ArticleAtencion primaria2014
[Application of the Community Assessment Risk Screen in Primary Care centres of the Valencia Health System].
Article in Atencion primaria, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Use of hospital care services by chronic patients according to their characteristics and risk levels by adjusted morbidity groups.PloS one · 2022Article
- Predicting unplanned hospital visits in older home care recipients: a cross-country external validation study.BMC geriatrics · 2021Article
- Telemedicine in Primary Care for Patients With Chronic Conditions: The ValCrònic Quasi-Experimental Study.Journal of medical Internet research · 2017Article
- [Tourism as a healthy and active ageing strategy: The effects of tourism on the health of the elederly].Atencion primaria · 2015Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveApplication of The Community Assessment Risk Screen (CARS) tool for detection of chronic elderly patients at risk of hospital readmission and the viability study for its inclusion in health information systems.
designRetrospective cohort study. LOCATION: Health Departments 6, 10, and 11 from the Valencia Community.
participantsPatients of 65 and over seen in 6 Primary Care centres in December 2008. The sample consisted of 500 patients (sampling error=±4.37%, sampling fraction=1/307). VARIABLES: The CARS tools includes 3items: Diagnostics (heart diseases, diabetes, myocardial infarction, stroke, COPD, cancer), number of prescribed drugs and hospital admissions or emergency room visits in the previous 6months. The data came from SIA-Abucasis, GAIA and MDS, and were compared by Primary Care professionals. The end-point was hospital admission in 2009.
resultsCARS risk levels are related to future readmission (P<.001). The value of sensitivity and specificity is 0.64; the tool accurately identifies patients with low probability of being hospitalized in the future (negative predictive value=0.91, diagnostic efficacy=0.67), but has a positive predictive value of 0.24.
conclusionsCARS does not properly identify the population at high risk of hospital readmission. However, if it could be revised and the positive predictive value improved, it could be incorporated into the Primary Care computer systems and be useful in the initial screening and grouping of chronic patients at risk of hospital readmission.
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