Predicting the likelihood of emergency admission to hospital of older people: development and validation of the Emergency Admission Risk Likelihood Index (EARLI).

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.

[1]  P Shelton,et al.  The community assessment risk screen (CARS): identifying elderly persons at risk for hospitalization or emergency department visit. , 2000, The American journal of managed care.

[2]  Stuart Parker,et al.  Follow up of people aged 65 and over with a history of emergency admissions: analysis of routine admission data , 2005, BMJ : British Medical Journal.

[3]  C. Dibben,et al.  The English indices of deprivation 2004 , 2011 .

[4]  J. Habbema,et al.  Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. , 2001, Journal of clinical epidemiology.

[5]  I. Gemmell,et al.  Factors influencing emergency medical readmission risk in a UK district general hospital: a prospective study. , 2005, BMC emergency medicine.

[6]  P. Dexter,et al.  Risk factors for nonelective hospitalization in frail and older adult, inner-city outpatients. , 2004, The Gerontologist.

[7]  A. Bowling,et al.  Just One Question: If One Question Works, Why Ask Several? Single Compared with Multi- Item Measures , 2005 .

[8]  D. Lyon,et al.  The Castlefields Integrated Care Model: The Evidence Summarised , 2006 .

[9]  Emmett Keeler,et al.  Development of a Method to Identify Seniors at High Risk for High Hospital Utilization , 2002, Medical care.

[10]  E. Turiel.  The Development of Morality , 2007 .

[11]  Michael Greenstone,et al.  Hospital at home for patients with acute exacerbations of chronic obstructive pulmonary disease: systematic review of evidence , 2004, BMJ : British Medical Journal.

[12]  Yunhwan Lee.  The predictive value of self assessed general, physical, and mental health on functional decline and mortality in older adults , 2000, Journal of Epidemiology and Community Health.

[13]  T A Brennan,et al.  Factors associated with unplanned hospital readmission among patients 65 years of age and older in a Medicare managed care plan. , 1999, The American journal of medicine.

[14]  N. Korner-Bitensky,et al.  Predictive validity of a postal questionnaire for screening community-dwelling elderly individuals at risk of functional decline. , 1996, Age and ageing.

[15]  D. Collett.  Modelling Binary Data , 1991 .

[16]  M. McDonagh,et al.  Measuring appropriate use of acute beds. A systematic review of methods and results. , 2000, Health policy.

[17]  Bernadette Porter,et al.  Supporting people with long-term conditions. , 2005, British journal of nursing.

[18]  C. Sanderson,et al.  Conditions for Which Onset or Hospital Admission is Potentially Preventable by Timely and Effective Ambulatory Care , 2000, Journal of health services research & policy.

[19]  C. Dowrick,et al.  Design of a clustered observational study to predict emergency admissions in the elderly: statistical reasoning in clinical practice. , 2007, Journal of evaluation in clinical practice.

[20]  Evercare Evaluation Interim Report: Implications for supporting people with long term conditions in the NHS. , 2005 .

[21]  M. Clarke,et al.  Increasing response rates to postal questionnaires: systematic review , 2002, BMJ : British Medical Journal.

[22]  R. Logan,et al.  A comparison of sub-bandage pressures produced by experienced and inexperienced bandagers. , 1992, Journal of wound care.

[23]  C. Boult,et al.  Predictive Validity of a Questionnaire That Identifies Older Persons at Risk for Hospital Admission , 1995, Journal of the American Geriatrics Society.

[24]  Chad Boult,et al.  Screening Elders for Risk of Hospital Admission , 1993, Journal of the American Geriatrics Society.

[25]  D. Bloch,et al.  A simple method of sample size calculation for linear and logistic regression. , 1998, Statistics in medicine.

[26]  J. Coast,et al.  Alternatives to hospital care: what are they and who should decide? , 1996, BMJ.

[27]  Daniel B. Mark,et al.  TUTORIAL IN BIOSTATISTICS MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS , 1996 .

[28]  D. Hosmer,et al.  Applied Logistic Regression , 1991 .

[29]  J. McMurray,et al.  Randomised controlled trial of specialist nurse intervention in heart failure , 2001, BMJ : British Medical Journal.

[30]  Denise Kendrick,et al.  Health care needs assessment , 1993 .

[31]  M. Egger,et al.  Predicting the Risk of Hospital Admission in Older Persons—Validation of a Brief Self‐Administered Questionnaire in Three European Countries , 2006, Journal of the American Geriatrics Society.