Tsai Chu-Lin, Clark Sunday, Sullivan Ashley F, Camargo Carlos A
EMNet Coordinating Center, Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, 02114, USA.
Health Serv Res. 2009 Oct;44(5 Pt 1):1701-17. doi: 10.1111/j.1475-6773.2009.00998.x. Epub 2009 Jul 13.
To develop and prospectively validate a risk-adjustment tool in acute asthma.
Data were obtained from two large studies on acute asthma, the Multicenter Airway Research Collaboration (MARC) and the National Emergency Department Safety Study (NEDSS) cohorts. Both studies involved >60 emergency departments (EDs) and were performed during 1996-2001 and 2003-2006, respectively. Both included patients aged 18-54 years presenting to the ED with acute asthma.
Retrospective cohort studies.
Clinical information was obtained from medical record review. The risk index was derived in the MARC cohort and then was prospectively validated in the NEDSS cohort.
There were 3,515 patients in the derivation cohort and 3,986 in the validation cohort. The risk index included nine variables (age, sex, current smoker, ever admitted for asthma, ever intubated for asthma, duration of symptoms, respiratory rate, peak expiratory flow, and number of beta-agonist treatments) and showed satisfactory discrimination (area under the receiver operating characteristic curve, 0.75) and calibration ( p=.30 for Hosmer-Lemeshow test) when applied to the validation cohort.
We developed and validated a novel risk-adjustment tool in acute asthma. This tool can be used for health care provider profiling to identify outliers for quality improvement purposes.
开发并前瞻性验证一种急性哮喘风险调整工具。
数据取自两项关于急性哮喘的大型研究,即多中心气道研究协作组(MARC)和国家急诊科安全研究(NEDSS)队列研究。两项研究均涉及60多个急诊科,分别于1996 - 2001年和2003 - 2006年进行。两项研究均纳入了18 - 54岁因急性哮喘就诊于急诊科的患者。
回顾性队列研究。
通过病历审查获取临床信息。风险指数在MARC队列中得出,然后在NEDSS队列中进行前瞻性验证。
推导队列中有3515例患者,验证队列中有3986例患者。风险指数包括九个变量(年龄、性别、当前吸烟者、曾因哮喘住院、曾因哮喘插管、症状持续时间、呼吸频率、呼气峰值流速以及β受体激动剂治疗次数),应用于验证队列时显示出令人满意的区分度(受试者操作特征曲线下面积为0.75)和校准度(Hosmer - Lemeshow检验p = 0.30)。
我们开发并验证了一种新型急性哮喘风险调整工具。该工具可用于医疗服务提供者概况分析,以识别异常值用于质量改进目的。