Donnan Peter T, Dorward David W T, Mutch Bill, Morris Andrew D
Epidemiology and Biostatistics, Tayside Centre for General Practice, Community Health Sciences, University of Dundee, Dundee, Scotland.
Arch Intern Med. 2008 Jul 14;168(13):1416-22. doi: 10.1001/archinte.168.13.1416.
Current international health policy has emphasized the importance of managing long-term conditions in the community with the aim of preventing emergency hospitalizations. Previous algorithms and rules have been developed but are limited to those older than 65 years and generally only for readmission. Our aim was to develop an algorithm to predict emergency hospital admissions in the whole population of those 40 years or older.
The design was a historical cohort observational study from 1996 to 2004 with at least 1 year of follow-up and split-half validation, set in the population of Tayside, Scotland (n = 410 000). Participants were 40 years or older with a 3-year history of prescribed drugs and hospital admissions. The main outcome measure was first emergency hospital admission in the following year, analyzed using logistic regression.
A total of 186 523 subjects 40 years or older were identified at baseline. A derivation data set (n = 90 522) yielded 6793 participants (7.5%) who experienced an emergency hospital admission in the following year. Strong predictors of admissions were age; being male; high social deprivation; previously prescribed analgesics, antibacterials, nitrates, and diuretics; the number of respiratory medications; and the number of previous admissions and previous total bed-days. Discriminatory power was good (c statistic, 0.80) and split-half validation gave good calibration, especially for the highest decile of risk.
A population-derived algorithm provided the first easy-to-use algorithm, to our knowledge, to predict future emergency admissions in all individuals 40 years or older. The model can be implemented at individual patient level as well as family practice level to target case management.
当前国际卫生政策强调在社区管理长期疾病以预防紧急住院的重要性。此前已开发出一些算法和规则，但仅限于65岁以上人群，且通常仅用于再入院情况。我们的目标是开发一种算法，以预测40岁及以上全体人群的紧急住院情况。
本研究为1996年至2004年的历史性队列观察研究，随访至少1年，并进行了对半验证，研究对象为苏格兰泰赛德地区的人群（n = 410 000）。参与者年龄在40岁及以上，有3年的处方药使用史和住院史。主要结局指标为次年首次紧急住院情况，采用逻辑回归进行分析。
基线时共识别出186 523名40岁及以上的受试者。一个推导数据集（n = 90 522）中有6793名参与者（7.5%）在次年经历了紧急住院。住院的强预测因素包括年龄、男性、社会剥夺程度高、既往使用过镇痛药、抗菌药、硝酸盐和利尿剂、呼吸类药物数量、既往住院次数和既往总住院天数。判别能力良好（c统计量为0.80），对半验证显示校准良好，尤其是对于最高风险十分位数人群。
据我们所知，一种基于人群得出的算法首次提供了一种易于使用的算法，可预测所有40岁及以上个体未来的紧急住院情况。该模型可在个体患者层面以及家庭医疗层面实施，以进行病例管理。
