87. Functional status and inflammatory profiles of older people admitted to hospital with collapse, an ‘ill-defined condition’ (292)

David Voegeli, PhD Student, School of Nursing and Midwifery, University of Southampton, Southampton, United Kingdom
Co authors: Katherine Hunt, Bronagh Walsh & Helen Roberts
D.Voegeli@soton.ac.uk

Abstract:

Background:

Admissions among older people are rising with older people now occupying two thirds of hospital beds (DH 2001). The largest growing subset of admissions is those for signs and symptoms. These admissions are not assigned a diagnosis and are often called ‘ill-defined conditions’ (Chapter 18, International Classification of Diseases). As a result, The Department of Health has suggested that this type of admission could be avoidable (DH 2000) although there is currently no evidence to support this view. These patients are thought to be less acute with frailty being a potential driver for admission. In this study, collapse, the presenting symptom most unlikely to receive a diagnosis, has been used as a model of an ill-defined condition. This allows investigation of the physiological/functional differences between patients with and without a diagnosis in order to assess whether these admissions really are ‘avoidable’. Sample 100 patients over 70 years admitted to hospital with collapse. Patients retrospectively assigned to two groups – defined group/ill-defined group dependent upon ICD disease code.

Methods:

Physiological measurement of body composition, hand grip strength, peak expiratory flow rate and vital signs combined with questionnaire data on functional and psychological status provide quantitative information on acuity and chronicity/frailty. A count of present inflammatory conditions, cytokine profiles, cortisol, vitamin D and other important parameters taken from blood samples add data on the role of inflammation in acuity of symptoms and development of frailty (Puts et al 2005).

Analysis:

This study is ongoing (n=61) but univariate analysis of variables within groups will be performed to establish associations between frailty, inflammation and diagnosis. Differences between groups will be analysed through logistic and multiple regression modelling. Data analysis will be completed by time of presentation.