Predicting frequent XXXX exacerbations using primary care data.

@entity1 @entity565 @entity1712 @entity1030
PURPOSE: Acute @entity1030 exacerbations account for much of the rising disability and costs associated with @entity1030 , 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. @entity1 AND METHODS: @entity1 identified from the Optimum @entity1 Care Research Database (OPCRD) with a diagnostic code for @entity1030 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 @entity1 without any @entity565 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. RESULTS: The full data set contained 16,565 @entity1 (53% male, median age 70 years), including 9,393 @entity1 without any recorded @entity565 and 3,713 @entity1 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 @entity1712 and @entity1030 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. CONCLUSION: @entity1 at risk of exacerbation can be identified from routinely available, computerized primary care data. Further study is needed to validate the model in other @entity1 populations.
