Omachi Theodore A, Yelin Edward H, Katz Patricia P, Blanc Paul D, Eisner Mark D
Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of California, San Francisco, California 94143-0111, USA.
COPD. 2008 Dec;5(6):339-46. doi: 10.1080/15412550802522700.
A survey-based COPD severity scoring algorithm could prove useful for targeted disease management and risk-adjustment. For this purpose, we sought to prospectively validate a COPD severity score that had previously been cross-sectionally validated. Using a population-based sample of 267 adults with self-reported physician-diagnosed COPD, we examined the extent to which this COPD severity score predicts future respiratory hospitalizations, emergency department (ED) visits, and outpatient visits. Structured telephone interviews, conducted at baseline and on an annual basis in two subsequent years, determined COPD severity scores and health-care utilization. A basic predictive model for respiratory-specific health-care utilization was developed using sociodemographics, tobacco history, and medical comorbidity data in multivariate logistic regression analysis. The added predictive value of the COPD severity score over and above this basic model was then evaluated by testing the change in model concordance indices. Our analysis demonstrated that the COPD severity score did, in fact, increase the concordance-index of models predicting future respiratory hospitalizations (increase from 80% to 87%; P = 0.03), ED visits (64% to 82%, P = 0.003), and outpatient visits (59% to 77%, P < 0.0001). In a separate analysis, both a greater baseline severity score and worsening of the severity score over time were prospectively associated with each outcome studied (P < 0.05 for each). In conclusion, the COPD severity score adds predictive value in estimating future respiratory-specific health-care utilization and is longitudinally responsive to evolving changes in COPD status over time. This severity score may be used to adjust for disease severity or to identify high-risk populations.
一种基于调查的慢性阻塞性肺疾病（COPD）严重程度评分算法可能对针对性的疾病管理和风险调整有用。为此，我们试图前瞻性地验证一个先前已进行横断面验证的COPD严重程度评分。我们使用了一个基于人群的样本，其中包括267名自我报告经医生诊断患有COPD的成年人，研究了该COPD严重程度评分在多大程度上能够预测未来的呼吸住院、急诊科就诊和门诊就诊情况。在基线以及随后两年每年进行的结构化电话访谈确定了COPD严重程度评分和医疗保健利用情况。在多变量逻辑回归分析中，利用社会人口统计学、吸烟史和合并症数据建立了针对呼吸相关医疗保健利用的基本预测模型。然后，通过测试模型一致性指数的变化，评估了COPD严重程度评分在该基本模型之上的额外预测价值。我们的分析表明，COPD严重程度评分实际上确实提高了预测未来呼吸住院（从80%提高到87%；P = 0.03）、急诊科就诊（从64%提高到82%，P = 0.003）和门诊就诊（从59%提高到77%，P < 0.0001）的模型一致性指数。在一项单独的分析中，较高的基线严重程度评分以及严重程度评分随时间的恶化与所研究的每个结果均呈前瞻性关联（每个P < 0.05）。总之，COPD严重程度评分在估计未来呼吸相关医疗保健利用方面增加了预测价值，并且对COPD状态随时间的演变变化具有纵向响应性。该严重程度评分可用于调整疾病严重程度或识别高危人群。
