Predicting frequent COPD exacerbations using primary care data
Received 12 August 2015
Accepted for publication 19 September 2015
Published 9 November 2015 Volume 2015:10(1) Pages 2439—2450
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Professor Hsiao-Chi Chuang
Peer reviewer comments 2
Editor who approved publication: Dr Richard Russell
Marjan Kerkhof,1 Daryl Freeman,2 Rupert Jones,3 Alison Chisholm,4 David B Price1,5
On behalf of the Respiratory Effectiveness Group
1Research in Real-Life, Cambridge, 2Mundesley Medical Centre, Norfolk, 3Plymouth University Peninsula School of Medicine and Dentistry, Plymouth, 4Respiratory Effectiveness Group, Cambridge, 5Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
Purpose: 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 and methods: 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.
Results: 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.
Conclusion: 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.
Keywords: prediction, risk factor, model, validation, FEV1
Corrigendum for this paper has been published.
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