Development and Validation of a Risk-Adjustment Tool in Acute Asthma

被引:4
作者
Tsai, Chu-Lin [1 ]
Clark, Sunday [2 ]
Sullivan, Ashley F.
Camargo, Carlos A., Jr.
机构
[1] Harvard Univ, Sch Med, Massachusetts Gen Hosp, Dept Emergency Med,EMNet Coordinating Ctr, Boston, MA 02114 USA
[2] Univ Pittsburgh, Div Gen Internal Med, Pittsburgh, PA USA
基金
美国医疗保健研究与质量局;
关键词
Asthma; emergency department; hospital admission; profiling; risk adjustment; 30-DAY MORTALITY-RATES; EMERGENCY-DEPARTMENT; PROSPECTIVE MULTICENTER; MEASURING PERFORMANCE; HOSPITAL ADMISSION; EARLY PREDICTION; SETTING BUDGETS; UNITED-STATES; CARE; SURGERY;
D O I
10.1111/j.1475-6773.2009.00998.x
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Objective To develop and prospectively validate a risk-adjustment tool in acute asthma. Data Sources 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. Study Design Retrospective cohort studies. Data Collection 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. Principle Findings 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. Conclusions 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.
引用
收藏
页码:1701 / 1717
页数:17
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