Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis
Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis
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DOI：
10.1258/jrsm.99.8.406
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发表时间：
2006-08-01
影响因子：
17.3
通讯作者：
Majeed, Azeem
中科院分区：
文献类型：
--
作者：
Bottle, Alex;Aylin, Paul;Majeed, Azeem
目的利用常规资料识别未来急诊入院高危患者。设计住院事件统计描述性分析。应用多元Logistic回归建立预测模型。在英国建立国家卫生服务医院信任。参与者所有在2000年4月1日至2001年3月31日期间在NHS医院急诊入院的患者。主要结果指标高影响用户被定义为在该指数开始入院后的12个月内至少有一次急诊住院并随后至少再入院两次的患者。结果2000/2001年度有2 895 234名患者因急诊入院，其中147 725人(5.1%)在第一次入院时未能幸存。在2 747 509名尚存病人中，269 686名(9.8%)其后在入院指标日期起计365天内有至少两次或以上的急症入院。在此期间，另有236 779人(8.6%)死亡。成为高影响力使用者的风险因素包括指数咒语前36个月内的紧急情况次数、合并症、年龄、因门诊护理敏感情况入院、种族、地区一级的社会经济数据、当地住院率、指数咒语中的发作次数、性别和入院来源。基于所有急诊入院情况的预测模型得到的受试者操作特征曲线得分为0.72。结论常规医院事件统计可用于识别未来多次急诊入院的高危患者。需要将防止部分后续住院的潜在成本节约与这些患者的病例管理成本进行比较。
Objective To use routine data to identify patients at high risk of future emergency hospital admissions.Design Descriptive analysis of inpatient hospital episode statistics. Predictive model developed using multiple logistic regression.Setting National Health Service hospital trusts in England.Participants All patients with an emergency admission to an NHS hospital between 1 April 2000 and 31 March 2001.Main outcome measures 'High-impact users' were defined as patients who had at least one emergency inpatient admission and who then went on to have at least two further emergency hospital admissions in the 12 months following the start date of that index admission.Results 2 895 234 patients were admitted as emergencies in 2000/2001, of whom 147 725 (5.1%) did not survive their first spell. Of the 2 747 509 surviving patients, 269 686 (9.8%) subsequently had at least two or more emergency admissions within 365 days of the index date of admission. A further 236 779 (8.6%) died during this period. Risk factors for becoming a high-impact user included the number of emergencies in the 36 months before index spell, comorbidity, age, an admission for an ambulatory care sensitive condition, ethnicity, area-level socioeconomic data, local admission rates, the number of episodes in the index spell, sex and the source of admission. The predictive model based on all emergency admissions produced a receiver operating characteristic curve score of 0.72.Conclusions Routine hospital episode statistics can be used to identify patients who are at high risk of suffering future multiple emergency hospital admissions. The potential cost savings in preventing a proportion of these subsequent admissions need to be compared with the costs of case management of these patients.