伯明翰肺改善研究(BLISS) COPD患者初级保健预后评分的发展:来自伯明翰COPD队列的数据

R. Jordan, D. Fitzmaurice, James Martin, J. Ayres, K. Cheng, B. Cooper, A. Daley, A. Dickens, A. Enocson, S. Greenfield, Martin R Miller, R. Riley, S. Siebert, R. Stockley, A. Turner, P. Adab
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引用次数: 1

摘要

预后评分可用于指导COPD患者的管理和降低住院风险,但现有评分效果不够好,对初级保健不实用。使用来自伯明翰初级保健COPD队列的数据,我们从23个候选变量中开发并内部验证了新的BLISS预后评分。71家全科医生诊所登记的1558名COPD患者和一项相关病例发现试验中新发现的331名患者被纳入研究,他们的自我报告和临床数据与常规医院发作统计数据相结合。主要终点为队列入组后2年内至少有一次呼吸入院记录。该模型采用逆向消去法建立。缺失数据采用链式方程进行输入。对鉴别和校准进行评估。引导用于内部验证。中位(最短,最长)随访时间为2.9年(1.8年,3.8年)。最终模型保留了6个变量:年龄、CAT评分、呼吸入院前12m、BMI、糖尿病、FEV1%预测。经乐观调整后,该模型在预测2年呼吸入院方面表现良好(c统计量=0.75 (95%CI 0.72, 0.79))。BLISS评分在预测呼吸入院方面比现有公布的评分表现更好。所有6个变量在初级保健记录中都很容易获得,或者很容易收集,并且一个简单的计算机程序可以计算得分。重要的下一步是外部验证,提出/评估使用模型来指导患者管理,并探索在初级保健实践中实施评分的最佳方法。
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Development of the Birmingham Lung Improvement Studies (BLISS) prognostic score for COPD patients in primary care: data from the Birmingham COPD cohort
Prognostic scores could be used to guide management of COPD patients and reduce risk of hospital admission but existing scores do not perform well enough and are not practical for primary care. Using data from the Birmingham primary care COPD cohort we developed and internally validated the new BLISS prognostic score from 23 candidate variables. 1558 patients on COPD registers of 71 GP practices and 331 newly-identified patients from a linked case-finding trial were included and their self-reported and clinical data were combined with routine hospital episode statistics. Primary outcome was the record of at least one respiratory admission within 2 years of cohort entry. The model was developed using backward elimination. Missing data were imputed using chained equations. Discrimination and calibration were assessed. Bootstrapping was used for internal validation. Median (min, max) follow up was 2.9 years (1.8, 3.8). 6 variables were retained in the final model: age, CAT score, respiratory admissions previous 12m, BMI, diabetes, FEV1% predicted. After adjustment for optimism, the model performed well in predicting 2yr respiratory admissions (c statistic=0.75 (95%CI 0.72, 0.79). The BLISS score showed better performance in predicting respiratory admissions than existing published scores. All 6 variables are readily available in primary care records or would be easy to collect, and a simple computer programme could calculate the score. Important next steps are external validation, proposing/evaluating a model of use to guide patient management and exploration of the best ways to implement the score in primary care practice.
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