC GERIATRICS, Vol. 20, no 1, article id 95Article in journal (Refereed) Published
Abstract [en]

Background The healthcare for older adults is insufficient in many countries, not designed to meet their needs and is often described as disorganized and reactive. Prediction of older persons at risk of admission to hospital may be one important way for the future healthcare system to act proactively when meeting increasing needs for care. Therefore, we wanted to develop and test a clinically useful model for predicting hospital admissions of older persons based on routine healthcare data. Methods We used the healthcare data on 40,728 persons, 75-109 years of age to predict hospital in-ward care in a prospective cohort. Multivariable logistic regression was used to identify significant factors predictive of unplanned hospital admission. Model fitting was accomplished using forward selection. The accuracy of the prediction model was expressed as area under the receiver operating characteristic (ROC) curve, AUC. Results The prediction model consisting of 38 variables exhibited a good discriminative accuracy for unplanned hospital admissions over the following 12 months (AUC 0.69 [95% confidence interval, CI 0.68-0.70]) and was validated on external datasets. Clinically relevant proportions of predicted cases of 40 or 45% resulted in sensitivities of 62 and 66%, respectively. The corresponding positive predicted values (PPV) was 31 and 29%, respectively. Conclusion A prediction model based on routine administrative healthcare data from older persons can be used to find patients at risk of admission to hospital. Identifying the risk population can enable proactive intervention for older patients with as-yet unknown needs for healthcare.

Place, publisher, year, edition, pages
BMC , 2020. Vol. 20, no 1, article id 95
Keywords [en]
Prediction; Hospitalization; Older persons
National Category
Gerontology, specialising in Medical and Health Sciences
Identifiers
URN: urn:nbn:se:liu:diva-164643DOI: 10.1186/s12877-020-1475-6ISI: 000519056900001PubMedID: 32143637OAI: oai:DiVA.org:liu-164643DiVA, id: diva2:1417586
Note

Funding Agencies|County Council of Ostergotland; Linkoping University from the strategic research fund for Health Care and Welfare [2016186-14]; Linkoping University

Available from: 2020-03-29 Created: 2020-03-29 Last updated: 2020-03-29

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Marcusson, JanNord, MagnusDong, Huan-JiLyth, Johan
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Division of Prevention, Rehabilitation and Community MedicineFaculty of Medicine and Health SciencesDepartment of Acute Internal Medicine and GeriatricsPain and Rehabilitation CenterDivision of Society and HealthResearch and Development Unit
Gerontology, specialising in Medical and Health Sciences

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