Predicting frequent COPD exacerbations using primary care data.
Predicting frequent COPD exacerbations using primary care data.
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DOI：
10.2147/copd.s94259
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发表时间：
2015
影响因子：
2.8
通讯作者：
Respiratory Effectiveness Group
中科院分区：
文献类型：
--
作者：
Kerkhof M;Freeman D;Jones R;Chisholm A;Price DB;Respiratory Effectiveness Group
COPD急性加重是COPD相关的残疾和费用增加的主要原因，但预测风险因素的数据有限。本研究的目的是开发一个稳健的、基于临床的模型来预测频繁加重的风险。从最佳患者护理研究数据库（OPCRD）中识别出的COPD诊断代码和1秒用力呼气量/用力肺活量比值<0.7的患者，如果年龄≥40岁，且数据涵盖研究索引日期前一年（预测年）和研究索引日期后一年（结局年），则纳入本历史随访研究。数据集包含潜在的风险因素，包括人口统计学、临床和共病变量。单变量分析后，将两次或多次急性加重的预测因素纳入逐步多变量logistic回归。敏感性分析的亚群的患者没有任何哮喘诊断以往和那些有问卷调查数据的症状和吸烟包年。根据1年前瞻性OPCRD数据验证了完整的预测模型。完整的数据集包含16，565例患者（53%男性，中位年龄70岁），包括9，393例无任何记录的哮喘患者和3，713例有问卷数据的患者。完整模型保留了11个变量，这些变量显著预测了两次或多次急性加重，其中前一年的急性加重次数具有最强的相关性;其他变量包括身高、年龄、1秒用力呼气量和几种共病状况。以前未确定的重要预测因素包括嗜酸性粒细胞增多症和COPD评估测试评分。当应用于验证数据集（n= 2，713; C统计量0.735）时，全模型的预测能力（C统计量0.751）变化不大。敏感性分析的结果支持主要结果。可以从常规可用的计算机化初级护理数据中识别有加重风险的患者。需要进一步的研究来验证其他患者人群的模型。
Acute COPD exacerbations account for much of the rising disability and costs associated with COPD, but data on predictive risk factors are limited. The goal of the current study was to develop a robust, clinically based model to predict frequent exacerbation risk. Patients identified from the Optimum Patient Care Research Database (OPCRD) with a diagnostic code for COPD and a forced expiratory volume in 1 second/forced vital capacity ratio <0.7 were included in this historical follow-up study if they were ≥40 years old and had data encompassing the year before (predictor year) and year after (outcome year) study index date. The data set contained potential risk factors including demographic, clinical, and comorbid variables. Following univariable analysis, predictors of two or more exacerbations were fed into a stepwise multivariable logistic regression. Sensitivity analyses were conducted for subpopulations of patients without any asthma diagnosis ever and those with questionnaire data on symptoms and smoking pack-years. The full predictive model was validated against 1 year of prospective OPCRD data. The full data set contained 16,565 patients (53% male, median age 70 years), including 9,393 patients without any recorded asthma and 3,713 patients with questionnaire data. The full model retained eleven variables that significantly predicted two or more exacerbations, of which the number of exacerbations in the preceding year had the strongest association; others included height, age, forced expiratory volume in 1 second, and several comorbid conditions. Significant predictors not previously identified included eosinophilia and COPD Assessment Test score. The predictive ability of the full model (C statistic 0.751) changed little when applied to the validation data set (n=2,713; C statistic 0.735). Results of the sensitivity analyses supported the main findings. Patients at risk of exacerbation can be identified from routinely available, computerized primary care data. Further study is needed to validate the model in other patient populations.