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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. Region Östergötland, Primary Care Center, Primary Health Care Center Valla.
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.ORCID iD: 0000-0001-7051-1234
Linköping University, Department of Health, Medicine and Caring Sciences, Division of Society and Health. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Regionledningskontoret, Forskningsstrategiska enheten.
2020 (English)In: BMC Geriatrics, E-ISSN 1471-2318, 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: 2024-07-04
In thesis
1. Proactive Primary Care for Older Adults at High Risk of Hospital Admission
Open this publication in new window or tab >>Proactive Primary Care for Older Adults at High Risk of Hospital Admission
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Demographic change is leading to a higher proportion of older adults in most parts of the world. A minority of older adults have poor health, but this group has high care needs due to frailty and/or multimorbidity. Guidelines for the management of frailty emphasise early detection of frailty and recommend comprehensive care approaches in primary care, but the evidence for these interventions is low. To provide effective and individualised care, the health system needs to identify these patients and develop proactive interventions to improve quality of life and avoid treatments that are of no benefit to the individual.  

The aim of this thesis was to study the effects of a proactive primary care working model in which vulnerable older adults were identified and received individually tailored care, using an adaptation of comprehensive geriatric assessment (CGA). 

Methods: A pragmatic controlled trial was conducted in 19 primary care practices in Sweden from 2017 to 2020. A predictive model, using electronic medical records to assess the risk of hospital admission, selected participants at high risk. Participants in the intervention practices were offered a comprehensive geriatric assessment in their primary care practice and subsequent follow-up by a team consisting of a nurse and the patient's doctor. A new CGA tool - PASTEL (Primary care ASsessment Tool for Elders) was used for assessment and care planning. The primary outcome for the intervention was hospital care days and secondary outcomes were hospital care episodes, mortality, outpatient visits, healthcare costs and cost-effectiveness. The outcomes were adjusted for age, sex and risk score and ana-lysed according to intention-to-treat. 

The predictive model was validated, and performance was assessed using the C-statistic. Focus group interviews were conducted to explore primary care nurses' and doctors' experiences with the new tool PASTEL. 

Results: 1304 older adults were included in the trial. The mean age was 82.2 years, 51% were female. During the follow-up period of 24 months, the relative risk reduction of hospital care days in the intervention group was - 22% (CI 95% = -35% to - 4%, p = 0.02) compared with usual care. There was no significant difference in mortality and outpatient visits. The reduction in healthcare costs was - € 4324 (- € 7962 to - € 686, p = 0.02). The intervention was cost-effective compared with usual care, mainly due to lower costs.

The predictive model had an AUC of 0.69 (CI 0.68- 0.70). Primary care staff considered PASTEL valuable and feasible in the primary care context.

In conclusion, the results of this thesis indicate that vulnerable older adults at risk of hospitalisation can be identified by a predictive model. Proactive intervention with a comprehensive geriatric assessment adapted to pri-mary care can reduce the need for hospital care. Future studies in similar contexts are needed to determine whether these results are generalisable.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2022. p. 66
Series
Linköping University Medical Dissertations, ISSN 0345-0082 ; 1816
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Identifiers
urn:nbn:se:liu:diva-188233 (URN)10.3384/9789179293895 (DOI)9789179293888 (ISBN)9789179293895 (ISBN)
Public defence
2022-10-07, Belladonna, Building 511 and online via Zoom: https://liu-se.zoom.us/j/65061110461?pwd=azJ0UVpIbVNaMnZtelRtRnlxcFZXUT09, Campus US, Linköping, 09:00 (Swedish)
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Available from: 2022-09-07 Created: 2022-09-07 Last updated: 2022-09-15Bibliographically approved

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Gerontology, specialising in Medical and Health Sciences

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