Lyon David, Lancaster Gillian A, Taylor Steve, Dowrick Chris, Chellaswamy Hannah
Castlefields Health Centre, Chester Close, Runcorn WA7 2HY, UK.
Fam Pract. 2007 Apr;24(2):158-67. doi: 10.1093/fampra/cml069. Epub 2007 Jan 8.
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.
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.
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.
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.
开发并评估一种基于证据的工具，用于预测英国75岁及以上老年人紧急住院的可能性。
对英格兰西北部哈尔顿初级保健信托基金内17家全科诊所登记的老年人进行前瞻性队列研究。向年龄≥75岁的老年人发放了一份包含20个条目的问卷。问卷中的条目是从已发表文献和一项试点研究收集的信息中挑选出来的。主要结局指标是在完成问卷后12个月内紧急住院情况。进行逻辑回归分析以确定那些能预测紧急住院的条目。利用预测建模过程中确定的条目设计了一个评分系统，以识别低、中、高和极高住院风险的人群。
总共83%（3032人）返还了问卷。开发并验证了一种简单的六项工具——紧急住院风险可能性指数（EARLI）。该工具包含的条目如下：您有心脏问题吗？[比值比（OR）1.40，95%置信区间（CI）1.15 - 1.72]；您有腿部溃疡吗？（OR 1.46，95% CI 1.04 - 2.04）；您能在无人帮助的情况下出门吗？（OR 0.60，95% CI 0.47 - 0.75）；您有记忆问题并会感到困惑吗？（OR 1.46，95% CI 1.19 - 1.81）；您在过去12个月内有过紧急住院吗？（OR 2.16，CI 1.72 - 2.72）；您会说您的总体健康状况良好吗？（OR 0.66，95% CI 0.53 - 0.82）。该工具具有较高的阴性预测价值（>79%），并识别出超过50%的高或极高紧急住院风险人群。极高分数（>20）识别出6%的老年人，其中55%在接下来的12个月内有紧急住院情况。低分数（≤10）识别出74%的老年人群，其中17%住院。
在本研究中，我们开发并验证了一种易于应用的工具，用于识别英国在接下来12个月内有紧急住院风险的老年人。EARLI可作为一种简单的分诊筛查工具，以帮助识别最脆弱的老年人，要么针对干预措施和支持以减少对医院服务的需求，要么用于纳入测试不同预防干预措施的有效性。
