OBJECTIVE: To develop and evaluate an evidence-based tool for predicting the likelihood of emergency admission to hospital of older people aged 75 years and over in the UK. METHODS: Prospective cohort study of older people registered with 17 general practices within Halton Primary Care Trust in the north-west of England. A questionnaire with 20 items was sent to older people aged>or=75 years. Items for inclusion in the questionnaire were selected from information gleaned from published literature and a pilot study. The primary outcome measurement was an emergency admission to hospital within 12 months of completing the questionnaire. A logistic regression analysis was carried out to identify those items which predicted emergency admission to hospital. A scoring system was devised to identify those at low, moderate, high and very high risk of admission, using the items identified in the predictive modelling process. RESULTS: In total, 83% (3032) returned the questionnaire. A simple, six-item tool was developed and validated-the Emergency Admission Risk Likelihood Index (EARLI). The items included in the tool are as follows: do you have heart problems? [odds ratio (OR) 1.40, 95% confidence interval (CI) 1.15-1.72]; do you have leg ulcers? (OR 1.46, 95% CI 1.04-2.04); can you go out of the house without help? (OR 0.60, 95% CI 0.47-0.75); do you have problems with your memory and get confused? (OR 1.46, 95% CI 1.19-1.81); have you been admitted to hospital as an emergency in the last 12 months? (OR 2.16, CI 1.72-2.72); and would you say the general state of your health is good? (OR 0.66, 95% CI 0.53-0.82). The tool had high negative predictive value (>79%) and identified over 50% of those at high or very high risk of emergency admission. A very high score (>20) identified 6% of older people, 55% of whom had an emergency admission in the following 12 months. A low score (<or=10) identified 74% of the older population of whom 17% were admitted. CONCLUSIONS: In this study, we have developed and validated a simple-to-apply tool for identifying older people in the UK who are at risk of having an emergency admission within the following 12 months. EARLI can be used as a simple triage-screening tool to help identify the most vulnerable older people, either to target interventions and support to reduce demand on hospital services or for inclusion in testing the effectiveness of different preventive interventions.
OBJECTIVE: To develop and evaluate an evidence-based tool for predicting the likelihood of emergency admission to hospital of older people aged 75 years and over in the UK. METHODS: Prospective cohort study of older people registered with 17 general practices within Halton Primary Care Trust in the north-west of England. A questionnaire with 20 items was sent to older people aged>or=75 years. Items for inclusion in the questionnaire were selected from information gleaned from published literature and a pilot study. The primary outcome measurement was an emergency admission to hospital within 12 months of completing the questionnaire. A logistic regression analysis was carried out to identify those items which predicted emergency admission to hospital. A scoring system was devised to identify those at low, moderate, high and very high risk of admission, using the items identified in the predictive modelling process. RESULTS: In total, 83% (3032) returned the questionnaire. A simple, six-item tool was developed and validated-the Emergency Admission Risk Likelihood Index (EARLI). The items included in the tool are as follows: do you have heart problems? [odds ratio (OR) 1.40, 95% confidence interval (CI) 1.15-1.72]; do you have leg ulcers? (OR 1.46, 95% CI 1.04-2.04); can you go out of the house without help? (OR 0.60, 95% CI 0.47-0.75); do you have problems with your memory and get confused? (OR 1.46, 95% CI 1.19-1.81); have you been admitted to hospital as an emergency in the last 12 months? (OR 2.16, CI 1.72-2.72); and would you say the general state of your health is good? (OR 0.66, 95% CI 0.53-0.82). The tool had high negative predictive value (>79%) and identified over 50% of those at high or very high risk of emergency admission. A very high score (>20) identified 6% of older people, 55% of whom had an emergency admission in the following 12 months. A low score (<or=10) identified 74% of the older population of whom 17% were admitted. CONCLUSIONS: In this study, we have developed and validated a simple-to-apply tool for identifying older people in the UK who are at risk of having an emergency admission within the following 12 months. EARLI can be used as a simple triage-screening tool to help identify the most vulnerable older people, either to target interventions and support to reduce demand on hospital services or for inclusion in testing the effectiveness of different preventive interventions.
Authors: Michael A LaMantia; Timothy F Platts-Mills; Kevin Biese; Christine Khandelwal; Cory Forbach; Charles B Cairns; Jan Busby-Whitehead; John S Kizer Journal: Acad Emerg Med Date: 2010-03 Impact factor: 3.451
Authors: Nibu Parameswaran Nair; Leanne Chalmers; Michael Connolly; Bonnie J Bereznicki; Gregory M Peterson; Colin Curtain; Ronald L Castelino; Luke R Bereznicki Journal: PLoS One Date: 2016-10-31 Impact factor: 3.240
Authors: Leo D Westbury; Holly E Syddall; Shirley J Simmonds; Cyrus Cooper; Avan Aihie Sayer Journal: BMC Med Res Methodol Date: 2016-04-26 Impact factor: 4.615